Integrated Energy System Planning

Distributed generation (DG), particularly customer-sited solar and battery storage, is increasingly influenced by a diverse set of policy and incentive mechanisms implemented by states and electric companies. These incentives, including upfront rebates, performance-based payments, export compensation mechanisms, tax incentives, and behavioral rate structures play a critical role in shaping customer adoption and long-term distributed energy resource deployment. This project reviews existing incentive-based distributed generation programs across the United States, with a particular emphasis on Midwest electric companies in the U.S. The study evaluates the combination of policy levers and how they contribute to sustained distributed resource growth. In addition, the research assesses how DG programs and adoption drivers are currently represented in Integrated Resource Planning (IRP) processes. This project evaluates current modeling approaches and explores opportunities for more integrated representations of DG within planning models. The findings highlight the importance of layered policy frameworks and improved modeling approaches to support more accurate forecasting of distributed generation and its role in future power system planning.

Integrated planning for low-carbon energy systems may require models that can coordinate long-term investments and short-term operations across electricity, hydrogen, heat, fuels and storage. This report provides a structured comparison of open-source frameworks to help researchers and practitioners understand the features of various open-source integrated planning models. The report evaluates each tool along five dimensions: 1) scope (e.g., sector coupling, network representation, temporal and spatial resolution), 2) modeling language and formulation (e.g., software implementation, formulations), 3) data management (e.g., inputs, workflows, standards compatibility) 4) treatment of uncertainty (e.g., support for stochastic analysis, decomposition techniques, sensitivity analysis, design for alternatives), and 5) usability and ecosystem (e.g., documentation, licensing, community support). The comparison shows the different modeling choices available to the practitioner that can shape the analysis in a resource planning study.

As regional energy systems experience higher levels of variable renewable generation, rising electricity demand, and increasing climate and weather-related risks, electric company resource planners are increasingly interested in understanding the extent to which the systems they plan may rely on neighboring regions during periods of high system stress. Modeling improvements with respect to how regional imports are characterized may support identifying more reliable resource portfolios. This report reviews current approaches for representing market interactions and other regional imports in long-term resource planning models. Ten integrated resource plans (IRPs) and other long-term planning documents are reviewed to identify methods for modeling market interactions and regional imports. In addition, a survey conducted with electric company planning practitioners and interviews with subject matter experts in electric system planning reveal additional insights into modeling practices. The findings from this analysis provide guidance for companies seeking to better understand current industry practices for modeling import availability and to adopt improved methods in their own long-term resource planning efforts.

Integrated resource planning processes are continuing to evolve as energy company planners seek to bridge the gap between IRP development and implementation. Changing system dynamics are pushing the need for new planning methods and metrics, infrastructure upgrades, and advanced operating strategies and management tools. This white paper outlines several practical realities associated with integrated resource planning.

Integrated Resource Plans (IRPs) look decades into the future, recommending generation portfolios that must meet customer demands over a wide range of possible and uncertain futures. Drivers of this uncertainty include carbon prices, natural gas prices, load, technology capital costs, and renewable generation. Stochastic analysis plays a key role in helping planners evaluate the risks posed by these uncertainties for each of their candidate portfolios. This report provides a review of stochastic planning practices employed by electric companies for their IRPs and other long-term resource plans. For this report, the IRPs and long-term plans from 21 companies were reviewed to identify trends in stochastic modeling practices. Five companies that extensively employ stochastic planning were also selected for a deep dive of their methods. The findings from both reviews informed guidance provided in this report for companies looking to adopt stochastic planning in their own IRPs. Additionally, an Excel workbook outlining illustrative examples for developing stochastic parameters from data is provided in an attachment to this report.

EPRI's Integrated Strategic System Planning (ISSP) Initiative developed a new framework and analytical toolbox for more comprehensively planning across generation, transmission, distribution, and end-use systems, and realizing cost-effective and reliable low-carbon electric power systems. The overall framework consists of a series of soft-linked power system modeling tools, including (1) an economic energy-systems planning model to develop regional technology pathways; (2) a detailed, nodal generation and transmission capacity expansion planning model to develop system-level resource expansion portfolios; (3) a series of grid operations simulation models to evaluate resource adequacy and system risk; and (4) distribution planning tools to assess potential distribution network upgrades and non-wires alternatives.

