Our Publications

Wind, solar, and lithium-ion batteries have become more affordable, driving their adoption in the transition to a decarbonized electricity grid. However, these technologies have limitations in providing consistent power during extended periods of high demand when renewable generation may be insufficient. This challenge has led to increased interest in long-duration energy storage (LDES). LDES encompasses a wide range of options, making it difficult to identify promising pathways or prioritize research efforts. In recent research, we and our colleagues employed a comprehensive long-term electricity system planning model to analyze various combinations of five key LDES parameters across 14 scenarios. This extensive analysis helps navigate the diverse landscape of LDES technologies and provides essential cost and performance targets. It offers valuable insights to guide innovation and commercialization efforts, ensuring the development of efficient long-duration energy storage solutions for reliable grid operations, even in scenarios of prolonged high demand.

Long-duration energy storage (LDES) is a potential solution to intermittency in renewable energy generation. This study evaluated the role of LDES in decarbonized electricity systems and identified the cost and efficiency performance necessary for LDES to substantially reduce electricity costs and displace firm low-carbon generation. We find that energy storage capacity cost and discharge efficiency are the most important performance parameters. Charge/discharge capacity cost and charge efficiency play secondary roles. Energy capacity costs must be ≤US$20 kWh–1 to reduce electricity costs by ≥10%. With current demand profiles, energy capacity costs must be ≤US$1 kWh–1 to displace all modelled firm low-carbon generation technologies. Electrification of end uses in a northern latitude context makes full displacement of firm generation more challenging and requires performance combinations unlikely to be feasible with known LDES technologies. Finally, LDES systems with the greatest impact on electricity cost and firm generation have storage durations exceeding 100 h.

The global move towards a net-zero emission energy system focuses on reducing CO2 emissions, driven by technological advances and a shift to renewable energy. The U.S. aims to transition from fossil fuels to low-carbon sources, while addressing social and economic aspects for equitable community support. Advances in wind, solar, nuclear power, energy storage, and carbon capture are key. Major emission reductions are expected in electricity, transportation, and home heating, though decarbonizing sectors like aviation and heavy industry remains challenging. This shift is crucial for climate mitigation, potentially cutting global CO2 emissions by 10% and improving health. The U.S. targets net-zero by 2050, in line with global emissions mitigation goals. This report highligts the role of National Academies of Sciences, Engineering, and Medicine in overseeing this transition, advocating diverse energy solutions and inclusive policies, highlighting the importance of technological, economic, and social changes for an equitable transition.

The Net Zero America study aims to inform and ground political, business, and societal conversations regarding what it would take for the U.S. to achieve an economy-wide target of net-zero emissions of greenhouse gases by 2050. Achieving this goal, i.e. building an economy that emits no more greenhouse gases into the atmosphere than are permanently removed and stored each year, is essential to halt the buildup of climate-warming gases in the atmosphere and avert costly damages from climate change. A growing number of pledges are being made by major corporations, municipalities, states, and national governments to reach netzero emissions by 2050 or sooner. This study provides granular guidance on what getting to net-zero really requires and on the actions needed to translate these pledges into tangible progress.

The electric power sector is currently undergoing several important transitions, which individually and collectively have the potential to transform the design, operation, and characteristics of electricity systems, including: decarbonization of electricity supplies; increased adoption of variable renewable energy and distributed energy resources; digitization of power systems; and electrification of greater shares of heating, transportation, and industry. In the face of these transformations, many conventional electricity resource capacity expansion models are no longer adequate for rigorous decision support and policy analysis. This working paper describes the formulation of “GenX,” a highly-configurable electricity resource capacity expansion model that incorporates several state-of-the-art improvements in electricity system modeling to offer improved decision support for a changing electricity landscape. GenX is a constrained optimization model that determines the mix of electricity generation, storage, and demand-side resource investments and operational decisions to meet electricity demand in a future planning year at lowest cost subject to a variety of power system operational constraints and specified policy constraints, such as CO2 emissions limits. The appropriate level of model resolution with regards to chronological variability of electricity demand and renewable energy availability, power system operational detail and unit commitment constraints, and transmission and distribution network representation each vary for a given planning problem or policy question. As such, the GenX model is designed to be highly configurable, with several different degrees of resolution possible on each of these three key dimensions. The model is capable of representing a full range of conventional and novel electricity resources, including thermal generators, variable renewable resources (wind and solar), run-of-river, reservoir and pumped-storage hydroelectric generators, energy storage devices, demand-side flexibility, and several advanced technologies such as high temperature nuclear reactors and carbon capture and storage. The model has been implemented in Julia Language.