NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

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Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

Treatment of Solar Generation 

in Electric Utility Resource 

Planning

John Sterling

Solar Electric Power Association

Joyce McLaren

National Renewable Energy Laboratory

Mike Taylor

Solar Electric Power Association

Karlynn Cory

National Renewable Energy Laboratory

Technical Report

NREL/TP-6A20-60047

October 2013
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Treatment of Solar Generation 

in Electric Utility Resource 

Planning

John Sterling

Solar Electric Power Association

Joyce McLaren

National Renewable Energy Laboratory

Mike Taylor

Solar Electric Power Association

Karlynn Cory

National Renewable Energy Laboratory

Prepared under Task No. SM13.1570

Technical Report

NREL/TP-6A20-60047

October 2013
NOTICE

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Acknowledgments

This paper was prepared by the National Renewable Energy Laboratory (NREL) and the Solar 

Electric Power Association (SEPA), who worked together to engage utilities directly to research 

solar in long-term utility planning and author this report. NREL managed the project, directed 

research, and led analysis of results. NREL is a national laboratory of the U.S. Department of 

Energy Office of Energy Efficiency & Renewable Energy Operated by the Alliance for 

Sustainable Energy, LLC. SEPA is a 501 (c)(3) educational non-profit research organization that 

delivers the latest actionable information to help members (utilities, developers, manufacturers 

and installers) implement solar business plans, get smarter about the changing energy sector, 

meet peers, and exchange ideas.

NREL and SEPA would like to thank the following individuals and companies for providing 

valuable input, insights, and feedback throughout this project: James Wilde (Arizona Public 

Service); Myron Rollins, Bradley Kushner, Mon Hong, Ryan Pletka, Tim Mason, and Scott 

Olson (Black & Veatch); Andrew Mills (Lawrence Berkeley National Laboratory); Tom Stanton 

(National Regulatory Research Institute); Jennifer Szaro (Orlando Utilities Commission); Pat 

O’Connell (Public Service Company of New Mexico); Bob Long (SCANA); Jon Bertolino 

(Sacramento Municipal Utility District); Gary Brinkworth (Tennessee Valley Authority); Brad 

Harrison, Norm Richardson, and Eric Hughes (Ventyx); David Berry (Western Resource 

Advocates); and additional contributors who choose to remain anonymous. We also are grateful 

to Mark Dyson (NREL) for providing insights and some text about capacity expansion modeling

and how solar technology characterization can significantly impact the simulated value of solar 

generators.

We would also like to thank the report’s peer reviewers for their expert advice and counsel, 

including many of the utility industry individuals we interviewed or that answered our 

questionnaire, as well as Lori Bird, Jaquelin Cochran, Lynn Billman, Maggie Mann and Douglas 

Arent of NREL. Finally, we would like to thank our project sponsors, Chris Nichols and Kelly 

Knutson of the U.S. Department of Energy SunShot program. 

We offer our appreciation to Anthony Castellano and Jim Leyshon for providing support on 

some of the graphics and to NREL Communications for providing editorial support. 
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List of Acronyms and Abbreviations

AC alternating current

APS Arizona Public Service

CA IOUs California investor-owned utilities

CEM capacity expansion model

CPV concentrating photovoltaics

CSP concentrating solar power

DC direct current

DG distributed generation

DOE Department of Energy

Duke Duke Energy

EE energy efficiency

EERS energy efficiency resource standard

ELCC Effective Load-Carrying Capability

GHG greenhouse gas

Idaho Idaho Power

IID Imperial Irrigation District

IRP integrated resource plan

IOU investor-owned utility

ITC Investment Tax Credit

LADWP Los Angeles Department of Water and Power

LBNL Lawrence Berkeley National Laboratory

LOLP Loss of Load Probability

MW megawatt

MWh megawatt-hour

NEM net energy metering

NREL National Renewable Energy Laboratory

NVE NV Energy (Nevada)

PCM production cost model

PGE Portland General Electric

PNM Public Service of New Mexico

PPA purchased power agreement

PSCo Public Service of Colorado

PV photovoltaic

PVRR Present Value Revenue Requirements

RE renewable energy (or electricity)

RFP request for proposals

RPS renewable portfolio standard

SCANA An electric and gas utility in SC and NC

SEPA Solar Electric Power Association

SREC solar renewable energy credit

T&D transmission and distribution

TEP Tucson Electric Power

TMY typical meteorological year

Tri-State Tri-State Generation and Transmission Association
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Executive Summary

Today’s utility planners have a different market and economic context than their predecessors, 

including planning for the growth of renewable energy. State and federal support policies, solar 

photovoltaic (PV) price declines, and the introduction of new business models for solar PV 

“ownership” are leading to increasing interest in solar technologies1 (especially PV); however, 

solar introduces myriad new variables into the utility resource planning decision. Most, but not 

all, utility planners have less experience analyzing solar than conventional generation as part of 

capacity planning, portfolio evaluation, and resource procurement decisions. To begin to build 

this knowledge, utility staff expressed interest in one effort: utility exchanges regarding data, 

methods, challenges, and solutions for incorporating solar in the planning process. Through 

interviews and a questionnaire, this report aims to begin this exchange of information and 

capture utility-provided information about: 1) how various utilities approach long-range resource 

planning; 2) methods and tools utilities use to conduct resource planning; and, 3) how solar 

technologies are considered in the resource planning process.

The National Renewable Energy Laboratory (NREL) and the Solar Electric Power Association 

(SEPA) worked together to engage utilities directly to research this topic and author this report.

An advisory council of electric industry experts guided the methodology used and provided 

feedback on draft analysis. The main sources of information captured in this report were 

predominantly from utilities. The authors conducted interviews with electric sector 

representatives from 13 entities (9 of which were utilities) and developed a utility questionnaire 

that secured more specific modeling data sources and methodologies from 28 utilities in 22 

states. Key questions from the questionnaire and a summary of utility responses are included in 

Appendix B.

Resource Planning Background

Integrated resource plan (IRP) procedures and long-term planning refer to the processes utilities2

take to evaluate a wide range of potential supply- and demand-side resource options to meet 

energy (MWh) requirements and peak demand (MW), plus a reserve margin. Utilities look for 

the best set of future decisions to reliably serve customers and meet regulatory requirements over 

a long-term period, usually approximately 20 years. This process is traditionally designed to 

maintain reliability and meet load at the lowest reasonable cost (i.e., least-cost). In recent 

decades, IRP processes have included other factors, including renewable energy mandates.

Additionally, some utilities have recognized that generation diversity provides direct benefits and 

utilities have started to place increased emphasis on lowering risk and uncertainty of future 

regulations and fuel prices, on “affordable” costs, or other non-cost metrics (unless prevented by 

state law or statute). Valuing diversity and considering the risk of the status quo is one way 

utilities are starting to incorporate non-cost metrics in decision-making. Some utilities note that 

an exclusive focus on least-cost may not yield the optimal portfolio of resources.

 1 This report considers distributed solar PV, utility-scale solar PV with and without tracking, concentrated solar 

power with and without storage, PV with battery storage, and concentrating PV. Because distributed PV and fixedaxis utility-scale PV are the dominant applications in the market, these technologies are the main focus. 2 For the purposes of this report, “utility” refers to any entity that engages in long-term supply- and demand-side 

planning to serve the load requirements of its customers. This typically applies to vertically-integrated utilities, 

which may include investor-owned utilities (IOUs), municipal utilities, and electric cooperatives.
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Through interviews and a questionnaire, the authors gathered information on utility supply 

planning. Utilities were asked to provide their resource planning process details, key assumptions 

(e.g. whether DG is represented as supply or negative load), modeling methodology (e.g. type of 

risk analytics and candidate portfolio development), and capacity expansion and production 

simulation model software. Utilities performing the most detailed long-term planning3 include 

the following steps (and within steps there are many variations):

1. Evaluate State Policies and Mandates. Evaluate state laws and regulations that 

influence future electricity demand and supply (e.g. efficiency and renewable energy 

mandates), because they impact the amount and timing of new supply procurement

needs and the characteristics of demand increases through the year and over time.

2. Review Existing Generation Fleet. Consider existing baseload, intermediate, peaking, 

and variable renewable generation that is owned or under a power purchase agreement 

(PPA). 

3. Forecast Load. Study each customer class and forecast current and future energy and 

demand requirements. Roll up the forecasts to determine peak demand periods on a 

daily, monthly, seasonal and annual basis. Attempt to anticipate future disruptive 

changes that could increase demand (e.g., plasma televisions and electric vehicles), 

decrease demand (e.g., energy efficiency program success), or moderate demand (e.g., 

smart meters, load shifting/shedding).

4. Plan Capacity Expansion. Analyze options for meeting long-term demand using either 

capacity expansion modeling or engineering judgment. Popular commercial models 

include Strategist, System Optimizer (previously known as Capacity Expansion), 

AURORAxmp and EGEAS, although a few utility interviewees use in-house models. 

These models can include one or more specific constraints, such as: specific resource

limits, minimum renewable requirements, transmission plans and capabilities, resource

restrictions, and forced plant additions or retirements. Generally speaking, the goal of 

the capacity expansion process is to minimize future costs, given the constraints in 

question. While not always available in commercial models, there are a wide range of 

supply- and demand-side technologies and a variety of configurations possible. Utilities 

interviewed wish more technology options were available in commercial models. Most 

utilities consider scenarios/plausible futures that need to be tested (e.g., forced plant 

retirements, carbon tax, or high natural gas prices) to ensure a robust process.

5. Production Cost Modeling. Simulate hourly dispatch over the entire planning horizon

and perform complex sensitivity analyses by: 1) using Monte Carlo simulation to 

randomly change multiple key input variables hundreds or even thousands of times; 4 2) 

varying single variables to examine specific impacts of major assumptions on a 

portfolio; or 3) a combination of both. Variables that can be modified include fossil fuel 

prices, wholesale market prices, load, environmental costs, renewable energy levels, 

hydro availability, energy efficiency, and incentive availability.

 3 Based on interviews and questionnaire results, it appears that most – but not all – utilities take the steps detailed 

here. For each step, utilities use different levels of detail, models or alternative calculation methodologies. The 

differences in the baseline supply planning processes were stark enough to capture the different methodologies used, 

as they provide a basis for including solar in long-term planning. 4 For simplicity, the terms Monte Carlo simulations and stochastic modeling are used interchangeably in this report.
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6. Select Portfolio. Select a portfolio, in which key metrics for each mix of future 

resources are tracked for comparison purposes. Metrics include revenue requirements, 

capital expenditures, emissions, fuel diversity, water usage, and average system cost. 

Future risk (including generator diversity and potential regulations) is a more recent 

metric used by some utilities to incorporate potential future risks into today’s decisionmaking. To select a resource portfolio that is both low cost and resilient to upward price 

risk, utilities can focus on the expected Present Value Revenue Requirements (PVRR) as 

well as the potential price risk (or risk tail, represented by the upper 5% of PVRRs or 

some other value).

Inclusion of Solar in Resource Planning Processes

Through interviews and a questionnaire, the authors gathered information on solar project 

representation in long-term utility plans (project size, capacity value and integration cost adder).

Interviewees gathered solar PV profile and cost information from a variety of sources. Solar 

generation profiles were either taken from existing operational solar plants, or from external 

sources like PVsyst or NREL’s PVWatts program, which provide a typical year of data for a 

limited number of specific locations. While there is interest in the combination of storage and 

PV, storage is not modeled in long-term planning by the majority of utility interviewees, because 

utilities feel they need more credible data and analysis before storage can be successfully

incorporated.

Solar cost information was less consistent – and there were big questions about the future, 

especially for PV technologies. Some utilities interviewed believed that U.S. Department of 

Energy SunShot goals for PV may be achieved by 2020; others said costs will continue to go 

down but at a slower pace, and still others said that solar costs have reached their minimum and 

will flatten out for the foreseeable future. These solar PV cost assumptions are reflected in their 

solar cost assumptions used in their long-term planning. Notably, utilities indicated they would 

like more credible estimates of future solar PV prices, because predicting future solar PV prices 

is considered a key uncertainty, especially over a multi-decade planning horizon. While prices 

have come down rapidly, questions remain about how long the prices will continue to decline 

and when they will bottom out (or if they already have).

One key question discussed at length with interviewees was the capacity value utilities attribute 

to the addition of solar capacity on their system. In essence, a solar generator’s capacity value is 

the percentage of its nameplate capacity that is anticipated to be reliably available to meet daily 

and seasonal peak demand, and is a region-specific metric.

5 Fully dispatchable conventional 

generation can be called upon to meet load as it fluctuates throughout the day and year. In 

contrast, the most commonly deployed solar technology, fixed-axis solar PV, generates only 

when the sun is available. Maximum energy production is mid-day; as the sun sets the 

production capability can diminish rapidly, depending on the panel orientation (westward panels 

can moderate this impact, although overall production is likely reduced). Most utilities peak later 

in the afternoon (e.g., between 4-6 p.m. in the summer months), so solar energy may not 

correlate perfectly with a utility’s need, depending on their specific situation (e.g., latitude, 

 5 Utility resource planners interviewed in this report routinely referred to the percentage of nameplate assigned to 

solar or other variable resources as its “capacity value”. The authors chose to use this term rather than “capacity 

credit”, which is also an acceptable industry term for this characteristic.
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insolation). Different solar technology configurations can moderate this potential for rapid 

impact through the addition of storage, or PV tracking (one or multiple axes). 

Figure ES 1 shows the ranges of capacity values attributed to a variety of solar configurations, 

based on questionnaire responses. 60–76% of respondents assigned some capacity value to the 

first three solar PV configurations, ranging from 0–60% for fixed PV and up to 80% with 

tracking. Conversely, only 26–44% of utility respondents assigned any capacity value to CSP 

(with or without storage), PV with battery storage or concentrating photovoltaics (CPV); 56–

74% used a 0% capacity value.6

Note: Numbers in circles represent the number of utility responses

Figure ES 1. Range of solar capacity values used in planning analysis, by technology, 

provided by utility questionnaire respondents 

A few other considerations for including solar in resource planning were briefly discussed during

interviews, including solar integration costs (the costs to manage solar variability with other 

generating resources, typically $2-11/MWh – as indicated by utility IRPs investigated by LBNL

(Mills and Wiser 2012)), whether to treat customer-sited distributed generation as net load 

 6 These utilities either have no or virtually no solar on their systems today, or have specifically stated that they have 

not determined the capacity value assigned to solar at this time.
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(typical) or as a resource (just starting), and ways to link long-term resource planning with power 

procurement through request for proposal processes.

A number of key challenges of incorporating solar into supply planning analysis were raised 

during the utility interviews. Most of these utilities have not experienced notable levels of solar 

PV, CSP or CPV penetration, and have not always modeled solar in their IRP studies. Most

utilities expressed an interest in incorporating and refining distributed- and utility-scale solar PV 

in their modeling (since they anticipate more PV coming onto their system in the next decade), 

although some utilities noted that CSP and CPV are not appropriate technologies for their service 

territories. Several utilities mentioned that they participated in this project specifically to learn 

from their peers about best practices. 

Improving Solar Within Utility Supply Planning

Utility interviewees identified a number of benefits and challenges associated with solar 

technology, listed in Table ES 1 and described in more detail in the report. And with declining 

technology costs and increasing customer interest, the participants stated that they are likely to 

include solar PV as a resource in their forthcoming plans. 

Table ES 1. Benefits and Challenges of Solar, Based on Utility Interviews

With these benefits and challenges in mind, utilities can more accurately incorporate solar 

generation into their long-term planning processes. The following list highlights some of the 

leading, utility-identified best-practices and analysis needs discussed in Sections 4 and 5 of this 

report:

1. Analyze and assign appropriate capacity values to solar resources – While not 

always available at 100% of potential output when a utility’s demand peaks, solar 

generation provides some ability to meet that peak. Utilities can carry out capacity 

valuation analyses for solar resources that are specific to their system and region (PV 

with or without tracking and with or without storage, CSP with or without storage and 

CPV). For PV, differentiation between fixed tilt and tracking systems will clarify results.

Further, utilities can update all solar values on a routine basis as penetrations increase 

Benefits of Solar

• Meet renewable standard 

requirements

• Fuel diversification

• Cost stability

• Geographic dispersal benefits and 

modularity

• Partial correlation with peak demand

• Mitigation of environmental 

compliance risks 

• Avoid line losses (typically DG only)

Challenges of Solar

• Variableand uncertain output

• Ramping issues

• Economics

• Lack of current capacity need

• Cross-subsidization concerns (DG)

• Reduced capacity benefit over time 

with increasing solar penetration
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(which may result in declining capacity values over time). Consider the benefits of 

geographic diversity and be sure to include integration cost add-ons, if needed.

2. Analyze solar individually, to get more accurate aggregate results – Individual solar 

assets are subject to output variability relative to the specific conditions at their sites; 

however, when solar generation is aggregated across multiple systems in various sites 

this variability becomes substantially reduced. For long-term planning purposes, 

consider aggregating individual solar profiles to create a more accurate aggregated 

geographic dispersion of specific systems.

3. Improve modeling assumptions and methods – If not modeled accurately, the 

inclusion of solar can yield erroneous results. Based on analysis experience to date, 

results can be skewed when solar is added to previously-optimized generation portfolio, 

decreased marginal capacity credit is not accounting for with higher solar penetrations, 

neighboring utility activities are unaccounted for, or accurate thermal generator 

operating limits are not appropriately captured.

4. Pursue sub-hourly sensitivities – The vast majority of production cost modeling work 

undertaken today is done based on an hourly dispatch over the planning horizon. 

Attempting to run those same models on a more granular level (such as 15-minute 

dispatch) would prove both time-consuming and computer-intensive. As an alternative, 

utilities can consider modeling sample periods (such as one week a season for each 

planning year) on a more granular sub-hourly basis. This approach could provide insight

into the integration issues caused by variable resources like solar, show the impacts to 

generation ramping, dispatch, and system operations, and lead to refinements in the 

amount of flexible resources or energy storage included in future portfolios of resources.

5. Evaluate whether to treat distributed generation as a resource – The majority of 

utilities today treat distributed generation (DG) resources as a net load impact. This 

approach, while acceptable, in essence embeds distributed solar penetration variability 

within the utility’s load forecast. Utility load is one of the key drivers of long-term 

supply planning, so combining DG and actual load forecast may make it harder to 

determine the impact of either alone. Alternatively, it might make sense to analyze solar 

DG as a resource that can be dynamically added in capacity expansion models. Utilities 

can then test how changes to key input variables impact the cost viability of distributed 

solar resources, and begin to optimize the amount of DG they want to target in the 

future.

6. Utility-identified analysis needs – Finally, utilities identified a number of key analysis 

needs that would better inform their supply planning, including:

• Credible PV price and performance data (preferable from other utilities or another 

third-party; not from industry)

• Analysis of how to incorporate geographically diversified resources into modeling

o Example: Instead of one 50 MW PV plant; model ten 5 MW PV across territory

• Analysis of the potential relationship between energy storage and PV

• Easier ways to predict impacts of increased PV penetration

• Better risk/uncertainty analysis methods (beyond scenario planning)
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• Improved commercial models that include:

o Currently unavailable technologies (e.g. solar PV, CPV, CSP, wind, etc.)

o Intra-hour sensitivity capability

o Improved performance (e.g. runtime when running sub-hourly)

• Capture distribution system impacts of DG and other technologies/activities in longterm plans (broader issue than solar)

• Clarity about when to include DG in supply modeling

o What is the tipping point that will impact results?

o What is net-energy metered DG’s impact on revenue requirements?7

• Supply additions during periods of low/no load growth.