The research presented in this report focuses on the modeling linking efforts between tools identified in (1), (2), and a portion of (3) above. The motivation for this work is that cost-effective, low-carbon electricity transition planning requires analytical tools and processes that consider key policy, technology, and market impacts across broad, interconnected power systems; as well as critical grid operations and reliability needs given higher levels of variable renewable energy, distributed energy resources, and storage assets. And simply, existing long-term planning modeling tools do not adequately meet both requirements.

Focusing on the bulk power system, this research leverages and links a zonal economic energy-systems planning model, a nodal unit-level capacity expansion planning model, and resource adequacy and production cost models for planning low-carbon and high-renewable long-term resource portfolios that are robust to potential future reliability and resource adequacy deficiencies.

Electric companies are searching for new modeling approaches and tools to support strategic resource planning and asset investment across generation, transmission, and distribution systems, with an aim to identify cost-effective, resilient, and technologically-robust decarbonization resource strategies. Studying the capabilities of existing power system modeling tools and current practices is critical to understanding and advancing an integrated planning framework. This work reviews the objectives, features, and capabilities of various capacity expansion planning tools, production cost model tools, resource adequacy tools, and network reliability modeling tools; and provides an overview of select studies that have been completed over the past few years in the integrated electricity and energy system modeling space.

EPRI’s Integrated Strategic System Planning (ISSP) Initiative develops a new resource planning framework and supporting analytical toolbox. This framework uses a series of soft-linked existing power system modeling tools, selected based on the review of modeling tools enlisted in this work. The new framework is tool-agnostic however, and may be used for more comprehensively planning reliable, low-carbon resource portfolios across electric power system supply, delivery, and end-use.

The pace of utility-scale battery storage deployment has accelerated since 2020, partly driven by continued technology cost reductions, renewable portfolio standards and, more recently, by storage targets set by some U.S. states.

Given the growing importance of energy storage in the future, resource planners are interested in understanding how this technology should be integrated into their long-term planning studies and modeling tools. Energy storage is seen as a valuable resource to support grid decarbonization efforts because of its capability to provide flexibility to systems with an increasing penetration of renewables. Questions that planners are asking include:

• What types of energy storage technologies and features should be included? What services should be considered when modeling energy storage?
• How spatially- and temporally- detailed does the model need to be in order to accurately value energy storage?
• What simplifications affect the assessment of energy storage in long-term planning?

This technical brief reviews information from recent integrated resource plans (IRPs) and planning studies, peer-reviewed journal articles, and several EPRI technical reports, to understand approaches and modeling practices used by electric companies and planners, as well as use cases of storage in long-term resource planning.

Existing long-term electric power system resource planning tools do not fully account for the potential reliability challenges introduced by large-scale deployment of many decarbonization technologies such as renewables, energy storage, and distributed energy resources. This is because the stress a system may face from these technologies (e.g., inadequate generation flexibility, insufficient resource adequacy, network deficiencies) often occurs on timescales, at locations, and within sub-systems of the power system that long-term planning models do not typically capture to keep solutions tractable. Electric companies are thus searching for new modeling approaches to support resource planning and asset investments—across generation, transmission, and distribution systems—with the aim of identifying cost-effective, resilient, and technologically-robust decarbonization resource strategies.

EPRI’s Integrated Strategic System Planning (ISSP) Initiative develops such a new resource planning framework and supporting analytical toolbox. Using a series of soft-linked existing power system modeling tools, the new framework is generalizable and may be used for more comprehensively planning reliable, low-carbon resource portfolios across electric power system supply, delivery, and end-use. This report presents the ISSP modeling framework and an application of the analytical toolbox through an initial study on the New York electric power system. The demonstration study illustrates the process of enhancing traditional capacity expansion modeling tools with more spatially- and temporally-granular power system operations modeling tools to validate initially preferred resource portfolios and update portfolios when the more granular tool(s) uncover economic inefficiencies or reliability deficiencies. Key insights on implementing the ISSP framework and lessons learned from the demonstration study are shared.

This report documents the experiences of a set of electric companies in the United States that are performing coordinated planning activities across generation, transmission, distribution, and customer-sided resources. The transition to decarbonized energy systems will substantially transform the electric sector in the next few years, and companies are recognizing that a more integrated planning process may help guide planning more cost-effective and reliable power systems than planning individual parts of the system in isolation.

