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Conference Paper

NREL/CP-6A20-77888

October 2020

Metrics and Analytical Frameworks for 

Valuing Energy Efficiency and 

Distributed Energy Resources in the 

Built Environment

Preprint

Monisha Shah, Dylan Cutler, Jeff Maguire, Zac Peterson, 

Xiangkun Li, Josiah Pohl, and Janet Reyna

National Renewable Energy Laboratory

Presented at the 2020 ACEEE Summer Study on Energy Efficiency in 

Buildings

August 17-21, 2020
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Conference Paper

NREL/CP-6A20-77888

October 2020

Metrics and Analytical Frameworks for 

Valuing Energy Efficiency and 

Distributed Energy Resources in the 

Built Environment

Preprint

Monisha Shah, Dylan Cutler, Jeff Maguire, Zac Peterson, 

Xiangkun Li, Josiah Pohl, and Janet Reyna

National Renewable Energy Laboratory

Suggested Citation

Shah, Monisha, Dylan Cutler, Jeff Maguire, Zac Peterson, Xiangkun Li, Josiah Pohl, and 

Janet Reyna. 2020. Metrics and Analytical Frameworks for Valuing Energy Efficiency and 

Distributed Energy Resources in the Built Environment: Preprint. Golden, CO: National 

Renewable Energy Laboratory NREL/CP-6A20-77888. 

https://www.nrel.gov/docs/fy21osti/77888.pdf. 
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Metrics and Analytical Frameworks for Valuing Energy Efficiency and 

Distributed Energy Resources in the Built Environment

Monisha Shah, Dylan Cutler, Jeff Maguire, Zac Peterson, Xiangkun Li, Josiah Pohl, and 

Janet Reyna, National Renewable Energy Laboratory 

ABSTRACT

This paper summarizes efforts to develop new, and enhance existing, analytical 

frameworks and metrics to quantify the value that grid-interactive efficient homes with solar

(GEB-solar homes) can provide. Industry is working to characterize and understand these 

capabilities and benefits, but existing analytical frameworks for evaluating energy efficiency

(EE) are often siloed from those that evaluate distributed energy resources (DERs). 

Five metrics were adapted from an extensive literature review and applied to case studies 

of a modeled home in Riverside, California: ramp up/down, cover factor demand/supply, and 

curtailable load. Eight different technology scenarios were analyzed using a connected suite of 

building, site, and grid models (BEopt, REopt, ReEDS, and PLEXOS). Additionally, timevarying marginal electricity costs were developed, based on NREL’s 2018 Standard Scenarios. 

These marginal costs were used to generate time-varying proxy retail rates, and were also 

applied directly to calculate a new grid alignment metric. 

In the results, a more integrated combination of GEB-solar technologies led to a higher 

cover factor demand—the percentage of gross home load covered by on-site solar—however, a 

benchmark was required to determine what range of cover factor was “best” for given grid 

conditions. To that end, a grid alignment cost metric was applied to the case study scenarios. The 

average cost to serve the net load of the home decreased from a median of ~$0.24/kWh to 

~$0.10/kWh when the most integrated technology scenario was optimized towards the grid

pricing proxy versus the time-of-use (TOU) rate. 

Introduction

This work focuses on analytical frameworks for valuing grid-interactive efficient singlefamily residential buildings with solar, GEB-solar homes, which, in aggregate, could provide 

substantial grid-oriented services. GEB-solar can impact the local and bulk power grids in 

positive and negative ways. Industry is working to characterize and understand these impacts, 

capabilities, and benefits as described by NASEO-NARUC (2019). To date, there has been 

limited development of metrics or analytical approaches for assessing the value that buildings 

harnessing EE, flexible loads (FLs), battery energy storage systems (BESS), and photovoltaics 

(PV) can provide to different energy sector stakeholders, such as building owners, distribution 

utilities, grid operators, and society (Shah et al. 2018). Existing analytical frameworks for 

evaluating the costs and benefits of EE are often siloed from those that evaluate DERs and are 

often not equipped to consider the cost and benefits of these resources as a combined package. 

Consequently, decision makers may even view resources such as EE and PV as competing, 

rather than considering the combined value of pairing these assets together (Perry et al. 2019). 

New analytical frameworks and metrics must be developed, or existing ones enhanced, to more 

accurately quantify the value buildings can provide to different energy system actors, particularly 

in a temporally and spatially specific context. 
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In this analysis, we highlight the relevant benefits by stakeholder group, evaluate the 

metrics that have heretofore been used to quantify these benefits, and evaluate select metrics 

(including a newly developed grid-centric metric) through a new analytical modeling framework. 

