Reliability versus Renewables: Modeling Decision Making in CAISO and PJM to Identify Market Barriers and Supports to Renewable Energy Adoption in the US

While electrification has been proposed as a key mechanism for combating global warming, in the US, fossil fuels are still the mainstay of the electricity sector. This work seeks to identify market barriers and supports for the adoption of renewable energy. A framework to represent the evaluation process for generating capacity is proposed and applied to two regions: California (CAISO), where legislation requires most electricity to be supplied by renewables by 2030, and the Mid-Atlantic (PJM), where renewables have barely penetrated the market. Generation technologies are ordered via multiobjective optimization using genetic algorithms to resolve a bounded knapsack problem. Price factors for each technology are established. A net present value is assessed for representative projects in each region and technology. Local sensitivity analysis is performed. Results suggest that capacity payments in PJM offer an incentive to fossil-fuel based plants, which calls into question the suitability of carbon pricing in the region. While solar PV has the lowest costs, profitability was highest for natural gas based plants in PJM. Sensitivity is higher in CAISO than in PJM due to lower profit margins. Renewables were selected most frequently in the cost optimization, with natural gas present in ~10% of results.