This report shows that applications of emerging integrated system planning processes are diverse across utilities. Their organizational structure, and market and regulatory environments influence the level of integration across departments and affiliated external organizations. This is particularly the case across planning functions, analytical tools, data environments and stakeholder engagement. For many utilities, integrated system planning is a new and evolving process. Companies are either implementing a process that is not yet mature, or they are engaging in preliminary discussions to delineate the nature of their collaborations.

This discussion paper presents a practical framework to help power system planners evaluate how much additional load, particularly from rapidly growing data centers (DCs), can be integrated without expanding generation, storage, or transmission infrastructure. As DC growth creates unprecedented planning challenges and opportunities for the electricity sector, the framework defines and quantifies available system “headroom” through a staged analytical approach. This approach combines probabilistic resource adequacy assessments to capture operational uncertainty; hourly nodal operations simulations to represent generator constraints and transmission limits; sub-hourly operations simulations to account for fast-response dynamics such as load variability and ramping; and power flow analyses to evaluate locational risks and transmission reliability requirements. At each stage, the framework compares inflexible and flexible DC operating profiles, demonstrating how DC flexibility—aligned with EPRI’s Flex MOSAIC™ flexibility classes—can mitigate reliability risks and unlock additional capacity. The paper also highlights practical considerations for realizing this headroom, including co-simulation of grid-enhancing technologies (GETs), and positions the framework as a complementary tool to follow-on analyses supporting faster, reliability-conscious large-load interconnection planning.

This report compiles information about load forecasting practices, focusing on forecast development that relates to electric resource and integrated system planning. A sample of documents focused on integrated resource plans, load forecast methodology, or supporting documents from electric companies and planning agencies was analyzed. Load forecasting methods, processes, data, and assumptions are investigated. The research also conducted a broad, high-level comparison of industry practices with EPRI’s internal long-term load modeling tools. Connections to resource planning needs and directions for future research are identified.

Data centers have become the fastest-growing source of U.S. electricity demand, and regional clusters of facilities are transforming local grid dynamics, fueled by increased consumer demand for streaming and other data-intensive services, cryptocurrency, and artificial intelligence (AI). Drawing upon state-level data on operational capacity, construction in progress, and announced plans, EPRI developed Low, Medium, and High scenarios for U.S. data center capacity growth through 2030. Data centers are projected to consume 9% to 17% of U.S. electricity by 2030, up from 4% to 5% today. The projected range of 2030 data center electricity demand is 60% higher than prior EPRI scenarios, which reflects the accelerated pace of data center development. Capacity continues to accumulate in primary data center markets, but the emergence of new capacity in other states suggests increased prioritization of power access and land availability, particularly for large AI training centers. Under reference policies, natural gas dominates incremental supply, while carbon-free energy commitments shift investment portfolios toward low-emitting generation and energy storage. Collaboration is essential to maintain and enhance grid reliability and to address affordability and community impacts as data centers connect to the grid.

Integrated planning for low-carbon energy systems may require models that can coordinate long-term investments and short-term operations across electricity, hydrogen, heat, fuels and storage. This report provides a structured comparison of open-source frameworks to help researchers and practitioners understand the features of various open-source integrated planning models. The report evaluates each tool along five dimensions: 1) scope (e.g., sector coupling, network representation, temporal and spatial resolution), 2) modeling language and formulation (e.g., software implementation, formulations), 3) data management (e.g., inputs, workflows, standards compatibility) 4) treatment of uncertainty (e.g., support for stochastic analysis, decomposition techniques, sensitivity analysis, design for alternatives), and 5) usability and ecosystem (e.g., documentation, licensing, community support). The comparison shows the different modeling choices available to the practitioner that can shape the analysis in a resource planning study.

Modeling to support utility scale resource planning is data intensive; the sourcing, organizing, processing, and analyzing of needed data can be challenging and time consuming. Advancements in Artificial Intelligence (AI) tools, such as Large Language Models (LLMs) can boost researchers’ efficiency and accuracy working on such tasks. LLMs are text based predictive models trained to input and output natural language, code, and data. By using LLMs, energy system inputs such as forecasted demand, existing and planned generators, and/or fuel prices can be sourced or developed. Further, processing this data with the help of LLMs can help to develop the code and analysis to input such data into generalized data structures that work across tools. This report explores the practical application LLMs as coding partners for developing inputs to capacity expansion models, focusing on hands-on tasks where LLMs supported coding and data preparation.