 7 Although the resource planning process and resulting documents are not part of ratemaking procedures, utility 

supply planning does occur within the broader context of utility decision-making and is impacted by the utility's 

overarching goals. As such, during the discussions of resource planning, the issue of the impacts of distributed 

generation on utility revenues and the implications for utility planning processes arose. Here we present only the 

results of our discussions with utility representatives, and do not holistically explore the issue of utility revenue loss 

resulting from distributed generation. This key question indicates that further research is needed. 
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Table of Contents

Executive Summary.................................................................................................................................... v

Resource Planning Background ............................................................................................................. v

Inclusion of Solar in Resource Planning Processes..............................................................................vii

Improving Solar Within Utility Supply Planning.................................................................................. ix

1 Introduction........................................................................................................................................... 1

1.1 The Task for Utility Resource Planners........................................................................................... 1

1.2 Purpose of this Report...................................................................................................................... 1

1.3 Key Audiences................................................................................................................................. 2

1.4 Related Research to Date................................................................................................................. 2

1.5 Research Methodology .................................................................................................................... 2

1.5.1 Research Team.................................................................................................................. 2

1.5.2 Advisory Council.............................................................................................................. 3

1.5.3 Utility and Stakeholder Participation................................................................................ 3

1.6 Report Organization......................................................................................................................... 3

2 Resource Planning Process................................................................................................................ 4

2.1 Key IRP Metrics: Least-Cost and Lowest-Risk............................................................................... 4

2.2 The IRP Process............................................................................................................................... 5

2.3 Electricity Supply and Demand Laws and Regulations................................................................... 6

2.4 Existing Generation Fleet Review ................................................................................................... 7

2.5 Load Forecast................................................................................................................................... 9

2.6 Capacity Expansion Planning ........................................................................................................ 10

2.6.1 Capacity Expansion Modeling ........................................................................................ 12

2.6.2 Engineering Judgment..................................................................................................... 13

2.7 Production Cost Modeling ............................................................................................................. 13

2.8 Portfolio Selection ......................................................................................................................... 15

3 Inclusion of Solar in Resource Planning Processes ...................................................................... 18

3.1 Background.................................................................................................................................... 18

3.2 Solar Data ...................................................................................................................................... 18

3.2.1 Solar Profiles................................................................................................................... 18

3.2.2 Solar Cost Information.................................................................................................... 19

3.3 Solar Capacity Valuation ............................................................................................................... 21

3.4 Additional Considerations for Solar in Resource Planning ........................................................... 25

3.4.1 Solar Integration Costs.................................................................................................... 25

3.4.2 Customer-Sited Distributed Generation.......................................................................... 25

3.4.3 From Solar Planning to Procurement.............................................................................. 25

4 Improving Solar Within Utility Supply Planning.............................................................................. 27

4.1 Utility-Identified Benefits and Challenges .................................................................................... 27

4.1.1 Solar Benefits.................................................................................................................. 27

4.1.2 Solar Challenges ............................................................................................................. 28

4.2 Utility-Identified Key Questions and Answers.............................................................................. 30

4.2.1 What Are Credible Estimates of Future Solar PV Prices and Performance?.................. 30

4.2.2 How Should Solar Capacity Be Valued? ........................................................................ 31

4.2.3 How Should Solar Resource Variability Be Considered?............................................... 32

4.2.4 How Can Scenario Planning More Optimally Manage Risk and Uncertainty?.............. 33

4.2.5 How Can Software Models More Effectively Incorporate Solar? .................................. 33

4.2.6 Should Customer-Sited, Net Energy Metered Photovoltaics Be Treated as a Net Load in 

Modeling?................................................................................................................................ 34

4.2.7 How Should DG Impact on the Distribution System Be Managed?............................... 35

4.2.8 How Should Net Energy Metered DG’s Impacts on Revenue Be Considered? ............. 36
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4.2.9 Can Solar Supply Planning Be More Appropriately Linked to Other Utility Planning and 

Operations?.............................................................................................................................. 37

4.2.10 Can Transmission and Distribution Planning Be More Appropriately Linked to Supply 

Planning?................................................................................................................................. 38

4.2.11 How Can a Utility Consider Solar During Periods of Low/No Load Growth? .............. 39

4.2.12 Other Considerations for Solar in Capacity Expansion Models and Ongoing Research 39

4.2.13 Summary of Solar Supply Planning Challenges and Solutions ...................................... 42

5 Utility-Identified Analysis Needs....................................................................................................... 43

Glossary..................................................................................................................................................... 46

References................................................................................................................................................. 50

Appendix A – Tennessee Valley Authority Case Study ........................................................................ 54

I. Who is TVA? ................................................................................................................................. 54

II. Envisioning the Future: Developing the TVA Scenarios............................................................... 54

III. Developing the TVA Strategies..................................................................................................... 55

IV. Selecting Preferred Strategies........................................................................................................ 57

V. Involvement and Role of Distribution Companies ........................................................................ 57

VI. Renewable Energy and Energy Efficiency Program Considerations............................................. 58

VII. Conclusion............................................................................................................................... 58

Appendix B – Select Utility Questionnaire Responses......................................................................... 59
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List of Figures

Figure 1. States with IRP requirements as of 2011.................................................................................. 6

Figure 2. States with RPS requirements................................................................................................... 7

Figure 3. Load duration curve by resource category.............................................................................. 8

Figure 4. Average annual consumption growth (Net of EE/DG)........................................................... 10

Figure 5. Sample resource selection options ........................................................................................ 11

Figure 6. Resource characteristics used in capacity expansion planning ......................................... 11

Figure 7. Common factors in PCM analytics.......................................................................................... 14

Figure 8. Key metrics for IRP and long-term planning selection......................................................... 16

Figure 9. Sample scatter plot of portfolios for risk analytics............................................................... 16

Figure 10. Opinions on future of solar costs ......................................................................................... 20

Figure 11. Range of capacity values used in IRP plans, by solar technology.................................... 22

Figure 12. Range of solar capacity values used in planning analysis, by technology, provided by 

utility questionnaire respondents..................................................................................................... 23

Figure 13. Impact of increasing PV penetration on ability to meet system peak............................... 24

List of Tables

Table 1. Historical and Projected Load Growth ....................................................................................... 9

Table 2. Examples of Capacity Expansion Model Constraints............................................................. 12

Table 3. Common Resource Planning Tools and Their Associated Functions.................................. 15

Table 4. Primary Capacity Valuation Approaches ................................................................................. 21

Table 5. Challenges and Utility-Identified Best-Practices for Incorporating Solar Into Utility Supply 

Planning, Based on Utility Interviews .............................................................................................. 42
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1 Introduction

1.1 The Task for Utility Resource Planners

When large coal facilities were constructed 50 years ago, utility planners had never heard of 

production cost models or integrated resource plans. Climate change was still an unfamiliar 

term, and utilities were tasked with meeting a steady six-percent load growth. Committing to 

large coal-fired generating stations, despite the risk and investments they required, was likely 

viewed as a necessity to provide baseload generation, rather than one option of many.

Today’s utility planners are presented with a very different landscape: near-zero load growth, 

the near-term retirement of large baseload facilities, fuel price uncertainties, a variety of 

evolving generation and grid technologies, state and federal efficiency and renewable support 

policies, and a host of environmental considerations. Planners are asked to balance a wide 

variety of factors relating to reliability, cost, risk, environmental protection, and equity. They 

have an aging grid, new customer demands, and a host of stakeholders to consider. Utility 

resource planning has evolved into a resource-intensive process that requires significant 

expertise, substantial time, and sophisticated tools (Wilson and Peterson 2011). Even so, 

there are many benefits to undertaking a thorough planning process to determine the lowest 

practical costs of reliably meeting expected future electricity demand. For this reason, at least 

27 states now require utilities to produce formal IRPs or similar plans (Wilson and Biewald 

2013).

8

1.2 Purpose of this Report

One of the relatively new elements that resource planners are challenged to consider is solar 

energy technology.

9 The goal of this report is to review current utility practices in long-term 

supply planning, and to understand how solar technologies are represented and modeled.

Federal and state policy developments, solar PV price declines, and the introduction of new 

solar PV “ownership” business models are stimulating increasing interest in solar 

technologies, at every scale. However, solar introduces a host of technology types, a wide 

variety of system sizes (from residential to utility-scale), variability in the generation profile, 

and rapidly changing costs. In addition, the established rules, regulations, and laws that 

govern utility supply procurement were developed during a time when large-scale, centrallylocated, conventionally-fueled generation was the only real option considered. Now that 

generation options and different configurations are increasing, the policies may need to be 

adapted to new technological, operational, and market needs, such as distributed solar 

generation.

Most, but not all, utility planners have less experience analyzing solar than conventional 

generation as part of capacity planning, portfolio evaluation, and resource procurement 

decisions. In the last 5-10 years, some utilities have developed tools and methods to model 

solar PV, if not other solar technologies. However, aside from publicly-available IRPs, there 

have been few resources through which utility planners can learn from each other regarding 

 8 For the purposes of this report, the authors use integrated resource plans, integrated resource planning, and 

long-term planning interchangeably. 9 This report considers distributed solar PV, utility-scale solar PV with and without tracking, concentrated solar 

power with and without storage, PV with battery storage, and concentrating PV. Because distributed solar PV 

and utility-scale PV are the dominant applications in the market, these technologies are the main focus.
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methods, challenges, and solutions about how to consider solar in planning process.

10 This 

report aims to begin this exchange of information. 

In sum, this report aims to build understanding of:

• Time frames and methods for utility long-range resource planning

• Tools and assumptions to conduct utility resource planning

• How solar technologies are considered in the resource planning process. 

1.3 Key Audiences

The primary audience for this report is utility resource planning departments. The report’s

aim is to share information about how others in the industry incorporate solar generation –

both utility-scale and customer-sited – into their resource planning processes. A secondary 

target audience is the U.S. Department of Energy, who is interested in understanding how 

solar electricity is represented in the market and whether there are analysis needs the 

Department can address. 

1.4 Related Research to Date

The research for this report builds upon work completed in 2012 by Lawrence Berkeley 

National Laboratory (LBNL), as reported in “An Evaluation of Solar Valuation Methods 

Used in Utility Planning and Procurement Processes” (Mills and Wiser 2012). For that effort, 

LBNL conducted a literature review of a sample of utility planning studies and procurement 

processes. Their aim was to understand the methods utilities use to design resource portfolios 

for consideration, as well as how the utilities evaluate the various bids during the 

procurement process. They looked at how the utilities approached a variety of factors –

including capacity value, energy value and integration costs, risk reduction, grid integration,

and operability issues – as they relate to solar. 

The LBNL team found that utility approaches vary widely. While many utilities have a 

method to evaluate the value of solar, only a few conducted detailed analyses that considered 

key factors. Rarely were the tradeoffs between various solar options or the full range of 

benefits and costs to the grid considered. The authors noted that improvements could be made 

to current utility methods in many areas, including the ranking of resource options, estimating 

capacity, evaluating the energy value of solar, estimating integration costs, and considering 

storage.

This report expands on the LBNL report. While the LBNL report used information found in 

utility IRPs and looked only at methodology, this report relies on interviews and a 

questionnaire to investigate the planning process and assumptions more thoroughly. 

1.5 Research Methodology

1.5.1 Research Team

NREL managed the project, directed research, and led analysis of results. Together, NREL 

and SEPA conducted electric sector interviews, delivered a utility questionnaire, and 

analyzed results. NREL and SEPA worked together to author this report.

 10 The Joint Resource Planner’s Forum, sponsored by the Western Governors’ Association, has also provided a 

forum for information exchange for planners in the U.S. West. See: http://westgov.org/newsite/crepc/pastmeetings/. 
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1.5.2 Advisory Council

Prior to beginning the research for this report, a small advisory council was formed. The 

council helped focus the project’s goals, inform its design and review the questions that were 

used in interviews with select utilities and in a written questionnaire. The council consisted of 

6 members representing two utilities, two national labs (NREL and LBNL), and two 

stakeholder groups (National Regulatory Research Institute and Western Resource 

Advocates).

1.5.3 Utility and Stakeholder Participation

Much of the data for this report was obtained through interviews with utility planners and 

non-utilities involved with the resource planning process, including software providers and 

consultants. Most, but not all, are identified in the acknowledgements; a few prefer to remain 

anonymous given current or near-term resource planning activities. In total, the research team 

conducted interviews in-person or by phone with representatives from 13 entities in the 

electric sector, including nine utilities. The authors gathered information on utility supply 

planning and how solar is represented, including:

• Resource planning process details (planning horizon, expected net load growth), 

• Key assumptions (whether DG is represented as supply or negative load, solar cost 

assumptions, existing solar projects) 

• Model methodology (inclusion of distribution and transmission planning, type of risk 

analytics, candidate portfolio development), 

• Model software (which models for capacity expansion and production simulation); and

• Solar representation (project size, capacity value, integration cost adder).

To supplement the interviews, the research team designed and administered a questionnaire. 

The questionnaire, which was distributed to 58 utilities using a web-based platform, included 

25 questions focusing on the methods and tools used in the planning process and the 

incorporation of solar into this process. Key questions and a summary of utility responses 

from the utility questionnaire are included in Appendix B.11 Twenty-two responses were 

received, representing 28 utilities in 22 states.

12

1.6 Report Organization

In Section 2, the report provides background information, including the IRP/long-term 

planning process, laws and regulations that shape electricity supply and demand (e.g. state 

mandates for renewable energy and energy efficiency), and related research to date. Section 2

also covers details of the resource planning process and explains considerations about the 

existing generation fleet, load forecasts, capacity expansion planning, production cost 

modeling, and long-term portfolio selection. The inclusion of solar in resource planning 

processes is explored in Section 3. This section includes a solar PV technology overview, 

information on where to get solar data, experiences in capacity valuation, and additional 

considerations in how to include solar in resource planning. Section 4 explores challenges

and benefits with including solar in the supply planning process that were identified by the 

utility participants and stakeholders, as well as some utility-identified answers for thirteen 

key questions around including solar in utility long-term planning. And finally, next steps and 

utility-identified analysis needs are discussed in Section 5.

 11 Individual utility responses are not provided in this report. 12 Some utilities reported similar planning processes across multiple affiliated companies, and some utilities 

operate in multiple states.
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2 Resource Planning Process

Integrated resource/long-term planning is a utility function that looks across a wide range of 

potential resource options to decide the best combination to reliably serve customers over a 

long-term period, often 20 years or more. This long-term view is often codified in an IRP or 

other long-term plan.

13 Based on interviews and questionnaire results, it appears that most –

but not all – utilities follow a similar structure for long-term planning. It is also clear that for 

each step, utilities use different levels of detail, models or alternative calculation 

methodologies to analyze their generation portfolio. While resource planning is much broader 

than just the incorporation of solar, the differences were stark enough that the authors decided 

to capture the different methodologies used, as they provide a basis for incorporating solar 

into long-term planning.

2.1 Key IRP Metrics: Least-Cost and Lowest-Risk

Historically, IRPs were judged almost exclusively on a least-cost perspective, meaning 

utilities must choose whatever mix of future resources result in the lowest cost. Cost still 

appears to be the dominant metric guiding long-term planning decisions, particularly because 

the least-cost requirement may be codified in state law. With the advent of renewable energy 

and energy efficiency standards, coupled with several natural gas market disruptions and 

associated price spikes, some state commissions and utilities are increasingly looking toward 

incorporating supply diversity and risk-reduction into their planning model. For example, 

some utilities are placing an emphasis on risk-adjusted metrics that consider future 

regulations, future fuel prices, “affordable costs” or other non-cost metrics. 

However, some states may require state law updates in order to consider least-risk in addition 

to least-cost. For example, the Wyoming Public Service Commission updated their guidelines 

for IRPs to specifically state that the commission’s review of resource plans may include 

“least-cost/least-risk planning” (WY PSC 2013). Utilities who note that an exclusive focus on 

least-cost may not yield the optimal portfolio of resources, may decide to push for changes in 

state law and statute to allow for inclusion of least-risk considerations in addition to leastcost.

Incorporating regulatory and fuel price risk and variability into the planning process 

introduces significant complexity but provides a much fuller picture of how different 

assumptions regarding future resource mixes will impact key metrics for the utility in 

question. For example, the following questions can be more closely analyzed when 

considering least-risk in addition to least-cost:

• What is the impact of a 30% price swing in natural gas on the future revenue 

requirements of multiple portfolios?

• How do more stringent environmental regulations impact the economic viability of 

the existing coal fleet? 

• Will higher load growth materially impact the resource decisions being made in the 

next five years? 

• In 30 years, what are the potential risks and uncertainties with building resources 

today?

 13 To illustrate an example of a successful and thorough process, a case study covering Tennessee Valley 

Authority’s recent IRP is included at Appendix A.
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To determine the answers to these questions, the utility must examine relevant supply and 

demand policies, look at its existing fleet of generation resources, project the longevity and 

availability of each asset and contract, consider demand-side opportunities to reduce load 

and/or peak demand, forecast future energy and peak demand requirements, and determine 

the best set of future resource additions to meet customer needs. As a result, there are a large 

number of variables that could change over a 20–30 year planning horizon, and thus a large 

number of potential futures that could come to fruition. 

It is important to note that utilities plan to meet two related yet distinct customer needs as part 

of this assessment: peak demand (in MW) and energy requirements (in MWh). From a peak 

demand standpoint, utilities typically plan to meet the forecast load growth plus a reserve 

margin. This reserve margin is often based on a Loss of Load Probability study, i.e., 

determining the reserves needed to ensure a probability of no worse than one outage in a 10-

year period. Reserve margins vary slightly for each utility, but typically fall in the 12–18% 

range of peak demand (meaning that for every 1,000 MW of peak demand, utilities plan for 

1,120–1,180 MW of generation availability). 

From an energy perspective, the mix and type of each generation resource becomes much 

more critical. A utility could conceivably meet all of its needs by adding combustion 

turbines; however, these resources typically have a poor heat rate (or lower fuel efficiency), 

which translates into a high overall production cost to the consumer. Alternatively, the utility 

could meet all of its future needs with renewable energy, which effectively has no production

cost due to its fuel source being free; however, renewable energy is often variable in nature 

and not perfectly aligned with system needs. This could result in a significant over-building

of capacity than would otherwise be pursued. It is for these reasons that integrated resource 

planning is such a critical activity for so many utilities – the decisions contemplated in this 

process can have huge ramifications to customers over the long term.

2.2 The IRP Process

Integrated resource planning is the process of determining the lowest practical costs of 

reliably meeting expected future electricity load through the combination of supply-side 

generation and demand-side resources, while balancing reliability of service, environmental 

protection, and other goals. 

Given the benefits of careful planning, particularly to ratepayers, some states require utilities 

to undertake IRP or long-term planning processes that are overseen by the state regulators. As 

of 2011, 27 states had IRP requirements, two states were developing or revising IRP rule or 

filing requirement, and ten states had long-term plan filing requirements (see Figure 1). The 

planning horizons generally range from 10 to 20 years, with the majority using a 20-year 

period. Plans must be updated every 2–5 years, with most states requiring biannual updates

(Wilson and Biewald 2013). In general, state rules require that all feasible supply and demand 

side resources be considered, with some states specifying the resource types. 

Long-term planning can be done differently, depending on the structure of the market and the 

authority of the regulators. In states with regulated retail markets (mainly in the West and 

South), generation suppliers are regulated and often have state requirements to produce an 

IRP or long-term plan. In states with competitive retail markets (mainly the Northeast, MidAtlantic, parts of the Midwest and California), the suppliers of generation are not regulated, 

and thus do not have state requirements that suppliers produce an IRP. Distribution 

companies that operate in competitive markets, however, are the “provider of last resort” and 
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are thus responsible for procuring power for customers that do not choose a competitive 

generation supplier. These distribution companies are subject to oversight by the public 

utilities commission. As of 2011, four states with competitive retail markets (Ohio, Delaware, 

Connecticut, and Rhode Island) required distribution utilities to undertake an IRP process, 

although the resource plans are generally not as comprehensive as in states with verticallyintegrated utilities (SEE Action 2011). Even in the states that do not require the submission of 

IRPs or long-term plans, utilities often undertake their own resource planning efforts as a 

matter of prudent business practice either on regular intervals or as needed based on market 

conditions.

