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DOE OSTI2024

Powering Data Centers with Clean Energy: A Techno-Economic Case Study of Nuclear and Renewable Energy Dependability

Rising data demands from artificial intelligence (AI) and large language models (LLMs) generating images, videos, and text have prompted increased need for larger and more robust data centers in the United States. Major companies interested in these larger data centers face the choice of linking them to existing regional grids, building stand-alone power supplies onsite, or a combination of both. The request, review, and approval process for new transmission lines to grids in the United States, however, has grown in recent years to times spans rivaling those of new construction for nuclear power plants. Building an islanded power supply for each data center is therefore becoming a prominent option. In this case study, several technologies are modeled in techno-economic simulations for long-term system costs subject to fixed electricity demand from a singular data center. A 250 MWe data center is assumed with additional 50 MWe for resiliency. Techno-economic simulations are conducted using the Holistic Energy Resource Optimization Network (HERON) software, which is a part of the Framework for Optimization of Resources and Economics (FORCE) tool suite. Technologies considered include solar, wind, lithium-ion batteries, and several types of nuclear reactors: large-scale reactors, small modular reactors, and microreactors. A low- and high-cost estimate for each technology is assumed to develop a range of expected economic performance. Low-cost estimates included several clean energy production tax credits. Different combinations of renewable energy generators with nuclear reactors are considered, ranging from a fully renewable-powered data center to a fully nuclear-powered data center. Historic time series of wind and solar availability from the Texas grid are used to train a reduced order model; this model then generates unique time series with similar characteristics of the training dataset. Multiple scenarios of weather and subsequent operations are simulated for each renewable-nuclear combination to determine total costs throughout the project lifetime. Fully renewable-powered configurations required large amounts of installed capacity (GW scale) in the simulations to meet the fixed demand of the data center. This is due to some scenarios in the historical dataset which captured low-wind and low-solar days, requiring over-building of these technologies as well as batteries to compensate for the low amounts of electricity generation. Fully nuclear-powered configurations outperformed the fully renewable and mixed renewable-nuclear configurations in terms of cost, with ranges between $1B and $10B in 2023 USDs compared to $40B+ for fully renewable configurations. Of the nuclear technologies, small modular reactors performed better economically than large-scale nuclear models due to lower projected capital costs, and both performed better than the microreactor models. These results demonstrate the applicability of firm, dispatchable electricity resources from baseload generators like nuclear power plants for operating facilities that run at constant power without daily variability.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Simulating Impacts of Extreme Events on Grids with High Penetrations of Wind Power Resources

As extreme weather events become more frequent and intense, the demand for connecting grid operation and infrastructure planning with extreme event models will increase as well. We present a methodology for creating damage contingencies and scenarios for electric transmission grids during a hurricane strike. Included is an example case study: Hurricane Dolly damaging a synthetic 2000 bus test system during its landing in Southern Texas. Using WIND Toolkit meteorological data in conjunction with fragility curves for various electric grid elements, we generate stochastic damage scenarios that can be used for short- and long-term planning problems including emergency asset management. We perform statistical analysis of damages and quantify topological effects on example synthetic grid. Also, we investigate loss-of-load events during two-day economic dispatch experiment. Finally, we point out various shortcomings of our method and suggest how it can be improved.

data-driven forecasting↗

DOE OSTI2022

Simulating Impacts of Extreme Events on Grids with High Penetrations of Wind Power Resources: Preprint

contingencies↗

DOE OSTI2021

Application of Site Controllers for Electrification of Commercial Fleet Vehicles

Electrification of transportation fleets presents a significant challenge for commercial customers. These challenges can be specific to region, weather, operating schedule, charging infrastructure, and other factors. This paper presents the value of integrating a site controller to monitor the health of assets and co-optimize the operation of commercial sites with multiple distributed energy resource technologies and electric vehicle fleets. The tests demonstrate smart-charging and vehicle-to-building uses compared to business-as-usual cases. The secondary objectives of power management for the customer vehicle are optimized by a planning tool (REopt) and integrated using the site controller. Any deviations from the planned dispatch are addressed by the real-time controllers offering tertiary controls, such as energy management, and limiting the reverse power flow (back into the grid). The results indicate that site controllers offer an efficient solution to manage the health of the connected assets and a scalable means to optimize the operations of a commercial customer with electrified transportation fleets.

ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗

DOE OSTI2025

A method for assessing economic, environmental, and reliability tradeoffs of interregional transmission connecting ERCOT (the Texas grid) to the eastern and western grids

Reliable development of the power grid is an evolving concern for humanity due to extreme weather that frequently threatens power sector infrastructure. The state of Texas is a uniquely structured testbed for grid planners to study when looking for solutions to development, innovation, and overcoming such challenges. Because of its size and islanded structure, Texas is small enough to model, but big enough to matter. Texas is a global leader in energy production, energy consumption, and maintains an unusually diverse fuel mix. In addition, the state has experienced winter freezes, heat waves, wind storms, droughts and floods that have threatened power sector infrastructure or caused recent blackouts and calls for demand side conservation. One of the most devastating of these events was the North American winter storm, dubbed “Winter Storm Uri” by the Weather Channel, that froze the region in February 2021 and led to an extended power outage event that put the majority of Texan residents in darkness for days. While preparing to avoid such outage events in the future, various tools have been proposed to improve grid reliability, including energy efficiency, demand response, and distributed energy resources. An additional option would be to develop interregional transmission that connects the Texas grid to other national grids. To assess the merits of this idea, we developed a novel, universally-applicable and internationally-relevant framework to study how the Texas grid would evolve alongside access to various interregional ties. This method allows us to stress the synthetic grid structure and analyze how it would respond to the shock of a simulated winter storm event. Our method leverages open-source modeling tools, such as PowerGenome, pyGRETA, and GenX to synthesize unique zonal grid data, construct a consolidated network of model regions, and simulate different developmental pathways of capacity expansion and operational dispatch. We demonstrate our method with an analysis connecting the Electric Reliability Council of Texas (ERCOT), the grid that serves most of Texas, the Western Electricity Coordinating Council (WECC), the grid that serves the western half of the contiguous U.S., and the Eastern Interconnect, the grid that serves the eastern half of the contiguous U.S. Our results indicate that the cost-optimal capacity of interregional transmission connecting the ERCOT grid to other grids lies between 9–13 GW assuming baseline conditions. Building this amount of connecting capacity in one or multiple directions lowers the costs and emissions of development and operation by up to $16 billion and 257 million metric tonnes (MMT) respectively. Additionally, our results show that the interregional connections between ERCOT and other national grids reduce the amount of total load shed required through mild winter storm events. However, our results also show that there is a threshold of very extreme winter storm conditions, spanning multiple service areas, above which the connections exacerbate resource adequacy problems. Therefore, the results indicate that the connections need to be carefully planned alongside the rest of the grid infrastructure to avoid over-reliance on specific resources or technology options.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Hydrogen Storage for Load-Following and Clean Power: Duct-firing of Hydrogen to Improve the Capacity Factor of NGCC Plants (Final Report, Phase I Conceptual Study)

The 12-month Feasibility study in Phase I study was completed and confirmed the system is feasible and the proposed system is an improvement over alternate low carbon dispatchable power options. Our demonstration will include 54 MWh of hydrogen storage. CO 2 capture inherent to the CHG process can capture 90% of the CO 2 (with upgrades to >98%) in a commercial system (~300 MWth) for sequestration or other uses. The hydrogen will be utilized in a duct burner in a Heat Recovery Steam Generator (HRSG) integrated with the existing Southern Company fossil asset. Here, the firing rate of the duct burner is varied to let the plant respond to fluctuations of electrical load. However, H 2 production is relatively constant by storing H 2 , and revenues are improved by arbitrage between use of low-cost off-peak variable electricity generation or use of stored H 2 under peak demand. The study enabled the fidelity of the concept to be improved and allowed identification of the requirements for the system. Defining the individual system and component requirements was performed via system requirements review with the whole team. These requirements were then incorporated into/iterated with our Heat & Mass Balance models and process flow diagrams were generated to reflect the overall system. This information was then used to complete the TEA and show economic feasibility. Our system generates power at 17.4% lower cost than other low carbon approaches for the H 2 generation, H 2 compression and storage, carbon sequestration, and HRSG added electricity production for a large-scale duct-fired system. Our team recommends completing the Phase II Pre-FEED study for the proposed system as the next step for the project and its tasks will achieve the overall objective and be ready to launch the FEED. The Pre-FEED tasks include updating the requirements, Concept of Operations, plant scope/process description, component modeling, system modeling, performance estimates, emission estimates, block flow diagrams, fluid/process conditions (PFD), utility usage, and facility sizing/definition. The approach will be to complete these updates in a greater level of detail for the selected site. The approach for developing the EIV is to use the updated results, including model outputs, for carbon dioxide and waste streams.

