Considerations for Implementing PV plus Storage Systems at Federal Buildings and Campuses
Fact sheet estimates the value of PV plus storage and discusses sizing and dispatching it for federal site managers.
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Fact sheet estimates the value of PV plus storage and discusses sizing and dispatching it for federal site managers.
We present novel stochastic optimization models to improve power systems resilience to extreme weather events. We consider proactive redispatch, transmission line hardening, and transmission line capacity increases as alternatives for mitigating expected load shed due to extreme weather. Our model is based on linearized or "DC" optimal power flow, similar to models in widespread use by independent system operators (ISOs) and regional transmission operators (RTOs). Our computational experiments indicate that proactive redispatch alone can reduce the expected load shed by as much as 25% relative to standard economic dispatch. This resiliency enhancement strategy requires no capital investments and is implementable by ISOs and RTOs solely through operational adjustments. We additionally demonstrate that transmission line hardening and increases in transmission capacity can, in limited quantities, be effective strategies to further enhance power grid resiliency, although at significant capital investment cost. We perform a cross validation analysis to demonstrate the robustness of proposed recommendations. Our proposed model can be augmented to incorporate a variety of other operational and investment resilience strategies, or combination of such strategies.
TTightly coupled nuclear-renewable hybrid energy systems (N-R HESs) are systems that link subsystems to generate dispatchable electricity and produce at least one industrial product from two or more energy resources. Because N-R HESs are designed to produce different products based on the value of those products in markets, their optimal designs and operations can be complex. This paper summarizes some key conclusions from a set of economic analyses of N-R HESs. Each N-R HES use case analyzed includes a nuclear reactor, a thermal power cycle to convert nuclear energy into electricity, either a wind or photovoltaic solar subsystem producing electricity, and an industrial process producing an energy or an industrial product. The analyses focused on identifying the optimal configuration and hours of operations for each N-R HES within ranges of hypothetical future electricity price profiles and industrial product prices. Four important insights are drawn from the results of those analyses.
For solar electricity generating technologies to be cost competitive at a large scale with conventionally generated electricity, cost reductions are needed for both concentrating solar power (CSP) and solar photovoltaic (PV) systems. In 2011, the U.S. Department of Energy (DOE) established solar cost targets that corresponded to reducing CSP and PV prices by approximately 75% in order to achieve a levelized cost of electricity (LCOE) of $0.06 per kilowatt-hour (kWh) for both utility-scale PV and high-capacity factor CSP-TES systems in 2020. Utility-scale PV achieved its 2020 cost target in 2017, and recent estimates for the LCOE of CSP-TES with a molten-salt power tower system are approximately $0.10/kWh for projects that are expected to come online in 2020, which represents a substantial reduction since 2010 - when the LCOE for CSP-TES was around $0.21/kWh. To continue the momentum for cost reductions in solar technologies, DOE recently established cost targets for 2030 that would make solar one of the lowest-cost sources of new electricity in the United States. For CSP-based systems, the new targets correspond to an LCOE in 2030 of $0.05/kWh for a dispatchable, high-capacity factor CSP-TES plant configuration. This aggressive target would have been unimaginable a decade ago. However, building on the previously described reduction in CSP-TES costs over the past decade, recent announcements suggest the next phase of projects will continue this downward trend through lower installation costs, attractive financing, longer-duration PPAs, and the ability to capitalize on the value that the flexibility of storage brings CSP. This presentation summarizes a recent report that evaluates the potential impacts of simultaneously achieving the 2030 cost targets for PV and CSP-TES, and it includes a detailed evaluation of the role that CSP-TES could play in realizing those impacts. The scenarios in this analysis are designed to isolate and assess the potential impacts of achieving DOE's 2030 cost targets for CSP-TES and PV, and they do not reflect the potential benefits or system impacts associated with success in other DOE research programs. The low-cost solar scenarios include DOE's 2030 solar cost targets--which are represented via a roughly 50% reduction in LCOE by 2030 (from current levels) with additional cost reductions thereafter representing technology learning and/or improvements that could result from innovation - coupled with a variety of market, technology, and demand assumptions. With these assumptions, the evolution of the contiguous U.S. electricity system is evaluated with NREL's Renewable Energy Deployment System (ReEDS) model, which was specifically designed to represent the temporal and locational value of renewable generation technologies in the U.S. power system. ReEDS relies on system-wide least-cost optimization to estimate the type and location of future generation and transmission capacity. In addition, it accounts for the locational and temporal variations in variable renewable technologies, including the need for new transmission, curtailment, dynamic capacity value, and the need to hold operating reserves to account for the uncertainty and variability of these technologies. This presentation summarizes the key findings that arise from a detailed evaluation of the impacts of achieving DOE's 2030 cost targets for CSP-TES and PV systems, noting again the inherent challenges associated with modeling future scenarios of the large, complex system electricity system in the contiguous United States within the context of various market features.