Adapted from Wilson and Biewald 2013

Figure 1. States with IRP requirements as of 2011

2.3 Electricity Supply and Demand Laws and Regulations

Many states have laws or regulations that can impact the mix of generation supply, or the 

total amount of customer demand. The dominant supply policy is the renewable portfolio 

standards (RPS). RPS policies require electric utilities to generate or buy renewable energy 

(RE) equal to a certain percentage of their retail electricity sales, or a set minimum quantity,

within a specified timeframe. Currently, 29 states and the District of Columbia have 

established RPS mandates, while eight states have less binding goals. RPS policies vary by 

state in their requirement, deadlines, types of utilities covered, eligible resources, penalties 

for non-compliance, and technology eligibility. Several states have specific solar or 

distributed generation “carve-outs,” shown in Figure 2.
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Adapted from DSIRE 2013

Figure 2. States with RPS requirements

Together, current policies require 3–5 GW of new renewable energy capacity to be added 

each year between now and 2020, with a total of 94 GW of new renewables by 2035, if full 

compliance is attained (Barbose 2013). States sometimes adjust the standards, historically to 

increase the requirements, though several unsuccessful legislative initiatives to limit or cancel 

RPS requirements occurred in 2013.

Renewable energy mandates may be incorporated into resource planning by setting 

constraints in capacity expansion and production cost modeling and by including the mandate 

as a criterion during the evaluation of resource portfolios.

Demand-related policies can include energy efficiency mandates, utility energy efficiency 

programs, demand response/shifting and technologies such as smart meters. These programs 

are evaluated to estimate program growth and then rolled into the overall load forecast (see 

section 2.5) to determine what peak demand is and when it will occur.

2.4 Existing Generation Fleet Review

The first step in resource planning is determining the longevity of the existing fleet of 

generation assets and purchased power contracts. Generation can be categorized into four 

main categories: baseload, intermediate, and peaking. Baseload generation assets are 

typically larger plants that have very low operating costs and are intended to run on a nearly 
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constant basis. They also require a long lead time to start and therefore ramping on and off 

are not particularly viable for these facilities, particularly as it could have an impact on 

minimum loading requirements for the generators. Nuclear and coal generation are often 

treated as baseload resources. The degree that baseload facilities can ramp up and down is 

dependent upon the design of each individual plant. NREL research shows that repeated 

cycling of coal plants can have large impacts on cost and wear (Lew et al. 2012).

Intermediate resources, typically natural gas combined-cycle plants, dispatch next. These 

resources are typically more flexible (compared to baseload plants) and able to follow load 

during the day. They can also be started more quickly than baseload resources (in a few 

hours), can be cycled on and off daily, and can be ramped up and down relatively easily. 

Peaking resources such as combustion turbines are used to meet demand during the peak 

hours of the day when load is at its highest. These assets can start and stop very quickly 

(often in 10 minutes or less), but are more expensive to operate. Graphically the three 

dispatchable resource types can be depicted as serving distinct portions of a utility’s load 

duration curve, shown in Figure 3.

Figure 3. Load duration curve by resource category

Certain renewable resources can be placed into a fourth category called “variable.” 

Renewable resources like solar and wind are not dispatchable; that is, they cannot be turned 

on and off at the utility’s discretion. Moreover, they are effectively “must take” resources, 

meaning that when the sun shines or the wind blows, the utility will be taking the energy. 

Separate from utility-owned generation assets, many utilities rely upon purchased power 

agreements (PPAs), power exchanges, storage, and short-term market transactions to meet a 

portion of their load requirements. Contracted power could be in the form of fixed blocks 

(100 MW delivered for a given time period), tolling agreements (where the utility is 

Baseload

Intermediate

Peaking

MW (Capacity)

Hours

Load Duration Curve

8760
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responsible for the delivery of gas and dispatching the third party’s unit), or call option 

contracts (executable when the power price exceeds an agreed upon strike price). PPAs differ 

significantly from owned assets in that when their contract term ends the resources must be 

replaced or renegotiated, whereas with owned assets there is the potential to extend the 

plant’s life.

When approaching long range planning, the utility must understand what tools it has in the 

proverbial toolbox and how long those tools will be accessible. As infrastructure ages, more 

and more generation assets will be nearing the end of their useful life and plant retirement 

decisions will loom large. This is particularly relevant with existing coal-fired generation, 

where environmental regulations requiring expensive upgrades to emissions control 

equipment, coupled with low natural gas prices, cause resource planning groups to perform 

detailed economic analyses of repair-versus-replace scenarios. Plant retirements could require 

the addition of new generation assets regardless of whether the utility forecasts any customer 

load growth during its planning horizon.

2.5 Load Forecast

A key component in long-term planning is predicting future customer demand. This involves 

detailed analytical reviews of each customer segment and the likelihood of each segment 

adding new customers as well as increased (or decreased) consumption on a per-customer 

basis. Residential customers can change their electricity needs without physically changing 

their footprint in several ways. Increased load can result from more computers, smart devices, 

and video games, as well as replacing old technology with new, higher energy-use 

technology (e.g., plasma TVs). Decreased load can occur when appliances are replaced with 

more energy efficient models. For each customer class, the utility rolls up the load forecasts 

to determine the peak demand periods on a daily, monthly, seasonal and annual basis.

According to data collected by the Energy Information Administration, electricity sales 

growth has slowed significantly over the last several years compared to the previous several 

decades (EIA 2013).

Table 1. Historical and Projected Load Growth

Time Period Average Annual 

Load Growth

1981 – 2005 2.3%

2006 – 2012 0.23%

2013 – 2040 (est.) 0.78%

Source: EIA 2013

Utilities as recently as five years ago were building resource plans based upon compound 

growth assumptions of more than 2% per year. The economic slow-down that began in the 

late 2000s is a principle cause of the reduced demand growth in recent years. In addition, 

many utilities have implemented energy efficiency programs and goals, which further reduce 

energy and demand projections. Others have implemented programs designed to reduce 

demand (utility energy efficiency programs), or to moderate demand including the use of 

smart meters, demand side management and programs designed to shift or shed peak loads. 

Based on a questionnaire conducted in support of this report, utilities appear to have lowered 

their overall internal load growth forecasts and are now more in line with what EIA is 

predicting (see Figure 4).
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Source: Utility questionnaire, as part of this project14

Figure 4. Average annual consumption growth (Net of EE/DG)

Aligning a growth assumption with the existing generation and purchased power assets gives

the resource planning group a vision of how much new generation capacity is required and

when those additions are needed. This task is tackled as part of capacity expansion planning.

2.6 Capacity Expansion Planning

Capacity expansion planning is the act of determining the most viable long-term resource 

plan that meets projected electric demand. The process includes reviewing a wide array of 

new resource additions or contract renewals to meet growth or fill needs created by asset 

retirements and purchase contract expirations. Multiple future energy resource mixes, based 

on a wide array of potential options, are considered in aggregate. As mentioned previously, 

many utilities have operated or continue to operate in a least-cost planning environment; the 

determination of what is truly least-cost is a difficult task. Utilities have at their disposal 

dozens of technology choices and multiple configuration options within those choices. Figure 

5 shows some of the choices for natural gas and solar.

 14 Load growth is highly region-specific, as it depends in large part upon a utility’s local economic outlook and 

population growth.

0 2 4 6 8 10 12

< 0%

0-1%

1-2%

2-3%

> 3%

Average Annual Consumption Growth - Net of 

EE/DG
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Figure 5. Sample resource selection options

For each viable technology option, the utility will track and calculate a series of cost and 

performance characteristics that will drive decisions about whether the resource will be 

included in any year of a future potential resource plan. Representative characteristics are 

shown in Figure 6.

Figure 6. Resource characteristics used in capacity expansion planning

Based upon these options and their unique characteristics, a plan for future resource additions 

can be developed. Two general forms of capacity expansion planning occur today: capacity 

expansion modeling and engineering judgment.

Natural Gas

Combined 

Cycle

1x1

2x1

Duct-fired

Combustion 

Turbine

Reciprocating 

engine

Aeroderivative

Wet- vs. DryCooled

Solar

PV

Fixed tilt

1- or 2-axis 

tracking

West- vs. 

south-facing

Concentrating

CSP

Parabolic trough

Central receiver

Trough w/ storage

Plant Statistics

Nameplate MW

Summer/Winter Net 

Dependable Capacity

Capacity Value (RE only)

Construction Time

Useful Life

Plant Operating 

Characteristics

Capacity Factor

Heat Rate (combustion only)

Water Use

Emissions

Ramp rate, minimum load, 

start times

Planned/Unplanned Outages

Plant 

Economics

Capital Cost ($/kW)

Incentives (e.g.tax credits, 

MACRS, state incentives)

Variable O&M ($/MWh)

Fixed O&M ($/kW-yr)

Integration Costs
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2.6.1 Capacity Expansion Modeling

Capacity expansion modeling is a process of creating a series of resource plans using a 

computer software tool called a capacity expansion model (CEM). Utilities provide an array

of inputs into the model with details on their existing generation assets and purchased power 

contracts, load growth assumptions, cost curves for fuel sources, capital and O&M costs for 

potential resource additions, and other key variables. The models then typically run through a 

dispatch simulation (often a sample week in every month of the planning horizon) that 

reviews existing resources against load requirements and determines when new resources are 

required. The model then reviews the resource options available and identifies the best choice 

based on set criteria, often times using least-cost as the primary criterion (as noted earlier, 

some utilities are also including one or more lowest-risk criteria). To do this, the model looks

at literally thousands of potential resource addition combinations and uses filters or “screens” 

to reduce them down to either one optimized plan or a ranked series of plans.

Certain constraints can be forced on the model so that multiple outcomes result, which can be

further investigated with additional modeling. For example, Table 2 shows the potential 

capacity expansion constraint criteria, along with an explanation of how each criterion is 

applied.

Table 2. Examples of Capacity Expansion Model Constraints

Potential Capacity Expansion Constraint Criteria Motivation

Limiting the number of specific resources that can be 

added in a given window of time

This approach is taken if the utility knows that 

it will be constrained from a capital or 

workforce perspective and could not build 

more than a given number of resources (i.e., 

combustion turbines) in any given year or over 

a set period of time.

Setting a minimum level of capacity or energy from a 

specific resource type

If a utility is subject to an RPS standard, this 

approach could be used to force a renewable 

resource to be selected even if it is not the 

least-cost option.

Restricting certain resources from being selected A utility may wish to run a scenario where the 

model is not allowed to select certain resource 

types, like new coal generation, or limits how 

much capacity can be from PPAs.

Requiring a specific resource to be built at a certain 

point in time

The utility could force the model to place a 

specific unit in service during the planning 

horizon. This could be done if the utility 

wished to see the impacts of adding new 

nuclear capacity.

Forcing a plant retirement prior to its book life Certain environmental regulations could 

warrant early closure of coal-fired generators. 

While capacity expansion models often can 

run “repair versus retire” analytics, the utility 

may wish to see the retirement scenario play 

out regardless of the least-cost option.

By varying these constraints with each successive run of the CEM, a utility will be able to see 

the resource plans optimized to different end goals. Another way of thinking about this 

process is the concept of scenario planning. Several utilities use a series of “future states,” or 

potential scenarios that could play out in the future, to drive how they could approach 

building out resource plans. 
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Scenario planning often tries to set plausible boundary cases based on existing unknown 

conditions. For example, one scenario could contemplate a future with extremely restrictive 

federal environmental policies. The utility could then look at constraining the CEM by 

forcing closure of coal plants, using high natural gas price curves, setting a carbon tax, or 

other activities. The CEM would then look to build a portfolio that operated at the least cost 

possible in that potential future, possibly by focusing on renewable additions. Alternatively, a 

scenario could be developed that postulated persistently low natural gas prices due to shale 

gas availability. This low-cost gas curve would likely lead to natural gas generation being the 

dominant new resource of choice.

The result of these constrained scenarios is a series of resource plans optimized to different 

assumptions and that outline the construction timeframes and availability dates of new 

resource additions throughout the planning horizon.

2.6.2 Engineering Judgment

One criticism of reliance on CEMs is that many of the resulting portfolios look quite similar. 

This is because the software is trying to optimize around cost given all of the constraints it is 

working against, and it will not make wildly different selections on its own. 

Some utilities rely instead on engineering judgment to come up with distinct portfolio 

options. Developing portfolios using this approach often starts with an end goal and works

backwards. A utility could look to create portfolios that met substantively different goals, 

such as a plan that would consume the least amount of water or a plan that used 30% 

renewable energy. These end goals could be internally generated or could result from gauging 

the interests of key stakeholders in the IRP process. Regardless, it is a viable method to 

determine the timing and addition of key resources.

2.7 Production Cost Modeling

The next stage in the long-term planning process is to run a production cost model (PCM). 

PCMs simulate hourly dispatch over the entire planning horizon rather than a representative 

sample as is done in a CEM. Utilities use these hourly dispatch models to perform complex 

sensitivity analyses. One approach is called Monte Carlo simulation, where multiple key 

input variables are randomly changed hundreds or even thousands of times in order to see 

how robust each potential resource plan is and how well it performs against a handful of key 

metrics. Other utilities will vary one variable at a time in order to see the specific impacts that 

major assumptions have on the outcome of a portfolio. Some utilities use both methods.

There are a group of assumptions utilities commonly vary as part of this process, as shown in 

Figure 7.
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Figure 7. Common factors in PCM analytics

For multi-variable Monte Carlo simulations, variables can be correlated or allowed to move 

randomly. While capacity decisions may already have been decided, the changes in these 

inputs can align with the future state/scenario planning described previously. The utility 

could limit variables to certain ranges for each future state and test all of the portfolios 

against those cases to see how they would perform in a wide variety of situations. 

By running an hourly PCM model, the utility is effectively looking at unit-by-unit dispatch 

across their system and instituting a dispatch of the resources based on economic merit. 

Modifications in certain assumptions can cause a significant shift in dispatch merit order. For

example, adding a tax on carbon emissions reduces the economic viability of coal generation 

compared to other resources; depending on the level of the tax, it could result in coal 

resources being dispatched less frequently than would otherwise occur. 

With this increased level of detail and precision, the resource plans developed in the capacity 

expansion planning phase can be compared with each other across a wide variety of 

sensitivities. This process can also give the utility an understanding of how resistant each 

resource plan may be to market unknowns and risks; this robustness (or lack thereof) factors 

into the decision-making process in many IRPs and long-term plans.

In order to simulate the complete picture, utilities often run these models in conjunction with 

each other, since capacity expansion and PCM tend to run as separate models. Table 3

provides a list of some common models currently utilized by utilities in resource planning.

PCM 

Factors

Fossil fuel

prices

Wholesale 

market 

prices

Load

Environmental 

costs

RE levels

Hydro 

availability

Energy 

efficiency

Incentive 

availability
15

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Table 3. Common Resource Planning Tools and Their Associated Functions

Software Tool Capacity 

Expansion 

Modeling

Production 

Cost 

Modeling

Transmission

Simulation

Notes

Strategist ✔ Pairs with PROMOD IV

System 

Optimizer

(Capacity 

Expansion)

✔ Pairs with Planning and Risk

PROMOD IV ✔ ✔

Pairs with Strategist

Regional scale

Hourly load profile

Planning and 

Risk 

(PROSYM)

✔ Pairs with System Optimizer

AURORAxmp ✔ ✔

Strategic 

Planning 

(MIDAS)

✔ Used mostly for financial 

analysis.

EGEAS ✔

A modular package that 

optimizes to a load duration 

curve, with the single 

objective of least cost

GENTRADER ✔

Modular tool 

Regional scale 

Hourly load profile

PLEXOS ✔ ✔

Models transactions to 

consider competition of 

supply, and is thus suitable for 

use in a competitive 

environment.

2.8 Portfolio Selection

The end result for many IRPs is a recommendation on a resource portfolio. Selecting one 

resource plan from several (or dozens) created in the capacity expansion planning process 

and analyzed through production cost modeling can be a daunting task. To assist with this, 

utilities rely on a handful of key metrics to guide their selection. These metrics could be 

viewed qualitatively or quantitatively as part of a detailed scorecard. Metrics typically

examined are shown in Figure 8.
16

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Figure 8. Key metrics for IRP and long-term planning selection

Of particular note is the inclusion of risk parameters in the long-term planning process. Leastcost planning dominated the IRP landscape for many years; however, several states have 

introduced least-risk planning and metrics as a way to gauge the potential for long-term rate 

stability. These can include the value of generator diversity to lower potential future risks, as 

well as the potential risk of future regulations that could increase the risk of today’s supply 

decisions. Risk metrics are often a result of detailed Monte Carlo simulations, where it is 

possible to create a bell curve of potential present value of revenue requirements (PVRR) for 

each portfolio. If a utility wanted to ensure that it selected a portfolio of future resources that 

was both low cost and resilient to upward price risk, it could focus on the expected PVRR as 

well as the potential price risk (or risk tail, represented by the upper 5% of PVRRs or some 

other value).

Figure 9. Sample scatter plot of portfolios for risk analytics

Portfolio 

Selection

Present Value 

of Revenue 

Requirements

Cumulative 

CapEx

Emissions

Fuel Diversity

Water Usage

Average 

System Cost

Future Risk

Expected PVRR

95th Percentile - Expected PVRR

PVRR and Risk Tail Analysis
17

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In the sample scatter plot in Figure 9, a handful of potential resource portfolios are plotted 

based on two key metrics: their expected PVRR, and the delta between their 95th percentile 

PVRR (i.e., upper tail of cost projections) and the expected PVRR. Portfolios falling in the 

lower left quadrant (as highlighted by the black line in Figure 9) would be considered to have 

lower expected cost as well as lower overall risk of price increases for ratepayers. While only 

a few utilities follow this approach, many have considered adopting this or some other similar 

method as part of their portfolio selection process. This can be augmented by also looking at 

the environmental impacts of each portfolio (carbon emissions, water usage, etc.), or even 

impacts to economic development criteria like job creation. 

It is important to note that portfolios selected as part of an IRP process are rarely binding; 

rather, they are treated as roadmaps based on a given set of assumptions. Virtually all utilities 

have a separate process to receive approval for building new assets and bringing them into 

their rate base. Indeed, IRPs are often only “acknowledged” by state commissions, a term of 

art that provides little assurance to utility financial officers and investors, as it does not 

contain any level of approval or surety of future cost recovery; however, IRPs and long-term 

plans do provide an opportunity for the utility to lay out its vision of potential futures and the 

resource mix that is most appropriate for its customer base.
18

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3 Inclusion of Solar in Resource Planning 

Processes

3.1 Background

Solar generation has become increasingly prevalent in recent years, driven by an increased 

focus on renewable energy coupled with rapid decreases in the cost to deploy solar assets. 

Over the last three years alone, total annual solar installations have risen from 781 MW in 

2010 to nearly 2,400 MW in 2012, and cumulatively nearly 6.1 GW of solar is now installed 

and operating in the United States as of the end of 2012 (Krishanmoorthy et al. 2013).

15 

Three interesting characteristics exist for that installed capacity:

1. More than 99% of the installations are customer-sited (DG).

2. Nearly 80% of the cumulative U.S. solar capacity serves the load requirements of 10 

utilities, with 30% of the overall capacity at one utility (Krishanmoorthy et al. 2013).

