• Energy Research

A framework to quantify the value of model enhancements (VOMEs) in transmission planning models is proposed and applied to a case study of the large‐scale, long‐term planning of the Western Electricity Coordinating Council (WECC) system. The VOME, which is closely related to the concept of the value of information from decision analysis, quantifies the probability‐weighted improvement in the system performance resulting from changes in decisions that result from model enhancements. The WECC case study shows that it is practical to quantify VOME and illustrates the type of insights that can be obtained. The values of four types of model enhancements are compared. The results show major benefits from considering long‐run uncertainty using multiple scenarios of technology, policy, and economics; these benefits are as much as 14% of total benefits of new transmission built in the first ten years. However, less benefit is obtained from more temporal granularity, more complex network representations, and inclusion of unit commitment constraints and costs. This framework can be applied to quantify the VOMEs in any planning context, such as integrated resource planning.

Energy systems planning models identify least-cost strategies for expansion and operation of energy systems and provide decision support for investment, planning, regulation, and policy. Most are formulated as linear programming (LP) or mixed integer linear programming (MILP) problems. Despite the relative efficiency and maturity of LP and MILP solvers, large scale problems are often intractable without abstractions that impact quality of results and generalizability of findings. We consider a macro-energy systems planning problem with detailed operations and policy constraints and formulate a computationally efficient Benders decomposition separating investments from operations and decoupling operational timesteps using budgeting variables in the master model. This novel approach enables parallelization of operational subproblems and permits modeling of relevant constraints coupling decisions across time periods (e.g., policy constraints) within a decomposed framework. Runtime scales linearly with temporal resolution; tests demonstrate substantial runtime improvement for all MILP formulations and for some LP formulations depending on problem size relative to analogous monolithic models solved with state-of-the-art commercial solvers. Our algorithm is applicable to planning problems in other domains (e.g., water, transportation networks, production processes) and can solve large-scale problems otherwise intractable. We show that the increased resolution enabled by this algorithm mitigates structural uncertainty, improving recommendation accuracy. Funding: Funding for this work was provided by the Princeton Carbon Mitigation Initiative (funded by a gift from BP) and the Princeton Zero-carbon Technology Consortium (funded by gifts from GE, Google, ClearPath, and Breakthrough Energy). Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoo.2023.0005 .

A local jurisdiction that regulates power plant emissions, but participates in a larger regional power market faces the issue of emissions leakage, in which local emissions decrease, but emissions associated with the imported power increase. Border carbon adjustment (BCA) schemes can be imposed on imports in an attempt to lessen leakage. This paper explores the potential cost and emission impacts of alternative BCA policies that could be implemented in the California AB32 carbon pricing system. We focus on cost and emission impacts on the power sector in California and the rest of the Western Electricity Coordinating Council (WECC) region, the latter of which provides approximately 23.5% of California’s electricity requirements. With both a simple schematic model and a detailed WECC generation-transmission expansion planning model for the year 2034 called JHSMINE, we examine the following deemed emission rate schemes for estimating and charging for emissions associated with electricity imports: no BCA, facility (import source)-specific deemed rate, a facility-neutral and constant deemed rate, and a facility-neutral and dynamic deemed rate. Our results suggest that, compared with cases with either no BCA or a BCA using facility-based deemed emission rates, facility-neutral schemes can provide efficiency gains by simultaneously lowering WECC-wide emissions and costs without raising payments by California consumers. Emissions leakage declines greatly. The precise value of the deemed rate affects these gains. One particular facility-neutral dynamic scheme in which rates are set by marginal emission rates external to California provides the greatest gain in economic efficiency. Our results also show the impact of carbon pricing and BCAs on transmission investment economics: California’s unilateral AB32 carbon pricing encourages more interstate transmission expansion because power imports are more profitable; however, BCAs that are cost-effective in lowering total regional emissions will dampen those incentives.