As regional energy systems experience higher levels of variable renewable generation, rising electricity demand, and increasing climate and weather-related risks, electric company resource planners are increasingly interested in understanding the extent to which the systems they plan may rely on neighboring regions during periods of high system stress. Modeling improvements with respect to how regional imports are characterized may support identifying more reliable resource portfolios. This report reviews current approaches for representing market interactions and other regional imports in long-term resource planning models. Ten integrated resource plans (IRPs) and other long-term planning documents are reviewed to identify methods for modeling market interactions and regional imports. In addition, a survey conducted with electric company planning practitioners and interviews with subject matter experts in electric system planning reveal additional insights into modeling practices. The findings from this analysis provide guidance for companies seeking to better understand current industry practices for modeling import availability and to adopt improved methods in their own long-term resource planning efforts.

Long-term resource planning in the electric sector plays a critical role in guiding infrastructure investment, shaping energy prices, supporting decarbonization goals, and promoting economic growth in Canada. This report reviews resource plans from major electric companies across provinces with regulated electricity markets, which collectively generate most of Canada’s electricity. It offers insights into the uncertainties prioritized by companies, including load forecasts and policy futures, scenario planning, and near-term resource additions or changes. The report also examines resource planning in Alberta and Ontario's deregulated electricity markets, focusing on system operators' planning outlooks. While investment decisions are primarily driven by competitive market signals, resource planning in these markets supports informed decision-making for electric companies.

Long-term resource planning in the electric sector plays a critical role in guiding infrastructure investment, shaping energy prices, supporting decarbonization goals, and promoting economic growth in Canada. This report reviews resource plans from major electric companies across provinces with regulated electricity markets, which collectively generate most of Canada’s electricity. It offers insights into the uncertainties prioritized by companies, including load forecasts and policy futures, scenario planning, and near-term resource additions or changes. The report also examines resource planning in Alberta and Ontario's deregulated electricity markets, focusing on system operators' planning outlooks. While investment decisions are primarily driven by competitive market signals, resource planning in these markets supports informed decision-making for electric companies.

plexos2duckdb extracts results from a PLEXOS solution file to a duckdb SQL database.

To access PRE-SW: plexos2duckdb v0.1.0 Beta, click here: https://github.com/epri-dev/plexos2duckdb

Benefits & Values

• Efficient Data Access & Analysis: Converts PLEXOS solution files into DuckDB for quick, efficient SQL based querying
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Platform Requirements

• Windows
• MacOS
• Linux

Navigating uncertainty is fundamental to building a successful long-term electric system resource plan. Whether it is short-term hourly fluctuation in system conditions; medium-term variation in seasonal states; or longer-term changes in factors such as government policy, technology cost, and load growth, the principal objective of the electric system planner is to identify 10- to 25-year system resource portfolio plans that can perform cost-effectively and reliably. Practices such as scenario analysis and sensitivity analysis are widely used in resource planning to navigate uncertainty and manage risk. Both approaches can provide important information about the range of potentially beneficial decisions but may miss identifying adverse outcomes or further opportunities due to the limited futures explored.

In contrast, stochastic analysis often explores a relatively large set of potential futures and explicitly considers risk. It is important to note that, in practice, scenario, sensitivity, and stochastic analysis complement one another across the different phases of a resource planning assessment. Scenario and sensitivity analyses can help identify candidate resource plans as they are well-suited to efficiently exploring the range of potential plans for a discrete set of uncertain futures. The outcome is thus a tractable set of candidate plans to consider. Sensitivity analysis and stochastic analysis may then be used to evaluate these plans, with the latter affording a means to characterize risk over more possible futures. Overall, the combination of scenario, sensitivity, and stochastic methods balances computational efficiency of the planning assessment with providing the most insight into risk and uncertainty.

This brief focuses on demystifying (1) the use of stochastic analysis in evaluating candidate resource plans; and (2) the interpretation of results from a stochastic resource planning analysis, including how the planner’s risk tolerance intersects with the potential selection of a “preferred” resource plan. To solidify the concepts presented, a simple, illustrative example is outlined and used throughout the discussion. Comparisons to traditional or “deterministic” resource planning analyses are made.

Uncertainty is inherent in the planning of electric power systems, an issue with which electric companies have grappled for decades when developing long-term resource portfolios. This primer provides an overview of techniques used in electric company resource planning to consider uncertainty, and guidance to implement one of the most common techniques in practice today to manage uncertainty and develop robust portfolios—Monte Carlo-based stochastic analysis. The material within is targeted towards electric company planners tasked with developing a long-term resource plan with an interest in integrating an industry-standard approach for considering stochastics but without significant prior experience with such techniques. The information can also be used by other internal company or external stakeholders who simply wish to learn more about stochastics and risk management as applied to electric company resource planning.