3. In the last 2 years, centralized solar (projects > 5 MW in size) have increased from 

less than 1 GW to more than 3 GW (SEPA 2013).

The proliferation of DG systems across multiple jurisdictions has been driven by many 

disparate factors. In some regions, it is due to aggressive customer adoption incentives. In 

others, the systems can compete on a cost basis against existing utility retail rates. Utilityscale systems, on the other hand, must compete more directly against wholesale costs, either 

all-source or renewable-only. The declining costs of PV in recent years have helped close the 

gap between what it costs to develop and deploy solar and what the market can bear. This 

became crystallized in 2012 when large-scale solar systems made up approximately half of 

all new solar capacity installed (slightly more than 1,100 MW), an increase of 2.5 times the 

capacity from 2011(SEPA 2013).

Not all utilities are adopting solar generation at the same pace; however, as solar deployment 

costs continue to decline and reach parity with other resource options (especially solar PV

recently), more and more utilities are likely to turn their attention toward solar generation and 

how it can play a part in future resource plans. In fact, most if not all utility interviewees 

stated that they are likely to include solar PV as a resource in their forthcoming plans.

3.2 Solar Data

There are two main types of solar data required in long-term planning according to utility 

interviewees, which include solar profiles and solar costs.

3.2.1 Solar Profiles

Solar profiles characterize the potential solar generator output, which includes resource 

potential on an hourly basis. Utilities that participated in the research for this report identified 

three main sources for solar profile data that they incorporate into their IRPs/long-term plans: 

National Renewable Energy Laboratory’s PVWatts (PVWatts 2013), PVsyst (PVsyst 2013), 

and data from existing operating plants in their service territory. PV Watts and PVsyst are 

only relevant for PV systems, the dominant technology included in long-term plans (if any).

PVWatts is an online program developed by NREL that provides the capability to create a 

typical meteorological year (TMY) of profile data for a solar resource in a given geographic 

 15 All values listed in MW-ac.
19

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region. This tool can provide a first glimpse into what kind of production could be expected 

from a proposed solar facility. Using a DC rating in kW, a derate factor, the type of array 

(fixed or tracking), and the array’s tilt and azimuth (where south-facing is stated as 180°), the 

model will create an hourly expected output based on typical weather patterns and solar 

irradiance, as experienced over several decades’ worth of recorded data. The downside to 

PVWatts is that the analysis can be run only for a limited number of sites. For example, there 

are only four sites available in Arizona: Flagstaff, Phoenix, Prescott, and Tucson.16 This data 

can be particularly helpful if utilities do not have many PV systems in operation in their 

jurisdiction, or do not have access to those systems’ output and want an extremely simple 

methodology. 

PVsyst is a software package that allows users to study the design of PV systems and perform 

sizing and data analytics. It includes component and meteorological databases, and is 

intended for use by architects, engineers, and researchers. PVsyst performs three general 

levels of study: preliminary design, including sizing the project; project design, where 

detailed hourly modeling is conducted; and measured data analysis, which allows for the 

actual data from the fully operational plant to be compared against simulated variables

(PVsyst 2013).

Data from operational plants is also used by utilities with access to system data within their 

service territory. This data provides the utilities a view into the real operational characteristics 

of solar PV and other solar generation. However, because these profiles are based on actual 

plant output, they may require some review and potential adjustments prior to inclusion in 

any kind of analytics. For example, if a PV plant has only been in operation for a short period 

of time, it may be necessary to supplement the profile with PVWatts data. Alternatively, 

severe or abnormal weather occurrences may dictate the need to augment with PVWatts data. 

This scrubbing is appropriate, as long-term plans are based first and foremost on projected 

load requirements which themselves have been “weather normalized,” or stripped of any 

impacts from abnormal weather conditions. As solar output is also predicated on weather 

patterns, it is appropriate to ensure an “apples to apples” comparison.

While there is interest in the combination of storage and PV to improve the profile of PV 

overall, storage is not typically modeled in long-term planning by the majority of utility 

interviewees, because utilities feel they need more credible data and analysis before storage 

can be included in long-term planning analysis. A few utilities that do consider the 

combination of storage and PV consider the combination to have dispatchability, although on 

a limited basis.

3.2.2 Solar Cost Information

For the most part, solar cost information is collected from publicly available sources, and 

from existing plants in the utility territory. However, for PV technologies the market is 

changing so fast, it is hard for utilities to understand the future cost trajectory.

Utility interview participants universally commented on the rapid decline in PV costs over 

the last several years. In particular, most utilities had forecast a declining cost curve in their 

planning assumptions, only to see the actual costs decline much more steeply than 

 16 The preceding statement is specific to TMY2 data, which is based on solar data from 1961–1990, and covers 

239 sites. TMY3 (1991-2005) is available for 1,020 locations, but is used less frequently due to the smaller 

sample size.
20

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anticipated. Common beliefs regarding the root cause of the nearly exponential cost 

reductions included:

• U.S. tax subsidies

• Foreign manufacturing credits

• Efficiency gains

• Market surplus of panels.

On a going-forward basis, there was still consensus but less overall agreement on where PV

costs would trend. Companies that were questioned on this issue fell into three general groups 

of thinking shown in Figure 10.

Figure 10. Opinions on future of solar costs

Several utilities specifically mentioned the Department of Energy’s (DOE) SunShot Initiative 

goals for reduced costs of PV deployment by 2020 as being potentially obtainable.17 Others 

commented that (while the dramatic declines seen over the last 5 years are not sustainable)

reductions will continue, albeit in a more measured fashion. Finally, several companies 

believed that solar costs were at or near a bottom and would levelize for the foreseeable

future. These solar PV cost assumptions are reflected in their solar cost assumptions used in 

their long-term planning. While prices have come down rapidly, questions remain about how 

long the prices will continue to decline and when they will bottom out (or if they already 

have). 

A few items have contributed to the thinking of utilities that believe PV costs may have 

bottomed out. First and foremost, there has been a series of bankruptcies in the solar industry 

over recent years, which has taken several manufacturers out of the market. Second, demand 

for solar panels globally is increasing and the market surplus is expected to dissipate over 

 17 The SunShot Initiative’s target is a 75% reduction in installed costs by the year 2020, down to $1.50/watt for 

utility-scale systems.

DOE SunShot 

Initiative cost 

targets are 

achievable

Reductions will 

continue but at a 

more gradual pace

Costs have already 

stabilized

?
21

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utility planning horizons. Finally, there is concern over post-2016 when the ITC reverts from 

30% back to 10%; demand could potentially slow.

Publicly available data sources for solar cost data that can be used in utility analysis are 

explained in Section 4.2.1.

3.3 Solar Capacity Valuation

One key question discussed at length with interviewees was the capacity value utilities 

attribute to the addition of solar capacity on their system. In essence, a solar generator’s 

capacity value is the percentage of its nameplate capacity that is anticipated to be reliably 

available to meet daily and seasonal peak demand, and is a very region-specific metric. There 

are multiple terms of art for this occurrence, capacity credit and capacity value being two 

more commonly used.

Conventional generation resources, like natural gas combustion turbines and coal-fired plants, 

are fully dispatchable and can be called upon to meet load as it fluctuates throughout the day 

and year. In contrast, the most commonly deployed solar technology, fixed-axis solar PV, 

generates only when the sun is shining.18 Typically, the sun provides the most energy for 

production between noon and 1 p.m. each day, and the production capability can diminish 

rapidly as the sun sets for projects facing south. PV facing west can moderate this drop-off, 

although overall production of the facility will be lower. Most utilities commonly peak later 

in the afternoon, between 4 p.m.–6 p.m., due to air conditioning load in the summer months. 

For this reason, solar energy may not correlate perfectly with a utility’s need for it, depending 

on their specific situation (e.g., latitude, insolation). Different solar technology configurations 

can moderate this potential for rapid impact through the addition of storage, or PV tracking 

(one or multiple axes) – but these configurations are not always included in long-term 

planning modeling.

Several methods are used to calculate the capacity value. Some of the more commonly used 

approaches are explained in Table 4.

Table 4. Primary Capacity Valuation Approaches19

Method Definition

Effective Load-Carrying Capability 

(ELCC)

Represents the ability of a power plant to increase the total 

load of a local grid without increasing its Loss of Load 

Probability (LOLP).

Peak Time/Season Windows Calculates capacity credits across predefined hours, months, 

and/or seasons. This approach can look at either the 

minimum or median output likely to occur with a given 

probability. It does not account for the impact of different 

levels of grid penetration.

Capacity Factor Quantifies a power plant’s average output relative to its 

installed capacity; while this approach is simple to calculate, 

it bears no relation to load served, and cannot account for 

the impact of different levels of grid penetration for solar as a 

whole.

 18 Solar generation with storage, whether from battery or thermal technologies, can be considered dispatchable. 19 Additional information on capacity valuation approaches can be found in: Hoff et al. 2008
22

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The appropriate capacity value for a given solar resource is dependent upon its generation 

profile and the utility’s load that it is intended to serve. Fixed-tilt systems that are facing 

south, while producing more energy, will have a lower capacity value than similar systems 

facing west, toward the setting sun. Similarly, single-axis tracking systems will have a higher 

capacity value (and capacity factor) than fixed tilt because of their ability to follow the sun. 

Figure 11 shows results from a recent LBNL report that outlines the wide array of solar 

capacity values currently in use, as reported in utility IRPs (Mills and Wiser 2012). As 

shown, dispatchable technologies were granted the highest capacity credit/value (well over 

80%), CSP (no storage) and PV with tracking were granted capacity credit/value in the range 

of 50–85%, and fixed PV was given a credit/value of 0–50%. General PV had the widest 

range, between 5% and 100%.

Source: Adapted from LBNL 2012

Figure 11. Range of capacity values used in IRP plans, by solar technology

To build upon LBNL’s work, the authors conducted direct interviews and gathered 

questionnaire responses. Most utilities interviewed in this effort generally utilized capacity 

values in the 20–60% range, depending upon technology selection. Adding storage 

capabilities or some other form of dispatchability generally would increase the assumption to

60–100% capacity value. However, several utility interviewees stated that they do not 

provide any capacity value to solar, with or without storage capabilities. 

As part of the questionnaire issued in support of this report, utilities provided additional data 

about their internal planning assumptions, as shown in Figure 12. For different configurations 

of solar PV (first three technologies), 60–76% of utility questionnaire respondents assigned a

capacity value between 0–60% for fixed PV and up to 80% with tracking. 24–40% of 
23

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responses indicated no value assumed for PV technologies.20 Of utilities that did assign 

capacity value, the greatest value was assigned to utility-scale tracking axis PV; more than 

half assigned a capacity value greater than 40%. This makes sense because PV with tracking 

capability can help meet early evening peak demand and provide more capacity value overall. 

Fixed axis PV was allocated a lower capacity value than tracking overall – with a slightly 

higher value for utility-scale compared to distributed PV. 

Conversely, only 26–44% of utility respondents assigned any capacity value to CSP (with or 

without storage), PV with battery storage or concentrating photovoltaics (CPV); 56–74%

used a 0% capacity value.

21 Utilities that assigned capacity values used a range of 0–100%.

Half the respondents that assigned value to CSP with storage assumed a value of 80% or 

above and 88% assumed a capacity value of 40% or greater. For CPV technology, of those 

that assigned value, 80% assumed a value of 40% or greater.

Note: Numbers in circles represent the number of utility responses

Figure 12. Range of solar capacity values used in planning analysis, by technology, provided 

by utility questionnaire respondents

When looked at in aggregate, the incremental capacity value for PV generation resources can 

actually decrease as penetration increases. This occurs because a utility must still meet 

system summer demand after the sun goes down around 7 p.m.–8 p.m. As its penetration 

 20 These utilities either have no or virtually no solar on their systems today, or have specifically stated that they 

have not determined the capacity value assigned to solar at this time.

21 Same comment as Footnote 20.
24

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increases, solar PV’s ability to provide any relief to system needs diminishes rapidly after 5 

p.m., as shown in Figure 13. It should be noted that this decrease does not become marked 

until a non-trivial level of penetration is reached (Perez et al. 2006). Five utility respondents 

to the questionnaire indicated they factor in declining capacity values for solar PV in their 

planning process; however, others were aware of the issue and indicated that it would be 

studied in the future if penetration levels warranted such an analysis.

Figure 13 depicts a typical utility load curve, with increasing levels of PV penetration netted 

out of the load. The solar peaks in the middle of the day, resulting in the full nameplate level 

of PV impacting the load. As the day goes on, the solar drops in production providing less 

and less generation coincident with the utility’s typical peak hour. 

Note: HE denotes hour ending

Figure 13. Impact of increasing PV penetration on ability to meet system peak

Over half of the utilities participating in this report indicated that they perform their own 

analytics comparing solar resources in their region directly to their own load profile or were 

basing their capacity values on data from systems currently in operation. The remaining 

companies rely upon a generic value (either from an industry report or a consulting firm), or 

use the same capacity value for solar as they do for wind generation. Several utilities 

indicated that they were looking at developing their own capacity valuation for solar 

generation in the near future. Many utilities noted a desire to have access to a larger data set 

including other utilities – to compare their experience or have a good place to start in lieu of 

their own data. Appropriate and accurate capacity values are important in the long-term 

3,500

4,000

4,500

5,000

5,500

6,000

6,500

7,000

7,500

8,000

HE1HE2HE3HE4HE5HE6HE7HE8HE9

HE10HE11HE12HE13HE14HE15HE16HE17HE18HE19HE20HE21HE22HE23HE24

MW

Impact of Increasing PV Penetration on System Peak

Load Net 100 MW Net 500 MW Net 1,000 MW Net 2,000 MW
25

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planning context because they will allow for solar generation to meet at least a portion of 

future demand requirements, thereby deferring the need for some new fossil fuel generation.

3.4 Additional Considerations for Solar in Resource Planning

3.4.1 Solar Integration Costs

One risk associated with solar generation without storage is adequately integrating its 

production fluctuations into the overall resource mix for a given utility. Based on responses 

to the questionnaire, discussions during interviews, and industry research, one commonly 

used approach is to estimate the cost (in $/MWh) to the system at large that was caused by 

the variable renewable resources. These studies look at the operating reserve requirements 

and what is needed on a 5- or 10-minute system operation timeframe. While the cost 

estimates are typically low, often in the $2–11/MWh range – as indicated by utility IRPs 

investigated by LBNL (Mills and Wiser 2012), they do represent a cost to the system once 

these resources are operating.22 Utilities can incorporate this cost into their CEM to provide a 

more robust view of the costs of this variable resource.

3.4.2 Customer-Sited Distributed Generation

DG systems are typically installed behind the utility’s meter. Because the system operator 

does not have complete control over DG, and because the production is generally used to 

reduce total site demand for utility power, most utilities today treat DG as a net load impact 

rather than as a resource (see Appendix B, question 3). This net load approach can happen 

implicitly or explicitly, by either assuming that the lower load growth projections that are 

now used already incorporate some assumption of DG behind the meter or by projecting a 

DG adoption curve and then subtracting it from a gross load growth projection for their 

overall customer base. 

Alternatively, a small number of utilities treat DG (or are strongly considering treating DG)

as a generation resource option, rather than a net load impact. This approach can allow the 

CEM to determine a more optimized approach to the amount of DG that could be included in 

the resource plan, and allows for independent investigation of customer load profiles (a major 

driver of long-term planning results). Some potential solutions for addressing DG in 

modeling can be found in Section 4.2.7.

3.4.3 From Solar Planning to Procurement

Resource planning focuses on long-term visions for where the utility wants to take its

resource mix. The capacity expansion plan that results from the capacity expansion analytics 

performed in support of an IRP outlines the timing and type of resources that will likely be 

pursued. It then falls upon the power procurement division of the utility to turn those plans 

into projects that deliver energy and capacity to the grid. This section focuses mostly on PV 

procurement. 

While long-term resource planning is used to inform power procurement at many utilities, 

these two operations are often run by two different departments within the utility. And the 

level of coordination between these groups can vary significantly from utility to utility.

 22 Table 6 “Assumed integration costs used by LSEs to adjust production costs for portfolios with solar.” One 

important caveat is that few utilities have done system-specific studies for integration costs. Several rely upon 

studies done for wind integration and simply use the same $/MWh cost for solar.
26

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Utility procurement procedures typically start with a request for proposal (RFP) that is 

generally targeted to a specific need, be it meeting an RPS goal, adding additional peaking 

generation, or replacing an expiring PPA. Some utilities may use the same/similar key 

metrics from the IRP reviews to inform the RFP process to determine the best mix of future 

resources. As discussed above, PVRR is the main metric on which utilities focus. Once an 

RFP is issued, the focus shifts from comparing large portfolios against one another to

comparing specific projects on a case-by-case basis. One approach is to compare the 

proposals to an avoided cost proxy. At this stage, it is critical that the utility be able to model 

the specifics of each proposal to determine their relative ranking. And some utilities who 

want to have their IRP processes drive their procurement will review the RFP criteria and 

outputs with the IRP planning team to make sure the procurement process is lining up with 

the planning process results.

The main variable in solar bids will be the site-specific production profile. Where many 

utilities may rely today upon generic TMY data for modeling solar in their long-term 

planning analysis, each proposal submitted as part of an RFP will, by necessity, contain much 

more granular information that impacts the solar profile. Site footprints, technology 

selections, array orientations, and site irradiance levels all aggregate together to produce a 

specific solar profile that must be analyzed. As part of the RFP process, the utility can request 

expected production profiles that can be evaluated and compared against each other. In fact, 

many utilities keep a database that compiles a historical record of all of the responses to solar 

RFPs; this is used as a baseline to compare against future bids.

Some renewable developers have begun to study utility valuation methods and preferences to 

create tailored proposals. Often, these unique proposals may have a higher development cost 

or lower production capabilities, but actually provide greater value to the utility reviewing the 

bid. As mentioned previously, a system facing west will not produce as much energy as one 

facing due south, but the production profile could line up much more favorably with the 

utility’s system peak and therefore offer greater capacity value (boosting its overall value to 

the utility).

From an analytical perspective, reviewing individual bids as part of an RFP process is 

mechanically similar to the analytics performed in the IRP process. The utility is still 

comparing the resources and looking for the overall lowest cost and/or best value for its 

customers. 

By integrating more solar into the IRP process in general, utility staff will be more familiar 

with the modeling characteristics associated with solar generation and be more adept once 

bids begin coming in for those resources. Over the next several years, assuming solar 

development costs continue to decline and/or existing supply costs increase, more utilities 

will see solar generation approach grid parity with conventional generation options. It is 

anticipated that this trend will first become noticeable as part of traditional RFP processes, at 

which point those reduced pricing trends will filter back into routine long-term planning 

processes. Reductions in solar costs could also drive CEMs to begin selecting solar resources 

above and beyond what may be required to meet RPS requirements.
27

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4 Improving Solar Within Utility Supply Planning

A number of key challenges of incorporating solar into supply planning analysis were raised 

during the utility interviews. Most of these utilities have not experienced notable levels of 

solar PV, CSP or CPV penetration, and have not always modeled solar in their IRP studies. 

Most utilities expressed an interest in incorporating and refining distributed- and utility-scale 

solar PV in their modeling23 because they anticipate more PV coming onto their system in the 

next decade. Some utilities noted that CSP and CPV are not appropriate technologies for their 

service territories. Several utilities mentioned that they participated in this project specifically

to learn from their peers about best practices for including DG and utility-scale solar PV in 

their models. 

This section explores some of the key benefits and challenges of incorporating solar PV into 

supply planning analysis and decision-making raised during the utility interviews. Note that 

this list is not comprehensive. It captures only items/characteristics raised by the 

interviewees; others may exist or emerge. This section also provides questions and answers to 

these challenges – many of which were identified directly by the interviewees. The goal of 

capturing this dialogue is to help advance the discussion of how solar could be better 

represented in both utility supply planning and broader utility operations.