The first two sections of the paper cover value streams and a review of existing metrics for GEBsolar. The last two sections outline the case studies for a modeled single-family home in 

Riverside, California, that were performed with a connected suite of building, site, and grid 

models (BEopt, REopt, ReEDS, and PLEXOS) and the associated results and conclusions. 

Potential Value Streams of GEB-Solar Homes

The enhanced capabilities of GEB-solar buildings, including flexibility, can provide a 

spectrum of benefits to various stakeholders in the energy system (Carmichael et al. 2019; DOE 

2019). This project explores new and existing methods for valuing these benefits for three main 

stakeholder groups: building owners/occupants, utility or grid actors, and society as a whole. 

From the bulk power system perspective, aggregates of single-family homes have the 

potential to provide a number of grid services. For example, a group of houses could provide 

capacity during peak times at the bulk power level by reducing net load based on a signal from 

grid operators. GEB-solar can provide transmission congestion relief by exporting energy to 

provide electricity in constrained areas. In addition, a group of homes could shift energy 

consumption to time periods with overgeneration of PV. Another way GEB-solar could provide 

value is through operational cost savings (e.g., power plant fuel, O&M, and startup costs) by 

enhancing the ability of a building to reduce energy requirements during times with high 

marginal cost of generation. 

The distribution system has needs unique from the bulk power system. One of the most 

significant ways a GEB-solar can support the distribution system is by managing the 

real/reactive power output from inverter-based devices like PV coupled to the grid. Doing so can 

aid in mitigating voltage violations (unrelated to PV), decrease distribution grid energy losses, 

and reduce or defer distribution system infrastructure upgrades. Additionally, GEB-solar can 

reduce distribution system congestion by curtailing net load or increasing or decreasing the 

export of on-site generation during times of congestion. 

Homeowners can accrue value from GEB-solar through utility bill cost savings, monetary 

compensation for providing grid services, or through non-monetary values such as improved 

comfort or resiliency. For example, a utility bill management strategy could utilize storage or 

load scheduling to shift energy consumption to lower-cost time periods or simply reduce 

consumption during high-cost time periods (assuming the retail tariff provides some time-based 

price differentiation or demand charge component). Either would result in reduced utility bills, 

assuming the price differential is large enough to make up for any roundtrip efficiency or other 

losses. New revenue streams can be developed by participating in demand response events which 

compensate for managed energy consumption based on temporal parameters. GEB-solar with 

storage can also provide increased resiliency by supporting critical loads during a grid outage, 

allowing occupants to continue operations. This capability will depend on inverter technology, 

islanding technology, and presence of AC-coupled batteries, as most solar inverters are not able 

to generate during grid outages for safety purposes. 

From the societal perspective, GEB-solar could also provide value by reducing the 

greenhouse gas emissions, reducing criteria pollutants, and increasing system resiliency. One 

aspect of greenhouse gas and criteria pollutant reduction is reducing total annual energy 

consumption (Langevin et al. 2019), but grid emissions also vary on a temporal basis (Vuarnoz 
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et al. 2018). BESS and/or FL in tandem with EE and PV could be optimized to shift energy 

consumption to a less carbon-intensive time period or to self-consume during carbon-intensive 

periods on the grid. Additionally, GEB-solar homes could provide flexibility to markets with 

high penetrations of variable resource renewable generation or provide low carbon electricity by 

exporting on-site renewables. Finally, benefits of generation sited near to load, and more 

controllability on the load side, could provide additional resiliency to the system. Capturing these 

benefits for GEB-solar requires the application of either existing or new metrics and analytical 

frameworks to measure the building capabilities needed to provide these benefits. 

Existing Metrics and Analytical Frameworks

A literature review was conducted to collect existing metrics that could quantify the value 

streams associated with GEB-solar and measure the building capabilities needed to provide 

them. A wide net was cast across five different bodies of literature: zero energy buildings, grid 

impact, demand response, value of DERs, and bulk power system; and from that search we 

identified over 125 potentially relevant metrics. 

