2021 Standard Scenarios Report: A U.S. Electric Sector Outlook
This presentation provides a summary of the 2021 Standard Scenarios report. It was presented in a public webinar on December 15, 2021.
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This presentation provides a summary of the 2021 Standard Scenarios report. It was presented in a public webinar on December 15, 2021.
The Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100) is a 2-year study by the U.S. Department of Energy's (DOE's) Grid Deployment Office and six national laboratories to comprehensively analyze stakeholder-driven pathways to Puerto Rico's clean energy future. In Year 1 of the study, the PR100 team rigorously modeled and analyzed scenarios that meet Puerto Rico's renewable energy targets and achieve short-term recovery goals and long-term energy resilience. This presentation will be showcased in the form of a public webinar, which summarizes PR100 progress in Year 1, provides considerations that can inform potential funding and implementation decisions by key federal and local agencies and stakeholders. The presentation follows the publication in July 2022 of a PR100 Six-Month Progress Update (in English and Spanish), as well as public webinars in February 2022 to kick off the study and in July 2022 to present the 6-month update. A final written report and web-based visuals will be published in late 2023. All publications and public events associated with the study will be available in Spanish and English. This report is also available in Spanish: https://www.nrel.gov/docs/fy23osti/85201.pdf.
ReEDS-India is an open-access tool for mid- and long-term capacity expansion modeling that finds the mix of generation, transmission, and storage technologies that meet the anticipated requirements of the electric sector at least cost.
Deep decarbonization towards a net-zero emissions future and the associated growth in power demand, renewables, and changes in water availability will require significant evolution in grid planning. Many obstacles threaten the viability of near-term generation capacity build out, including the need for large-scale transmission expansion. Current very-long term planning tools do not adequately model physical representations of economic dispatch and system topology down to inter- and intra-regional transmission and distribution infrastructure. This project attempts to fill that gap.
Recent technology cost and performance improvements and the federal investment tax credit (ITC) have driven growing interest in coupling solar photovoltaic (PV) and battery systems. Combining these technologies into co-located or hybridized PV+battery systems has the potential to lower costs and increase energy output relative to multiple independent systems. In this work, we provide an overview of PV+battery systems and demonstrate methods for incorporating them into NREL’s Regional Energy Deployment System (ReEDS) capacity expansion model. Although the methods are applied to a specific model, we anticipate that the approaches used here can be useful for informing PV+battery method development for other capacity expansion models. The implemented method relies heavily on the native representations of PV and battery technologies; therefore, the focus of this work is on capturing and parameterizing the interactions between them for a configuration in which the PV and battery technologies share a single bi-directional inverter. This work also demonstrates the impacts of including PV+battery systems in the ReEDS optimization for the conterminous United States through 2050. In particular, we perform parametric sensitivities for input assumptions that are uncertain and expected to influence PV+battery deployment levels, including (a) the cost of PV+battery systems relative to independent PV and battery systems, (b) the battery component’s qualification for the ITC, and (c) future cost trajectories for PV and battery systems. We find that PV+battery deployment could occur throughout the conterminous United States if there are cost savings associated with DC coupling PV and battery technologies. If even modest (5%) cost savings can be achieved (through a shared inverter and balance-of-system costs), then approximately one-third of utility-scale PV deployment through 2050 adopts the DC-coupled hybrid configuration, resulting in total PV+battery deployment that exceeds the magnitude of PV+battery projects in U.S. interconnection queues in 2020. If greater cost savings can be achieved through DC coupling (e.g., due to a growing amount of shared balance-of-system costs, reduced financial risk, or modularity) or more rapid cost and performance improvements are realized for PV and battery technologies, then total PV+battery deployment and the share of PV and battery deployment that adopts the hybrid configuration grows (to >50%). In all cases, growing PV+battery deployment primarily displaces independent PV and battery technologies, indicating the strong competition between the hybrid and independent configurations comprising technologies with similar performance characteristics.
