An efficient method to estimate renewable energy capacity credit at increasing regional grid penetration levels

Abstract

The wide scale deployment of variable renewable energy technologies (VREs) offers a pathway to decarbonize the electric grid. One challenge to reliably operating the grid is ensuring that sufficient generating capacity is available to meet demand at all hours. By determining an individual generator's contribution to resource adequacy based on its expected availability when power is needed, the capacity credit for these resources is estimated. The objective of this study is to quantify the contribution of VRE to resource adequacy as a function of VRE penetration, across several regions, technologies, and resources. A computational model was built using the effective load carrying capability (ELCC) method to calculate capacity credit values for regions spanning the contiguous United States. As the deployment of VRE increases, we show its marginal contribution to meeting peak load decreases, which in turn requires additional generating capacity to maintain reliability. In addition, a rapid approximation method is demonstrated to estimate solar and wind capacity credit, relying on the capacity factors during hours of peak net demand. We find that estimates with the lowest error relative to capacity credits calculated using the ELCC method occur using the average renewable resource capacity factors of the top net 10 demand hours, regardless of resource type. Using context-specific values for capacity credit can improve long-term decision making in generation capacity expansion, cultivating more economical long-term resource planning for deep decarbonization.

Keywords

• Capacity credit
• Effective load carrying capability
• Loss of load probability
• Monte Carlo simulation