Most of the metrics identified through literature review fell into two high-level 

categories: (1) operational metrics; or (2) asset metrics. Asset metrics—“leading” metrics—

measure potential performance, evaluating the theoretical or technical potential of a GEB-solar

depending on its design characteristic. Operational metrics—also referred to as “lagging” 

metrics—measure past performance, typically based on empirical data. Metrics were also 

categorized as: directly applicable to GEB-solar, indirectly applicable (i.e., requiring minor 

adaptations before being used to evaluate a GEB-solar home), or out-of-scope. Out-of-scope 

metrics were those that measured impacts not considered in this analysis. Five metrics were 

selected to test their effectiveness through the case study analysis, and these are described in 

more detail below. 

Ramp up/down. The ramp up/down metric was adapted from the bulk power flexibility 

literature from a metric called “maximum ramp rate in net load” (GMLC 2020) and is usually a 

maximum value calculated over all hours of the year. To avoid outliers, the metric can also be 

calculated as the mean of the absolute value of the top 1% largest ramps over the year.

RRii = PPii − PPii−1 ii = 1, 2, . . . , 8760 (Eq. 1)

The ramp metric (in kW/hr) was calculated by subtracting the net load1 (in kW) for each hourly 

time step by the net load of the previous time step for the entire year. This is seen in Eq. 1, where 

Ri is the ramp for the time step, Pi is the power at the time step, Pi-1 is the power at the previous 

time step, and this was considered on an hourly basis. 

The ramp metric measures the change of net load (e.g., kW) for a given time step (e.g., 1 

hour) over a time period (e.g., 1 year), units kW/hr. Ramp could be a useful metric in 

understanding how variable the net load of a building might be and quantifying the magnitude. 

The ramp metric could be used to ascertain building performance against other grid behaviors. 

For example, it may be useful to measure ramp for a building during a time of day when systemwide ramps in load are occurring. The ramp metric could also be useful in determining 

1 Net load refers to the gross load of a GEB-solar home minus any on-site generation used to meet gross load, 

including on-site solar electricity stored in the BESS and discharged to meet on-site loads at a later time. 
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opportunities for smoothing the load profile to reduce demand charges through storage or FL

capabilities. If the ramp metric were calculated for individual technologies in the building instead 

of for the whole building, one may be able to determine how different equipment contribute to 

rapid changes in net load in a building. 

Curtailable load. Curtailable load measures the absolute decrease in net load a building can 

provide during a time step (e.g., 1 hour), in units of kW. This metric originates from the bulk 

power system literature and was originally defined as the ramp capability of a dispatchable fleet 

over various time periods (GMLC 2020). For a building, curtailable load would need to consider 

all FLs in the building operating during the time step of interest while taking into account 

thermal comfort requirements. This metric could be calculated on an asset basis and provide the 

potential to curtail net load or on an operational basis and provide the actual net load that was 

curtailed due to economic drivers or building controls. Curtailable load can be considered over 

various time scales, and measurement becomes more complex when storage is included. This 

information could be helpful to utilities who may need to curtail load during times of system 

peak or to building owners who seek to curtail loads to avoid high demand charges or TOU rates.

Cover factor supply/demand. Cover factor demand and supply are derived from the net zero 

energy buildings literature and are usually calculated on an annual basis (Verbruggen et al. 

2011). Both of these metrics are unit-less and vary from 0 to 100%. Cover factor supply (CFs) 

measures the ratio of on-site generation consumed by the building to gross on-site generation. A 

CFs of 100% means the building is consuming all on-site generation, whereas a cover factor of 

0% means the building is exporting all on-site generation. This metric can indicate the amount of 

electricity the building is exporting during a given time period and also provide a sense for what 

percentage of the on-site electricity is being used for self-consumption. CFs is normally 

calculated on an annual basis (Eq. 2) but can be modified for different time periods (e.g., a daily 

time period). 

CCCCss = CCCCCCCCCCCCCCCC oo PPPP gg gggg

GG oo PPPP gg gggg

xx100 (Eq. 2)

Cover factor demand (CFd) measures the ratio of total on-site generation serving load to the 

gross load of the building. This metric provides insights on both load matching2 or selfconsumption,3

 as well as how reliant the building is on electricity from the grid. For example, a 

building with CFd of 100% on an annual basis means the building is a zero-energy building over 

the course of a year, whereas a cover factor of 0% means the building lacks on-site generation or 

the generation is completely misaligned with demand, and is thus fully reliant on the grid. This 

metric can be modified as needed to consider how different net load components (e.g., 

electrochemical storage, thermal storage, FLs) change the cover factor. For this analysis, CFd

was calculated by dividing the annual on-site generation consumed by the building by the 

building’s gross load for the entire year (Eq. 3). The gross load in the scenarios includes the 

consumption from the traditional loads (e.g., lighting, appliances), as well as consumption from 

the battery system and FLs (if applicable). 