Power system planning software remains fragmented across organizational boundaries, with specialized tools for capacity expansion, production cost modeling, power flow, and dynamic analysis operating on incompatible data models and assumptions. This article argues that the fragmentation is not merely a technical problem but a predictable consequence of Conway's law: software architectures mirror the departmental structures within which they are developed. Regulatory milestones like Federal Energy Regulatory Commission (FERC) Order 888 formalized these divisions, but the roots trace back to the distinct engineering disciplines-mechanical, chemical, and electrical-that staffed generation and transmission planning departments in vertically integrated utilities. As the industry moves toward integrated system planning (ISP) that coordinates generation, transmission, and distribution investment decisions, the software ecosystem must evolve accordingly. We identify five categories of software requirements to enable this transition: coherent data inputs decoupled from individual applications, unified and extensible data schemas, modular component representations that support multiple abstraction levels, lifecycle management of planning datasets, and well-defined application programming interface (API) contracts that separate data exchange from algorithmic control. We examine how these requirements interact with three common workflow patterns-serial gate clearing, sequential multiapplication, and convergence oriented-and discuss the interface design principles each demands. We then outline a vision for platform-based planning architectures where specialized analytical services compose through standardized interfaces and where artificial intelligence (AI)/machine learning (ML) tools augment decision support within a disciplined software infrastructure. The practices proposed here offer a path from today's siloed tool collections toward collaborative planning ecosystems capable of handling the complexity of modern power system transformation.
The growing recognition of the value of hydrogen as an energy intermediate in supporting future power systems with high shares of variable renewable energy has prompted many studies to quantify the economic potential of multi-output hybrid systems, which are one type of integrated energy systems (IES). Because of the complexity of modeling multiple sectors, these studies typically use simplified modeling approaches to capture the interactions between sectors. In this study, we explore the implications of alternative modeling approaches for nuclear-hydrogen IES focusing on a power system in the Midwest United States. We combine highly resolved capacity expansion and production cost modeling tools of the power system with a detailed hydrogen system optimization tool to determine the optimal electrolyzer and storage sizing and optimal operations of the nuclear-hydrogen hybrid resource across three future study years. We compare economic and operational outcomes across a spectrum of modeling approaches, including a non-hybridized base approach; a traditional price-taker approach that does not include the impact of hydrogen production on the electricity system; a power-system-focused price-maker approach that does not account for temporal hydrogen constraints; and two improved price-taker and price-maker approaches that each address the impact of revenue-optimal levels of electricity production on the resulting power system and temporal hydrogen constraints on the overall feasible solution. Results show how a traditional price-taker approach can overestimate the economic benefits of multi-output nuclear-hydrogen IES compared to our two improved approaches that estimate both hydrogen system constraints and power system interaction. We find that hydrogen output requirements and storage size limits are key drivers to overall operations and some economic outcomes. Under our assumed constant hydrogen output requirement, storage costs, test system, and modeling approaches, our results indicate that hybridization can provide a net benefit, but results are sensitive to the treatment of hydrogen revenues and electricity prices as impacted by the power system evolution.
Through Net Zero World, the National Renewable Energy Laboratory (NREL), the National Energy Secretariat of Argentina, La Pampa's Energy and Mining Secretariat, Pampetrol, and the Administracion Provincial de Energia de La Pampa (APELP) conducted an integrated analysis of the power system of the La Pampa province in Argentina. The study assessed the impact that solar and battery energy storage systems could have on the region grid's reliability and electricity costs, providing insights into long-term decarbonization strategies across Argentina. The analysis demonstrated that these systems are expected not only to help with decarbonization efforts but also to bolster grid resiliency, reduce line losses, and improve voltage regulation while lowering electricity costs. The paper offers a replicable framework for similar studies across other Argentine provinces, which could inform a national grid reliability plan and promote regional coordination. Such coordination will play a key role in supporting a nationwide clean energy transition as Argentina increases its share of renewable energy. By coordinating subnational energy plans with national strategies, Argentina can secure a resilient clean energy future, leveraging the substantial renewable resources available across its provinces.
Electrification of end-use technologies could have important implications for bulk power sector planning, operations, and impacts. By combining previously-defined electrification scenarios with capacity expansion modeling, we evaluate the potential challenges and opportunities associated with electrification. Our scenario analysis reveals that electrification reduces energy consumption and air emissions from across the energy sector, and its impacts on energy system costs are modest due, in part, to the abundance of low-cost resources in the United States.