03 NATURAL GAS↗

DOE OSTI2023

Electric Vehicle Managed Charging: Estimating the Potential Bulk Power System Value

With more and more electric vehicles (EVs) on the road, the grid of the future can greatly benefit from EV managed charging, which coordinates charging based on people's travel needs, electricity supply, and grid conditions. The added flexibility could be especially valuable for the bulk power system as it transitions to high shares of variable renewable generation, like wind and solar. Numerous studies have estimated the potential value of EV managed charging. In this study, NREL leveraged more detailed modeling of EV adoption, use, charging, and bulk power system operations to understand the potential value. Unique to this study, NREL modeled different charging flexibility types and dispatch mechanisms - as well as participation rates among drivers in having their EV charging managed - starting from vehicle-specific descriptions of charging flexibility. The study is based on a passenger light-duty vehicle adoption scenario with 100% EV sales by 2035 and a New England power system in 2038 with 84% clean generation and 26% of the electric load met by net imports. The 2038 New England light-duty vehicle feet is modeled as 45% electric.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

DOE OSTI2026

Analysis of Molten Salt Thermal Energy Storage Tank Sizing and Preheating

Molten Salt Thermal energy storage (TES) is essential for concentrating solar power (CSP) plants, enhancing their capacity factor and dispatchability to ultimately reduce electricity costs. This technology is becoming more important for other technologies including nuclear power, thermal-electrical storage, and concentrating solar thermal (CST). While molten salt TES tanks have been successfully deployed in lower-temperature parabolic-trough plants, the current state-of-the-art central receiver systems operating at temperatures up to 565 degrees Celsius have experienced costly and frequent failures. These failures are associated with the immaturity of the technology and problems in tank design, fabrication, commissioning and, sometimes, aggressive operation. A primary contributing factor is the absence of a dedicated technical standard for high-temperature molten nitrate TES tanks; designers must currently rely on existing codes like API 650 and ASME Section II, which are insufficient because they fail to account for the unique demands of high temperatures, thermal cycling, and transient conditions.

25 ENERGY STORAGE↗

DOE OSTI2022

Electric Vehicle Managed Charging: Forward-Looking Estimates of Bulk Power System Value

When and where electric vehicle charging occurs has significant implications for power systems supporting widespread electric vehicle deployment with high shares of wind and solar generation. Numerous studies have estimated the value of scheduling or otherwise managing electric vehicle charging in such power systems. This study improves on those earlier works by leveraging detailed simulation models for electric vehicle adoption, electric vehicle use, electric vehicle charging, and bulk power system operations; and linking them with methods for describing charging flexibility at both the individual vehicle and aggregate levels. This study closely analyzes electric vehicle managed charging (EVMC) performance along the dimensions of flexibility type (within-charging session or within-week scheduling), dispatch mechanism (direct load control or one of several price-based mechanisms), and participation rate, under the assumptions of ubiquitous chargers and all trips completed on time. The study is located in a passenger light-duty vehicle adoption scenario with 100% electric vehicle sales by 2035, and in an envisioned 2038 New England power system for which within-region generation is 84% clean.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Computational Math Problems for a Clean Energy Future

Cutting edge computational mathematics are ubiquitous in renewable energy research. Problems in resilient and reliable electric grid operations, infrastructure planning, wind farm yaw control, and more demand sophisticated and scalable computational tools that enable the transition of renewable energy technologies from proof of concept to deployment into our energy system. The mission of the Computational Science Center at NREL is to lead the lab's efforts to solve energy challenges using high-performance computing (HPC), computational science, applied mathematics, scientific data management, visualization, and informatics. In this poster, we provide a short overview of three areas of computational mathematics research at NREL: wind power scenario generation for stochastic grid operations and infrastructure planning, improved rational function approximations for electromagnetic transients codes, and wind farm yaw control using a combination of the Alternating Direction Method of Multipliers (ADMM) and reinforcement learning (RL). Increasing penetrations of renewable energy into power grids motivate the investigation of new approaches to characterizing uncertainty for five-minute economic dispatch problems. Similarly, as the penetration of distributed energy resources on power grids increases, it becomes important to revisit our methods of modelling transient phenomena, i.e. electromagnetic transients programs. Finally, the combination of ADMM and RL for wind farm yaw control presented here can potentially increase the efficiency of the deployed distributed controllers by orders of magnitude.