2 Managing energy consumption to match with energy supply. 3 Maximizing consumption of energy generated on-site. 
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CCCCdd = CCCCCCCCCCCCCCCC oo PPPP

GG ll xx100 (Eq. 3)

Case Studies

To evaluate the applicability and usefulness of existing and new metrics, an analysis

workflow was developed to assess the interactions between PV, EE, FL, and BESS in a modeled 

single-family home in Riverside, California. The analysis workflow allowed the project team to 

study simulated home design and operations with respect to grid information. Existing metrics 

identified through the literature review were calculated using this analysis workflow to begin 

assessing their applicability and effectiveness in measuring the various benefits of GEB-solar. 

The analysis workflow (Figure 1) included two models developed by the National Renewable 

Energy Laboratory, the Building Energy Optimization (BEopt) (BEopt 2014) and the Renewable 

Energy Optimization (REopt) (Cutler et al. 2017) models, which were both deployed to 

determine cost-optimal adoption and operation of EE, FL, and DER technologies in homes. 

Figure 1. Analysis workflow for evaluation of customer investment decisions

Home EE and DER investment and operation models. BEopt is a physics-based building 

energy modeling platform for residential buildings that finds cost-optimal combinations of 

building equipment and technologies. The major limitations to BEopt with regards to this project 

are the limited options for modeling FLs and the inability to model battery storage. REopt is a 

techno-economic optimization model that can optimally size and dispatch mixes of generation 

and storage technologies to minimize the cost of energy for a site. It is formulated as a mixed 

integer linear program (MILP), with the objective function being the minimization of the life

cycle cost of energy for a specific site or building. The limitation to REopt in the context of this 

project is that it simplifies the building load modeling and cannot fully simulate and compare EE

improvements with renewable energy or storage options. While both of these models optimally 

select combinations of technologies to minimize the life cycle cost of energy for a site, neither 

contains the complete set of technologies that were of interest for this project. Using a 

coordinated model workflow, these two models were utilized in conjunction, with data being 

passed from BEopt into REopt, enabling a more complete evaluation of the technological space 

under consideration. 

These two models approach the control of dispatchable technologies in very different 

ways. BEopt uses algorithmic approaches for how to control a single technology (e.g., HVAC 

systems or smart appliances). These are typically schedule-based and are input based on analysis 

of the TOU rate. REopt, on the other hand, dispatches a suite of technologies to minimize cost of 

electricity for the homeowner and considers the technical capabilities and cost/benefit trade-off 

(as formulated in the optimization constraints) as it optimally dispatches any combination of the 
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technologies selected by the model using perfect foresight. This approach reflects a centralized 

controls approach—similar to what an advanced home energy management system (HEMS)4

would attempt to deliver—and may be considered to represent a theoretical upper bound. Actual 

operation of an integrated HEMS would depend on how the system was programmed and its 

ability to coordinate the loads with uncertain forecasts. To adequately capture a range of 

approaches to controls, both types of device controls were included in the modeling framework. 

Figure 2. Summary of technology combination scenarios

BEopt was utilized to suggest optimal packages of energy efficient and FL technologies 

that minimize life cycle costs for the homeowner when designing a single-family home. With EE 

and, in some scenarios, FLs modeled in BEopt, the top 100-200 performing sets of technology 

combinations were sent to REopt to find optimal combinations of PV and BESS to minimize cost 

to the homeowner. Additionally, for coordinated controls scenarios, the FLs were characterized 

in BEopt but then modeled explicitly in REopt using state space modeling of resistancecapacitance networks to allow for coordinated dispatch.5

 This approach allows for the 

exploration of a full range of EE, FLs, BESS, and PV, but because of the sequential nature of the 

modeling flow, not everything is co-optimized in the same platform.

Time-varying Marginal Grid Costs. To determine the grid value of different demand-side 

technologies when the technology owner is operating it in their own self-interest, the project 

team used two models to generate time-varying marginal costs of electricity, using NREL’s 2018 

Standard Scenarios to characterize future bulk power system scenarios.6

 Using the capacity 

investments from the 2018 NREL Standard Scenarios Mid-Case, we used PLEXOS, a 

commercial production cost model, to obtain hourly marginal costs of energy and ancillary 

services for the projected 2040 system. We then constructed a total marginal electricity cost by 

4 Home energy management systems exist but are not widely deployed in US homes. For more information see: 

https://www.energystar.gov/products/shems_key_product_criteria 5 A resistance-capacitance network is an equivalent circuit representation of the thermal flows through a building. 

