The paper addresses the inefficiencies in large-scale energy systems planning models, traditionally based on linear programming (LP) or mixed integer linear programming (MILP). These models often struggle with tractability due to necessary abstractions that compromise result quality. To overcome this, the authors introduce a novel Benders decomposition approach. This method separates investment from operational decisions and decouples operational time steps using budgeting variables, enabling parallel processing of subproblems and accommodating policy constraints over time. This new approach significantly improves runtime, scaling linearly with temporal resolution, and shows marked runtime reductions for all MILP and some LP formulations, varying with problem size. Beyond energy, this algorithm is applicable to planning in domains like water, transportation, and production processes. Notably, it can tackle large-scale problems otherwise intractable. The enhanced resolution achieved through this method reduces structural uncertainty, thereby improving the accuracy of planning and investment recommendations.
The Inflation Reduction Act (IRA) in the U.S. offers significant incentives for low-carbon hydrogen and liquid fuels, impacting their cost-competitiveness by the early 2030s. This study examines the IRA’s effects on producing hydrogen and synthetic liquid fuel from natural gas, electricity, biomass, and ethanol. Findings show that with IRA credits, green hydrogen and blue hydrogen (from natural gas with carbon capture) become cost-competitive with traditional gray hydrogen. Biomass-derived hydrogen isn’t cost-competitive under current IRA provisions. However, if biomass gasification with carbon capture could claim IRA credits, it would be cheaper than gray hydrogen. For synthetic liquid fuels to compete with petroleum fuels, the IRA’s clean fuels credit, ending in 2027, requires extension or additional policy support. The subsidies per unit of CO2 mitigated for these pathways, except electricity-derived synthetic fuel, range from $65 to $384 per ton, within or below U.S. estimates of the social cost of carbon for 2030–2040.
To meet ambitious global decarbonization goals, electricity system planning and operations will change fundamentally. With increasing reliance on variable renewable energy resources, energy storage will probably play a critical accompanying role to help balance generation and consumption patterns. As grid planners, non-profit organizations, non-governmental organizations, policy makers, regulators and other key stakeholders commonly use capacity expansion modelling to inform energy policy and investment decisions, it is crucial that these processes capture the value of energy storage in energy-system decarbonization. Here we conduct an extensive review of literature on the representation of energy storage in capacity expansion modelling. We identify challenges related to enhancing modelling capabilities to inform decarbonization policies and electricity system investments, and to improve societal outcomes throughout the clean energy transition. We further identify corresponding research activities that can help overcome these challenges and conclude by highlighting tangible real-world outcomes that will result from pursuing these research activities.
This paper analyzes how the rise of wind and solar power impacts electricity market design, focusing on the shift towards carbon-free technologies. The growth of renewables is expected to increase price volatility daily and seasonally. Traditionally, market designs don’t consider volatility a problem. However, the potential for higher revenue volatility could increase investment costs in competitive markets, raising doubts about the sustainability of such models as renewables grow. We introduce a stochastic equilibrium model with financial entities providing hedging for generation capacity investments. This model uniquely calculates the cost of capital based on revenue volatility and market participants’ risk measures. Initial findings suggest that systems dominated by renewables might have lower investment risks due to less fuel price uncertainty. However, the risk reduction isn’t uniform across all resource types. The paper highlights that increased risk for peaking and backup resources could result in lower reliability in future modeled electricity systems.
The Inflation Reduction Act (IRA) significantly impacts the U.S. energy sector, particularly the PJM Interconnection. It’s projected to increase PJM’s clean electricity to 60% by 2030, up from 48% without the IRA, necessitating faster renewable energy and transmission growth. The IRA could reduce PJM’s CO2 emissions by 37% from 2019/2021 levels, but emissions may rise post-2032 without extended policy support. The Act also lowers electricity costs in PJM. Deeper decarbonization in PJM will require more rapid expansion of low-carbon resources, possibly including advanced technologies like carbon capture and storage (CCS) or long-duration storage by 2035. Implementing a clean electricity standard and CO2 emissions cap and trade could help achieve up to a 90% CO2 reduction by 2035, while maintaining or lowering electricity costs.
