Dynamic analysis of demand curve adjustment and learning in response to generation capacity cost dynamics in the PJM capacity market

We investigate the effect of demand curve adjustments and combustion turbine (CT) cost uncertainty in the PJM Interconnection (PJM) Reliability Pricing Model (RPM). RPM is the PJM capacity market construct that is intended to provide incentives for sufficient generation investment. A dynamic Monte Carlo simulation model, originally used to help design the RPM, is modified to simulate the impacts of dynamics of curve adjustment and capacity costs in the PJM capacity market. To attract new generation investment and retain existing capacity, the demand curve of the PJM RPM is designed to provide more payments when capacity is short in order to maintain a target reserve margin. The RPM includes a provision to adjust the capacity demand curve based on the history of revenues earned that CTs would earn from the energy and ancillary services markets. Our analysis shows that this adjustment contributes to higher generation reserve margins and reduces scarcity costs in the energy markets. A second set of analyses addresses the adaptation of RPM over time to changes in CT costs. One analysis examines the response to possible decreases in capital costs as a result of “learning-by-doing”, while another examines the effect of sudden shocks in the form of large increases or decreases in costs. The results of the learning-by-doing analysis shows that decreasing CT cost results in improved reserve margins and lower consumer payments in the model.