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Contract No. DE-AC36-08GO28308

REopt: A Platform for Energy 

System Integration and 

Optimization

Preprint

T. Simpkins, D. Cutler, K. Anderson, D. Olis, 

E. Elgqvist, M. Callahan, and A. Walker

Presented at the 8th International Conference on Energy and 

Sustainability (ES2014)

Boston, Massachusetts

June 20 – July 2, 2014

Conference Paper

NREL/CP-7A40-61783

August 2014
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REOPT: A PLATFORM FOR ENERGY SYSTEM INTEGRATION AND OPTIMIZATION

T. Simpkins*

, D. Cutler, K. Anderson, D. Olis, E. Elgqvist, M. Callahan, A. Walker

National Renewable Energy Laboratory

Golden, CO

 * Corresponding author. Tel: +1-303-275-4242

E-mail address: travis.simpkins@nrel.gov

ABSTRACT

REopt is an energy planning platform offering concurrent,

multiple technology integration and optimization capabilities to 

help clients meet their cost savings and energy performance 

goals. The REopt platform provides techno-economic decision

support analysis throughout the energy planning process, from 

agency-level screening and macro planning to project 

development to energy asset operation. REopt employs an 

integrated approach to optimizing the energy costs of a site by 

considering electricity and thermal consumption, resource 

availability, complex tariff structures including time-of-use, 

demand and export rates, incentives, net metering, and

interconnection limits. Formulated as a mixed integer linear 

program, REopt recommends an optimally sized mix of 

conventional and renewable energy, and energy storage 

technologies; estimates the net present value associated with 

implementing those technologies; and provides the costoptimal dispatch strategy for operating them at maximum 

economic efficiency. The REopt platform can be customized to 

address a variety of energy optimization scenarios including 

policy, microgrid, and operational energy applications. This 

paper presents the REopt techno-economic model along with 

two examples of recently completed analysis projects.

INTRODUCTION

Energy asset modeling tools are a class of decision support 

tools that provide analysis of energy generation and 

consumption at all stages of the project development cycle, 

from master planning and project screening to detailed 

feasibility analysis to real-time control of energy assets. During 

the screening phase, these tools often leverage geospatial

renewable energy resource datasets in conjunction with 

standardized technology models to provide estimates of the 

energy production capabilities of a given technology. After a 

potential project has been identified during the screening phase, 

more detailed techno-economic analysis is performed during 

the development phase. After commissioning is completed, 

real-time energy modeling tools may be used to help operate 

the asset. A few examples of such energy modeling tools that 

provide analysis and decision support at one or more stages of 

the development cycle include RETScreen [1], PVWatts [2],

SAM [3], DER-CAM [4-7], and HOMER [8,9].

A common limitation of many energy asset modeling tools 

is that they require the user to specify the size of the system to 

be considered, a detail which—by definition—is not known at 

the screening stage. Although the user can make an estimate of 

the system size, the economic feasibility may be significantly 

worse if the analysis is performed at a size other than the 

optimal, possibly leading the user to incorrectly conclude that a 

particular technology is not economically viable. The more

detailed energy modeling tools improve the fidelity of the 

analysis during the feasibility stage, but they typically only 

consider one technology at a time and therefore fail to account 

for the interactions that occur when multiple technologies are 

operating concurrently. Finally, many tools do not accurately 

model the interaction between the load and the onsite 

generation, particularly the periods when an asset is outproducing the load. If this electricity cannot be sold back to the 

utility, the energy may be of no economic value.

In 2007, NREL began developing a platform for energy 

system integration and optimization that could be used 

throughout the project development cycle. This tool was called 

REO and was specifically developed to provide early estimates 

of sizing, even during the screening phase of the project when 

only basic data is available [10,11]. Later in the project, the 

default estimates could easily be replaced with more detailed 

data, and the analysis rerun. It was also designed to support 

batch mode automation such that a large portfolio of sites could 

be analyzed programmatically to help clients meet macro-level 

energy goals.

The REO tool was recently converted to a Mixed Integer 

Linear Program (MILP) to improve the speed and accuracy of 

the solver. Time series integration was also implemented for 

considering the effects of multiple technologies operating 

concurrently. The resulting platform for energy system 

integration and optimization was rebranded as REopt.