This can be represented in a state-space formulation, which is linear set of equations that can be embedded in a 

MILP. 6 NREL’s Standard Scenarios is an annual product that uses the ReEDS capacity expansion model to project the 

evolution of the bulk power system through 2050 under a range of potential futures (Cohen et al. 2019; Cole et al. 

2018). 
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allocating the marginal cost of capacity (from the ReEDS model) to the top 40 net-load hours.7 

Lastly, to construct a time-varying proxy retail rate for this future scenario, we took the marginal 

cost patterns described above and scaled them up, such that the same amount of revenue would 

be collected from that load profile as the revenue collected from the actual retail rate’s energy 

prices.8 These proxy retail rates could then be used in BEopt and REopt to influence technology 

investment and operations decisions. 

Eight technology scenarios were studied in the analysis workflow (Figure 1); all 

scenarios included traditional EE measures that lower overall energy consumption but do not 

allow for load shifting. The “no FL” scenario refers to scenarios with only traditional EE

upgrades and no FL options. The “FL-Sch” scenario includes the FL options included in BEopt, 

such as certain household appliances or the HVAC systems with manually programmed 

operating schedules around the peak periods of a local TOU rate. Since flat rates do not 

incentivize load shifting and the time-varying marginal grid costs do not align well with 

scheduled control, they were not applied to the FL-Sch scenarios. Finally, the “FL-Adv” 

technology scenarios include FLs for which the operations are optimized in REopt based on the 

rate structure, other incentives and, when available, with PV and battery sizing and dispatch 

decisions. This approach maximizes synergies across flexible residential assets.

In the analysis, several economic and technology assumptions were taken. The analysis 

period was 30 years, and the technologies were assumed to be owned by the building owner. The 

PV and battery systems were assumed to utilize the Investment Tax Credit at 30% and 26.25% 

respectively and grid charging was allowed, as well as the 5-year Modified Accelerated Cost 

Recovery System (MACRS) depreciation deduction. The PV systems were assumed to have an 

installed cost of $2.80/W-DC (Fu et al. 2017) with an O&M cost of $20/kW/year. The battery 

costs were assumed to be $930/kWh + $1,115/kW, with a single replacement occurring during 

the analysis time period with a cost of $471/kWh + $565/kW9 (WMPR 2018). 

BEopt and REopt were both exposed to a suite of utility tariffs to understand what 

technologies (and operational approaches) provided the most value to the homeowner. Four sets 

of rate structures were applied to the scenario analysis: a flat volumetric rate, a 2018 TOU rate 

for Southern California Edison (SCE),10 the net metering rate for SCE in 2018, (NREL 2018) 

and the aforementioned proxy retail rate based on the projected 2040 system. This last rate was 

included to provide the most direct, price-based communication of grid needs to the homeownerfocused modeling workflow. 

A distinct single-family home model was developed for Riverside, California, and 

location-specific weather files were used to model realistic conditions. Riverside is simulated in 

the SCE service territory, which is part of the California Independent System Operator, and is 

located in IECC climate zone 3B. By design, BEopt selects packages of energy efficient 

technologies based on capital cost, installation costs, equipment lifetime, and electricity rates to 

minimize life cycle costs. For Riverside, 24 potential efficiency measures in nine major 

technology categories were considered: thermal mass (3), wall insulation (2), wall sheathing (4), 

7 The future marginal electricity costs were developed with a prototype version of a grid model post-processing tool 

called Cambium (Hale et al., 2019) 8

 This should not be mistaken for a maximally economically efficient pricing scheme. It would be by chance if the 

actual costs that create the difference between wholesale and retail rates followed the patterns of the marginal costs. 9

Battery costs are assumed to decline at a rate of 6%/year (Manghani, R. 2014. The Future of Solar-Plus-Storage in 

the U.S. Boston: GTM Research) 10 SCE rate: TOU-D-T-Region 10 

(https://openei.org/apps/USURDB/rate/view/586be52d5457a30d661c9607#1__Basic_Information). 
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ceiling insulation (3), windows (1), basement/floor insulation (3), infiltration (3), ventilation (1), 

space conditioning (2), and lighting (2). The base model was an all-electric home to focus on 

exploration of the electric impacts of space conditioning upgrades/controls. The capital and 

installation costs, as well as the expected lifetime of each of these technologies, are standard to 

BEopt and come from the National Residential Efficiency Measures Database (NREL 2018). 