4.1 Utility-Identified Benefits and Challenges

During the course of interviews with utilities and stakeholders, several benefits and 

challenges associated with solar were brought up and discussed. These are not meant to be 

all-inclusive or applicable to all utilities. Rather, they give a sense for how solar generation is 

currently perceived, which may lead to a stronger understanding of how it could be adopted 

in the future. 

4.1.1 Solar Benefits

• Meeting renewable standards requirements – Many states have renewable 

standards in place, and several of those have specific requirements for solar energy 

and/or solar renewable energy certificates (SRECs). Several utilities identified 

renewable mandate compliance as a key factor in their pursuit of solar generation.

• Fuel diversification – Many utilities are beginning to look at fuel diversification as a 

way to mitigate rate volatility for their customers. Utilities that are coal heavy 

expressed concern about future environmental regulations, while those that rely upon 

natural gas generation viewed severe historical price volatility as a motivating factor 

to diversify their fuel mix. 

• Cost stability – Solar energy has effectively a zero dollar dispatch cost, as virtually 

all of the costs to operate the facility are fixed (i.e., construction cost and some fixed 

O&M). Utilities were drawn to avoid fuel price-related risks by essentially 

guaranteeing a portion of its energy requirements will be met with a very stably priced 

resource. 

• Geographic dispersal benefits and PV modularity – Solar PV’s variability can be 

mitigated by dispersing solar projects across a wide geographic region. Utility 

interviewees liked that solar PV can be deployed on a very incremental basis; there is 

no standard footprint or capacity size that must be considered. PV generation is 

 23 All utilities interviewed were interested in PV. CSP was of interest to a few utilities, but is not a viable option 

for others due to the quality of resource, land constraints, etc.
28

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immensely flexible in this regard as it can be sized on a scale of hundreds of kilowatts 

to hundreds of megawatts. By deploying solar in smaller amounts across a wider 

region, a utility can smooth out any site-specific cloud variability and related quick 

ramping up and down. Targeting specific geographic locations for PV installations 

can also allow the utility to solve localized voltage concerns or avoid transmission

curtailment issues within load pockets, where siting generation assets (particularly 

those with emissions) can be problematic. PV generation also benefits in its 

substantial difference in construction schedules compared to conventional natural gas 

and coal. Solar PV facilities can be constructed and brought online in as few as six 

months, while natural gas resources can take anywhere from three to five years.

• Partial correlation with peak demand – Utilities’ summer peak demands are driven 

primarily by air conditioning load, which results in system peaks in mid- to late 

afternoon each day, when the sun is still in the sky and solar generators are still 

producing some energy. The correlation of solar production to peak demand varies 

with each utility, location, technology selection, and orientation (Perez et al. 2006). 

Utilities interviewed for this report repeatedly stated that they believed there was a 

capacity value associated with solar – while not always 100%, it can be higher 

capacity value than with wind generation. This subject was covered in more detail in 

Section 3.

24

• Mitigation of environmental compliance risks – Solar energy is produced with zero 

emissions of CO2 or other greenhouse gases (GHGs). With the federal government 

considering a carbon tax, cap-and-trade system, or some other mechanism intended to 

curb GHG emissions, utilities are contemplating future compliance costs. 

Diversifying portfolios with solar generation provides the added benefit of displacing 

emissions-generating resources.

• Avoided line losses (typically DG only) – DG resources are located at the load (the 

end user of electric power) rather than on the high-voltage transmission system like 

other forms of centralized generation. Because it is generated near the end user, the 

energy does not have to be transmitted across transmission and distribution lines, nor 

does it need to undergo any transformation (step-up or step-down) to serve load, 

meaning all of the typical line losses associated with remote, centralized generation 

resources are saved. Utilities liked that small-scale central station PV can also be 

located near load centers and interconnected at distribution voltages, avoiding line 

losses in a similar manner.25

4.1.2 Solar Challenges

• Variable and Uncertain Output – Solar is a variable resource that ramps down (e.g. 

cloud cover or storms) and is non-dispatchable. This means that utilities treat it as a 

“must take” resource and modify their economic dispatch decisions around its profile 

or production curve. The variability of solar is difficult to predict in advance and can 

lead to sudden drops and rises in the production profile at a given location. Utilities 

account for this variability by maintaining both spinning reserves and quick-start 

natural gas capacity. One third of the utilities responding to the questionnaire 

 24 Correlation with peak demand is also highly penetration-specific. Section 3 of this report discusses the impact 

of higher penetration of solar PV, and how the incremental capacity value of the next system falls. 25 Deferred transmission and distribution (T&D) costs are often discussed as another benefit of solar generation; 

however, during the report’s interview process, utilities either did not mention deferred T&D costs as a benefit, 

or stated that it may be a benefit but it had not been quantified to date.
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indicated that they include a cost add-on when reviewing variable resource proposals 

to account for the additional ramping requirements placed on their natural gas fleet. 

• Ramping issues – With sufficiently high penetration levels of solar generation, 

operational issues can arise due to the solar generation ramping up rapidly in the 

morning, as the sun rises, and then ramping down rapidly in the afternoon as the sun 

sets. These severe ramps require the utility to have a large amount of flexible 

resources available (CA ISO 2013).

26 

• Economics – Utilities operating in a least-cost planning state must be able to prove 

that solar generation is economic for its customers compared to other available 

resources, either all-source or renewable-only. In regions where the solar irradiance 

levels are high, like the desert Southwest, large-scale solar has begun to compete on 

an economic basis with conventional natural gas-fired resources; however, in other 

states this convergence has not occurred. Utilities in least-cost planning paradigms 

that choose a more expensive resource are at risk for cost disallowances, unless they 

are assured there will not be a prudency review, or some form of cost recovery is preestablished. Utilities also mentioned difficulty in pursuing solar now while prices are 

still in steep decline, and the consequences of locking in prices that are significantly 

higher than what could be available 1–2 years later.

• Lack of current capacity need – The economic downturn experienced over the last 

several years has not only stalled load growth for utilities; in many cases it has 

actually caused a load reduction. Because utilities plan several years in advance to 

meet load, this change from the predicted load growth left many utilities long on 

generation capacity. In states with RPS requirements, additional generation capacity is 

being pursued regardless of overall capacity need, mainly because utilities want to 

avoid paying RPS non-compliance penalties. Adding solar generation in states that are 

not subject to an RPS could result in prudency concerns and associated cost 

disallowances.

• Cross-subsidization concerns (DG only) – When solar is installed behind a 

customer’s meter, the generation from that system offsets the customer’s electric 

usage. When that usage is less than the solar production, the generation gets exported 

to the utility’s distribution system. This transaction is often completed under a rate 

paradigm known as net energy metering (NEM). The concern from utilities comes 

from the fact that most retail rates (particularly those for residential customers) are 

designed to recover both fixed and variable costs primarily through consumptionbased variable rates. As DG is installed behind the meter and offsets (or reduces) 

energy and load requirements, these fixed costs can go under-recovered. This issue is 

largely separated from utility planning requirements, but was mentioned during 

interviews as part of the research for this report.

• Reduced capacity benefit over time with increasing solar penetration – As solar 

penetration increases and begins to have a larger impact on the system in aggregate, 

the overall benefits that each solar system provides decreases. Utilities are concerned 

about this decreasing capacity benefit. This concern is expected to rise unless utilities 

are rigorous about adequate dispersal of resources or energy storage solutions become 

commercially viable from a cost and technology perspective. Potential analysis 

solutions are explained in Section 4.2.2.

 26 This solar ramp can be viewed every day on the California ISO website (second chart), 

http://www.caiso.com/Pages/TodaysOutlook.aspx.
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4.2 Utility-Identified Key Questions and Answers

Utilities and stakeholders identified their key questions about including solar as part of their 

long-term planning and analysis activities. Some even mentioned potential innovations or 

analysis methodologies to get answers to these questions. Both the key questions and answers 

that emerged as themes in our conversations are captured here, as best practices. Note that 

this section is not meant to be all-inclusive or applicable to all utilities. 

4.2.1 What Are Credible Estimates of Future Solar PV Prices and 

Performance?

Many utilities acknowledged that in the last few years, solar prices are declining in a nonlinear fashion, and a few mentioned that it appears the market will reach the U.S. Department 

of Energy SunShot cost goals. However, collectively, utilities had many questions about how 

long the solar prices will continue to decline rapidly and when prices will bottom out. 

The challenge of predicting future solar PV prices was seen as a key uncertainty by most 

interviewees, particularly because planning is done over a multi-decade horizon. In order to 

consider the full range of potential resources that will be available, an accurate depiction of 

the trajectory of future solar PV prices would be extremely helpful. 

Research and utility interviews revealed a number of reasons why PV prices are hard to 

estimate. There are a number of different references and data sources, and they are not always 

in agreement about the future of solar PV prices. It does seem that the further out in time you 

go, the fuzzier the price of PV becomes and the wider the range of predictions grows. There 

appear to be geographic distinctions in terms of resource quality, and experience of installers 

and other cost factors that aren’t clearly articulated in the literature. And there were questions 

about the current market oversupply of PV – when will it be exhausted and the supplydemand balance be reestablished? Could PV prices increase once the oversupply is 

exhausted, and if so, by how much? Together, these factors make PV prices seem hard to 

predict.

NREL and SEPA both strive to create credible, objective analyses on a variety of topics,

including the cost and performance of solar PV. As such, both entities have assembled a list 

of useful datasets, information, tools, and reports that could help inform solar PV predictions. 

They include:

Data Sources that are Regularly Updated

1. NREL’s Transparent Cost Database –utility scale data 

(http://en.openei.org/wiki/Transparent_Cost_Database)

2. NREL’s Energy Technology Cost and Performance Data for Distributed Generation 

(http://www.nrel.gov/analysis/tech_cost_data.html)

3. NREL’s Open PV Project, which captures voluntary historical cost and performance 

data (https://openpv.nrel.gov/)

4. LBNL’s Tracking the Sun report (Barbose et al. 2013 –

http://emp.lbl.gov/sites/all/files/LBNL-5919e-REPORT.pdf) 
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Data and Analysis Snapshots in Time

1. U.S. Department of Energy November Technical Report: PV Pricing Trends: 

Historical, Recent, and Near-Term Projections (Feldman et al. 2012 -

http://www.nrel.gov/docs/fy13osti/56776.pdf)

2. U.S. Department of Energy November Technical Report: Benchmarking NonHardware Balance of System (Soft) Costs for U.S. Photovoltaic Systems Using a 

Data-Driven Analysis from PV Installer Survey Results (Ardani et al. 2012 – 

http://www.nrel.gov/docs/fy13osti/56806.pdf)

3. Western Wind and Solar Integration Study - Phase 2 (integration costs only) (Lew et 

al. 2013 – http://www.nrel.gov/electricity/transmission/western_wind.html)

Analysis Tools

1. NREL’s System Advisor Model - advanced tool for estimating levelized cost of 

energy (LCOE)

(http://sam.nrel.gov/) 

2. NREL’s Cost of Renewable Energy Spreadsheet Tool - simplified spreadsheet tool 

for estimating levelized cost of energy (LCOE)

(https://financere.nrel.gov/finance/content/crest-cost-energy-models)

3. NREL’s PVWatts - tool for modeling production profiles of solar resources at 

different geographic locations (http://www.nrel.gov/rredc/pvwatts/about.html)

4.2.2 How Should Solar Capacity Be Valued?

The authors asked the utilities to explain if and how they valued solar capacity in their input 

assumptions, modeling, and planning. As shown in Figure 12, some ignored it altogether; a 

few used wind capacity valuation as a benchmark or proxy, and others have studied solar PV 

capacity value in detail (believing that wind is a weak proxy).

Utilities that have specifically studied and analyzed the value of solar PV capacity on their 

system tend to assign some sort of capacity value to PV or they indicated plans to analyze 

and use values in the near future. Several utilities perform their own analytics comparing 

solar resources in their region directly to their own load profile, rather than relying upon a 

generic value or using the same capacity value for solar as they do for wind generation. More 

recent research found that PV plants in the U.S. West have capacity values that range 

between 52% and 93%, depending on location and sun-tracking capability (Madaeni et al. 

2013). This research also compared data- and computationally-intense reliability-based 

estimation techniques with simpler approximation methods and found that if designed well, 

simpler methods can provide accurate approximations. The most accurate of these simpler 

methods is weighted capacity factor of the plant (Madaeni et al. 2013). By valuing both the 

benefits and challenges of adding PV to their system, these utilities are able to better capture 

the positive and negative impacts.

The capacity value can be very specific to a particular location. Even in areas with a strong 

and consistent solar resource, there are concerns because the system summer peak occurs in 

the evening hours as commuters are returning home for the evening and just as solar facilities

without storage are ramping down. Only through analysis of a specific solar technology, 

location, and system characteristics can a utility identify the most accurate capacity credit that 

can be attributed to solar on any particular system.
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Also, utilities will perform analysis to compare actual solar performance data to actual load 

data for increased precision. Comparing actual data to a forecast weather normalized load 

curve can provide misleading results, due to the correlations between weather and load, as 

well as weather and solar. TMY data (which is an average) can be compared to weather 

normalized load, or actual solar output can be compared to actual load data. 

Importantly, only a few interviewees have considered the capacity valuation differences 

between fixed and tracking solar PV. The advantage of tracking solar PV is that it optimizes 

the amount of generation during the evening peak as the modules track the setting sun. The 

inclusion of tracking PV for those solar installations where it is feasible and cost effective can 

help optimize the output of the solar modules and reduce the overall impact of shifting the 

peak into the early evening hours. 

Appropriate and accurate capacity values are important in the long-term planning context 

because they will allow for solar generation to meet at least a portion of future demand 

requirements. Each technology operates differently in real-world situations and can provide 

distinct costs and values – only by studying the differences between the two can an accurate 

solar PV valuation be captured.

4.2.3 How Should Solar Resource Variability Be Considered?

Solar resources can vary greatly depending on where they are located. There are a few key 

factors that impact the amount of electricity that can be produced from a solar resource, 

including total solar insolation (or total sunshine on an hourly, daily, seasonal, and annual 

basis), cloud cover (minute-by-minute and hourly changes), shading (seasonal), and 

technology used. The combination of these factors and others will determine the scale of 

impact that one or more solar systems will have when interconnected to a utility’s electric 

grid.

There are several ways that a utility can reduce the potential impact on its system’s resiliency 

and reliability resulting from the variance of output from solar systems, particularly PV.

1. Geographic Diversity: Solar PV is a modular technology. When solar projects are 

scattered over a wide geographic area, the variability of the entire portfolio of solar 

projects is limited and greater certainty of the output of the solar portfolio can be 

achieved. It is similar to when an investor diversifies his/her investment portfolio so 

that any issues with one investment can be moderated by success in other areas. 

2. Integration cost add-on: Utilities can also address resource variability by 

incorporating an integration cost adder into their planning analytics. This practice is 

done at many utilities and could be considered a common practice today. If this 

approach is taken, utilities will get the best analytic results if they conduct a systemspecific (i.e., based on their own generation and transmission systems and resources) 

study to better understand this method. For example, detailed analytics could be 

conducted to quantify the potential need for incremental spinning and non-spinning 

reserves as PV penetration increases. Additional reserves can help the overall system 

deal with some of the ramping challenges and the best analysis will be on the 

utility’s own system.
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4.2.4 How Can Scenario Planning More Optimally Manage Risk and 

Uncertainty?

In some respects, today’s electric sector is starting to quickly move away from the one for 

which the rules and regulations were established. Utilities and regulators are focused on a 

wider range of options to meet increasing load than just a small number of large, centralstation generators, with transmission to interconnect these large plants. 

Today, there are many more supply and demand options to consider, including:

• Generation technology choices (e.g. wind, solar PV, geothermal, distributed 

generation) 

• Demand options (demand response, energy efficiency) 

• Flexibility options (storage, ramping existing generators differently than tradition)

• Co-optimization of transmission and generation simultaneously.

The potential combinations of supply, demand, and flexible technology options can feel 

overwhelming. Rather than address the full range of options, to save time and money several 

interviewees noted that they use scenario planning to consider risks and uncertainties. 

However, scenario planning cannot investigate the full range of technologies available and 

identify the optimal portfolio; it can only consider the handful of scenarios that are 

determined by the analysts. Moreover, examining a few optimum scenarios in depth may not 

allow for a full exploration of risks and uncertainty. While scenario analysis can test each 

variable individually, it may not allow for the full capture of interactions between variables. 

Utilities routinely incorporate some form of risk analysis into their long-term planning 

activities. One common approach is to look at the impacts of modifying one variable at a time 

on a portfolio’s overall metrics. This can isolate, for example, how the natural gas price 

forecast can impact generation dispatch, fuel mix, and the PVRR of a given set of resources.

Several utilities perform a more comprehensive and intricate set of risk analyses as part of 

their IRP/long-term planning procedures. This is often done by correlating key variables and 

running Monte Carlo simulations. These random-draw computer models can test how 

multiple variables can together impact both the potential results of capacity expansion 

decisions and the subsequent PVRR for each portfolio under consideration. Correlation 

among the variables has been used in the past, primarily between factors like load growth and 

natural gas prices. In the future, it is expected that other variables like carbon costs, prices of 

renewable energy credits, installed capacity costs, and other major items could be tied

together so that as one is varied, the rest move in a somewhat logical and predictable fashion. 

These more complex and interrelated risk methods can provide a more robust vision of how 

something as dynamic as a portfolio of resources can interact.

4.2.5 How Can Software Models More Effectively Incorporate Solar?

All of the interviewees model their electric system in order to support their supply planning 

decisions. While they all use different models for different purposes, the goal is the same: to 

better understand the impacts of their current decisions on their future outlook. 

According to several interviewees, commercially available models do not keep up with the 

technology options and innovations that are actually available. All utilities mentioned that the 

software they use has one or more constraints that make them less than ideal. Requested
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changes or updates to the software may result in customized “one-off” versions of the model 

for their particular situation. Many of the utilities interviewed expressed an interest in 

learning more about their software options. This interest included a better understanding of 

the full functionality of the software options and what software other utilities are using. 

However, none seemed ready to try another software option until they fully understood their 

options, due to the large investment of money and time needed. One potential solution could 

be having a third-party review and explore the modeling software available today.

Second, a few utilities mentioned they were potentially interested in software upgrades. In 

order to make the investment in the new software option, one utility mentioned a desire to see 

a direct comparison of the existing software to the new software – a comparison that explains 

the full range of new features, the errors in previous versions that were addressed and 

statistics like improved run time, computing power needed, and other key factors. Potentially 

the software vendors or a third party could compare the improvements and advantages of the 

new software more clearly so that a utility can feel more confidence in moving forward with 

an upgrade.

The ability of commercially available models to incorporate accurate wind and solar profiles 

is quite limited. To better represent wind and solar, one utility said it is working on 

stochastically varying the wind and solar inputs in its own modeling. Instead of using a 

simplified representation (either a typical week each quarter, or even an average wind or solar 

year), this utility hopes to utilize stochastic methodologies to vary the wind and solar weather 

patterns.27 So, in the first year, they see one type of pattern, and in the second, a totally 

different pattern is used to more closely approximate the variability of wind and solar 

generation over seasons and years. The authors did not investigate how these more accurate 

wind and solar inputs could be incorporated directly into the commercial models.

4.2.6 Should Customer-Sited, Net Energy Metered Photovoltaics Be Treated 

as a Net Load in Modeling?

