Solar+Storage: Understanding Commercial-Scale Project Economics through Cost-Modeling
Is solar+storage economical in my location? Which commercial building types are most likely to see cost-savings from solar or storage? Where are the emerging markets for solar+storage?
Use the tabs below to explore the results of cost-optimization modeling on the economics of solar+storage projects in commercial building types across the U.S.
About NREL’s Solar+Storage Optimization Modeling
This project explores the economics of solar+storage projects for commercial-scale, behind-the-meter applications. It provides insight into the near-term and future solar+storage market opportunities across the U.S.
Using NREL’s REopt Model, we explore the technically and economically optimal configurations of solar+storage for a variety of:
- climate zones,
- building load profiles,
- technology cost assumptions, and
- utility rate structures.
Questions answered include:
- Is solar+storage economical in my location?
- Which commercial building types are most likely to see cost-savings from solar or storage?
- Where are the emerging markets for solar+storage?
- Which utility rate structures encourage solar+storage deployment?
- How much do system sizes vary across buildings and locations?
- What is the role of policies and incentives in solar+storage economics?
To answer these questions, NREL identified the optimal solar+storage system design for:
- 16 commercial building types
- 17 locations across the U.S.
- 70 utility rate tariffs
- near term and long-term technology cost projections
- different incentives, policies and electricity price increases.
The results of this research are presented in the tabs at the left.
See the methodology tab for details about the assumptions, rates and building types.
Who can use these results?
- Building owners and energy managers can use the results to learn about typical system sizes for different building types or load profiles, and how utility rate structures impact economics costs.
- Policy makers and regulators can understand the impact of existing incentives, such as the investment tax credit (ITC) and net metering (NEM), on solar+storage economics. They can determine the cost at which solar+storage becomes economically viable, and design incentive and policies that encourage deployment in their location.
- Industry can use the results to explore where solar+storage markets may develop over time, as technology costs drop.
- Utilities can get a better understanding of how rate structures and load profiles impact customer decisions regarding behind-the-meter solar and/or storage investments.
Impact of Technology Costs on Solar+Storage Economics
- At what price points does solar+storage become economical?
- As lithium-ion battery system prices decline, the number of locations and building types in which batteries are economical increases significantly.
- Even at higher technology costs, solar with storage systems are economical in some building types in Anaheim, San Francisco and New York.
- Office buildings, hospitals, large hotels, and secondary schools may see cost savings from solar with storage in the near term.
- All of the 16 building types modeled see cost savings from solar with storage, in more than one location, under the lowest cost point.
- At lower technology cost points, solar with storage is economical in 10 of the 16 locations modeled.
Location of Emerging Solar+Storage Markets
- Where are commercial-scale solar+storage projects economical now?
- Where are markets likely to emerge in the future?
- Near term markets exist for solar+storage in locations such as California and New York.
- As technology prices drop, the number of building types that can benefit increase, and additional markets appear in Colorado, New Mexico, and Alaska.
- At the lowest price point modeled, systems also become economical in Florida and Minnesota.
- Solar systems without storage provide cost savings to many commercial building types across the country, at near term price points.
Payback Period for Solar and/or Storage Systems
- How many years would it take for a solar and/or storage system to pay for itself?
- The payback period for the locations and building types modeled ranges between 5 and 12 years in all cases
- Payback periods for solar-only systems have a slightly longer payback range.
- As technology costs decline, optimal solar system sizes increase, while simple payback periods decline.
Expected Bill Savings from Solar+Storage Systems
- How much could a solar+storage system reduce my electricity costs?
- Solar+storage systems have the potential to provide both demand charge savings and energy savings across a variety of building types and locations.
- Averaging the results for a building over multiple locations or all building types in single location, the total value of the energy savings is typically greater than the value of the demand charge savings. Results for individual buildings in a specific location may differ. (Those results can be explored in other tabs.)
Impact of Rate Design on Solar+Storage Economics
- How does the design of the utility tariff, such as demand charges and time-of-use elements, impact solar and/or storage economics?
- Do expected savings from solar and/or storage increase when utility rates have higher demand charges?
- Solar and/or storage systems are more likely to be economical under utility tariffs that have:
- demand charges (of any type), or
- time-of-use elements (including time-of-use energy rates or time-of-use demand charges).
- Solar and/or storage systems are more likely to be economical under utility tariffs that
- As the maximum demand charge of the utility tariff increases, one may expect expected savings to also increase.
- Looking across all locations, building types and technology combinations, once technology prices drop to Cost Point D, expected savings do increase with increasing demand charges.
- When considering only projects that include battery storage, expected savings do not necessarily trend upward with increasing demand charges. The trend is highly dependent on location and building type.
Impact of Load Profile on Solar+Storage Economics
- Which building types have the most peaks in electricity use?
- Do buildings with variable loads get more savings from solar+storage?
- Can a building with a flat electricity load see savings from a solar+storage system?
- Secondary schools, office buildings and retail stores have the most variation in electrical loads.
- Variability in building load over the course of the year does NOT impact expected savings from solar and/or storage systems. Buildings with high variability in demand (“peaky load profiles”) are not more likely to benefit from solar with storage more than buildings with flat load profiles.
- As technology prices decline, solar+storage systems may yield savings for most commercial building types, despite the amount of variation in load profile.
Impact of Policies & Incentives on Solar+Storage Economics
- How does the step-down of the Investment Tax Credit (ITC) from 30% to 10% impact solar+storage economics?
- How does the availability of net energy metering impact the economics of commercial-scale solar+storage?
- How much will it impact solar+storage economics if electricity price increases are slower than anticipated?
- Reductions in technology costs, given the assumed cost trajectories, make up for the ITC step-down to 10% in 2021.
