Reliability versus renewables: Modeling costs and revenue in CAISO and PJM

Abstract

While electrification has been proposed as a key mechanism for combating global warming, fossil fuels are still the mainstay of the electricity sector in the US. Power generation technologies were evaluated in two regions: California (CAISO) and the Mid-Atlantic (PJM). 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, including capacity payments and renewable energy credits in PJM. Local sensitivity analysis is performed. Renewables were selected most frequently in the cost optimization with no constraints on variable renewable energy. However, when solar photovoltaic and onshore wind generation are constrained to 30–70 % of the total capacity addition, natural gas is the most selected technology. Profitability was highest for natural gas based plants in PJM except where solar renewable energy credits are available. Selectivity of incentives to natural gas plants from PJM capacity payments are intensified by the recent order to extend the minimum offer price rule. This calls into question the suitability of carbon pricing in the region.