REopt, along with its predecessor REO, has been used to 

screen energy opportunities at over 1,000 sites for multiple 

government and private clients. The REopt platform has also 

been used for more detailed analysis of cost-optimal operating 

strategies for dispatchable technologies, energy security 

analysis using a combination of renewable generation and 

energy storage, and long range master planning to help a 

campus achieve its carbon neutral goals. The remainder of this 

paper describes the REopt techno-economic model as well as 

two analysis projects completed using the REopt platform.
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THE REOPT MODEL

The REopt model is a deterministic optimization model of 

the thermal and electrical energy use at one or more sites. The 

model achieves an energy balance between consumption and 

generation during every time period by building and 

dispatching a cost-optimal combination of renewable 

generation, conventional generation, and energy storage. The 

renewable energy technologies that REopt considers include 

photovoltaics (PV), wind, solar ventilation air preheating 

(SVP), solar water heating (SWH), biomass, waste-to-energy 

(WTE), and landfill gas (LFG).

The major outputs of REopt include the optimal system 

size, dispatch strategy, and installation cost for each technology 

in the solution set as well as the present value of all future 

energy costs associated with implementing it. Additional 

metrics such as the Levelized Cost of Energy (LCOE) are also 

computed for each technology. 

Formulation of the Mixed Integer Linear Program 

The objective function of the MILP is to minimize the 

present value of all future energy costs over the analysis period, 

including:

• The capital cost of building new energy generation and 

storage capacity.

• The present value of any incentives and depreciation.

• The present value of all operating expenses and revenues, 

such as operations and maintenance (O&M) costs, biomass 

feedstock costs, WTE tipping fees, fuel costs, or utility 

purchases.

The constraints governing how REopt builds and dispatches 

technologies fall into several categories, including:

• Load constraints: The electrical and thermal loads must be 

fully met by some combination of renewable and 

conventional generation during every time step. Additional 

load constraints restrict the amount of energy that a 

particular technology can replace; for example, solar water 

heating can only replace the energy that is used to heat 

domestic hot water.

• Resource constraints: The amount of energy that a 

technology can produce is limited by the amount of 

resource within the region. Biomass and WTE, for 

example, may only consume the material within close 

proximity of the plant. The energy production of variable 

technologies is limited by the renewable resource at the 

location. The utility grid is assumed to be able to produce 

unlimited amounts of energy. 

• Operating constraints: Dispatchable technologies such as 

biomass, WTE, and LFG may have minimum turndown 

limits that prevent them from operating at partial loads less 

than a specified level. Other operating constraints may 

limit the number of times a dispatchable technology can 

cycle on and off each day. 

• Sizing constraints: Most sites have limited land and roof 

area which may restrict the size of technologies such as 

PV, wind, SWH, and SVP. LFG is limited by the gas 

generation of the nearest candidate landfill.

• Policy constraints: Utilities often impose limits on the 

cumulative amount of renewable generation that a site can 

install and still qualify for a net metering agreement. 

Similarly, interconnection limits may restrict the total 

amount of renewable energy systems that may be 

connected to the grid. Other policy constraints may restrict 

the size of variable technologies or the size of a system 

that may be eligible for a production incentive. 

• Emissions constraints: CO2 and other greenhouse gases are 

tracked in the model and constraints may be included such 

that the solution meets specified emissions objectives.

• Scenario constraints (optional): Net zero electricity 

constraints require that a site produce as much electricity 

from renewable generation over the course of a year as it 

consumes. Similar constraints may require a site to obtain 

a specified percentage of its total energy from renewable 

generation or that it achieves some measure of energy 

security by always meeting the critical load with onsite 

renewable generation.

Financial Calculations: The REopt economic model 

considers an N-year analysis period. Energy consumption and 

production, however, are assumed to be constant in all years

such that the optimal energy balance achieved in year 1 holds 

for subsequent years in the analysis period. By making this 

assumption, the present value of the total energy costs over the 

next N years can be determined by escalating the current 

energy cost (using an electricity or fuel escalation rate) and 

then discounting those costs back to the present using an 

appropriate discount rate. In this way, REopt models the 

economics of the entire N-year analysis period while only 

optimizing the energy balance in the first year. All projects are 

assumed to be built immediately and then start operating in 

year 1. 