ADMM↗

DOE OSTI2021

The Technical and Economic Potential of Hydrogen within the United States

Hydrogen markets have an opportunity to grow as its value as an energy intermediate increases. Hydrogen’s estimated serviceable consumption potential in the United States is 106 MMT/yr which is a ten-fold increase over the current market size. That opportunity is driven primarily by increased demands for metals refining, biofuel and hydrocarbon production, supplementing the natural gas system, seasonal storage for the electricity grid, and fuel cell electric vehicles. The U.S. has many renewable, nuclear, and fossil resources that each could meet that demand itself. The economic potential, which considers both costs to produce hydrogen and willingness of applications to pay for it, is 22 to 41 MMT/yr. The range is impacted by factors that impact the cost to produce including the price of natural gas and the cost of electrolyzers. It is also impacted by competition to provide the same services as hydrogen including electrification.

electrolysis↗

DOE OSTI2021

Final Scientific Technical Report for "Microgrid RD&D and Testing of PIDC and PWD Systems"

Alstom Grid Inc’s (ALSTOM Grid) Research Design and Development (RD&D) project, “Microgrid RD&D and Testing for PIDC and PWD” was conducted in partnership with Philadelphia Industrial Development Corporation (PIDC) in its role as owner’s representative and manager of a vibrant commercial and industrial community, involving critical loads in one of the nation’s largest unregulated, non-military electric distribution systems. PIDC needed to develop solutions to address the planned considerable growth in Distributed Energy Resources (DER) Combined Heat Plan (CHP), Renewables, Distributed Generation (DG), Demand Response (DR) and Storage. In supporting the corporate objective, PIDC needed a new class of control systems for achieving enhanced energy resilience of their critical infrastructure operation during adverse conditions together with carbon emission reduction and optimization of the overall system operation economics through system energy efficiency during normal and emergency operating conditions. Additionally, PIDC anticipated the need to support a new class of commercial agreements with tenants, such as those for Urban Outfitters, for guaranteed one hundred percent (100%) grid resilience and electric power supply in case of utility outage conditions. The project was designed to address the challenges for the commercial & industrial (C&I) communities. But more importantly, the project included development of scalable and replicable solutions intended to target a multitude of the nation’s electric distribution communities. The project researched and developed a fully comprehensive prototype consisting of microgrid operation and control functions including islanding, synchronization and reconnection, protection, voltage, frequency, and power quality management, dispatch, and system resiliency. The project provided the foundation required to significantly enhance the overall national objectives set by the DOE for energy resilience, emission reduction and system energy efficiency improvement, including protection of critical infrastructure and public resources.

14 SOLAR ENERGY↗

DOE OSTI2024

Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach

The escalating energy demand and the adverse environmental impacts of fossil-fuel use necessitate a shift towards cleaner and renewable alternatives. Concentrated Solar Power (CSP) technology emerges as a promising solution, offering a carbon-free alternative for power generation. The efficiency and profitability of CSP depend on the Direct Normal Irradiance (DNI) component of solar radiation; hence, accurate DNI forecasting can help optimize CSP plants’ operations and performance. The unpredictable nature of weather phenomena, particularly cloud cover, introduces uncertainty into DNI projections. Existing DNI forecasting models use meteorological factors, which are both challenging to estimate numerically over short prediction windows and expensive to model through data at a sufficiently high spatial and temporal resolution. This research addresses the challenge by presenting a novel approach that formulates DNI prediction as a multi-class classification problem, departing from conventional regression-based methods. The primary objective of this classification framework is to identify optimal periods aligning with specific operational thresholds for CSP plants, contributing to enhanced dispatch optimization strategies. We model the DNI classification problem using four advanced deep neural networks – rectified linear unit (ReLU) networks, 1D residual networks (ResNets), bidirectional long short-term memory (BiLSTM) networks, and transformers – achieving accuracies up to 93.5% without requiring meteorological parameters.