Electricity system planners encounter many uncertainties when developing future generation and transmission infrastructure portfolios that are robust to potential future hazards. Among these, weather and climate are fundamental – impacting all aspects of system operation. While traditional bulk electric system planning practices consider historical weather and climate, changing average and extreme conditions place new risks on assets and challenge existing and future system reliability. To respond to these challenges, planners may first identify how changes to weather-normal conditions and extreme events could impact system operations, and subsequently use these relationships to identify adaptations to maintain reliability and ensure resilience.

While climate risks are system- and hazard-specific, a common planning framework that incorporates climate impacts and conveys system needs during critical periods can help navigate the uncertainties present. Through the Climate Resilience and Adaptation initiative (Climate READi), EPRI has developed a climate-informed integrated electric system planning framework to identify cost-effective adaptation and resilience investments. This report guides planners through the integration of climate data and asset impacts across models to support integrated bulk electric system planning.

Integrated Resource Plans (IRPs) look decades into the future, recommending generation portfolios that must meet customer demands over a wide range of possible and uncertain futures. Drivers of this uncertainty include carbon prices, natural gas prices, load, technology capital costs, and renewable generation. Stochastic analysis plays a key role in helping planners evaluate the risks posed by these uncertainties for each of their candidate portfolios. This report provides a review of stochastic planning practices employed by electric companies for their IRPs and other long-term resource plans. For this report, the IRPs and long-term plans from 21 companies were reviewed to identify trends in stochastic modeling practices. Five companies that extensively employ stochastic planning were also selected for a deep dive of their methods. The findings from both reviews informed guidance provided in this report for companies looking to adopt stochastic planning in their own IRPs. Additionally, an Excel workbook outlining illustrative examples for developing stochastic parameters from data is provided in an attachment to this report.

As rising numbers of inverter-based resources (IBRs), largely from wind, solar, and battery energy storage systems are deployed in power systems around the world, their contribution to the grid and the demand for services from them are undergoing transformation. To maintain grid stability and reliability, the participation of IBRs in some of the services will be critical. IBRs have the capability to provide some of these grid services such as operating reserves, planning reserves, and voltage support. The procurement and deployment of the services can be implemented either as mandatory interconnection requirements or as market products.

This report provides a comprehensive overview of both current and emerging opportunities for IBRs within bulk power systems and electricity markets. It concentrates on the reliability services (ancillary services) of the bulk power system, with a special emphasis on the ability of variable energy resources, like wind and solar, to deliver these services. It presents a thorough examination of both traditional and emerging ancillary services within the bulk power system, discussing the potential roles for IBRs. This includes technical definitions and characteristics, contemporary performance criteria, and compensation mechanisms for each of the identified services.

Variable resources such as wind and solar have the technical and control capability to provide most grid services; however, they rarely do in practice due to eligibility rules, grid operator confidence and forecast uncertainty, and for economic reasons. Further actions may be necessary to investigate these barriers further and explore the potential benefits of more robust participation from these resources across the globe.

The integration of electric storage resources (ESRs) into wholesale electricity markets in the United States, catalyzed by FERC Order 841 in February 2018, has reshaped the energy landscape. ESRs, crucial for grid flexibility and renewable integration, have expanded in key regions where market operators have gained experience on best ways to manage them efficiently and reliably. This insight report delves into their participation, examining key aspects and the evolving landscape in electricity markets across the United States.

Hydrogen is a promising emerging resource that may be a critical enabler of a low-carbon future. The development of large-scale hydrogen turbines for power generation is accelerating in response, with encouraging results. Electric companies are increasingly interested in understanding and incorporating hydrogen facilities into their resource plans. By monitoring advancements in both the techno-economic space and its inclusion in resource plans, resource planners can assess their progress and level of understanding against industry norms while also identifying gaps and next steps for their own plans.

This technical brief discussed future research needs and addresses the following questions:

• What are current perspectives on the role of hydrogen in electric company integrated resource planning?
• What challenges and benefits can electricity companies expect when incorporating hydrogen into long-term planning?
• How can current efforts inform priorities and lessons for future studies?