Temporal Resolution: REopt uses time series integration 

to combine the energy production from concurrently operating 

technologies. The typical time step is one hour. Since the 

optimization model assumes that production and consumption 

are constant across all years, only the energy balance of year 1 

need be considered, and thus there are 8760 time steps in a 

typical N-year optimization. To simplify the complexity for 

lower fidelity screenings of multiple sites, 288 time steps are 

used such that a typical day of 24 hours is included for each 

month. Detailed analysis using 35,040 time steps (or more) can 

be performed when 15-minute load and resource data is 

available.
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Thermal and Electrical Loads: REopt is designed to 

consider the entire energy consumption for a site, including 

electricity and up to five fuels such as natural gas, propane, #2 

oil, #6 oil, or coal. The actual hourly or 15-minute load data is 

used, where available. Since many sites do not have detailed 

load profile information, REopt queries a database of 

EnergyPlus simulations based on building type (or mix of 

building types in the case of a campus or installation) and 

climate zone to obtain a simulated hourly load profile. The 

commercial reference buildings are used for this purpose [12].

These load profiles are then scaled in proportion to the annual 

or monthly consumption data.

Cost Models: NREL maintains a cost dataset containing 

capital, O&M, and variable operating costs for each 

technology. It is regularly updated and is based on market data, 

NREL cost research, and actual costs of recently constructed 

projects. The capital cost curve for each technology 

incorporates economies of scale for larger system sizes, and is 

approximated as piecewise linear in the MILP. The capital cost 

models may also be adapted to a specific location using a local 

cost adjustment factor.

Utility Policy and Tariff Structures: Net metering refers 

to a policy agreement whereby electricity generated onsite and 

delivered to the grid by an electric utility customer can be used 

to offset electricity provided by the utility to the customer.

Specifically, a site can overproduce electricity in one time 

period, export it to the grid, and then consume it in a later time 

period without incurring any transactional cost.

Utilities that offer net metering programs may impose 

limits on the size of the system that can engage in net metering.

For sites which lack a net metering program, the export of 

electricity may still be allowed, but it may have no economic 

value. Similarly, some utilities also restrict the size of 

renewable energy systems that can be interconnected to the 

grid. REopt obtains both the default net metering limit and the 

interconnection limit from the Database for State Incentives in 

Renewable Energy (DSIRE1

) and models the resulting value of 

the energy produced and consumed accordingly. 

REopt supports complex tariff structures that include both 

peak demand charges and time-of-use (TOU) consumption 

rates. Demand rates may be specified for on- and off-peak 

hours, which can vary by season. TOU consumption rates may 

vary by the time of day, the season, or both.

Economic Parameters and Incentives: REopt considers 

any available federal, state, and local incentives for each 

technology, including capital cost incentives and production 

incentives. Incentive information is queried from DSIRE. A 

matrix of energy escalation rates based on fuel type and region 

is obtained from the National Institute of Standards and 

Technology, or through consultation with the local utility. The 

 1 http://www.dsireusa.org/

appropriate discount rate is selected in consultation with the 

client.

REopt is capable of considering both owner financed 

projects as well as third-party financed projects, where the 

owner and financier may each have different costs of capital 

and corporate tax rates. Depreciation schedules such as the 5-

or 7-year Modified Accelerated Cost Recovery System are also 

applied based on the relevant tax code.

Scenarios: Typical REopt scenarios include:

• Base Case / Business as Usual. In this scenario, the site is 

constrained to continue purchasing energy from the grid or 

using their existing energy assets; no new technologies are 

considered. The solution is the present value of all future 

energy costs over the analysis period. The net present 

value (NPV) of other scenarios is the difference between 

the energy cost associated with their optimal solutions and 

the base case energy cost, and may be positive or negative.

• Minimize energy costs. The objective of this scenario is to 

reduce energy costs by building new technologies. If one 

or more technologies are cost effective, the present value 

of energy costs in this case will be lower than that of the 

base case and the resulting NPV will be positive. If no 

technologies are cost effective, the solution set for this 

scenario will contain only the utility grid and the present 

value will be the same as the base case.

• Net zero electricity. In the net zero scenario, the site must 

produce at least as much electricity from onsite renewable 

energy technologies as it consumes over the course of the 

year. The solution consists of a set of technologies that 

achieve this goal at the minimum present value of future 

energy costs. Depending on the resources and land 

available for building projects, this scenario may be 

infeasible. The NPV of this scenario may be negative,

indicating that there is a cost to achieving net zero.

• Energy security. The energy security scenarios typically 

require the site to meet some fraction of the load for a 

defined period of time using onsite energy assets.

Description of the Technologies

A common set of characteristics define each of the 

technologies. These characteristics include the capital, O&M, 

and operating costs as well as the temporal production factors.