Results 

Though all the of the metrics described above were applied in the case study, a few will 

be highlighted in this report. The application of cover factor demand, a metric that can measure 

building capabilities of both load-matching and self-consumption, and the results of a grid 

alignment metric will be explained. 

Impact of control options. One component of the scenario analysis was to explore how different 

types of control options—scheduled controls (BEopt) and advanced dispatch controls (REopt), 

which more closely integrate EE and DER options—could impact the value of GEB-solar and, in 

particular, the contribution of solar to that value. Figures 3 and 4 compare the impact of these 

control options, with and without solar and storage, respectively, in diagrams displaying the 

dispatch results for 1 week in early June for the modeled home in Riverside, California (the 

optimal EE package generated by BEopt). 

In Figure 3, we show the impact of the two control options (smart water heaters and 

HVAC) without any PV or BESS. The scheduled controls minimize HVAC use during peak 

pricing, as opposed to the advanced controls, where the HVAC is either turned off or precooled. 

Note that REopt, with its more advanced dispatch, was allowed to choose different setpoints 

from BEopt so long as it maintained thermal comfort (leading to a precooling strategy), whereas 

BEopt was scheduled to turn HVAC off at certain times and temperature would drift 

accordingly. Additionally, the scheduled controls do not include water heater control, where the 

advanced controls model does (note shifting of the DHW [yellow area] out of peak-pricing 

periods in the lower tile of the figure). The scheduled controls include appliance control where 

the advanced controls approach does not (note shifting of the light blue area out of peak-pricing 

periods in the upper tile of the figure). Despite these differences, the two controls approaches, in 

Figure 3, result in relatively similar net load profiles (solid black trace), with the largest change 

being the advanced controls shifting of the DHW load. 

Next, Figure 4 mirrors the dispatch differences between the two controls approaches from 

Figure 3 but now includes the cost-optimal configurations for PV and BESS systems. In these 

instances, the advanced controls shift a large amount of load into the hours where the PV system 

is generating electricity, enabling a larger system size (3.3 kW vs. 5.4 kW for scheduled and 

advanced controls, respectively). The scheduled controls optimally select a larger BESS, whereas 

the advanced control selects a smaller system and utilizes the enhanced load coordination to 

effectively utilize more of the cost-effective PV. Net load is also more consistently low and 

smooth with the advanced controls. The detailed comparison of these two approaches highlights 

the ability of the controls to impact both system sizing and resulting metrics characterizing the 

operation of these buildings. 
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Figure 3. A week of operations comparing the scheduled (top) and advanced (bottom) controls. The profiles are for 

just EE and FLs (no PV or BESS). Light red bands show peak-pricing periods.

Solar and storage sizing. Building on the previous section, the scenarios with scheduled controls 

lead to a slight decrease in both average PV and battery sizes as compared to scenarios with only 

traditional EE. This is likely due to high-cost peak periods in the TOU rate aligning well with 

peak PV generation hours, so by scheduling FL outside of peak TOU periods, the direct value of 

PV generation is reduced.11 It is not cost-effective for a homeowner to invest in a larger storage 

system to shift the electricity generated by the PV system to match the new load schedules, so 

the technology sizes decrease; however, when advanced control is available, there is a noticeable 

increase in average PV size with a corresponding decrease in battery size. The increase in 

building load flexibility allows for better utilization of renewable energy integration. PV sizes 

increase as load can be dynamically shifted to match daily generation profiles, reducing the need 

for grid purchases. Simultaneously, the increased flexibility allows the building to act as a 

storage unit itself, through space temperature and hot water temperature setpoint modification, 

reducing the need for additional electrochemical storage capacity to align on-site consumption 

with self-generation. 

11 This results from the EE being implemented/controlled separately from the PV/BESS. The scheduled controls are 

not aware of potential PV generation, therefore only reducing load in the high value periods, and thus the PV system 

is optimally sized smaller.
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Figure 4. A week of operations comparing the scheduled (top) and integrated (bottom) controls with optimal PV and 

BESS sizes included. Light red daily bands show peak-pricing periods. 

The solar and storage sizing is not just affected by available control options but also by 

the type and shape of the rate structures in place. When comparing the sizing results under the 

TOU rate with the proxy retail rate, we see that PV sizes are reduced (for a given control 

strategy, e.g., FL-Adv TOU and the proxy retail rate) when optimized under the proxy retail rate. 