Several techno-economic models exist to evaluate solar PV plus battery storage opportunities for behind-the-meter, commercial application. This tool comparison evaluates the methodological differences between several solar plus storage modeling tools and employs a case study to compare their outputs. The tools selected for comparison include NREL's REopt Lite, NREL's System Advisor Model (SAM), Homer Energy's Homer Grid, Geli ESyst, EPRI StorageVET, and LBL's DER-CAM. The comparison focuses on each tool's simulation and optimization capabilities, PV and battery performance modeling, dispatch strategies, and economic model assumptions and calculations, with brief mention of additional capabilities outside the scope of behind-the-meter PV and battery storage. This report describes the case study's tool inputs and outputs and dives into the differences in assumptions, methodologies, and capabilities between tools.
Tightly coupled nuclear-renewable hybrid energy systems (N-R HESs) are systems that link subsystems to generate dispatchable electricity and produce at least one industrial product from two or more energy resources. Because N-R HESs are designed to produce different products based on the value of those products in markets, their optimal designs and operations can be complex. This paper summarizes some key conclusions from a set of economic analyses of N-R HESs. Each N-R HES use case analyzed includes a nuclear reactor, a thermal power cycle to convert nuclear energy into electricity, either a wind or photovoltaic solar subsystem producing electricity, and an industrial process producing an energy or an industrial product. The analyses focused on identifying the optimal configuration and hours of operations for each N-R HES within ranges of hypothetical future electricity price profiles and industrial product prices. Four important insights are drawn from the results of those analyses.
Deployment of distributed energy resources including combined heat and power (CHP), photovoltaics (PV), and battery storage is growing as building and campus owners seek to reduce energy costs and increase resilience to grid outages. While deployment of a single technology is widespread, integrated systems of multiple technologies are also becoming more common to provide maximum economic and resilience benefits. To accelerate deployment, decision makers require tools to evaluate the technical and economic feasibility of these integrated systems. This paper presents a framework for optimizing the size and dispatch strategy of CHP with PV and battery storage to minimize life-cycle cost of energy. The modeling methodology is presented, with a particular focus on the linearization of cost and performance metrics required for implementation in a mixed integer linear program. A case study is used to examine the technical and economic viability of integrated CHP, PV, and battery systems, compared to CHP-only or PV+battery storage systems. In the case study we show how the integrated systems can provide increased economic and resilience benefits compared to single technologies on their own. The case study presented shows the combined CHP, PV, and battery system resulted in a 7% lower life cycle cost than CHP only, and a 22% lower life cycle cost than PV+battery storage. The life cycle emissions for CHP, PV, and battery storage resulted in a 24% reduction than CHP only and a 17% increase than PV+battery storage.
The embodiments described herein provide for a system including a processor. The processor is configured to select at least one grid system contingency from a plurality of grid system contingencies. The processor is further configured to derive one or more eigen-sensitivity values based on the at least on grid system contingency. The processor is also configured to derive one or more control actions at least partially based on the eigen-sensitivity values. The processor is additionally configured to apply the one or more control actions for generation re-dispatch of a grid system.
In northern climates, developing a zero-energy community is increasingly costly. Building loads are higher, thanks to the colder weather, and solar PV is both less effective, due to lower solar incidence, and misalignment with the buildings' energy needs (summer production, winter demands). We present a novel application for geothermal energy production in support of a zero-energy community, combining appropriate energy efficiency (demand design), geothermal production (supply design), and asset dispatch as an integrated technoeconomic package. This poster presents the process used to explore this system integration challenge, initial results, and discusses some of the technical opportunities for increasing the community-scale adoption of geothermal as an electric and thermal resource. Results indicate that under certain conditions, community-scale geothermal resources are quite competitive with today's grid tariffs and with PV, showing an LCOE within $0.01/kWh before consideration of important grid modernization factors such as resiliency and reliability.
In northern climates, developing a zero-energy community is increasingly costly. Building loads are higher, thanks to the colder weather, and solar PV is both less effective, due to lower solar incidence, and misalignment with the buildings' energy needs (summer production, winter demands). We present a novel application for geothermal energy production in support of a zero-energy community, combining appropriate energy efficiency (demand design), geothermal production (supply design), and asset dispatch as an integrated technoeconomic package. This poster presents the process used to explore this system integration challenge, initial results, and discusses some of the technical opportunities for increasing the community-scale adoption of geothermal as an electric and thermal resource. Results indicate that under certain conditions, community-scale geothermal resources are quite competitive with today's grid tariffs and with PV, showing an LCOE within $0.01/kWh before consideration of important grid modernization factors such as resiliency and reliability.