The production factor for a given time step is the percent of the 

rated output that can be obtained for that time step. For 

example, if a system has a rated capacity of 10 MW but the 

production factor for a given time step is only 10%, it would be 

expected to produce 1 MW of power during that time step.

Dispatchable technologies are those that can adjust their 

power output on demand or be switched on or off by a 

controller. Examples include biomass, WTE, LFG,

conventional generators, and the utility grid. These 
4

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technologies are both sized and dispatched by the solver for 

maximum economic benefit. The production factors for these 

technologies are based on the availability of the system, 

including both planned and unplanned maintenance. These 

technologies can be dispatched at partial loads so long as the 

minimum turndown ratio is upheld. Further, the dispatch can 

vary temporally, subject to ramp rate restrictions of the 

technology.

Variable technologies are those that cannot be dispatched. 

Examples include PV, wind, SWH, and SVP. The production 

factors for these technologies are a function of the typical 

meteorological year (TMY) time series resource data. The 

solver optimally sizes these technologies. In general, they are 

assumed to generate energy at a rate proportional to their 

production factor at all times and therefore do not curtail.

Energy storage is similar to the dispatchable technologies 

in that the solver both sizes and dispatches the charging and 

discharging of these devices. A detailed description of the 

variable and dispatchable technologies is provided in the 

following sections.

Photovoltaics: The PV model assumes a fixed panel tilted 

towards the equator at an angle equal to the latitude of the 

installation. Since REopt assumes that the energy output from a 

given technology stays constant in all years, but PV production 

is known to degrade over time, REopt uses an annualized PV 

energy output over the analysis period to properly account for 

the economic impact of degradation [13].

The O&M cost for PV includes annual maintenance costs 

plus an amortized inverter replacement. The solar resource is 

comprised of the direct, diffuse, and global irradiance obtained 

from the TMY geospatial dataset2

. The PV system size is 

limited by the amount of land or roof area available.

Wind: The model includes three categories of wind power 

turbines: 10 kW, 100 kW, and utility scale (> 1 MW). Further, 

the utility scale wind turbine is based on the wind class of the 

wind resource.

REopt utilizes time series TMY wind resource data (at 

heights of 50 m, 80 m, and 110 m) which is obtained from

AWS Truepower3 for locations within the continental US. 

REopt then calculates and adjusts for wind shear between the 

different heights in the TMY file to determine the time series 

production factor at the hub height of the selected turbine. The 

wind size is limited by the land area available.

Solar Water Heating: The solar water heating model 

assumes an indirect closed-loop system comprised of a glazed 

flat-plate collector, storage tank, and heat transfer fluid 

circulating pump. SWH uses the solar resource including direct 

normal and diffuse irradiance obtained from the TMY 

geospatial dataset. The production of hot water need not be 

temporally aligned with consumption (due to the existence of a 

 2 http://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/tmy3/ 3 www.awstruepower.com (subscription only)

storage tank), but must occur within the same day – i.e. water 

heated on a given day must be consumed the same day. This 

allows for SWH thermal energy to be used at a different time 

period within a day without explicitly modeling the storage 

system. SWH can replace either a conventional gas or electric 

system and the energy that such a system can provide is limited 

by the domestic hot water load. 

The efficiency of the SWH system is inversely 

proportional to the solar fraction that the system achieves. For a 

given SWH size-to-load ratio, a constant collector efficiency is 

used (based on simulations performed in higher fidelity SWH 

models.) This efficiency is assumed to apply during each time 

step of the analysis.

Solar Ventilation Air Preheating: The SVP model 

consists of a transpired solar collector on the south-facing wall 

of a building coupled with a circulating fan which draws air 

into the outside air intake of the HVAC system. The preheated 

air produced by such a system can reduce the heating costs 

associated with either electrical or thermal space heating. The 

quantity of preheated air produced, and therefore the fuel 

saved, is limited by the outside air heating load. The system 

size is limited by the area available on the south-facing wall of 

the building. 

The heat production of the modeled SVP system is based 

on a preprocessed geospatial dataset that considers ambient 

temperature and solar insolation [14-15]. The efficiency of the 

modeled system is inversely proportional to penetration, i.e. as 

the fraction of ventilation air preheated increases for a given 

space heating load, the efficiency decreases. 