A recent article by Sovacool et al. used cross-sectional regression to examine the relationship between clean energy deployment and national carbon dioxide (CO2) levels. They reported that nuclear energy deployment isn’t significantly linked to lower CO2 emissions, unlike renewable energy, questioning nuclear power’s effectiveness in reducing emissions from fossil fuels. This study critically reviews Sovacool et al.’s claims and methods, identifying several limitations. It conducts a reanalysis using the same data and time frames but with revised cross-sectional and more robust panel data analyses. The findings contradict Sovacool et al., showing that both nuclear power and renewable energy are associated with lower per capita CO2 emissions, with similar magnitude and significance. Sensitivity analysis confirms this association is resilient to potential omitted variables, indicating that both nuclear power and renewable electricity contribute significantly to reducing CO2 emissions.
Advanced geothermal systems have the potential to deliver significant U.S clean electricity by 2050, using innovative drilling and well stimulation. While traditionally providing continuous “baseload” power, these systems are shifting towards flexible generation to compete in markets with increasing variable renewable energy (VRE). This study explores the potential of future geothermal plants with engineered geothermal reservoirs for flexible, load-following generation and energy storage. Using a linear optimization model based on reservoir simulations, the study evaluates plant operations and investment decisions against electricity price trends. Findings reveal that geothermal plants with operational flexibility and in-reservoir energy storage can significantly increase market value, up to 60% more than conventional baseload plants. These reservoirs provide large, efficient energy storage, enabling both short and long-duration storage, ideally during high-price periods. The study’s sensitivity analysis across various subsurface and cost scenarios underscores the enhanced value of flexible geothermal energy in markets with high VRE penetration.
In electricity systems with high variable renewable energy, flexible operations of natural gas combined cycle (NGCC) power plants with carbon capture and sequestration (CCS) can enhance economic value. A new model efficiently represents these flexible NGCC-CCS plants. It dissects plants into subcomponents with linear constraints for energy and mass balances, and simplifies complex unit commitment (UC) constraints. This study explores linear relaxation of discrete UC variables with a generator clustering method, integrating this into a power system model. A case study shows the hourly operations of NGCC-CCS components and their impacts on environmental and economic performance. The findings reveal that linear relaxation with generator clustering significantly speeds up runtime (18–527 times faster) and minimizes errors. The model demonstrates that flexible NGCC-CCS plants reduce power system operating costs by adjusting output during peak demand, indicating their increased relevance in future grids dominated by renewable resources.
Europe’s reliance on Russian natural gas, highlighted by the Russian-Ukraine conflict, poses a significant energy security risk. To address this, Europe must urgently reduce its dependency on Russian fossil fuels through demand reduction, alternative energy sources, and rapid clean energy infrastructure development. The European Commission’s REPowerEU plan targets a two-thirds reduction in Russian gas imports by 2022 end, but stops short of total elimination until 2027 and lacks a detailed policy analysis for immediate energy security. A new report offers detailed strategies for the EU and the UK to quickly sever Russian gas dependence. It suggests enhancing the REPowerEU plan with additional gas reductions for electricity and adjusting gas storage targets. This report, aligning with other studies, outlines immediate strategies for a Russian gas embargo, assessing policy implications and climate commitments. Its integrated electricity and gas models confirm the feasibility of eliminating Russian gas dependency while maintaining supply security.
This study examines the environmental and economic viability of different fuel production methods as part of U.S. net-zero greenhouse gas emission strategies. It conducts lifecycle assessments for hydrogen (H2), synthetic natural gas (SNG), and Fischer-Tropsch liquid (FTL) fuels, considering three sources: biomass with carbon capture and storage (CCS), natural gas with CCS, and renewable electricity. The study finds that hydrogen production, especially from biomass with CCS, is more cost-effective and efficient in reducing greenhouse gas emissions than FTL and SNG. Sensitivity analysis reveals the significant impact of capital costs and capacity factors on the levelized costs of carbon mitigation (LCCM), with natural gas-based processes being particularly sensitive to feedstock price changes. Biomass-based fuels with CCS show the highest potential for carbon mitigation and become increasingly cost-effective at higher carbon pricing. These results emphasize the critical importance of biomass-based hydrogen production with CCS in achieving U.S. net-zero emissions goals.