Tightly coupled nuclear-renewable hybrid energy systems (N-R HESs) are designed to both generate dispatchable electricity and produce at least one industrial product from nuclear and renewable energy sources. This presentation summarizes findings from NREL's technoeonomic assessment of several N-R HES configurations. It summarizes key conclusions and communicates the next steps in NREL's work on these technologies.
Hydrogen is a promising clean and renewable source of energy. Its high energy density offers a unique and effective means to store energy. The integration of hydrogen production devices with the power grid could lead to a cleaner and more reliable grid operation at reasonable costs. In this work, the capability of Proton Exchange Membrane (PEM) electrolyzers to perform grid services is assessed using a battery equivalent model approach. The Battery Equivalent Model (BEM) has been developed to represent the physics and operation of a fleet of PEM electrolyzers. The BEM model has been designed to be modular and readily incorporated into grid planning studies. This application of PEM electrolyzers can be integrated with other power generation and consumption devices, aggregating capabilities of each device to enhance the ability of the integrated system to provide grid services and improves grid stability. The developed model is evaluated using ancillary support services including peak management, frequency regulation, voltage regulation, artificial inertia, spinning reserve, and wholesale market price response using a representative price and power system profiles. The BEM model has also been evaluated to provide frequency regulation service that conforms to the IEEE 1547-2018 standard. Metrics such as energy efficiency, charging efficiency, hydrogen production rate, and service efficacy have been incorporated into the model to quantify the performance of the fleet while responding to grid services. Results show the electrolyzer fleet's effectiveness in quickly adjusting its real power consumption based on grid service requests. The energy efficiency of the fleet is maintained relatively high – in the range of 78% and 90% during the service operation. These results demonstrate the ability of PEM electrolyzers to effectively provide fast dispatch capability through storing off-peak hour energy while regulating power consumption during peak hours. The BEM model can assist in quantifying the stability and variability impacts from integration of PEM electrolyzers with intermittent renewable resources in grid integration studies.
In northern climates, developing a zero-energy community is increasingly costly. Building loads are higher, thanks to the colder weather, and solar PV is both less effective, due to lower solar incidence, and misalignment with the buildings' energy needs (summer production, winter demands). We present a novel application for geothermal energy production in support of a zero-energy community, combining appropriate energy efficiency (demand design), geothermal production (supply design), and asset dispatch as an integrated technoeconomic package. This paper presents the process used to explore this system integration challenge, initial results, and discusses some of the technical opportunities for increasing the community-scale adoption of geothermal as an electric and thermal resource. Results indicate that under certain conditions, community-scale geothermal resources are quite competitive with today's grid tariffs and with PV, showing an LCOE within $0.01/kWh before consideration of important grid modernization factors such as resiliency and reliability.
The H2@Scale concept envisions hydrogen as an energy intermediate across multiple energy sectors. The concept is motivated both by the opportunity to produce hydrogen from low-cost energy resources, and by the many potential applications of hydrogen, including as energy for transportation - used directly in fuel cell electric vehicles (FCEVs), as a feedstock for synthetic fuels, and to upgrade oil and biomass - feedstock for industry (e.g., for ammonia production, metals refining, and other end uses), heat for industry and buildings, and electricity storage. A key hydrogen production opportunity is using variable renewable electricity (i.e., electricity generated by wind turbines or solar photovoltaic panels) during hours when generation exceeds the load on the grid as a source of low-cost, dispatch constrained electricity (LDE). In addition to producing low-cost hydrogen, electrolysis can also serve as a form of responsive load on the electric grid, which can enhance grid stability, reduce curtailment, and create a supplementary revenue stream for generators. The future use and adoption of hydrogen across end-use applications depends both the cost of hydrogen production and on each application's threshold price (i.e., the maximum price each application will pay for hydrogen - above that price, the application has an alternative that is more economic). We estimate economic potential of the hydrogen market - the quantity of hydrogen at an equilibrium price at which suppliers are willing to produce and consumers are willing to buy the same quantity of hydrogen - by developing supply and demand curves for hydrogen production and use. The demand curves are based on the threshold price for various quantities of hydrogen in each market, which is affected by projected prices for alternatives that could provide the same service (e.g., fuel for vehicles, feedstock for metals refining). The supply curves include hydrogen produced via natural gas steam methane reforming, high-temperature electrolysis coupled with nuclear power plants, biomass gasification, and low-temperature electrolysis using LDE. We use a range of market and technology-advancement assumptions to estimate the economic potential for five scenarios in the mid 21st century timeframe, and the corresponding impacts on energy use and emissions. We estimate hydrogen's economic potential to be 14-48 MMT/yr in the contiguous United States - corresponding to 1.5-5 times current annual consumption - depending on key assumptions about the future market conditions and the success of hydrogen production research and development (R&D) in our five scenarios. As a result of the estimated economic potential markets, we also approximate that grid-integrated electrolysis can increase the penetration of wind generation in the electricity market by providing a value stream for LDE, and that total U.S. fossil fuel use could decline below a scenario with high renewable penetration on the grid. We find that hydrogen has potential to provide technical and economic synergies when integrated across multiple sectors of the U.S. energy system, but continued R&D, infrastructure, and market development for LDE are required.