Biomass and Waste-to-Energy: The model includes four 

configurations of biomass systems each of which is modeled as 

a mass-burn combustion system. The four configurations

include an electric-only system using a condensing steam 

turbine, a combined heat and power (CHP) system using a 

condensing turbine and main steam extraction, a CHP system 

with a backpressure turbine, and a thermal-only option. The 

primary difference between the two CHP systems is that the 

backpressure system provides a fixed ratio of electricity to 

thermal output, while the condensing turbine is able to vary this 

ratio, though at a higher capital cost.

Each system is assumed to have a minimum turndown 

ratio, meaning that it can operate at partial loading down to a 

given fraction of its nameplate capacity, but then it must shut

off. Four types of biomass resource are included and are 

programmatically queried from the geospatial database: crop 

residues, forest residues, primary mill residues, and secondary 

mill residues [16]. A cost to acquire and transport the material 

is assumed, unless a biomass resource exists onsite, in which 

case it is assumed to be free. An additional fee is included for 

material being transported an extended distance.

The WTE module contains the same four configurations as 

the biomass module and has similar turndown requirements. 

The primary difference between these models is the boiler and 
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turbine efficiencies as well as the capital costs to build the 

systems. The system efficiencies are different due to the lower 

operating pressures of the WTE systems resulting from the 

chlorides released from the combustion of municipal solid 

waste (MSW).

The MSW within a given area is calculated by multiplying 

the population within that area by the waste generation per 

capita. Although the procurement of a contract diverting MSW 

to a WTE plant can be difficult to obtain, the client is assumed 

to be able to obtain sufficient quantities of this waste during the 

prescreening process. WTE facilities are paid to accept MSW, 

and this significant source of revenue is referred to as a tipping 

fee. The tipping fee is estimated based on published data [17].

Landfill Gas: The LFG model includes three 

configurations: an electric-only generator coupled to an internal 

combustion engine, a CHP internal combustion engine

generator with heat recovery system, and a thermal-only gas 

collection system. A minimum turndown ratio is assumed for 

each of the system types.

Landfills that are candidates for energy generation are 

identified by the Landfill Methane Outreach Program4 of the 

Environmental Protection Agency (EPA). This program uses a 

first-order decay model to estimate the gas production of a 

landfill based on the dates of operation, quantity of waste in 

place, and the fill rate. For a site to access the gas at a candidate 

landfill, a pipeline must be constructed and the cost of this is 

included in the model. The size of an LFG system is limited by

the maximum size that the EPA has designated for the 

associated landfill. 

Conventional Generators: The conventional generator 

model includes both diesel and natural gas engine generators. 

The fuel consumption of these generators is modeled using a 

linear fuel curve with nonzero y-intercept (which allows for 

nonlinear generator efficiency) based on data provided by the 

manufacturer. These technologies incur capital, O&M, and fuel 

costs and may be subject to turndown and cycle limits. 

Utility Grid: The utility grid is assumed to be able to 

supply an unlimited amount of electricity and thermal fuel. 

Energy from the grid incurs only the costs specified by the 

tariff structure; there are no capital or O&M costs. For 

microgrid analyses, the utility grid can be disabled. 

Energy storage: REopt models energy storage as a device 

that allows energy to be shifted from one time period to 

another. A round-trip efficiency is assumed and limits are 

imposed on the minimum state of charge, the charging and

discharging rates, and the number of cycles per day. Energy 

storage devices incur capital and O&M costs. By default, any

technology can charge the energy storage device, but charging 

can also be limited to specific technologies.

 4

http://www.epa.gov/lmop/

RENEWABLE ENERGY SCREENING FOR THE NAVY

In 2012, the Secretary of the Navy announced five energy 

goals, two of which were as follows: 

1. Produce at least 50% of shore-based energy 

requirements from alternative energy sources, and 

2. Achieve net zero status at 50% of Department of Navy 

installations, meaning that those installations would produce at 

least as much electrical energy onsite as they consume over the 

course of a year. 

The Navy requested assistance with identifying and 

prioritizing cost effective renewable energy project 

opportunities across 69 domestic and international installations 

to help meet these goals. REopt was used to analyze two 

scenarios of particular interest to the Navy: 

1. The optimal mix of renewable generation to achieve net 

zero electricity status at the minimum present value of energy 

costs for a given installation, and

2. The optimal mix of renewable and conventional

technologies to meet electrical and thermal loads at a given 

installation for the lowest present value of energy costs.

The process began by collecting and collating data for the 

installations. At the beginning of the process, only the most 

basic data was needed, such as the geographic coordinates of 

the installations and their annual utility costs and consumptions 

for all fuels and electricity. REopt was then run to determine 

the most cost effective technologies that meet the energy goals. 