- Under higher technology costs, the availability of Net Energy Metering at a retail rate significantly impacts savings provided by solar-only systems in some locations, most notably Anaheim and San Francisco.
- Storage systems are economical in cases with and without net energy metering, however savings are higher in the No NEM and NEM at the wholesale rate cases.
- There is little difference in solar+storage economics between the “no NEM” and “wholesale NEM” cases. This trend was the same at all technology cost points modeled.
- Differences in electricity price increases do not significantly impact solar+storage economics. Higher price increases show slightly higher levels of savings. However a limited range of escalation rates were explored. More extreme electricity price growth would likely have more significant impacts on system economics.
Solar+Storage System Sizes
- What is a typical solar+storage system size for my building type?
As a rule of thumb, a 10kW solar PV system would require about 1000 square feet of roof space. A 1MW ground mounted system would require approximately 6 acres of land.
Explore the Data
- Is solar+storage economical in my location and building type?
Explore Rate Switching
- Should I switch rates when I build a solar+storage system?
Methods and Modeling Assumptions
Commercial buildings were modeled for 16 locations, representing every climate zone across the U.S.
Commercial utility rates were selected for the utility with the largest commercial customer base within each climate zone. Rates and building types were matched, based on the load profile of the building and the eligibility requirements stated in the utility’s rate tariff sheet.
The modeling included a variety of tariffs. Some have demand charge elements, some have time-of-use elements, some have both time-of-use and demand elements, and a few were flat rates. All of the tariffs were taken from NREL’s Utility Rate Database and were up to date as of January 2017.
Hourly load profiles were generated for the 16 Department of Energy Commercial Reference Buildings (1980’s stock). Different profiles were created for each ASHRAE climate zone (see map below). These annual, hourly load profiles were used as inputs into the optimization model. You can explore the load profiles using the interactive chart, below.
Optimization modeling was conducted for seven solar photovoltaic and battery storage price points, representing anticipated cost trajectories.
Cost Point A represents conservative technology costs in the current market. Some stakeholders are reporting current costs closer to Cost Points B and C.
Cost Point G represents estimated technology costs by 2037.
The solar technology cost trajectory is based on NREL’s Annual Technology Baseline. Battery storage costs are based on NREL discussions with a variety of battery suppliers and developers.
The Renewable Energy Optimization Model (REopt) provides cost-optimal technology solutions at a single site, or across a portfolio of sites.
REopt is a mixed integer linear program that outputs optimal technology sizing and hourly dispatch strategies, along with financial data.
REopt can identify optimal system sizes, given other parameters, or can output financial data for set system sizes. Multiple on-site technologies, including existing diesel generators, can be considered in the optimization.
The REopt model is currently run by NREL analysts, in-house. A web-based version of the tool is currently in development, and expected to be released as a beta-version in September 2017.
For more information about REopt, visit: https://reopt.nrel.gov/
Citations & Links
Solar+Storage: Reducing Barriers through Cost-Optimization and Market Characterization is a joint endeavor of the National Renewable Energy Laboratory (NREL) and Clean Energy Group. The research seeks to elucidate the emerging market for distributed solar paired with battery energy storage (solar+storage). The two-year research initiative, ending in 2017, is funded under the Department of Energy’s Sunshot Initiative.
Additional products from this project include:
NREL Solar+Storage Modeling Input Assumptions (NREL 2016)
PV cost assumptions:
NREL (National Renewable Energy Laboratory). 2016 Annual Technology Baseline (ATB). Golden, CO: National Renewable Energy Laboratory. http://www.nrel.gov/analysis/data_tech_baseline.html
Storage cost assumptions are based on cost data collected by NREL, summarized in: http://www.nrel.gov/docs/fy17osti/67235.pdf
Utility Rate Database: http://en.openei.org/wiki/Utility_Rate_Database
Renewable Energy Optimization Model description: https://reopt.nrel.gov/
Load profiles are based on the Department of Energy Commercial Reference Buildings (https://energy.gov/eere/buildings/commercial-reference-buildings) using Energy Plus Software (https://www.energyplus.net/).
Previous Products and Continuing Work
When installed with the proper islanding and control systems, behind-the-meter solar+storage systems can provide backup power to critical electrical loads and extend the capacity of existing backup generators during extended outages. However, the value of this resilient power benefit is difficult to quantify. Despite being valued highly by stakeholders, resiliency is not easily monetizable by developers.
NREL has developed a methodology to consider the value of resiliency in our optimization model. This methodology is employed as part of this cost-modeling project, and is used in NREL technical assistance efforts as part of the NREL Resilience Roadmap for Federal, State, Local disaster planning.
Results of the resiliency modeling portion of the solar+storage optimization project will appear here by the end of 2017.
Providing Frequency Regulation
Solar+Storage projects have the capacity to tap multiple value streams to shorten project pay-back periods. Value streams include both cost reductions for the system owner, such as reduced utility demand charges or energy consumption charges, as well as direct payments from participation in energy services markets, such as frequency regulation.
As part of this research project, NREL has developed a methodology to explore the existent to which there is competition between the use cases when a behind-the-meter battery is used to provide both frequency regulation and bill-reduction.
To what degree can a system owner stack value streams? Does using a battery to provide frequency regulation services result in less capability to provide bill savings?
Does using a battery system to provide frequency regulation change the optimal system size for a building?
Market Adoption Modeling
NREL is also employing it’s distributed generation adoption model (dGen) to model the market adoption of solar + storage across the U.S. over time, given the cost assumptions used in the project-level modeling presented here. The results of this market-level modeling will also be presented here.
The results of this continuing research will appear here before the end of 2017.
For more information, please contact Joyce.McLaren_at_nrel.gov