Historically, utilities have not had to model or plan for a substantive amount of distributed 

generation. In the last 5–10 years, the number of customer-sited PV systems has increased 

dramatically, and utilities are struggling with when and how to incorporate customer-sited PV 

into their planning and modeling. More background on customer-sited DG can be found in 

Section 3.4.2. Interviewees raised concerns that span several different aspects of customer 

on-site generation using solar PV.

Solar is often treated by utilities as a net load. The advantage is that the modeling run time of 

the analysis is greatly reduced. There are several challenges with this simplification. Several 

utilities note that load assumptions are one of the main drivers of differences between 

scenarios tested. Therefore if solar is treated as net load, the analyst is unable to capture the 

direct impact of solar PV on the system at the distributed or bulk level. Variations in load 

growth include DG penetration variability, but are not explicitly visible.

One utility clarified that because solar PV penetration is still “in the noise,” it is easier to 

model solar as netted out of load. Even when that utility modeled distributed solar PV as a 

resource, it found the results didn’t differ from the baseline model. Only as PV penetration 

reaches a certain level does it make sense to model PV as a resource, according to this utility. 

Several utilities did ask questions about the threshold level – when is this point reached and 

 27 These profiles would also be correlated to the load forecast, which is already typically part of a utility’s 

Monte Carlo simulations.
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how will they know they are approaching it? One solution could be for a third party to 

analyze where the point of inflection is: at what point does including distributed solar as a 

resource impact the system and is that inflection point different depending on the 

characteristics of the system?

Another utility treats customer-sited generation as a resource and does not net it out of load. 

In others words, even customer-sited (residential and commercial) generation competes 

against other generation in the dispatch queue. Similarly, they treat energy efficiency and 

conservation measures as a resource that can also compete and whatever resource is most cost 

effective wins. 

This utility operates in more than one state, which can complicate the modeling because some 

PV and conservation measures are more cost effective in some states than in others. Even so, 

the utility believes that treating DG and EE as resources is a more accurate way to plan for 

and model these non-traditional resources. By building in DG, EE, and utility-scale 

generators as resources, this process allows all technologies to compete against each other on 

the biggest driver – cost. Together, the combination determines how much supply there will 

be, what resources will be available in the energy profile, and what choices are accessible 

during the system peak. 

If a utility were interested in treating DG as a resource instead of as a net load impact, it 

could take its DG forecast curve (or curves, if multiple scenarios are possible) and assign it as 

a zero cost “must take” resource. The CEM would select it each year in lieu of other options. 

The PVRR of this portfolio could be compared to the PVRR of the base case run (i.e., 

without DG) to determine the overall cost savings from this resource. This type of 

information could be useful should the utility be interested in leveraging the resource 

planning process for DG incentive structures.

Additionally, another utility suggested including reserves for these “resources.” If DG and 

EE are included as resources, then utilities will likely want to include this when calculating 

their reserve requirements. While taking this approach does increase modeling run times and 

expense, one utility feels the trade-off is worth it to the increased accuracy of choosing the 

best combination of resources to lower overall system and operational costs.

4.2.7 How Should DG Impact on the Distribution System Be Managed?

The impact of DG on the distribution system is uncertain because it has not been fully 

analyzed by most utilities. Utilities are interested in both the positive and negative impacts of 

solar on the distribution system. Until solar reaches a high level of penetration on most utility 

systems, especially at the feeder level, doing this type of detailed analysis is not considered to 

be warranted. 

Most of the participating utilities acknowledged that DG penetration is expected to become 

notable in the next 10 years; however, a few interviewees said they were not sure how to 

determine the point at which DG should be included in their supply planning analyses. One 

utility also said it was not sure about the methodology needed to include DG penetration in its

bulk power analysis studies. 

New analyses could help define what is needed and when. Since this is an issue that spans 

across utilities, it might make sense for a national research institute, like a national 

laboratory, EPRI, or SEPA, to scope out and perform this analysis to inform the utilities 
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about the general conditions for including DG. This broad analysis would allow for a starting 

point from which each utility can consider its unique situation.

4.2.8 How Should Net Energy Metered DG’s Impacts on Revenue Be 

Considered?

Although the resource planning process and resulting documents are not part of ratemaking 

procedures, utility supply planning does occur within the broader context of utility decisionmaking and is impacted by the utility's overarching goals. As such, during the discussions of 

resource planning, the issue of the impacts of distributed generation on utility revenues and 

the implications for utility planning processes arose. Here we present only the results of our 

discussions with utility representatives, and do not holistically explore the issue of utility 

revenue loss resulting from distributed generation. This key question indicates that further

research is needed. For more thorough discussions of the topic, please refer to other reports 

(Newcomb et al. 2013, Aliff 2013, EEI 2013, Lehr and Binz 2013, and Rábago et al. 2012).

The rapid adoption of distributed generation is a concern for utilities as it represents a 

paradigm shift within the traditional electric utility business model. This is a hot topic in 

many forums held across the country and was identified as a critical issue in utility 

interviews.28 Utilities may want to consider examining the tipping point to determine when 

DG should be included in supply modeling, as well as looking at DG as both a net load and as 

a resource to quantify the difference in revenue required per kWh. With this information, any 

differences that exist can be discussed as part of a broader long-term planning stakeholder 

initiative.

Several interviewees mentioned their anxiety about the possibility that their distributed 

generation incentive programs and/or NEM programs might be more popular than 

anticipated. In that case, one utility mentioned that the success could lead to an overall rate 

increase, particularly as the utility sells less power and has fewer customers to cover the cost 

of the transmission and distribution system.29 Few utilities would be interested in creating 

incentives that result in a rate increase; utilities do not want to fundamentally change the way 

the revenue forecast is developed. As directed by state laws and regulations, utilities strive to 

be careful when developing their revenue requirements, so that their attention is on least-cost 

planning. This least-cost planning focus is especially important for any analyst that considers 

the market from the wholesale power provider perspective. Their main question is what 

happens to the rate revenues if this new program is successful?

None of the interviewees identified a direct solution to the possibility of rate increases that 

result from successful customer-sited on-site generation programs. This is likely because 

regulatory approval for any potential solution is required and could be challenging. 

 28 In 2014, NREL has several activities to analyze this space more thoroughly including quantifying 

methodologies for valuing costs and benefits of solar, utility operationalization of value of solar in program 

design and implementation, and examining DG solar issues from the regulatory perspective. Contact Karlynn 

Cory (Karlynn.cory@nrel.gov) if you have questions or would like to get involved. 29 Most retail rates are recovered as per kilowatt-hour (kWh) payments (e.g., ¢/kWh). The fewer customers that 

make the kWh payments, the more the remaining customers have to pay to cover the system’s transmission and 

distribution costs. It can be a self-defeating cycle where rising rates make it more attractive for the customers to 

consider on-site generation, so more of them sign up to self-generate, which results in more rate increases for 

the remaining customers. There is also a concern of cross-subsidization where the few who participate in on-site 

generation programs (generally more affluent homeowners) are subsidized by the remaining customers 

(generally middle- and low-income ratepayers).
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Considering alternative rate mechanisms may also create economic, social, and political angst

within the traditional rate setting process. 

Three examples of alternative rate mechanisms are currently under discussion or at the early 

stages of deployment30 in the United States, (and more potential solutions are expected to 

develop in this rapidly emerging field):

1. Fixed cost recovery via demand charges, customer charges, or standby charges

In this approach, the utility modifies their residential rate schedules to recover some 

fixed costs through either the creation of a demand charge (which is currently very 

rare on a residential customer level) or by implementing a fixed customer or standby 

charge for customers who have DG behind their meter (Newcomb et al. 2013). This 

approach can be difficult to implement, as stakeholders are likely to see it as a direct 

charge for “going solar.” As one utility pointed out, new technologies may require 

new ways of thinking on the rates side as well.

2. Create a “Value of Solar” rate schedule

A second approach used in Austin, Texas actually creates two separate transactions

(Rábago et al. 2012). The customer continues to pay all applicable charges on their 

bill to the utility. Under a separate transaction (combined on the bill for simplicity), 

the utility purchases all of the PV system’s output from the customer at a rate that 

represents the value provided to the utility of that resource. This approach is 

beginning to gain traction. It will require a sound methodology on how to calculate 

the value of the resource, how to translate that into a contract rate, and what the 

update frequency of that rate will be. Lessons learned are starting to emerge on the 

program design, which others may want to consider. One utility mentioned that this 

is very complex and confusing for all parties (particularly customers), and that it is 

challenging to get consensus on appropriate methodology and the values to use. 

3. Reframe the cost of service framework

The final method is to develop and implement correct market and regulatory 

incentives to reframe the cost of service framework (Lehr and Binz 2013). One way 

is to perform a “line item” cost review of all of the services that the utility provides 

to the customer, and likewise the services that the DG customer provides back to the 

utility. These two buildups can then be netted for purposes of customer billing. 

While technically sound, this is a very complex approach.

4.2.9 Can Solar Supply Planning Be More Appropriately Linked to Other Utility 

Planning and Operations?

Long-term supply planning performed at utilities does not always impact supply procurement 

or transmission planning. For solar generation resources, particularly at the distributed level, 

this may not allow for the full value of these non-conventional supply sources to be captured 

 30 Another option that is covered in the literature, but not in the scope of the utility discussions, is to consider 

overhauling cost of service ratemaking used today. Some market analysts question the usefulness of cost of 

service ratemaking in the future, when the electric sector will be more complex with a wider variety of 

generation technologies, at the behind-the-meter distributed level, the wholesale distributed level (on the utility 

side of the meter), and at the transmission level. If regulatory approval could be secured, it may be possible to 

depart from the traditional model of cost of service ratemaking (Aliff 2013, EEI 2013, and Newcomb et al. 

2013).
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by the utility unless solar supply planning is linked to other utility planning and operations. 

This section explores two of these factors.

Several utilities interviewed acknowledged that the tasks of analysis for generation supply 

planning and issuing RFPs in order to procure new supply fall to different groups. These 

utilities indicated that the two groups may not extensively collaborate or coordinate. Without 

more coordination, the RFP evaluation criteria may not reflect the future needs or 

expectations of the utility adequately, and future supply planning could then have to be 

adjusted to accommodate different generation resources than were originally modeled and 

anticipated as optimal.

Several utilities discussed using their internal bid database from previous RFPs as a 

benchmark for their anticipated RFP price results (“mark-to-market”), which is standard 

practice. 

One utility mentioned that in addition to examining individual RFP responses and comparing 

them on a price basis alone, the short-list of potential awardees could be modeled in the 

current system to determine which of the bids actually delivers the best value to the existing 

portfolio, beyond just the price proposed. This is a tested approach used by other utilities 

(Mills and Wiser 2012). This type of detailed modeling allows more factors than least cost to 

be considered, including impacts on the local system, resource diversity, and supply 

resources that lower overall system risk. While the utility hasn’t yet implemented this type of 

approach, it is considering it (not only for renewables, but for all supply resources).

Another option that goes beyond the mark-to-market test is that the supply planners can be 

more directly involved in developing the RFP evaluation criteria. If there are specific 

characteristics that are desirable in the optimal supply portfolio, these can be communicated 

to the supply acquisition team; the supply planners also may be able to help develop specific 

criteria associated with each key factor. By including these criteria in the RFP and clearly 

articulating what the utility needs to best meet the optimal supply future (e.g., supply located 

in specific locations, specific technologies), the chances of getting new supply that meets 

those needs increases dramatically.

4.2.10 Can Transmission and Distribution Planning Be More Appropriately 

Linked to Supply Planning? 

For most of the interviewees, supply planning does not usually inform transmission planning 

analysis or decisions.31 The two are regularly performed separately and do not always inform 

each other. While this is standard practice at the utilities interviewed, the increase in 

penetration of renewables, and particularly solar at the distributed level, may mean that there 

are unique costs and benefits that could be considered. In these cases, it might be 

advantageous for the generation supply and transmission planning teams to more closely 

coordinate. Without this discussion or additional analysis, the value of T&D deferral benefits 

that result from DG is likely assumed to be zero.

One utility interviewee already links generation and transmission analyses. The utility 

integrates generation and transmission planning through a more complicated analysis, but the 

utility staff feels they can identify the optimal combination of supply and transmission by 

examining many more scenarios. The interviewee considers many different scenarios that can 

 31 In some states there are rules that are intended to prevent a utility giving itself an unfair advantage by favoring 

its generation resources on its transmission resources.
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dramatically change the generation supply portfolio (along with associated constraints – e.g.,

range of natural gas prices, CO2 scenario). The utility also identifies a handful of new 

transmission build-outs across its system footprint. Then it considers the full combination of 

generation supply under each transmission build-out scenario. The utility analyzes the full 

range of combined supply and transmission portfolios to satisfy all of its stakeholders in a 

transparent, collaborative way.

Another utility knows that it wants to better link supply and transmission planning and has 

started internal discussions toward that end. The supply planners are talking to the 

transmission planners to think about whether localized penetrations of solar will change the 

delivery point load forecast in their service territory – and to consider how it could result in 

changes or impacts to the transmission plan. This utility hasn’t yet chosen a path forward.

4.2.11 How Can a Utility Consider Solar During Periods of Low/No Load 

Growth?

Today, many utilities have more generating capacity than they need due to the economic 

downturn. The combination of less manufacturing and industrial activity and cost-cutting 

measures, as well as improved energy efficiency and conservation, has flattened out the 

overall demand for electricity more quickly than anticipated. As a result, some generation and 

capacity resources added in the past decade may not be fully utilized.

Utilities continue to study new supply technology options, despite low or no load growth, for 

two main reasons. Many utilities are studying the impacts of repairing/retrofitting existing 

plants instead of replacing them. One example is whether to retire a coal plant and build a 

new replacement facility, retrofit it with environmental controls, or to replace the generators

with alternative technologies. New supply options could inform which technologies are used 

for replacement.

Second, several utilities pointed out the current lack of demand growth is a near-term issue 

for all resources, not just renewables. Sometime in the future, new supply planning will be 

needed. One utility suggested the desire to investigate and analyze the full range of 

technology options available today, taking time to better understand those options. In so 

doing, the utility suggested that full range of costs and values could be considered, including 

ways to reduce overall portfolio risk (e.g., increased diversity), ways to hedge against or 

reduce uncertainty (e.g., potential future fuel price scenarios), and better understanding of the 

cost of the full range of alternatives.

4.2.12 Other Considerations for Solar in Capacity Expansion Models and 

Ongoing Research

Modeling an electric system can be challenging for sub-hourly timing specifically and for 

integration modeling more generally. This section examines the challenges with sub-hourly 

modeling, capacity expansion models, and explores the impacts of broader modeling 

approaches, based on current NREL research. Wind and solar can vary on a minute-byminute basis, whereas most production cost models run only hourly simulations over a year. 

Therefore it is challenging for today’s production cost models to run sub-hourly and to 

consider how to integrate the wind and solar resource more closely to actual production. 

Likewise, running detailed cost models at 5-minute levels can drastically increase run times 

and may not be practical overall.
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Some utility analysts have performed analysis of integration costs at 5- or 10-minute

intervals, but not many. These utility analysts estimate integration costs in the range of $2–

$11/MWh, as explained in Section 4.4.1 (Mills and Wiser 2012). Importantly, this range 

reflects assumptions used by utilities, not the range of estimates found in detailed studies of 

PV variability (due to the paucity of actual studies). Utilities can examine these previous 

studies and use the information found therein as proxies for the cost of solar integration on 

their system, as a first-cut approximation.

Another option is to run the analysis over shorter timeframes. If the utility’s goal is to 

determine solar feasibility, then detailed simulations of “tough” periods make sense. Instead 

of running a 5- or 10-minute model for the entire year, one utility suggested that running 

“one-off” cases can help, by examining a month or two at 5- or 10- minute intervals. Another 

utility suggested that modeling could be performed using a 5-minute dispatch on a 

representative day or week each month during the planning horizon. Either way, utilities 

could be able to study variability impacts without having to wait for the entire year to run. 

If the utility’s goal is to improve the accuracy of PVRR estimates, then the utility might only 

need to run an hourly model with additional operating reserves to represent the cost of 

resources that will be needed to chase sub-hourly deviations. 

Several of the methods and assumptions used to characterize renewable energy technologies 

in CEMs can significantly impact the simulated value of solar generators, and associated 

deployment of solar resources in utility IRPs. Ongoing research at NREL and within the 

academic community has identified some modeling choices that are likely to overestimate the 

value of solar, while others under-represent the contribution of solar to system performance. 

The magnitudes of these impacts are frequently utility- and scenario-specific, and this is an 

area of active research to identify modeling methodologies that can accurately capture the 

value of solar generators to system performance.

Based on current knowledge, four common modeling approaches are discussed below, along 

with their impact on simulated solar resource selection.

• Addition of solar capacity to previously-optimized generation portfolios can lead 

to suboptimal resource selection. Some utilities optimize capacity expansion 

without renewable resources, and then add solar resources to the conventional 

generation fleet without accounting for the energy and capacity contributions from 

solar generators. These portfolios do not represent a least-cost resource plan because 

they do not co-optimize renewable and conventional generation resources in the CEM

(Mills and Wiser 2012). This practice is akin to assigning solar a 0% capacity value, 

and results in higher simulated costs of adding solar resources to a utility portfolio 

than if the CEM had co-optimized the deployment of conventional and renewable 

generation resources. 

• Failing to account for decreasing marginal capacity credit can inflate the 

perceived value of solar generation to a utility system. As outlined in Section 3, the 

marginal or incremental capacity value of new solar installations can decrease as more 

solar resources are installed, because the utility’s peak in net load shifts from 

afternoon to evening. Failing to account for this by dynamically calculating the 

capacity credit of solar generation as a function of installed solar capacity will likely 

result in CEM scenarios where solar is expected to provide more firm peak capacity 
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than is feasible. This can over-estimate the value of solar generation in a utility 

system and lead to higher than optimal solar deployment.

• Renewable generation and other infrastructure investments by other, 

neighboring utilities can impact optimal solar investment in a utility’s own 

service territory. Utilities in the United States operate within synchronized electrical 

interconnections where new generation resources outside a utility’s service territory 

can impact transmission line congestion and the price and accessibility of power 

transfers from other regions. Several recent studies have analyzed renewable resource 

build-out pathways and their impacts on power flow and marginal energy costs (Mai 

et al. 2013; Milligan et al. 2012; Lew et al. 2010). These studies suggest that failing to 

account for out-of-state capacity expansion either directly or through dynamic 

boundary conditions can lead to sub-optimal investment within a utility’s jurisdiction. 

For example, if high solar deployment is expected in a neighboring region, a utility 

might cost-effectively choose to rely on market purchases during hours of peak solar 

generation when solar electricity may suppress marginal electricity costs in boundary 

regions, rather than developing solar resources within their service territory. The sign 

and magnitude of this impact is highly system- and scenario-specific, and could lead 

to over- or under-estimates in simulated optimal solar deployment.

• The representation of thermal generator operating limits can impact the optimal 

level of solar resource investment. To estimate production costs and determine the 

most cost-effective combination of generating resources to meet load, some CEMs 

rely on a load duration curve-based approach, and others use a simplified, 

chronological dispatch. The latter approach can be improved by modeling generator 

commitment states as integer or binary variables, rather than continuous variables, in 

order to capture important generator characteristics such as minimum stable 

generation level and minimum up and down time. A chronological approach with 

explicit representation of generator commitment status has been shown to more 

accurately model the impacts of solar production on the cycling and ramping behavior 

of thermal (gas and coal) plants (Palmintier and Webster 2011). CEMs that do not 

model these constraints are likely to find sub-optimal generation portfolios and fail to 

represent the true impact of solar production variability on system cost, which could 

lead to over or underestimations.

These and other modeling considerations are likely to have important impacts on the 

selection of solar resources in utility IRP processes. Building on prior analysis (Mai et al. 