The pace of utility-scale battery storage deployment has accelerated since 2020, partly driven by continued technology cost reductions, renewable portfolio standards and, more recently, by storage targets set by some U.S. states.

Given the growing importance of energy storage in the future, resource planners are interested in understanding how this technology should be integrated into their long-term planning studies and modeling tools. Energy storage is seen as a valuable resource to support grid decarbonization efforts because of its capability to provide flexibility to systems with an increasing penetration of renewables. Questions that planners are asking include:

• What types of energy storage technologies and features should be included? What services should be considered when modeling energy storage?
• How spatially- and temporally- detailed does the model need to be in order to accurately value energy storage?
• What simplifications affect the assessment of energy storage in long-term planning?

This technical brief reviews information from recent integrated resource plans (IRPs) and planning studies, peer-reviewed journal articles, and several EPRI technical reports, to understand approaches and modeling practices used by electric companies and planners, as well as use cases of storage in long-term resource planning.

Electric company resource planning in the United States determines decarbonization technology portfolios, energy costs, and investment plans for hundreds of billions of dollars in new electricity resources. Electric resource plans cover approximately half the United States electricity system, excluding most of the northeast, Texas, and many rural areas. The EPRI State of Electric Company Resource Planning 2023 report provides detailed reporting and analysis on electric company resource plans in the United States. It includes an analysis of resource portfolios and load projections, as well as 3 resource planning deep dive review analyses focused on CO2 reduction targets, technology cost expectations, and key IRP themes identified from a text review. An analysis of aggregate portfolios from recent resource plans show rapid electric resource capacity growth is planned through 2030. Nameplate capacity growth dominated by solar, followed by wind and battery storage. Expected future electricity demand growth is driven by industrial consumption and economy-wide electrification. Eighty percent of resource planning customers are covered by a 2050 net-zero target. Emerging focus areas for resource planning include adapting to extreme weather risk, integrating equity perspectives, and improved stakeholder engagement for communities impacted by resource plans.

U.S. consumption of natural gas for electric generation has increased significantly within the last 10 years and is likely to continue to grow to support renewable integration. Gas and electric modeling has been a challenge throughout the industry, and only a limited number of technically detailed models are available to support the co-simulation effort. Extensive simulations are limited by a lack of realistic data on supply and pipelines as well as the need for a better picture of heating load as the industry transitions from gas to electric. Such information will be especially relevant for transmission planning in already congested urban areas necessitating a detailed pipeline model.

This report describes the data needs for gas modeling for an electric sector audience including pipeline networks and their major components, necessary model inputs to simulate a gas network, typical results obtained from a hydraulic simulation, and the decisions or questions a gas model can inform. The last chapter provides data for a pipeline in the southeastern United States based on publicly available data. With this small but realistic data set, electricity modelers will be able to better simulate gas networks to increase understanding of the reliability, resilience, and economic impacts that gas supply has on the electric system – including the impacts of common cause and extreme events as well as many other gas/electric coordination challenges.

In recent years, prospects for the future deployment of battery energy storage resources in the electric sector have increased markedly, as battery costs have declined, and their technical performance has continued to improve. However, battery energy storage technologies have complex cost, value, and performance characteristics that make them challenging to model in long-term power system capacity expansion models.

Phase 1 of this project explored the potential value of incorporating five features that are not commonly represented in existing long-term capacity planning models: (i) battery energy storage system (BESS) degradation; (ii) grid (network) modeling; (iii) ancillary services; (iv) sub-hourly temporal resolution; and, (v) uncertainty. Simulation results identified that BESS degradation is the feature with the largest impact on planning outputs in models with energy storage.

Phase 2 of this study, which is described in this report, takes the next logical step and focuses on experimenting with different degradation models for Lithium-ion battery energy storage systems in a capacity planning model. The objective of Phase 2 is to answer the question: What is the most economically efficient (that is results in the lowest overall system costs when degradation is taken into account) and computationally efficient approach to battery degradation that can be incorporated into long-term power system resource planning models? To answer this question, five methods to make a capacity planning optimization problem “degradation-aware” were developed and simulation experiments were completed that incorporated one method at a time. These simulations were done using a “maquette” version of a large power system located in the Southeastern United States.