14 SOLAR ENERGY↗

DOE OSTI2021

A hub and spoke approach to optimizing energy wheeling of renewable resources

The deployment of zero carbon renewable energy sources needs to increase significantly to support the goal of net zero greenhouse gas emissions by 2050. At the same time energy end use needs to decarbonize. This will change both energy supply and energy demand patterns, requiring the energy delivery infrastructure (grid-based transmission circuits) to become increasingly flexible to maintain security of supply everywhere and always. The integration of zero carbon renewable energy requires cross-border and cross energy system coupling and a fit-for-purpose design. Nowadays, energy systems are planned, designed and operated in silos with a strong national focus. However, large-scale offshore wind production needs to be transported to deep inland locations, across country borders. The increased peak generation capacity of renewable energy sources will, at times, significantly exceed demand (Matthew Langholtz, 2020). The traditional solution of continuously reinforcing and extending the electricity grid is not sustainable from a cost and societal perspective. This paper will, however, propose a deterministic approach on how networked (interconnected grid) Points of receipt (POR) to Points of Delivery (POD) can be optimized for wheeling renewable energy resources while minimizing energy cost with a hub and spoke approach. The statistical approach will be done via using existing daily energy market clearing prices, available transmission capacity and firm daily transmission prices in open access energy markets. Renewable energy targets, including specific offshore wind targets, need to be in line with the ramp-up as implied by the Paris Agreement. These targets are required to provide industry with a secure market outlook that allows them to build up supply chains accordingly. Optimizing wheeled energy paths from carbon neutral resources such as renewables make them not only cost competitive on the unit commitment stack, but also more accessible on the dispatch stack to other carbon heavy forms of generation such as coal and natural gas turbines (Matthew Langholtz, 2020). This correlates to maximizing renewable resource inertia (wind, solar, biomass) within an interconnected grid without having to consider additional expansion of resources via land purchases and de-forestation.

Mukherjee, Srijib↗

DOE OSTI2022

Designing for Zero Energy and Zero Carbon on a Multi-Building Scale Using URBANopt: Preprint

Groundbreaking efforts are necessary to mitigate contributors increasing impacts of climate change. In parallel to inventing pioneering clean energy technologies it is even more fundamental to rethink designing energy systems within a singular facility and collectively to function as a district. Facilities should not be continuously passive by just consuming; there is a need to shift to perform more dynamically. Designing for zero energy and zero carbon on a multi-building scale can uncover opportunities for building energy efficiency, decarbonization, demand flexibility, and resiliency that are not accessible at an individual building scale. This approach can be challenging without innovative tools to evaluate the multitude of possibilities. As an investigated result, we highlight the use of a campus-scale energy modeling platform - URBANopt™ - for the expansion of the National Renewable Energy Laboratory's (NREL's) South Table Mountain campus in Golden, Colorado. Programmatic growth included the design of three new all-electric, zero-energy, and zero-carbon, mixed used buildings (a combination of research laboratories and office space). This investigation is critical to NREL reaching net-zero emissions for its operational footprint, which will occur in phases over the next decade. Leveraging URBANopt's capabilities, we evaluate 1) high-performance building energy efficiency and decarbonization measures, 2) 4th generation district heating and cooling (4th GDHC) systems, 3) optimized onsite generation and energy storage assets that meet zero-energy and zero-carbon targets at minimum life-cycle costs, and 4) cost-optimal distributed energy technology mixes, dispatch strategies, and associated capacities that increase resiliency to grid outages. This work demonstrates the use and capabilities of URBANopt through a real-world case study on a multi-building scale.

community energy model↗

DOE OSTI2023

Flexible Fully-Decoupled Nuclear Plants with Thermal Energy Storage - Technoeconomic Optimization