NLR Engage™ is a free, open-access energy system planning tool that allows multiple users/stakeholders to develop and collaborate on capacity expansion models. The tool, developed at the National Laboratory of the Rockies (NLR), provides a collaborative and easy-to-use interface built on Calliope, a multi-scale energy systems modeling framework. Visit the NLR hosted webtool at https://engage.nlr.gov/en/login/?next=/en/
Multiple capacity expansion models (CEMs) for the U.S. power system represent the balance of options among generation, transmission, and storage assets that can satisfy electric loads, operating and planning reserves, and policy requirements. These models are typically set up to find the least-cost portfolio of assets that meet specified requirements, and model decisions can include both investments in new, and retirement of existing, resources. The scenarios explored by CEMs can help inform strategies for meeting future electricity and energy needs under a range of future conditions. However, projections can differ between models, sometimes dramatically, for a seemingly similar scenario. Differences in model coverage, structure, and input assumptions contribute to the range of model outcomes. Understanding what drives the biggest differences in model outputs improves model insights and provides context for interpreting results. This summary presents analysis that was performed through a forum of analysts who own, update, and apply CEMs, as well as nuclear experts from national laboratories, industry, and the research community. The following sections describe methods, results, and findings from an original, innovative inter-model comparison that provides insights into what drives the greatest differences in nuclear retirement and deployment projections across models and a range of technology, market, and policy conditions.
Welcome to Engage! Engage is a capacity expansion modeling tool supported by the National Renewable Energy Laboratory and based on the Calliope open-source capacity expansion model developed by the ETH Zurich University, maintained at the TU Delft University. Engage is an accessible (free, open-access, web-hosted) and flexible web-based energy system planning application for rapid multiple-energy-form energy system scenario exploration. Its cloud-based, collaborator-sharable data model, intuitive interface and visualization capabilities facilitate collaboration and communication among teams, with experts, and among diverse stakeholder groups exploring energy system implications from district to national-scale models. This training session was presented to the National Aeronautics and Space Administration (NASA) to help them understand how capacity expansion modeling can help them develop single site/distribution analysis of energy to regional airports.
This slide deck is part of a broader program focused on supporting Indian states with long-term power system planning. More information about this program can be found at the National Renewable Energy Laboratory's "Supporting India's States With Renewable Energy Integration" web page at https://www.nrel.gov/international/india-renewable-energy-integration.html. The power sector in Uttar Pradesh, India's most populous state, is poised to transform over the next few decades due to a combination of national and state-level policies impacting both the supply and demand of electricity. The Government of Uttar Pradesh has policies and plans to develop in-state solar PV, pumped storage hydropower, and green hydrogen. Power system policymakers and utilities in Uttar Pradesh are faced with the challenges of planning a system that incorporates increasing amounts of renewable energy and storage resources, meets rising electricity demand due to economic development and green hydrogen production, and satisfies operational and reliability requirements. To support these various objectives, the National Renewable Energy Laboratory (NREL), RMI, and the Uttar Pradesh New and Renewable Energy Development Agency (UPNEDA) evaluated the least-cost pathways for the state's power sector through 2050. NREL developed a capacity expansion model that identifies investment and operational decisions for every year (2024-2050) for all of India, with detailed representation for the state of Uttar Pradesh, which can provide a framework for recurring planning studies. The purpose of this slide deck is to supplement the main study (published in May 2024) with additional modeling scenarios to explore hydrogen flexibility.
Well-designed electricity markets play a crucial role in maintaining reliable electric power systems, which are critical in modern society. Here, this study examines the impact of different electricity market designs and clean energy incentive schemes on supporting renewable energy integration and achieving clean energy goals. To this end, we utilize a game-theoretical generation expansion planning model where generation companies make investment and retirement decisions to maximize their expected profit. The model is structured as an equilibrium problem with equilibrium constraints (EPEC) and solved using a diagonalization approach combined with progressive hedging. We analyze three types of electricity market designs: an energy-only market, a capacity market, and a clean energy market, and consider a wide range of market parameters resulting in 14 total scenarios. Wind and solar capacity comprise the majority of new investments in all considered scenarios, but the resultant system planning reserve margin (PRM) can differ significantly depending on market parameters. We also find that profit-driven investments lead to lower PRMs than a traditional system cost minimization approach. These individual scenario results further demonstrate how different market designs and clean energy incentive schemes may influence investor decision-making and impact resource adequacy throughout the clean energy transition.