A list of the most technically and economically viable 

renewable energy technologies at each of the installations was 

produced. For each installation, the capital cost, estimated 

annual cost under third-party financing, and the NPV of the 

recommended technologies was provided. The LCOE for each 

technology at each installation was also calculated.

The results for a representative installation are shown in 

Table 1. The recommended portfolio of technologies to 

minimize the present value of energy costs at the installation

includes wind, PV and SWH, and saves $2.5M over the 25-

year analysis period compared to the base case. Net zero 

electricity can be achieved by implementing a PV and wind 

solution at a savings of $0.5M over the analysis period.

The NPV of the recommended technologies at each 

installation was compared to other installations in the region. A 

sample comparison of ten installations is shown in Fig. 1.

Table 1. REopt screening results for Installation 6

Goal Maximize NPV Net Zero

Solution

4 MW Wind

3 MW PV

5000 ft2 SWH

5 MW Wind

7 MW PV

NPV $2.5M $0.5M

Capital Cost $19M $32M
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Figure 1. NPV of the net zero solutions for ten installations

At the conclusion of the screening analysis, the Navy 

selected candidate projects at specific installations for more 

detailed analysis. During this phase, in-depth installation 

assessments were conducted and meetings with onsite energy

managers, master planners, command leadership, and utility 

representatives were held. During these visits some 

technologies originally in the optimal mix were deemed

infeasible and were excluded. At Installation 6, for example, 

wind was eliminated as a candidate due to the proximity of an 

airfield. In addition, discussions with utility representatives 

revealed that the local utility has a policy whereby only 10 MW 

of RE systems can be connected to the grid. This further limited 

the capacity of renewable generation at this installation.

Following the installation visit, the inputs to the model 

were updated and the two scenarios were rerun using batch 

mode processing. The revised optimal mix of technologies is 

shown in Table 2 and reflects the new constraints that exclude

wind and limit the cumulative system sizes of all renewable 

energy technologies to less than 10 MW.

To offset the lack of wind in the net zero case, the solver built

the maximum amount of PV but was only able to achieve 87% 

of the net zero goal. (The net zero goal became infeasible at 

this installation due to the interconnection limit imposed by the 

utility.) The NPV of this scenario was now -$0.8M, compared 

to a savings of $0.5M in the initial results.

Table 2. Revised optimal mix of RE at Installation 6

Goal Maximize NPV Net Zero5

Solution 5 MW PV

5000 ft2 SWH 10 MW PV 

NPV $2.0M -$0.8M

Capital Cost $16M $31M

DISPATCH STRATEGY ANALYSIS FOR THE ARMY

Fort Hunter Liggett is a US Army installation near King 

City, CA. The installation currently has 2 MW of PV installed 

and is in the process of adding additional PV and a 1 

MWh/1.25 MW lithium-ion energy storage system as part of 

their net zero initiative. The US Army was interested in using 

REopt to analyze cost-optimal economic dispatch strategies for 

operating the storage system while it was connected to the grid. 

Figure 2 illustrates the optimal economic dispatch of a 

combined battery and PV system considering on- and off-peak 

demand periods, TOU rates and the electrical load for a 

representative week in July. During the afternoons of the first 

three days, the PV generation out-produces the load and the 

excess energy is stored in the battery for use during evening 

hours. During the next four days, the peak demand is reduced 

by strategically discharging the battery during the late 

afternoon hours. The truncated peak of the utility purchases 

(~1950 kW) is determined by the solver to be cost-optimal and 

reflects the peak-shaving operation. During the first three days 

 5

This installation can only meet 87% of its net zero goal due to the 

interconnection limit.

Figure 2. Cost-optimal economic dispatch strategy for a combined battery and PV system at Fort Hunter Liggett
7

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

when the utility purchases were not expected to exceed the 

peak demand, REopt determined that the battery was not 

needed for peak-shaving and instead dispatched it to discharge 

during the peak TOU rate period. These results assume perfect 

prediction of both solar irradiance and electrical load.

The REopt platform was also used to conduct a 

preliminary energy security analysis using the PV and battery 

system in microgrid mode. The objective of this analysis was to 

determine the length of outage for which the PV and battery 

system could sustain the critical load as well as to estimate the 

amount of additional PV and storage necessary to sustain the 

critical load for a range of outage durations. 