New Jersey is among a vanguard of states pursuing a transition to a 100% carbon-free electricity system. The goal of this study is to provide a detailed assessment of key policy and technology options and choices and their implications for New Jersey���s pathway to 100% carbon-free electricity. In particular, this study examines least-cost pathways to reach New Jersey���s current laws and stated policy goals under a range of possible future conditions and explores the role of in-state solar PV, offshore wind, nuclear power, and imported electricity in the state���s electricity future. Our goal is to provide an independent assessment of costs and trade-offs associated with different choices facing New Jersey stakeholders provide actionable insights for decision-makers. For this study, we use a state-of-the-art open-source electricity system optimization model, GenX, which plans investment and operational decisions to meet projected future electricity demand while meeting all relevant engineering, reliability, and policy constraints at the lowest cost. We create a detailed model of the electricity system of New Jersey, the PJM Interconnection, and neighboring grid regions (15 total zones, two in NJ, nine in PJM) and explore a range of policy, technology, and fuel price scenarios to assess options for New Jersey to reach a 100% carbon-free electricity supply by 2050. Key Findings: 1. A transition to 100% carbon-free electricity is feasible while maintaining reliability and with reductions in bulk electricity supply costs (-29% to -10% vs. 2019 costs under a least-cost approach). 2. The lowest-cost strategy to reach 100% carbon-free electricity supply entails a significant increase in NJ���s dependence on imported electricity. Imports of wind, solar and other carbon-free resources from out of state are generally more affordable than available in-state resources. 3. Electricity demand could increase significantly (up to +70% total sales and +85% peak demand), and patterns of consumption shift dramatically (from summer afternoon to winter overnight peak demand) due to electrification of vehicles and buildings consistent with NJ economy-wide climate goals. 4. The lowest-cost pathway to 100% carbon-free electricity departs from NJ���s current policy approach, which prioritizes in-state and distributed generation (e.g., solar, offshore wind, nuclear). 5. Import dependence can be reduced by requiring in-state renewable resources and preserving the state���s existing nuclear reactors; the most affordable strategy to prioritize in-state resources increases bulk electricity supply costs by 7-10% relative to the least-cost 100% carbon-free pathway, but still results in costs comparable to or lower than today (-24% to -1% vs 2019). 6. If more states in the region pursue parallel deep decarbonization goals, the costs of reaching 100% carbon-free electricity in NJ increase by 16-20% in 2050, as greater demand for clean electricity across the region drives up import costs and NJ relies more on in-state clean energy resources. Bulk electricity supply costs in 2050 range from -17% to +5% relative to 2019 costs if all states in the region pursue 100% carbon-free electricity. Correction: This version, published March 26, 2022, corrects an error in the calculation of 2019 New Jersey bulk electricity supply costs used as a benchmark throughout this report. All depictions of 2019 costs and comparisons to this benchmark are updated and corrected in this version.

A generation and storage expansion co-optimization model is formulated as an MILP. This expansion model is characterized by the inclusion of unit commitment modeling, such as the minimum run requirement, the ramp-rate limit, minimum up/down time and start-up/shut-down costs. Storage operation considering energy and power capacity limits and energy losses are also modeled. Storage and generation expansion is modeled as continuous variables, and an operating reserve requirement is included. The new formulation is tested in the IEEE RTS-24 test system, and the numerical results show the importance of considering unit commitment in generation and storage operation expansion modeling. Furthermore, the complementary and substitution relationships between intermittent resources and storage are quantified using the model; sometimes storage enhances the values of wind, but in other circumstances the reverse is true.

The problem of whether, where, when, and what types of transmission facilities to build in terms of minimizing costs and maximizing net economic benefits has been a challenge for the power industry from the beginning-ever since Thomas Edison debated whether to create longer dc distribution lines (with their high losses) or build new power stations in expanding his urban markets. Today?s planning decisions are far more complex, as grids cover the continent and new transmission, generation, and demand-side technologies emerge.

As more variable renewable energy (VRE) and energy storage (ES) facilities are installed, accurate quantification of their contributions to system adequacy becomes crucial. We propose a definition of capacity credit (CC) for valuing adequacy contributions of these resources based on their marginal capability to reduce expected unserved energy. We show that such marginal credits can incentivize system-optimal investments in markets with installed capacity requirements and energy price caps. We simulated such markets using a LP-based capacity expansion planning model with convexified unit commitment (UC) constraints and ES. The impacts of UC and ES on capacity credits are investigated. Furthermore, we analyze technology and system cost distortions resulting from implementing inaccurate CCs in the capacity market. The results show that ignoring UC constraints can overestimate the CCs for VRE and ES. Building ES increases the CCs of VRE resources with higher capacity factors and a negative correlation with load. Assigning the wrong credit to VRE can significantly distort resource mixes and system cost. Implementing the proposed CCs can, in theory, eliminate those distortions and achieve the same overall optimum as a theoretical market without energy price caps.