Experimental results show that selecting a proper degradation approach can materially reduce total system costs relative to a result that assumed a fixed battery lifetime. Models that keep the battery’s state of charge at lower levels can extend the battery’s operational life and improve economic efficiency (that is the added benefit of extending the battery’s life exceeds the added cost of controlling the

battery’s use pattern). Other models that limited the total amount of energy discharged from the battery or simulated energy capacity fade are not as economically efficient. Many of these degradation simulation models can be solved in a computationally efficient manner and do not add a significant computational burden to existing battery optimization models.

Capacity expansion models used by electric companies to develop future long-term resource plans continue to evolve to be able to better assess the potential value and future deployment of energy storage and other emerging power system technologies. One challenge in developing these new and improved models is that doing so often requires tradeoffs between economic efficiency and computational complexity. The simulation results in this analysis pave the way for incorporating “degradation-aware” yet computationally light models for battery storage systems in power system capacity planning problems.

Improved forecasting of the adoption of Distributed Energy Resources (DERs) by end-use consumers can help power system planners account for the impacts of DERs on the planned system resource mix. This Technical Brief summarizes research on current practices used to forecast Electric Vehicles (EV) and distributed Solar Photovoltaic (PV) adoption and a quantitative analysis of the impact of EV adoption on expected future capacity expansion. Survey results show EV and PV adoption forecasts often are treated as exogenous inputs or modifiers to the main load forecast in resource planning. Granular (hourly, locational) forecasts of the impacts of solar PV and EVs on system load mostly are used for long-term distribution planning purposes. Our quantitative analyses show that EV adoption can significantly impact system planning decisions within a typical planning time horizon, particularly additional renewable resource capacity. Modeling results are highly dependent on specific system conditions, particularly the generation mix, load shapes, and the use of managed charging.

Expectations for the future role of energy storage resources in the electric sector have increased in recent years, as technological developments have been accompanied by policy support. However, energy storage technologies have complex cost, value, and performance characteristics that make them challenging to model.

This analysis aims to determine which features that are not commonly represented in existing long-term capacity planning models may, if included, materially alter key decisions related to how much energy storage is expected to be cost-effective. Using an integrated model of capacity planning and operations, the analysis varies the inclusion of five features to understand how model complexity can impact planning insights: degradation, grid (network) modeling, ancillary services, subhourly temporal resolution, and uncertainty.

Model results indicate that degradation has the largest impact on planning outputs, including those related to energy storage deployment and operations. Other features have smaller impacts on investment and dispatch outcomes. Grid modeling does not alter cost-effective levels of energy storage in the conventional capacity planning setting with a fixed planning reserve margin, though it impacts transmission planning decisions on the locational placement of resources. The extent of cost-effective battery storage capacity in these scenarios is primarily driven by capacity needs, but energy time-shifting represents a non-trivial fraction of the system value of storage.

Long-term electric sector capacity planning models continue to evolve to more comprehensively assess the potential value of energy storage and other emerging technologies. Model formulation decisions entail tradeoffs between the accuracy of the representation and model parsimony, and determining which model details matter can help to prioritize efforts and to ensure the appropriate valuation of resources under wide range of possible futures.

The electric power industry is shifting its generating portfolio towards variable energy resources and natural gas. As these changes are occurring, the industry needs to plan for resource adequacy that will make electric service more resilient to significant disruptions of supply whether they are the result of weather, cyber / physically attacks, fuel constraints or multi-factor events. Across each of these topics the power industry today employs planning methods that tend to understate the probability of supply disruptions affecting multiple units and their impact on consumers and the system itself.

This white paper focuses on planning for resource adequacy given a world in which supply disruptions are correlated and no longer limited to the outage of independent units and may be due to widespread or long-duration events with significant economic impacts on consumers. The paper highlights the following attributes of planning for resource adequacy in an environment of increasing numbers of extreme events:

• Supply disruptions that are common mode events caused by weather, cyber / physical attacks, natural gas constraints or combinations of factors.
• The occurrence of an event (zero/one), consideration of its physical impacts (the amount of unserved energy, breadth of customer base impacted, and duration) and its economic costs to consumers.
• The need for the definition of probabilistic metrics and methodologies that over time can be used to incorporate consideration of common mode and high impact supply disruptions.

The paper concludes with an identification of strategies that an individual utility and/or an ISO/RTO could follow based on its unique situation.