FUTURE WORK: MODEL PREDICTIVE CONTROL

When REopt is used during the planning and investment 

phases of a project to develop cost-optimal operating strategies 

for dispatchable energy assets, TMY weather data and 

historical load profiles are used. Consequentially, REopt is an 

omniscient agent; for every time step it knows exactly what has 

happened in the past, and it knows exactly what will happen in 

the future. As a result, it can make expert decisions that are 

guaranteed to be optimal, subject to the characteristics and 

constraints of the model and the backward-looking data with 

which it has been presented. This is equivalent to saying that 

REopt assumes perfect prediction of all future events, in 

particular, the weather and the load. 

One area of ongoing research is to adapt the REopt model 

to be a real-time, model predictive control (MPC) supervisory 

agent. The agent would receive weather predictions and load 

forecasts for the upcoming 24-72 hours which would be 

combined with the TMY weather data and historical load 

profiles to provide both short- and medium-term projections of 

upcoming conditions. This would allow REopt to use the same 

techno-economic models that are used during the planning and 

investment stage of the project to dispatch the installed energy 

assets thereby increasing the correlation between the predicted 

cost savings and those achieved in practice. 

CONCLUSIONS

REopt has been developed as a platform for energy system 

integration and optimization. Formulated as a MILP, it is a

deterministic optimization model with the objective of

minimizing the energy costs of one or more sites over an Nyear analysis period. REopt recommends the cost-optimal mix 

of RE technologies, including PV, wind, SVP, SWH, biomass, 

WTE, and LFG, conventional generation, including diesel,

natural gas engine generators, and the utility grid, and energy 

storage to meet the energy goals of a site or portfolio of sites. 

REopt considers both the electrical and thermal loads 

simultaneously. 

REopt can be used at various stages of the project 

development process. During the screening stage, REopt can 

quickly and inexpensively identify and optimally size 

economically viable renewable energy technologies. When 

additional input data is available such as electrical load profiles 

or detailed descriptions of the tariff structure, REopt can be 

used during the feasibility phase to refine system sizes and 

analyze cost-optimal dispatch strategies for the energy assets. 

Future work is underway to enhance the model by including 

forward-looking weather predictions and load forecasts such 

that REopt can be used in a real-time MPC environment.

NOMENCLATURE

CHP Combined Heat and Power

CO2 Carbon Dioxide

DSIRE Database for State Incentives in Renewable Energy

EPA Environmental Protection Agency

HVAC Heating, Ventilation and Air Conditioning

LCOE Levelized Cost Of Energy

LFG Landfill Gas

MILP Mixed Integer Linear Program

MPC Model Predictive Control

MSW Municipal Solid Waste

NPV Net Present Value

NREL National Renewable Energy Laboratory

O&M Operation and Maintenance 

PV Photovoltaics

RE Renewable Energy

REO Renewable Energy Optimization

SVP Solar Ventilation air Preheating

SWH Solar Water Heating

TMY Typical Meteorological Year

TOU Time-Of-Use

WTE Waste-To-Energy

ACKNOWLEDGMENTS

The Federal Energy Management Program, US Army and 

the US Navy provided funding to NREL to screen installations 

for renewable energy project opportunities using REopt. The 

authors would also like to acknowledge the following NREL 

staff for their contributions to the REopt model: Jeff Bedard, 

Nate Blair, Sam Booth, Larry Brackney, Kari Burman, Nancy 

Carlisle, Donald Chung, Jason Coughlin, Jerry Davis, Jesse 

Dean, Dennis Elliott, Jon Falcey, Dan Getman, Julieta Giraldez, 

Steve Gorin, Mike Helwig, Randy Hunsberger, Alicen Kandt, 

Jessica Katz, Lars Lisell, Nick Long, Danny Martin, Ian 

Metzger, Anne Miller, Nick Muerdter, Brett Oaklief, Mike 

Pacheco, Linda Parkhill, Dave Peterson, Billy Roberts, Owen 

Roberts, George Scott, Mic Stremel, Tim Tetreault, Gregg 

Tomberlin, Adam Warren, and Bob Westby.
8

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

REFERENCES

[1] RETScreen International, 2005, “Clean Energy Project 

Analysis: RETScreen Engineering & Cases Textbook,” 

Minister of Natural Resources Canada

[2] Dobos, A. P., 2013, “PVWatts Version 1 Technical 

Reference,” NREL Report No.TP-6A20-60272, National 

Renewable Energy Laboratory, Golden, CO.