This study employs an electricity system capacity panning model with detailed economic dispatch and unit commitment decisions/constraints to quantitatively answer two key questions: How does the enactment of the federal Inflation Reduction Act of 2022 impact the cost of electricity, greenhouse gas emissions, and investment in electricity capacity in the PJM Interconnection over the 2023-2035 period? Given new and expanded federal subsidies for clean electricity resources in the Inflation Reduction Act, what additional capacity investments and resource deployment would be required and at what cost for the PJM region to reduce greenhouse gas emissions 80-90% by 2035 while maintaining an affordable and reliable electricity supply? Executive summary: In August 2022, Congress passed and President Biden signed the Inflation Reduction Act (IRA), which enacts a comprehensive set of financial incentives (tax credits, grants, rebates, loans) that support all sources of carbon-free electricity, promote vehicle and building electrification and efficiency, and subsidize carbon capture and storage (CCS). The implementation of IRA means that the full financial weight of the federal government is now behind the clean energy transition. This will have transformative effects on the economics of decarbonization in the PJM Interconnection (and across the United States). IRA will spark a new, sustained period of growth in PJM electricity consumption, which could rise ~19% from 2021 to 2030. The law also subsidizes the cost of deploying new renewable energy capacity and maintaining the region’s existing nuclear fleet. As a result, this study finds that clean electricity could supply 60% [58-66% across sensitivities] of PJM demand in 2030, up from 48% [43-61%] without enactment of IRA. However, realizing this potential will require a dramatic acceleration in the pace of wind and solar interconnection and transmission expansion in the PJM Interconnection. The growth of lower-cost, carbon-free electricity under IRA will significantly reduce CO2 emissions from PJM power generation, which could fall 37% [3-66%] from 2019/2021 levels. In contrast, PJM emissions would increase 12% [0-15%] from 2021 levels without IRA. However, PJM emissions may rebound after 2032 when a production tax credit for existing nuclear reactors established by IRA is set to expire. Unless equivalent policy support is extended beyond 2032, our modeling finds 12 GW [0-33 GW] of the PJM nuclear fleet is likely to retire by 2035, with new natural gas capacity and generation increasing to fill the resulting gap and meet growing demand, reversing some of the emissions progress achieved through 2030. In addition to driving down greenhouse gas emissions, IRA also lowers the cost of electricity supply in the PJM region. We find the average cost of bulk electricity supply for PJM load serving entities (LSEs), including transmission expansion and state policy requirements, will be about $42/MWh [~$40-45/MWh] in 2030, about 5-10% lower than without IRA, and well below costs paid in 2019 ($50.2/MWh) and 2021 (~$61/MWh). The primary sources of cost savings are reduced wholesale energy prices, lower costs to meet state clean energy policy goals (due to federal subsidies), and growing demand (which spreads fixed costs over more MWh). While IRA puts the PJM region on a path to lower-cost electricity and lower greenhouse gas emissions, the new federal policy is not sufficient to drive deep decarbonization of the PJM interconnection on its own. Fortunately, by subsidizing the cost of all new carbon-free electricity resources, IRA also makes it cheaper and easier for PJM states to reduce emissions further while preserving affordability. Part 2 of this study presents a cost-optimized blueprint of the additional capacity investments and resource deployment required for the PJM region to deeply decarbonize over the 2023-2035 period. Specifically, we apply two stylized policy constraints and model the evolution of the PJM capacity mix and operations to meet those constraints: A clean electricity standard (CES) requiring increased shares of carbon-free electricity generation in the region (55% clean share by 2025, 70% by 2030, 85% by 2035), and; A CO2 emissions cap and trading scheme (cap & trade) requiring decreasing region-wide emissions (58% below 2005 emissions by 2025, 80% by 2030, 95% by 2035) This study finds that, due to passage of IRA, the PJM region could cut CO2 emissions from power generation by 80-90% by 2035 while keeping average bulk electricity supply costs for LSE’s comparable to or lower than levels experienced in recent years (2019 & 2021). However, deep decarbonization in the PJM region will require much more rapid expansion of low-carbon electricity resources and supportive transmission expansion above and beyond the rates of deployment made economical by IRA. By 2035, the region will also likely deploy more advanced ‘clean firm’ resources like gas power plants with carbon capture and storage (CCS) or long-duration electricity storage technologies (LDS), to replace coal- and gas-fired power capacity. We also identify and map several affordable resource portfolios and spatial patterns for clean electricity resource siting across the PJM region, demonstrating that the region has some flexibility to address local priorities and concerns.