New nuclear power plants may be one of the technologies for future zero carbon electricity production. These future systems will however also need significant flexibility to cope with fluctuating demand and large share of intermittent renewable supply, rather than running in conventional baseload generation. The present work explores the use of thermal energy storage (TES) as a buffer between the reactor heat and its conversion to electricity, enabling flexible operation of nuclear plants. The focus is on maximizing flexibility, ideally as full decoupling of the nuclear and power cycle islands, as well as on system efficiency in various modes. A system design presented for a high temperature gas cooled reactor (HTGR) presents a slightly complex solution, as separate high- and low-temperature TES systems are required in order to cover the extended temperature range. Although this configuration imposes certain limitations on the discharge system configuration, it also presents the opportunity to design a more efficient system. For this case in particular, a steam reheat cycle is advantageous. Dynamic models were developed for the decoupled system in order to generate insights into aspects of off design operation and system control. A technoeconomic analysis, furthermore, provides sizing and costing of the component, followed by size and dispatch economic optimization considering energy arbitrage. This optimization shows that in all explored markets, the addition of TES system with the proposed configuration results in positive impact on project net present value. Further sensitivity analyses show impact of multiple inputs and possible technical limitations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Behind the Meter Storage for Electric Vehicle Charging, Electrochemical and Thermal Energy Storage, and Solar Photovoltaic

In response to the potentially large and irregular demand from EVs, along with changing load profiles from buildings with on-site generation, utilities are evaluating multiple options for managing dynamic loads, including time-of-use pricing, demand charges, battery storage, and curtailment of variable generation. Buildings, as well as commercial, public, and workplace EV charging operations, can use a combination of electrochemical battery storage and thermal energy storage coupled with on-site generation to manage energy costs as well as provide resiliency and reliability for EV charging and building energy loads. We are completing a behind the meter storage analysis that focuses on determining the optimal system designs and energy flows for thermal and electrochemical behind the meter storage with on-site solar photovoltaic (PV) generation enabling electric vehicle charging in various climates, building types, and utility rate structures. In completing this analysis, we have developed a tool that combines existing battery models via the System Advisor Model (SAM) and building modeling software via EnergyPlus into a single interface. This tool allows us to simulate a building with a detailed battery model to properly size the battery, thermal energy storage, and solar PV systems to maximize profit for the system owner. This also allows us to assess how the battery degrades under various supervisory control dispatch algorithms to control charging/discharging; we can also see how thermal energy storage is created and used to complement the battery to reduce thermal loads in the building. With this project, we can analyze new batteries that are designed specifically for energy storage, rather than designed to be extremely energy dense for electric vehicle applications, using battery lifetime models from other national labs and the existing SAM battery model, which has detailed lifetime and degradation parameters. We can also assess novel thermal storage technologies by integrating them into the whole building energy simulation program EnergyPlus. Because the model calls both SAM and EnergyPlus, required inputs need to be compatible for both models. These inputs include, on a high-level, the following: weather files, building and electric vehicle load profiles, electricity rate tariff information, and system cost information for the stationary battery, solar PV, and thermal storage system. The various buildings we are studying for this analysis are retail big-box grocery store, commercial office building, fleet vehicle depot and operations facility, multi-family residential, and electric vehicle charging station. For these different applications, the battery and thermal storage will be dispatched differently, and the various technologies are sized differently to optimize cost.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2022

Wind and Solar Hybrid Power Plants for Energy Resilience

Wind-solar-storage hybrid power plants represent a significant and growing share of new proposed projects in the United States (U.S.). Their uptake is supported by increasing renewable energy market share, technical abilities for dispatch and control, and decreasing wind, solar, and battery storage costs. Simultaneously, generation and storage resources are increasingly used in distributed power systems. While concerns around the reliability of the aging, transforming U.S. electric grid are growing, diversifying energy resources through hybridization or spatial distribution provides an opportunity to enhance power system resilience compared to single-source generation. Understanding where to build hybrids for resilience value, rather than bulk power supply, has not been fully explored in previous studies. Therefore, in this study, we complete a national complementarity analysis to identify areas in the U.S. that are particularly suited for wind-solar hybrid power plant development. We show the importance of seasonal and diurnal patterns in assessing complementarity, and identify that regions in the Great Plains, Midwest, and Southeast are particularly suited for hybrid power plants. We demonstrate the resilience value of hybridization for a reference system based near Memphis, Tennessee, and show optimal sizing of wind, solar, and storage assets given 1.0 and 0.9 critical load factors. Results indicate that pairing wind and solar assets better meet constant load demand and reduce storage requirements compared to solar alone. These results enable future work integrating complementarity metrics in resilience frameworks and indicate a need for more finer resolution of local resource, demand, and hazard data.

14 SOLAR ENERGY↗