[3] Blair, N., Dobos, A. P., Freeman, J., Neises, T., Wagner, M., 

Ferguson, T., Gilman, P., and Janzou, S., 2014, “System 

Advisor Model, SAM 2014.1.14: General Description,” NREL 

Report No. TP-6A20-61019, National Renewable Energy 

Laboratory, Golden, CO.

[4] Stadler, M., Siddiqui, A., Marnay, C., Aki, H., and Lai, J., 

2010, “Control of Greenhouse Gas Emissions by Optimal DER 

Technology Investment and Energy Management in Zero-NetEnergy Buildings,” European Transactions on Electrical Power, 

21(2).

[5] Stadler, M., Marnay, C., Cardoso, G., Lipman, T., Mégel, 

O., Ganguly, S., Siddiqui, A., and Lai, J., 2009, “The CO2

Abatement Potential of California’s Mid-Sized Commercial 

Buildings,” Report No. CEC-500-07-043, 500-99-013, LBNL3024E, California Energy Commission, Public Interest Energy 

Research Program.

[6] Marnay, C., Venkataramanan, G., Stadler, M., Siddiqui, A., 

Firestone, R., and Chandran, B., 2008, “Optimal Technology 

Selection and Operation of Microgrids in Commercial 

Buildings,” IEEE Transactions on Power Systems, 23(3), pp. 

975-982.

[7] Stadler, M., Marnay, C., Siddiqui, A., Lai J., Coffey, B., and 

Aki, H., 2008, “Effect of Heat and Electricity Storage and 

Reliability on Microgrid Viability: A Study of Commercial 

Buildings in California and New York States”, Report No. 

LBNL - 1334E 

[8] Lambert T., Gilman P., and Lilienthal P., 2005,

“Micropower system modeling with HOMER” Integration of 

Alternative Sources of Energy, Farret F., Simões M. eds., John 

Wiley & Sons, Chap. 15.

[9] Lilienthal P., Lambert T., and Gilman P., 2004, “Computer 

Modeling of Renewable Power Systems”, Cleveland C.J. eds., 

editor-in-chief, Encyclopedia of Energy, Elsevier Inc., 1, pp. 

633-647

[10] Walker, A., 2009, “Heuristic Approach to Renewable 

Energy Optimization,” Paper No. ES2009-90456, Proceedings 

of the ASME 3rd International Conference on Energy 

Sustainability San Francisco, California, New York: American 

Society of Mechanical Engineers 1 pp. 419-426, NREL Report 

No. CP-7A2-45812.

[11] Walker, A., 2008, “Renewable Energy Planning: 

Multiparametric Cost Optimization; Preprint,” NREL Report 

No. CP-670-42921, National Renewable Energy Laboratory, 

Golden, CO.

[12] Deru, M., Field, K., Studer, D., Benne, K., Griffith, B., 

Torcellini, P., Liu, B., Halverson, M., Winiarski, D., Rosenberg, 

M., Yazdanian, M., Huang, J., Crawley, D., 2011, “U.S. 

Department of Energy Commercial Reference Buildings Model 

of the National Building Stock,” Report No. NREL/TP-5500-

46861, National Renewable Energy Laboratory, Golden, CO.

[13] Jordan, D.C., and Kurtz, S.R., 2013, “Photovoltaic 

Degradation Rates – an Analytical Review,” Progress in 

Photovoltaics, 21(1), pp. 12-29.

[14] Federal Technology Alert, 1998 “Transpired Collectors 

(Solar Preheaters for Outdoor Ventilation Air),” DOE/GO10098-528, U.S. Department of Energy, Washington, D.C. See 

also http://www1.eere.energy.gov/femp/pdfs/fta_trans_coll.pdf

[15] Christensen C., Hancock E., Barker G., and Kutscher C., 

1990, “Cost and Performance Predictions for Advanced Active 

Solar Concepts,” Proceedings of Solar 90 The National Solar 

Energy Conference, American Solar Energy Society, Inc., 

Austin, Texas, pp. 275–280

[16] Milbrandt, A., 2005, “A Geographic Perspective on the 

Current Biomass Resource Availability in the United States,” 

NREL/TP-560-39181, National Renewable Energy Laboratory, 

Golden, CO. See also http://www.nrel.gov/docs/fy06osti/

39181.pdf

[17] Van Haaren, R., Themelis, N., and Goldstein, N., 2008,

“The State of Garbage in America,” BioCycle, 51(10), pp. 16-

23. See also http://www.biocycle.net/images/art/1010/

bc101016_s.pdf