Because the proxy retail rate reflects future marginal costs, and the grid’s generation mix is 

expected to evolve to contain more low marginal cost renewables, daytime prices are driven 

down, decreasing the value of behind-the-meter PV generation. On the other hand, the real-time 

nature of the proxy retail rate, with greater volatility and larger price differentials, allow for more 

arbitrage opportunities causing battery sizes to increase relative to TOU sizing across all control 

approaches.

Building capability results. Though all the building capability metrics described above were 

calculated using the analysis workflow, this section will discuss the CFd metric results, as it is a 

metric well-suited to assess load-matching and self-consumption, which are unique building 

capabilities for GEB-solar homes. First, the annual CFd results for the California TOU scenarios 

are summarized in Figure 5. The scenarios without PV are not included in the graph because they 

would have a 0% cover factor demand. Each boxplot within the chart represents results for a 

~100-200 combination of technologies (depending on BEopt optimization search path), ranging 

from only EE options without FLs or controls to full PV/battery/FLs/advanced controls. Each 

boxplot may represent different cost-optimal combinations of EE or FL technologies, as well as 

different sizes of solar and storage. 
11 

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Figure 5. Cover factor demand, TOU rate

The small interquartile range seen in all scenarios suggests consistency of the cover 

factor value between the buildings in each scenario, highlighting the fact that FL and DER 

having a much larger impact than static EE measures for this metric. The results demonstrate that 

as the FL controls become more advanced, and BESS is deployed, the cover factor demand 

increases. The scenario with FL-Adv and BESS has a cover factor demand of over 90%, 

illustrating that the building is close to consuming zero energy from the grid on an annual basis. 

Interestingly, the scenarios with scheduled FLs have a lower cover factor than the comparable 

scenario without FLs. This is likely because the TOU peak is aligned with peak solar generation, 

and the scheduled controls move those loads to off-peak periods. 

Figure 6. Cover factor demand, proxy retail rate

The amount of PV deployed also plays a role in the magnitude for the cover factor. The 

median PV system size for the FL-Adv scenarios was approximately 5.5 kW, but for all other 

scenarios the median PV system size was close to 3.5 kW. The larger system size is likely due to 

the FL-Adv being able to shift the loads to be coincident with the on-site generation. The proxy 

retail rate scenarios only include the technology packages with PV and do not include the FLSch (Figure 6). CFd increases as BESS and FL-Adv are added, which is similar to the TOU 

scenarios. The PV system size also plays a large role in these results with the higher cover factor 

associated with a higher PV system size. Calculating CFd for a particular design and operation of 
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a home could indicate which homes might be best suited to minimize utility bill savings for 

customers but may need to be considered against the life cycle cost of the technology 

investments. Similarly, a high CFd could also indicate which homes are best suited to address 

local distribution peak shaving, especially if designed against a TOU set by the distribution 

utility.

A high CFd might also indicate which homes are best suited to minimize export impacts 

on the local grid or reduce net load during periods of peak demand on the bulk power grid. 

Although the project team could measure the building capability metrics in the case studies, there 

was still a need for context: Was a cover factor demand of 70% good, and in which instances? 

The project team needed to develop a benchmark for a GEB-solar home that could be considered 

an ideal grid asset under a given set of grid conditions. 

Figure 7. Average grid alignment cost for Riverside, California (TOU rate)

Grid alignment results. To that end, a grid alignment metric was developed and applied to the 

case studies to provide a benchmark for which homes might be best aligned with a future bulk 

power grid from a cost and emissions perspective. To explore this metric, we introduce the 

average marginal cost metric (CCpp,aaaaaa), and then discuss the associated results. This metric is 

calculated by multiplying the hourly net loads for each version of the Riverside home with the 

technology scenarios conducted with BEopt and REopt (Fig. 1) against the proxy retail rate 

pricing in each hour and then normalizing this by the annual electricity demand of the home to 

demonstrate the average cost of serving this load from the grid perspective: 

CCpp,aaaaaa = � EEtt

EEaaaaaaaaaaaa

∙ cctt

8760

tt=1

(Eq. 4)

Where EEtt is the net load for time step t (in units of kWh), EEaaaaaaaaaaaa is the total annual energy 

consumption of the home, and cctt is the proxy retail rate price for time step t.12 

Figure 7 shows the result for average grid cost for Riverside, California, when the EE and 

DER assets were selected and optimized for the TOU rate. In each graph, results are presented in 

boxplot form, where the underlying data are the average marginal cost metric results for every 

12 We note that in this metric we are concerned with energy (thus the unit of kWh for the hour), whereas in Equation 

1 we evaluate based on kW given that it is a ramping metric focused on power. These are both able to be used 

because it is an hourly model and the kW for the hour is equal to the energy in kWh for that hour.
13 

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building design selected during the BEopt optimization (and subsequently passed to REopt for 

DER optimization). 