2013), current work at NREL aims to more accurately quantify the impacts of these and other 

modeling approaches and assumptions on optimal solar deployment levels in utility planning 

processes (Mai et al. 2014 Forthcoming). This research is focused on identifying ways that 

commercial CEMs could be improved to more accurately represent renewable energy value 

within a utility portfolio. Specifically, near term research will use a newly developed CEM, 

NREL’s Resource Planning Model, to quantify the sensitivity of optimal solar deployment to 

different methods for quantifying solar capacity value, different representations of renewable 

development in neighboring utilities’ jurisdictions, and different methods for modeling 

thermal generation and transmission constraints. Preliminary findings using Colorado-area 

utilities as an example are expected to be published in early 2014.32

 32 For more information on this ongoing work, please contact Trieu Mai (Trieu.Mai@nrel.gov).
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4.2.13 Summary of Solar Supply Planning Challenges and Solutions

Table 5 summarizes the challenges utility interviewees identified for incorporating solar PV 

into utility supply planning, as well as some potential solutions. While this is not 

comprehensive, this information may advance the discussion of how to better represent solar 

energy in utility supply planning.

Table 5. Challenges and Utility-Identified Best-Practices for Incorporating Solar Into Utility 

Supply Planning, Based on Utility Interviews

Main 

Challenge Challenge Details Potential Solutions

Future Solar 

PV Price 

Uncertainty

What is a credible estimate of future 

solar PV prices? Or a range of 

predictions?

Gather and review reputable data sources, many of which 

are publicly available. Compare a wide variety of public 

historical costs and predictions of solar (and other 

renewable) costs.

Solar PV 

Technology 

Characteristics

How should I value solar capacity, if 

at all?

Utilities that have studied the value of solar capacity tend 

to assign some value through solar-specific analysis. 

Study the capacity value of solar specific to your location, 

technology and utility system. Consider tracking, as it may 

reduce the system peak shift into the early evening.

How concerned should I be that solar 

PV output can vary greatly?

Dispersal of solar PV over a wide geographic area can 

diversify output and create overall greater certainty in total 

solar output. 

Risk and 

Uncertainty –

Limitations of 

Scenario 

Planning

Scenario planning may not address 

full range of risk, uncertainty, and the 

full range of interactions between the 

supply, demand and flexibility 

technology options available today.

Develop more comprehensive and more inclusive risk 

analysis to address today’s complex electricity market and 

technology options.

Modeling

Commercially available models don’t 

keep up with or include technology 

innovations; software is sometimes

less than ideal.

Explore and compare software options – it would be 

particularly helpful for a third party to compare the 

software available today (to available packages as well as 

upgrades). Optimize wind and solar modeling inputs and 

move away from simplifications.

Integration at the sub-hourly level is 

challenging using today’s models.

Test 5-minute intervals for 1 or 2 key months instead of 

the entire analysis period. To improve PVRR, include 

additional operating reserves in hourly modeling.

CustomerSited PV

Load assumptions drive scenario 

results.

Treat both DG and EE as resources instead of as net load

Uncertainty of how DG impacts the 

distribution system.

Perform analysis to understand when and how DG needs 

to be included (perhaps through a third party). Begin to 

model on-site generation as its own resource to see 

impact on distribution.

Desire to avoid a rate increase.

Consider alternative rate mechanisms, including solar 

tariff rates (utility collects for services and self-generators 

get payment for their full benefits). 

Link Solar 

Supply 

Planning to 

Other Utility 

Planning and 

Operations 

Resource RFPs are not always linked 

to supply planning.

Go beyond mark-to-market solar database; consider 

modeling RFP finalists to understand their value in today’s 

system; also coordinate with supply planners to include 

specific RFP criteria in procurement.

Transmission planning and decisions 

are not always linked to supply 

planning.

Simultaneously examine optimal generation and 

transmission planning through combined analysis.

Low/No Load 

Growth

Load growth has slowed or halted 

and utilities have more generating 

capacity than they need.

Analyze the full suite of potential supply and demand 

technologies available for replacing retiring plants, 

compared to environmental control retrofits. Better 

understand options to meet future resource needs.
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5 Utility-Identified Analysis Needs

Solar generation is becoming a viable resource for inclusion in IRP/long-term planning

analytics across the country. In some regions solar generation already competes from a cost 

perspective, while in other states and regions, RPS requirements may still drive solar

adoption. Through utility discussions, the authors identified several areas that warrant further 

consideration and research to better understand the impact of solar on local and regional 

utility operations, and how it can be optimized to best support the resiliency of the electric 

grid. These include:

• Credible PV price and performance data – Identify and gather PV price and 

performance data. Utilities could consider centralizing the data into a database with a 

trusted third-party, who could aggregate the information so the breadth and depth of 

the data can be shared between utilities, without specific project or utility identifiers.

• Analysis of how to incorporate geographically diversified resources into

modeling – Utilities today typically allow their CEM to consider a standard size 

(MW) for each potential resource it may select (i.e., a 50-MW solar generator, or a 

110-MW combustion turbine). 

Solar is unique in that its footprint is scalable to any size requirement and can be 

integrated across a large geographic region. Rather than installing a static 50-MW 

solar farm at one location, a utility could choose instead a set of ten 5-MW solar 

farms spread out across its service territory. This approach is not typically used in IRP 

processes today, but further analysis could explore the possibilities and explain the 

modeling implications. Question for investigation could include – what size makes 

sense for these smaller farms? Where could they be realistically located? And how 

does spreading them out impact local and overall system operation?

These smaller systems would be easier to interconnect (due to the fact that they could 

tie in at lower voltages), and the variable and intra-minute fluctuations (i.e., quick 

ramping) due to cloud cover and other weather events could be smoothed. Staggering

systems from east to west in a service territory could stagger ramping up and down 

across the aggregate production. Such an approach, while not currently implemented 

in IRPs today, may be viable and provide a glimpse into how solar can be optimized

in the future. Additional analysis to explore different configurations of smaller solar 

farms could be informative to utility decision-makers.

• Analysis of the potential relationship between energy storage and PV – Energy 

storage applications are an intriguing match for solar generation. Although storage is 

less important at current low penetrations, at high penetrations storage could be useful 

in smoothing fluctuations in solar production, or as a method to provide firm output 

across the system peak. Storage may be particularly helpful for distributed 

applications, to help address potential islanding due to high percentages of distributed 

solar on a particular feeder. There are several pilot projects that tie solar and energy 

storage together, but to date there has not been long-term analysis of efforts to bundle 

these technologies. 

The primary roadblocks to incorporating energy storage are its high cost to deploy 

and uncertainty in how to value the resources it can provide. Unlike solar generation 

(or any fossil-fuel resource), energy storage does not actually generate power; rather, 

it stores it, presumably from a less expensive time to a more expensive (peak) time. 

This arbitrage by itself is difficult to cost justify because of the inherent losses in 
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storing energy. Pairing storage with solar generation to increase its capacity value and 

providing both smoothing and firming services may create sufficient justification for 

deploying energy storage resources. 

Current analysis explores some key aspects of energy storage including operational 

benefits (Denholm et al. 2013), market and policy barriers (Sioshani et al. 2012), high 

penetration of renewables in combination with storage (Augustine et al. 2012 and 

Denholm and Hand 2011), and ways that storage can reduce renewable energy 

curtailment (Denholm 2012). More analysis on the benefits and different 

configurations of solar and energy storage could inform ways to consider specific 

storage-PV combinations, and their direct impact on utility operations.

• Easier ways to predict impacts of increased PV penetration – In Figure 13, 

varying levels of PV penetration were shown with their relative impact on a utility’s 

peak load day. The higher the penetration, the less its incremental capacity will be 

able to contribute to meeting the utility’s peak demand.33 Currently, CEM software 

tools appear unable to model this dynamic impact to capacity values. 

High penetration of PV and renewables has been analyzed, including large-scale solar 

deployment (Drury et al. 2012) and technologies that enable high wind and solar 

penetration (Denholm 2011). Including these capabilities and modeling the 

interactions more closely could allow for a more robust picture of system interactions 

at higher levels of solar penetration.

• Better risk/uncertainty analysis methods (beyond scenario planning) – Examine 

and consider performing more comprehensive risk analyses as part of long-term 

planning, because scenario planning is not capable of investigating the full range of 

technologies available in order to identify the optimal portfolio. Using an advanced 

risk analysis method (e.g. Monte Carlo), correlate key variables (load growth, natural 

gas prices, carbon costs, renewable energy technology prices, installed capacity costs, 

etc.) so that if one is varies, the rest move in a logical fashion.

• Improved commercial production cost models –Include technologies in analysis 

that are not currently available or poorly represented in commercially available 

models.

Today’s PCMs are only capable of doing a resource dispatch hourly. However, solar 

output may fluctuate enough within the hour from cloud cover and other weather 

events to create operational impacts that are not currently captured. Several utilities 

mentioned a desire to be able to model the variability of solar (and other variable

renewables such as wind) on an intra-hour basis. 

Intra-hour cost modeling is not currently done because running PCMs on any time 

scale less than an hour would require massive amounts of computing power, or slow 

the model run time such that analysis would take significantly longer timeframes. As 

explained in Section 4.2.6, this timeframe could be run for a few key months, or for a 

few representative weeks each month. This level of analysis may be sufficient to 

reveal the implications of solar production variances and the level of flexible quickstart generators required. In addition, improvements in PCMs themselves (i.e. 

decreased run-times) or using faster, more powerful computers (i.e., high performance 

computing centers) could address these issues so intra-hour solar could be modeled 

more easily.

 33 For additional information on this decreasing capacity value, please see: Perez et al. 2006.
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• Translate distribution system impacts to long-term plans – Much discussion has 

occurred in recent years regarding the true value and cost of solar generation, 

particularly as it relates to the distribution grid. Solar advocates believe that system 

upgrades can be deferred and asset lives extended due to solar being located on local 

feeders. Utilities are more skeptical and see the potential need for significant system 

upgrades in the future because the existing distribution system was not designed for 

bi-directional flow. While IRPs do not often address long-term distribution system 

planning, effects on distribution system values and costs could become important for 

incorporation, particularly as NEM expands across the country.

• Clarity about when to include distributed generation in supply modeling – PV 

systems are often tied in behind a customer’s meter. As such, these systems can be 

modeled in an IRP as a net load impact that highlights reduced retail sales in the 

future, or as a stand-alone resource. The method used impacts the PVRR on a per 

kWh basis for all customers. Utilities may want to consider examining the tipping 

point to determine when DG should be included in supply modeling, as well as 

looking at DG as both a net load and as a resource to quantify the difference in 

revenue required per kWh. With this information, any differences that exist can be 

discussed as part of a broader long-term planning stakeholder initiative.
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Glossary

avoided cost The incremental costs of electric energy or other services, if a utility did not 

purchase from the existing power seller; the focus is on the cost of 

alternatives available to the buyer/utility.

34

azimuth The angle between the north direction and the projection of the surface 

normal into the horizontal plane; measured clockwise from north.35 More 

simply stated it is the angle between due north (0 degrees) and the direction 

from which the sunlight is coming. On the equinoxes, the sun rises at due 

east with an azimuth of 90 degrees, and sets at due west with an azimuth of 

270 degrees. At solar noon in the northern hemisphere, the azimuth angle is 

180 degrees.

baseload generation Electric generating facilities within a utility system that are operated to the 

greatest extent possible to maximize system mechanical and thermal 

efficiency and minimize system operating costs.36 Baseload generation 

typically has annual load factors that exceed 75%. Examples include coalfired, natural gas combined cycle, nuclear, very large, damned hydroelectric

(i.e. not run of river), geothermal and biomass.

37

capacity expansion 

model (CEM)

A computer software tool used in resource planning to determine potential 

expansion of electricity generation, storage and transmission systems over 

several decades. The model chooses the cost-optimal mix of technologies 

that meet reserve requirements, technology-specific resource constraints 

and policy constraints.38 Results are used to generate plans for electricity 

capacity additions or energy purchases, and are tested in production 

simulation models to determine the final cost and emissions outputs. Inputs 

to the model include variables such as existing generating assets and

purchased power contracts, potential resources, load growth assumptions, 

fuel cost curves, capital and operations/maintenance expenses. 

capacity factor (CF) The ratio of actual energy produced by an energy generating unit or system 

in a given period, to the hypothetical maximum possible (i.e. energy 

produced from continuous operation at full rated power).39

capacity value The value assigned to a generating facility, based on the extent to which the 

facility can help reliably serve load.

40 In this report, we use capacity value in 

the sense used by resource planners (expressed as a percentage of 

maximum generating capacity), rather than the monetary or market value 

assigned to the capacity.

derate factor (DC/AC) A number accounting for the loss of power in the conversion from DC to AC 

power. The overall derate factor for a system is the product of the derate 

factors for the components of the system. 

distributed 

generation (DG)

Generation located on a utility distribution system, typically at or near the 

load. For purposes of this report, DG is often intended to mean customersited PV.

 34 Adapted from EEI’s Glossary of Electric Industry Terms, 

http://www.eei.org/resourcesandmedia/products/glossry_electerm/Pages/default.aspx

35 Quoted from: http://www.solarbuzz.com/resources/glossary 36 Quoted from EEI’s Glossary of Electric Industry Terms, 

http://www.eei.org/resourcesandmedia/products/glossry_electerm/Pages/default.aspx

37 Adapted from http://www.renewableenergyworld.com/rea/news/article/2009/10/how-to-compare-powergeneration-choices 38 Quoted from NREL’s Energy Analysis description of the Regional Energy Deployment System model 

(ReEDS), http://www.nrel.gov/analysis/reeds/description.html 39 Quoted from OpenEI: http://en.openei.org/wiki/Definition:Capacity_factor 40 Adapted from http://www.ise.osu.edu/isefaculty/sioshansi/papers/pv_cv.pdf
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Effective LoadCarrying Capability

(ELCC)

The ability of a power generator to support additional peak load without 

reducing the reliability of the electrical system (in terms of loss of load 

probability or loss of load expectation). The amount of ELCC for a particular 

generator is calculated by determining the amount of existing supply 

capacity that can displaced by the source while serving the same load 

profile and maintaining the reliability of the electrical system.

energy storage Devices or technologies that can store electrical energy so that it can be 

used to meet demand at a later time.41 Adding an energy storage system to 

a solar photovoltaic system allows the solar energy to be used when the sun 

is not shining. This expands the flexibility of the photovoltaic system.

greenhouse gases

(GHG)

Gases that trap heat in the atmosphere.42 Specifically, they absorb and emit 

infrared radiation and contribute, either directly or indirectly, to the process 

of absorption and re-dispersion of thermal radiation coming from a planet’s 

surface (the greenhouse effect). Greenhouse gases include water vapor, 

carbon dioxide, methane, nitrous oxide, ozone and chlorofluorocarbons 

(CFCs).

grid parity The point where the cost of alternative energy sources equals the cost of 

electricity purchased from the grid.

43

heat rate A common measure of the efficiency of a steam power plant. The amount of 

energy input to an electric generator to generate one kilo-watt hour of

electricity generated; typically represented in Btu/kWh.

44

integrated resource 

plan (IRP)

A plan developed by an electric power provider, sometimes required by a 

public regulatory commission or agency, that defines the short and long term 

capacity additions (supply side) and demand side management programs 

that it will undertake to meet projected energy demands.45

integrated resource 

planning (or 

resource planning)

The process of developing an integrated resource plan.

intermediate 

generation (loadfollowing power 

plants)

Power-generating equipment that can vary its level of output (generation) in 

response to changes in electricity demand. Normally operated on a daily 

cycle to serve on-peak loads during the day but not off-peak loads during 

nights and weekends.

46 Typically, these plants have annual load factors 

ranging from 40% - 60%. Examples include smaller coal-fired power plants, 

natural gas combined cycle plants, most hydroelectric (when the weather 

cooperates), and in the future, offshore wind power, concentrated solar 

power, thermal solar power and wave energy.

47

load duration curve A graph that illustrates the average (or peak) hourly load, from highest to 

lowest, sorted in decreasing order for all 8,760 hours per year.48 The area 

under the load duration curve represents the total demand for the period of 

time. Load Duration Curves are used in capacity planning.

Monte Carlo A problem solving technique used to approximate the probability of certain 

outcomes by running multiple trial runs, called simulations, using random 

variables.49 A class of computational algorithms that use repeated random 

sampling, by running simulations many times over, in order to compute the 

probability of an occurrence; especially useful for simulating systems with 

many variables, or coupled degrees of freedom with significant 

uncertainty.

50 

 41 Adapted from http://energy.gov/oe/technology-development/energy-storage 42 Quoted from U.S. Environmental Protection Agency: 

http://www.epa.gov/climatechange/ghgemissions/gases.html

43 Adapted from NREL: http://www.nrel.gov/docs/fy12osti/54527.pdf 44 Adapted from: http://www.eia.gov/tools/faqs/faq.cfm?id=107&t=3 45 Quoted from http://www1.eere.energy.gov/tribalenergy/guide/glossary.html 46 Quoted from http://www.risk.net/energy-risk/glossary/2040393/intermediate-generation-cycling-generation 47 Adapted from http://www.renewableenergyworld.com/rea/news/article/2009/10/how-to-compare-powergeneration-choices 48 Adapted from RMI: http://www.rmi.org/RFGraph-load_duration_curve 49 Quoted from Investopedia: http://www.investopedia.com/terms/m/montecarlosimulation.asp 50 Adapted from Wikipedia: http://en.wikipedia.org/wiki/Monte_Carlo_method
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net dependable 

capacity

The maximum amount of electricity that a generating system or facility can 

reliably produce during the most restrictive seasonal conditions, minus the 

amount of electricity that is consumed by the facility itself, if any. 

net energy metering

(NEM)

A policy that allows a customer to receive a financial credit from the utility for 

power generated by their distributed energy system that is fed onto the 

electricity grid (net, or after most is used on-site by the customer, behind the 

meter). The credit is used to offset the customer's electricity bill and rules 

vary by jurisdiction.

peaking generation Generation from power plants that normally operate to serve loads during 

peak load times or during system emergencies, and that operate at very low 

annual load factors (5%-15%). Examples include natural gas combustion 

turbine, and simple cycle turbine (natural gas- or oil-fueled).51

peak demand/peak 

load

A period of time during the day, month, season or year during which the 

peak electrical demand/load/use occurs on the electric system. In warm 

climates, the peak electrical use may occur in the summer, when there is 

high demand for electricity to run air conditioning. In cold climates, the peak 

demand may occur during winter when there is high demand for heating.

planning horizon The period of time (in years) that a planning process covers. 

(generation) portfolio The collection of energy generation assets (power plants or purchased 

energy) available (or planned) to be used by a utility to meet the load.

present value The current worth of a cash flow, after considering interest. Future value 

becomes present value through the process of discounting.52

Present Value of 

Revenue 

Requirements

(PVRR)

A dollar amount that represents the total annual revenue, discounted to 

present dollars at the time of calculation that a utility must collect from

customers to pay all costs and expenses including a reasonable return on 

investment.

53

production cost 

modeling (PCM)

Production cost modeling simulates hourly electric generation dispatch over 

the entire planning horizon. Production costing models are used extensively 

in the electric power industry to forecast the expected amount of electricity 

produced by different power generation units and the expected cost of 

producing that electricity for a given power generation system.54 The models

can be used to forecast the expected amount of electricity production and to 

perform sensitivity analyses that account for the expected variation of load 

over time and the expected availability of generating facilities and, often 

times, transmission and distribution capacity, while accounting for 

uncertainties (e.g. fuel price, seasonal variations).