High-performance residential communities such as net-zero energy (NZE) and NZE-ready communities are emerging across the United States as many cities and states have committed to achieve ambitious energy efficiency and renewable energy goals between now and 2050. The achievement of these goals will transform the nation's communities into the holistic paradigms consisting of pervasive solar photovoltaics (PVs) and other renewable distributed energy resources, energy storage and flexible loads. However, integrating residential communities with high PV penetration may bring many challenges to the grid. For example, during many hours of the year, electric power is flowing out of PV-enabled homes and back into the grid. When this happens simultaneously in many nearby homes, distribution feeder voltage can be raised significantly. Intermittent solar power can also cause voltage flickers, leading to potential damages on both distribution feeder and home appliances. Existing solutions, such as PV curtailment, battery storage, and direct load control, are not able to address all the challenges in a cost-effective way. Better understanding is needed on how to deploy rooftop PV, how to predict building loads, and how to coordinate the homes in a community to enhance grid reliability. This seminar consists of four integral presentations that cover different aspects of a solution to these challenges. First, we will present an automated method to identify the best surfaces to deploy PV and size PV according to the PV penetration goals in a community. Second, we will present a streamlined way to create gray-box building models that are suitable for large-scale building-grid co-simulation. Next, we will present a data-driven method to estimate the end-use energy consumption to better coordinate the behind-the-meter resources to reduce utility cost, provide grid services, and improve renewable energy integration. Lastly, we will present a hierarchical control system that integrates the building models and the estimated non-dispatchable loads into the decision-making process to enhance the reliability and resilience of the residential community. Simulation results from an NZE-ready community in Fort Collins, Colorado will be presented in all four presentations and the guidelines on better grid integration of residential communities will be provided.
Traditional interactions between the built environment and the electric utility industry are changing rapidly. Rather than a one-way flow of electricity where utilities produce and transmit the electricity that buildings then consume, buildings are becoming efficient energy consumers that can dynamically adjust loads to ensure grid-provided energy is used effectively. Buildings are transitioning to now be at the intersection of forecasting loads and demand response, forecasting and control of output from distributed generation and renewable energy systems, and the optimal dispatch of energy storage. Because residential and commercial buildings consume 74.7% of the electricity generated in the United States, grid-integrated buildings represent an important solution to addressing challenges facing the power sector. In addition to the electric power grid, buildings will be increasingly interactive with water, transportation, and telecommunications networks, and will also more strongly affect surrounding buildings and other civic infrastructure.
Both the frequency and intensity of extreme weather events have been trending higher in recent years, leading to significant infrastructure loss in the electric grid. The impact of these extreme weather events is desired to be analyzed and quantified in order to help transmission and distribution system operators to prepare and prevent significant losses. In this paper, we developed an approach that models the impact of extreme weather on the power grid and identifies the worst impact zone using Q-learning (a reinforcement learning approach). The identification results reveal grid vulnerability to weather events and provide insights for system operators to help achieve optimal resource allocation and crew dispatch in order to minimize the adverse impact of extreme weather. Simulation studies are conducted on the IEEE 123-node system to demonstrate the performance of the proposed approach.
The costs of solar photovoltaics (PV) have been dropping in recent years, leading to increasing installations of solar PV systems and growing interest in how the technology will impact the electric grid. Additionally, as battery costs decline, there is increasing interest in understanding the benefits and limitations of battery storage for the grid as well as potential co-benefits of coupling battery storage and photovoltaics. This paper presents recent methodological developments to more accurately represent the value and limitations of coupled PV and battery systems in capacity expansion models. These are demonstrated using the Resource Planning Model (RPM), which co-optimizes capacity investments, transmission investments, and reduced-order dispatch through 2045. We simulate the evolution of the generation and transmission system in the Western Interconnection both with and without a coupled PV and battery technology option under two core scenarios - a baseline scenario and a high renewable penetration scenario - coupled with sensitivities assuming low and mid PV and battery cost projections. We analyze the degree to which a variety of configurations of coupled PV + Battery systems are able to meet future electricity needs, under which system conditions this coupled technology becomes beneficial to the electric grid and identify the displaced technologies when coupled solar and storage is introduced into the model. We find that capturing the benefits of co-locating these systems becomes increasingly important as the penetration of solar PV rises, and that in particular it is important to accurately capture the ability of these technologies to shift energy, provide firm capacity, and reduce expected curtailment.