Electric companies, industry stakeholders, and regulators are placing  increasing emphasis on accurately representing distributed energy resources (DER) in electric company long-term resource planning efforts. While there is substantial literature on the qualitative advantages and disadvantages of distributed energy resource modeling methods, there has been little research that quantitatively assesses implications across methods. This study quantitatively demonstrated the impact of a variety of approaches to representing energy efficiency (EE) and demand response  (DR) in electric company resource planning modeling and analysis. Overall, a comparison of resource planning modeling simulations showed there are conditions under which certain approaches may be more appropriate than others for integrated resource planning. Resource planners can use the results and insights developed to assist them in deciding upon EE and DR modeling approaches for their own resource planning. Results also can be used by other industry stakeholders to analyze resource plans in a more informed way.

In July 2018, EPRI published a white paper entitled, Developing a Framework for Integrated Energy Network Planning (IEN-P): 10 Key Challenges for Future Electric System Resource Planning (3002010821). This paper identifies and describes 10 complex, large-scale power system planning challenges that electric power system planners and regulators are beginning to confront today, and which are expected to become more pressing and widespread in the future. EPRI’s Technology Innovation (TI) program launched, in early 2018, Phase 2 of this research effort, which is designed to begin to assist electric companies with determining how to implement strategies to address these challenges.

In February 2019, EPRI published an initial set of case studies that highlight how different electric companies in the United States have started to address the IEN-P challenges identified in the white paper. This Technical Update follows the publication of Volume 1 and includes case studies for planning challenges not contained in the first volume. “Key Insights” are included in each case study to enable transfer of knowledge and learnings among peers and to show companies how others are addressing commonly-occurring challenges brought upon by a rapidly changing electricity sector.

To attain least-cost generation, transmission, and delivery of electricity at a reliable level, close coordination between generation and transmission operation and planning is fundamental. Optimizing these sectors in isolation can miss integrated generation and transmission solutions that are cost-optimal while meeting reliability targets. Before the introduction of competition, the level of coordination was sufficient—but competition forced separation of the generation and transmission functions, even within vertically integrated utilities. Nowadays, generation companies act independently, dealing at arm’s length with transmission planners. However, different groups (transmission planners and generation planners), within the same region—or even company—and across regions need to coordinate and anticipate others’ decisions to attain better global long-term development. The same needs are emerging in integrated systems where unaffiliated distributed resources are appearing at the grid edge. Such unbundled and distributed systems are also fraught with uncertainties, which, if inadequately considered, will lead to plans that are not resilient and cannot adapt in a way that maintains economic and reliable operations.

These challenges, referred to here as the coordinated expansion planning (CEP) problem, have come into focus over the last few years for several reasons, including deeper penetration of renewable energy sources, integration of emerging storage technologies, electrification of the transport sector, increased interdependencies with other sectors (for example, gas), and increased distributed generation in distribution grids. These changes result in increased short- and long-term uncertainties as well as a need for increased modeling fidelity to represent temporal dynamics more accurately (for example, hourly or sub-hourly intertemporal couplings in expansion models). These challenges, together with the progress in computational resources, have prompted the development of sophisticated tools able to produce expansion plans that not only approach system optimality, but are also flexible and robust against the various planning and operating uncertainties.

This report provides an in-depth view of the state-of-the-art methods and tools to produce coordinated expansion plans. In addition, it identifies the research and development needs for the new generation of coordinated expansion planning models and tools.

The report begins with some introductory concepts and a general layout; it is followed by several sections that cover specific aspects of the CEP problem. Together the sections give an integral perspective on the ongoing and future research efforts on the CEP problem. However, each section has been written to be read independently, if the reader is interested only in a particular aspect of the problem.

In July 2018, the Electric Power Research Institute (EPRI) published a white paper entitled, Developing a Framework for Integrated Energy Network Planning (IEN-P): 10 Key Challenges for Future Electric System Resource Planning (EPRI report 3002010821). This paper identifies and describes 10 complex, large-scale power system planning challenges that electric power system planners and regulators are beginning to confront today, and which are expected to become more pressing and widespread in the future. In early 2018, EPRI’s Technology Innovation (TI) program launched Phase 2 of this research effort, which is designed to begin to assist electric companies with determining how to implement strategies to address these challenges.

This Technical Update contains the first of a two-volume set of case studies that highlight how different electric companies in the United States have started to address the IEN-P challenges. The second volume is expected to be published later in 2019. “Key Insights” are included in each case study to enable transfer of knowledge and learnings among peers, and to show companies how others are addressing commonly occurring challenges brought upon by a rapidly changing electricity sector.