The average marginal cost increases with larger numbers of—and more integrated control 

of—DER and EE technologies. While the optimization models are effectively reducing the total 

cost of utilities for the homeowner, the reduction in utility purchases is happening at times when 

the grid’s cost of delivering that energy (as expressed by the proxy retail rate) are the highest. 

Therefore, the BEopt and REopt models have effectively minimized cost for the homeowner by 

moving consumption out of the high-price period of the day based on the 2018 TOU price (see 

Fig. 7). This has effectively moved the power consumption into times that are more costly from 

the future grid perspective. Figure 8 shows the daily average proxy retail rate profile for all hours 

of the year in comparison with the on-peak period from the SCE 2018 TOU rate. We note that 

the on-peak period for the TOU rate (shown in the band covering 12 p.m.-6 p.m.) covers the 

hours with the lowest marginal grid costs on average. This difference in price shapes affect the 

investment and operation of the suite of technologies analyzed in this work. Figure 9 shows 

similar boxplot results for the scenarios that were designed against the proxy retail rate. 

Figure 8. Comparison of daily average proxy retail rate for 2040 vs. SCE 2018 TOU rate (on-peak, 12 p.m.-6 p.m.) 

For the marginal costs, similar to the previous scenarios, the scheduled controls were not 

run under this pricing scenario. We note that when the customer-facing models are optimized 

against the proxy retail rate, the trends seen in Figure 9 reverse themselves: the average marginal 

cost metric now decreases in line with more technologies and advanced controls. This 

demonstrates the ability to align the objectives of the customer and the grid, with the increasingly 

dispatchable, responsive controls of the DER technologies being operated to minimize energy 

consumption during high marginal cost periods for the bulk power system. Additionally, we note 

that the y-axis scales are different between Figure 7 and Figure 9, and the average cost to serve 

the net load of the home shifts from a median of ~$0.24/kWh to ~$0.10/kWh when the most 

integrated combination of PV, battery, and FL control is optimized towards the TOU rate versus 

the marginal grid costs, respectively. 
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Figure 9. Average grid alignment cost for results optimized (proxy retail rate)

Conclusions

In this work, we developed a modeling methodology to evaluate the value of GEB-solar

homes from multiple perspectives, including: the homeowner, the bulk power system, and 

society. To achieve this multi-perspective analysis, we combined models from several sectors: 

building energy modeling and EE (BEopt), DER optimization and dispatch (REopt), capacity 

expansion (ReEDS), and unit commitment and dispatch (PLEXOS). We utilize the model 

outputs to assess several metrics to better understand how to quantify GEB-solar contributions. 

Based on the research and analysis undertaken, we drew several overall conclusions from 

the work. First, though the literature review resulted in over 125 metrics, only one-fifth of the 

metrics were readily applicable to GEB-solar homes and, even then, there were still building 

capabilities of interest that could not be readily measured from existing metrics. Second, 

although the project team could measure the building capability metrics in the case studies, there 

was still a need for context. Was a cover factor demand of 70% good, and in which instances? A 

grid alignment metric was developed and applied to provide a benchmark for which homes might 

be best aligned with the bulk power grid from a cost and emissions perspective. 

In this work, there was still a need to more clearly capture the strengths and weaknesses 

of the grid alignment approach and, more specifically, explore the types of costs and emissions 

utilized therein. Additionally, the current grid alignment methodology incorporates many grid 

events (e.g., capacity costs, duck curve ramping, PV curtailment events, and so on) during which 

a building strategy of self-consumption might be needed. These grid events could be more 

specifically isolated in the grid alignment methodology and used to benchmark the building 

capability metrics. It would also be valuable to understand “at what cost” the building 

capabilities and grid alignment values of a GEB-solar home are from the owner perspective. For 

example, is it beneficial to increase grid alignment slightly, but result in a huge capital 

expenditure for the homeowner? A benefit-cost approach could be constructed to better 

understand this trade-off in future work. Finally, the results here are drawn from a small set of 

case study scenarios. These should be expanded, and associated metrics should be stress tested 

across a much larger set of locations and building designs. 
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