Modeling Demand Complexities Driven by Net-Zero Emission Initiatives for Generation Adequacy Assessment

Abstract

Abstract

Conventional generation adequacy methods rely on historical load patterns and peak demand scaling assumptions. The integration of EV and EHS introduces temporal characteristics that differ significantly from historical patterns. These new loads show strong weather dependencies, creating correlations with weather-dependent renewable energy sources (RESs) and affecting system reliability. This requires a novel approach to incorporate correlations among multiple load types and RESs into the adequacy assessment framework. This paper presents a hybrid method for developing system load models that combines past performance data for conventional loads with predictive load models for decarbonized loads. The method is assessed on the Roy Billinton Test System (RBTS) and the IEEE Reliability Test System (RTS) to evaluate the reliability impacts of various decarbonized load growth scenarios. Results reveal that load-shape characteristics and temporal correlations significantly influence system adequacy, highlighting the insufficiency of existing load modeling approaches. The integration of decarbonized loads and RES amplifies the impact of the correlation between supply and demand on reliability indices. Various flexibility strategies can be applied to decarbonized loads to enhance system reliability, depending on the temporal alignment between decarbonized loads and RES.