Karnataka ranks fourth among Indian states in installed capacity of renewable energy, which accounts for over 50% of its resource mix. The state is positioned to play a leading role in India's energy transition and contribute to meeting national targets for renewable energy thanks, in part, to ambitious state goals to develop Karnataka's abundant zero carbon energy resources and plans to phase out new investments in thermal plants. Power system stakeholders in Karnataka are faced with the challenge of planning a generation portfolio that accounts for the variability of wind and solar resources, meets increasing and shifting electricity demand, and satisfies operational and reliability requirements. The objective of this study is to evaluate least-cost pathways for Karnataka's electric power system through 2050. The capacity expansion model developed for this study identifies power system investment and operational decisions for every year (2020-2050) for all of India, with detailed representation of the state of Karnataka. The study led to five high-level results and provides a framework for recurring planning studies to meet changing system conditions and needs.
In this policy brief, we assess the prerequisites for India to achieve 450 GW of solar and wind cumulative installed capacity by 2030. We examine requirements such as availability of land, new transmission buildout, financing and pace of deployment, as well as the impact on grid reliability and cost of generation. We also examine the impact of policies promoting domestic manufacturing. Deploying 307 GW of solar and 142 GW of wind capacity would use only about 1.25% of land that is categorized as barren or waste, which is equivalent to about 0.22% of the total land area in India. Because of the good solar resource across large swaths of India, the solar energy buildout—and thus the land use—potentially can be spread out. India would need about 280 GW of new interstate transmission capacity by 2030, a little over double the transmission expansion that has already been planned through 2025. However, most of the new transmission buildout is driven by the near doubling of electricity demand between 2020 and 2030. The total investment needed (in generation and storage resources) to realize this target is around USD 26.5 billion annually, which is 20% lower than the annual investment in India’s power sector across all generation resources between 2015 and 2019. We estimate that using domestically manufactured panels instead of imported panels may increase solar PPA prices by about 10%–15% in the medium term, but solar power would still be a cost-effective way to meet growing demand instead of building new fossil fuel-based power plants, because the price of electricity from solar plants has fallen below the variable cost of most existing coal units. To reach this target, India would need to build about 35–40 GW of solar and wind capacity every year in this decade. India’s power sector achieved a pace of capacity addition of 22 GW per year in the previous decade (including thermal and renewable). Policy and regulatory measures would be needed to increase the pace of deployment.
Decarbonizing the electricity system to zero-carbon emission is crucial for climate change mitigation. Previous studies have shown that such a transition in the United States (U.S.) may lead to higher system cost compared to a business-as-usual case, but it is not well-known how the cost of electricity generation varies at sub-regional level under the transition, and studies have rarely evaluated the trade-off between the cost and avoided climate damages, as well as the potential roles of negative emission technologies (NETs) in the electricity decarbonization. In this work, we present a regionally resolved national model to quantify the cost of decarbonizing the U.S. electricity system under a set of possible scenarios. The results show that, compared to the reference scenario without a decarbonization policy, reaching zero CO 2 emission by 2050 would incur, depending on the scenarios, 335-494 billion USD additional cost to the U.S. electric power sector during 2020-2050. The regional costs of electricity generation ranges from 2.4 to 4.7 cent/kWh, largely due to the generation profiles and renewable resources availability of those regions. The additional costs can be translated to an average CO 2 abatement cost of 29-59 USD/metric ton CO 2 (with 2%-7% discount rates), which are comparable to the social cost of carbon in the literature at around 4% discount rate. The results also show that the cost of mitigating the last few percent CO 2 emission from the U.S. electricity system may exceed the costs of NETs, indicating an opportunity for NETs to contribute to electricity decarbonization.