Power Purchase 

Agreement (PPA)

A legal contract between an electricity generator and a power purchaser for 

the sale of energy, capacity and/or ancillary services. The contract includes 

all the terms of the sale and often serves to determine the credit quality of 

the generating project by financers.55

renewable energy 

certificate

The property rights to the environmental, social, and other non-power 

qualities of renewable electricity generation. A REC, and its associated 

attributes and benefits, can be sold separately from the underlying physical 

electricity associated with a renewable-based generation source.56

renewable portfolio 

standard (RPS)

A legislated mandate for utilities to generate or purchase a portion (generally 

expressed as a percentage) of electricity sold from certain renewable 

generating sources, such as wind, solar, geothermal, biomass and biogas. 

 51 Adapted from Adopted from http://www.renewableenergyworld.com/rea/news/article/2009/10/how-tocompare-power-generation-choices 52 Quoted from: http://www.eei.org/resourcesandmedia/products/glossry_electerm/Pages/default.aspx 53 Adapted from Pennsylvania code: http://www.pacode.com/secure/data/052/chapter57/subchapktoc.html 54 Quoted from ERCOT: 

http://www.ercot.com/content/meetings/lts/keydocs/2011/0110/Production_Cost_Modeling_Presentation_10JA

N2011.pdf

55 Adapted from: http://en.wikipedia.org/wiki/Power_Purchase_Agreement 56 Quoted from the EPA: http://www.epa.gov/greenpower/gpmarket/rec.htm
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resource mix The different types of generating facilities that contribute to meeting the load

of an electric system within a defined area.

solar renewable 

energy credit (SREC)

A Renewable Energy Credit generated by a solar generating system, which 

indicates the production of a unit of solar energy. SRECs are tradable within 

some U.S states and regions that have Renewable Portfolio Standards, or 

voluntary green power markets. 

typical 

meteorological year

(TMY)

A set of hourly weather data for a particular location over a period of a year 

that represent a typical year and are consistent with the long-term averages. 

TMY files capture typical conditions for your location of interest and are 

available in TMY2 (most current 30 years) or TMY3 (most current 7, 10, or 

15 years) constructs.57 The data (which include temperature, solar radiation 

and precipitation) are often used for conducting simulations to facilitate the 

design and location of solar energy systems.

weather 

normalization

An adjustment methodology that accounts for weather. Normalization allows 

for the comparison of energy consumption between different periods of time 

or geographic locations that have different weather conditions by factoring 

out aberrations or unusual occurrences (e.g. variables such as higher than 

or lower than normal outside air temperature).

 57 Quoted from Weather Analytics: http://www.weatheranalytics.com/weather-products/simulation-andmodeling/tmy-files/
50

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This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

Appendix A – Tennessee Valley Authority Case 

Study

I. Who is TVA?

The Tennessee Valley Authority (TVA) is the largest publicly owned electric utility in the 

United States. It is a federal corporation that was created in 1933 by Congress as part of the 

New Deal to help move the country out of the Great Depression. 

TVA is the primary electricity provider in Tennessee and also covers areas of Mississippi, 

Alabama, Georgia, North Carolina, Kentucky, and Virginia. The company works with 155 

municipal and cooperative distributors that resell over 80% of the TVA-generated power to 

their consumers. The remainder of the TVA power is sold directly to 51 large industrial 

customers and six federal installations. Besides power production, TVA provides navigation, 

flood control, and land management services for the Tennessee River system. 

TVA is a self-regulated utility and is not subject to oversight by any state public service 

commissions, as are other regulated utilities. This means that state-level policies do not apply 

to the area within the TVA district and TVA is not required to produce an Integrated 

Resource Plan (IRP). 

The company’s 2011 IRP, TVA’s Environmental and Energy Future, was the result of over 

30 months of effort and was the first IRP TVA had completed since 1995. TVA was under no 

obligation to update the 1995 plan, but by 2008 it was clear that the time had come to 

reconsider the future. TVA staff redesigned the way the IRP would be developed, driven by a 

strong desire to improve the methodology for considering and planning for a variety of 

alternative futures. The resulting methodology and product highlighted here is an example of 

a thoughtful IRP process with clearly presented results.

II. Envisioning the Future: Developing the TVA Scenarios

As TVA approached the task of producing a new IRP, staff from across the company were 

brought together to brainstorm the attributes of all possible futures and describe these future 

worlds. Once the most likely futures had been agreed upon, they were presented to 

stakeholders using storyboards. After stakeholder vetting, the plausible futures were passed 

on to planning modelers for transformation into individual scenarios through the 

identification of appropriate assumptions and constraints. This initial phase of 

conceptualizing the scenarios and modeling the parameters to match those “futures” was a 

time-consuming step. In fact, it was so critical that it is now incorporated into TVA’s annual 

planning process. 

For their 2011 IRP, TVA developed eight future scenarios:
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• Dramatic Economic Recovery

• Environmental Focus as a National Priority

• Prolonged Economic Malaise

• Game-Changing Technology

• Focus on Energy Independence

• Carbon Regulation Creates Economic Downturn

• Reference Case: Spring 2010

• Revised Reference Case: Great Recession Impacts Recovery

These scenarios were based on distinct visions of the future that were vetted in collaboration 

with stakeholders. For each scenario, uncertainty values were identified (e.g. commodity prices, 

environmental regulations, etc.) and then used as attributes to describe each future. Forecasts for 

each attribute were developed, discussed with internal and external stakeholders, and compared 

across scenarios to ensure reasonableness. A review of the scenario definitions after completion 

of the study made it apparent that some of the scenarios were similar in many respects; that is, 

some attribute values did not show significant variations across the scenarios. Over time, TVA 

has realized the importance of ensuring that each imagined future is both distinct and credible. 

Making sure that each scenario is sufficiently different and likely to occur avoids wasting time 

and resources on duplicative efforts and far-fetched notions. For example, there is little need to 

consider a future with 2% load growth if there is little expectation that this future will occur. 

In their upcoming IRP,58 TVA plans to reduce the number of scenarios, perhaps by half. To 

accomplish this goal, the 2015 IRP will focus on more distinct futures based on key uncertainties 

of particular interest to stakeholders. This approach aims to satisfy specific cases of interest to a 

wide variety of stakeholders (e.g., assumptions regarding penetration of distributed generating 

resources). TVA anticipates that stakeholder involvement in the creation of these scenarios will 

improve understanding and buy-in during the more complex modeling phase. 

III. Developing the TVA Strategies

Once scenarios were finalized, strategies that represented potential business decisions and 

portfolio choices that TVA could control were developed. First, key components of the strategies 

were defined, such as the types of generation technologies from which TVA could select and the 

development of new transmission infrastructure. Company management and stakeholders were 

consulted during this phase, to identify what business strategies they were willing to consider. If 

management were not on-board with a particular component, such as the accelerated 

development of nuclear, there would be no need to consider it. 

Next, development of the strategies was accomplished by combining variations of the key 

components. A total of nine key components (or attributes) were selected, with input from public 

scoping efforts. These key components included four model inputs and four model constraints:

 58 In general, as long as the long-term plan remains in line with the outcomes of the annual planning cycle, TVA 

does not deem it necessary to update the IRP. Although originally scheduled to begin in 2015, TVA will begin 

updating the IRP in late 2013, with a completion date of 2015. This effort is in direct response to stakeholder 

requests to review the company’s plans in light of changing natural gas prices and other key uncertainties. The 

company has already begun to reach out to stakeholders, including the solar industry, to continue to improve the 

methods through which solar can best be considered within the IRP process.
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• The level of the energy efficiency and demand response included in each strategy

• The amount of renewable resources input in each strategy

• The schedule for coal-fired idling to be tested

• The option of including a pumped-storage unit

• Constraints on the addition of new nuclear capacity

• Constraints on technology and timing of new coal facilities

• Constraints on gas-fired unit expansions

• Constraints on the type and level of transmission infrastructure to support the resource 

options in each strategy.

Through the consideration of these key components, five business strategies, represented by 

different resource portfolios for the future, were developed: 

1. Limited change in current resource portfolio

2. Baseline plan resource portfolio

3. Diversity-focused resource portfolio

4. Nuclear-focused resource portfolio

5. Energy efficiency/demand response-focused resource portfolio.

The strategies were differentiated from each other by the extent to which each key component 

was included in the strategy. That is, each key component was assigned a numerical boundary or 

range for each strategy (e.g., the Energy Efficiency and Renewable Focused strategy included the 

addition of 5,100 MW of renewables by 2020). In response to stakeholder comments, the target 

ranges for energy efficiency, demand response, and renewable energy were increased in the 

strategies during the strategy development process. 

The attributes of the five strategies were used as modeling inputs to create optimized generation 

portfolios. First, an optimized capacity expansion plan was generated using Ventyx’s System 

Optimizer tool, which minimizes the PVRR for each portfolio. The resulting portfolios were then 

evaluated using an hourly production cost model with stochastics, Ventyx’s Strategic Planning 

Model (MIDAS). The model determined the range of plan costs based on Monte Carlo modeling

of thirteen key variables that account for commodity prices, financial variables, operating costs, 

dispatching costs, and load forecast uncertainties. These variables differ from the key attributes 

(described above) in that the variables are not business decisions, but variables that are not in the 

utility’s control.

The total PVRR for each resource plan was calculated by using the random set of uncertainty 

variables drawn for stochastic iteration. The mean value across the plan cost distribution from 

the set of iterations was used as the reported plan cost of each strategy in a particular scenario. 

One portfolio was generated for each of five planning strategies associated with each of the six 

future scenarios during the draft phase of the IRP study. In addition, there was a portfolio for 

each of the five strategies associated with the Reference Case. This resulted in a total of 35 

portfolios. Each portfolio represented a 20-year plan for capacity expansion. 
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IV. Selecting Preferred Strategies

To evaluate the 35 portfolios, TVA (with input from stakeholders) created a scorecard. The goal 

was to identify trends or common characteristics that could lead to the most desirable 

outcomes.59 The scorecard organized and communicated the evaluation process, which involved 

scoring each of the five strategies on its cost, risk, environmental impacts, and other strategic 

metrics.60 The scoring process was subject to public comment, during which there were 

discussions of the trade-offs, constraints and compromises. These discussions, as well as a 

sensitivity analysis to identify top performing strategies were used to refine the evaluation results 

during the final phase of the IRP. 

The results of this scoring process, as well as a narrative explaining the technology investments 

associated with each strategy, were included in a draft IRP study report. The report was released 

for public comment consistent with the requirements of the National Environmental Policy Act 

(NEPA). A companion Environmental Impact Study (EIS) report was also issued with the draft 

IRP results. Based on public comment and stakeholder input, adjustments were made to the 

assumptions and the strategies following the release of the draft findings. During the final phase 

of the IRP, additional modeling and analysis was completed and led to the development of a 

Recommended Planning Direction, which consisted of guideline ranges for key components of 

the resource plan that performed well across most scenarios. This Recommended Planning 

Direction was submitted to the TVA Board of Directors and was adopted in April 2011.The final 

IRP defined TVA’s short- and long-term strategic direction and identified short-term actions. 

V. Involvement and Role of Distribution Companies

In the years leading up to 2008, the 155 distributors of TVA contemplated separating from TVA. 

As a result, there was not significant involvement of individual distribution companies during the 

initial stages of the IRP process. However, the association that represents the distributors, The 

Tennessee Valley Public Power Association (TVPPA), did participate as a stakeholder during the 

IRP process. 

Today, there is more direct collaboration with the 155 distributors, and while they are not closely 

involved in the IRP, they do have involvement primarily through various committees of TVPPA. 

There is a wide variety of levels of experience and interest in capacity expansion planning 

amongst the distributors, with some having little knowledge or interest in the details of new 

capacity needs or construction. The fuel source and technology by which the electricity they 

distribute is produced has, to date, not been an expressed as the overriding concern, and 

distributors are more adamant that the cost of power from TVA remains as low as possible. 

The distribution companies are, however, the ultimate implementers of all distributed generation 

programs (such as distributed solar or energy efficiency and demand response products), and 

therefore need to understand the benefits and challenges of distributed generation on their 

system. Likewise, TVA needs to know which distributors will be offering distributed generation 

programs to their customers and the extent of the customer-interest, in order to estimate the role 

that distributed generation will play in meeting future load and its effect on future transmission 

 59 Determining which outcomes were the most desirable (i.e. what were the indicators of a successful portfolio) was 

challenging but critical. 60 See TVA 2011 IRP, Chapter 6, pages 103-1 for details on the scorecard evaluation.
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system needs. In some cases, the distribution companies have expressed some reluctance to share 

information regarding customer involvement in distributed generation with TVA.

The uncertainties associated with the TVA relationship to its distributors, including the potential 

of separation and the imperfect information regarding future involvement in efficiency, demand 

response, and distributed generation programs, introduces an element of risk into the TVA 

capacity planning process. If communication surrounding these uncertainties remains unchanged, 

this risk will increase with rising penetration of distributed generation on the system. Currently, 

TVA planners assume an overall level of DG penetration and adoption across the TVA territory; 

however, a methodology that includes sub-region-specific DG penetration curves may be 

necessary in the future.

VI. Renewable Energy and Energy Efficiency Program 

Considerations

For the 2011 IRP, a fixed portfolio for renewable energy and energy efficiency was created 

outside of the capacity planning model and input as must-take transactions. These portfolios 

were designed using standard cost-effectiveness tests and other techniques and during the IRP 

were tested to confirm overall benefit, although individual components of the defined portfolios 

were not optimized directly in the models. The renewable energy portfolios were pre-selected 

based on a determination of future markets and the availability of supply. In its upcoming IRP, 

TVA wants to allow the model to select the renewable energy portfolio from a variety of options 

based on current market and resource data. This will result in more optimized renewable energy 

costs and production profiles. A similar approach to energy efficiency resource selection is also 

planned for the next IRP, based on direct optimization of small blocks of efficiency based on 

end-use or customer segment groups.

VII. Conclusion

TVA executed a thorough resource planning process that includes significant and meaningful 

stakeholder involvement, and culminates in a clearly written Integrated Resource Plan. TVA 

dedicates significant resources to informing, surveying and responding to stakeholders through 

all phases of the planning process, from scenario formulation to portfolio selection. The TVA 

IRP explains this process; results are well-organized and clearly written and thus are accessible 

to all stakeholders. 
59

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Appendix B – Select Utility Questionnaire Responses

1. What is your integrated resource planning horizon?

Total number of responses: 22 

2. What is the average annual consumption growth percentage for your planning 

horizon, net of any energy efficiency or customer-sited generation impacts?

Total number of responses: 22 

14%

18%

41%

5%

9%

5%

9%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

10 years 15 years 20 years 25 years 30 years 35 years Other

0 2 4 6 8 10 12

< 0%

0-1%

1-2%

2-3%

> 3%

Number of Responses
60

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3. How is distributed, net metered solar (behind the meter) incorporated into the 

modeling?

Total number of responses: 22

46%

18%

14%

23%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

50.0%

Negative load

that changes

consumption

profiles

Generation

resource

Not

incorporated

due to

negligible

penetration

Other (or

Additional

Comments)
61

This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

4. (a) Does your utility incorporate long-term transmission system planning into its 

resource planning process?

Total number of responses: 22

(b) Does your utility incorporate long-term distribution system planning into its 

resource planning process?

Total number of responses: 22

86%

0%

14%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

Yes No, but will for next

planning cycle

No

41%

5%

55%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

Yes No, but will for next

planning cycle

No
62

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5. (a) What capacity expansion model software does your utility utilize? (ex. Strategist)

Other: Plexos, Aurora, P&R CapEx, EGEAS, Outsource to 3rd party

Total response count: 21

(b) What production cost modeling software system does your utility use? (ex. PROMOD)

Other: Plexos, Aurora, Ascend Analytics PowerSimm, Market Analytics, RTSim, MIDAS

Total number of responses: 21

29%

10%

14%

19%

29%

0%

5%

10%

15%

20%

25%

30%

Strategist System Optimizer In-House Model Unknown/None Other

10%

19%

10% 10%

14%

10%

29%

0%

5%

10%

15%

20%

25%

30%

Gentrader ProMod Pmonth PROSYM Planning and

Risk (PaR)

Unknown /

N/A

Other
63

This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.

6. (a) What type of risk analytics does your company perform?

Total number of responses: 21

(b) Which variables do you stress?

Total number of responses: 21

Other or additional comments:

• We vary hydro output

• Stochastic modeling stresses: load, gas prices, power prices, and outages

• Energy efficiency is a single-variable sensitivity; the other items are stressed 

stochastically. The other item stressed in the risk analysis is the market price for 

electricity

• Other commodity prices, financial parameters, O&M costs, availability

• Sensitivities are run around multiple variables.

• fuel consumption, fuel costs, as-available energy purchases, as-available energy 

curtailment, capital cost for new generation, independent power producer payments/costs

• High/Low Distributed Generation

• EE Costs, Inflation

• Fuel consumption, fuel costs, as-available energy purchases, as-available energy 

curtailment, capital cost for new generation, independent power producer payments/costs

67%

52% 57%

76%

10%

0.0%

20.0%

40.0%

60.0%

80.0%

Single-variable

sensitivities

Stochastic

analyses

Monte Carlo Discrete

scenarios

Other or

Additional

Comments

86%

71%

33%

95%

29%

57%

48% 43%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

Natural gas

fuel prices

Carbon

costs

Installed

capacity

costs

Load Federal or

state

incentives

or tax

decisions

Renewable

requirement

decisions

Energy

efficiency

levels

Other or

Additional

Comments
64

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7. How do you choose solar capacity sizes for candidate portfolio evaluation?

Total number of responses: 21

Other or additional comments:

• Solar capacity size is standard based on MWh needs (i.e., RPS MWh requirements)

• Based on customer request

• Sizes are based upon actual projects

• To meet our Sustainability Targets - 20% by 2020, solar is one of many sustainable 

options that is valued

• Policy directed from governing board

43%

19%

10%

29%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

50.0%

Standard size based on

nameplate megawatts,

i.e. 20 MW increments

Standard size based on

megawatt-hours needs,

i.e. RPS MWh

requirements

Dynamic/Scalable Other (or Additional

Comments)
65

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8. (a) What capacity values for resource adequacy purposes do you assign for each 

type of solar resource? Note: not capacity factors

Note: Numbers in circles represent the number of utility responses

Total number of responses: 21
66

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b) What are these capacity values based on?

Total number of responses: 21

Other or additional comments:

• Regional Transmission Organization

• We will continue to analyze PV levels of generation, PV ability to meet electricity 

demand, etc. to determine the capacity value of PV.

• Initially RFP's. Thereafter, we gather actual solar operational production data to track 

against estimates. If needed, we will adjust our capacity values accordingly.

c) What methodology was utilized to calculate the capacity value?

Total number of responses: PV – 21; CSP – 18

43%

29%

24%

10%

29%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

Internal utility

research

Experience with

operational

systems

General industry

reports

Consultant(s)

research

Other or Additional

Comments

0

5

10

15

20

25

PV CSP

Number of Responses

Unsure

Other

Solar Load Control Capacity

Capacity Factor

Peak Time/Season Windows

Effective Load Carrying Capacity
67

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9. (a) Do you incorporate an integration cost adder for solar resources?

Total number of responses: 21

(b) As solar PV is considered a non-firm resource, does your utility explicitly add a 

firming cost for valuation purposes?

Total number of responses: 21

If yes, please describe the methodology/approach:

• Estimate at this point, as we have little experience.

• Indirectly, by applying coincident capacity value

• Cost of natural gas back up is used

• It varies based on valuation method

33%

10%

57%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

Yes No, but will in next planning

process

No

14%

43%

0%

43%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

50.0%

Yes No, but it is implicitly

captured on a portfolio

basis in the capacity

expansion model

No, but will in next

planning process

No