Day-Ahead Risk Averse Market Clearing Considering Demand Response with Data-Driven Load Uncertainty Representation : A Singapore Electricity Market Study

Demand response program is being implemented in the National Electricity Market of Singapore, which boosts the flexibility of demand side to actively participate in the real-time electricity market. Meanwhile, it is also significant to implement such a program in the day-ahead market, since load providers are able to adjust their hourly purchasing schedules according to the clearing prices. However, uncertain factors should be considered in the DR program of the day-ahead market, such as the uncertain electricity load. Regarding the issue, this paper proposes a day-ahead bidding and clearing framework considering demand response with uncertain and correlated nature of electricity loads. To this end, a data-driven Dirichlet process mixture model (DPMM) is introduced to represent the load uncertainty, which might bring about the economic risk. To further reduce such a risk, a worst-case conditional value at risk ( WCVaR ) is integrated into our proposed framework, and a WCVaR based two-step risk averse market clearing model (MCM) is proposed. Finally, we conduct numerical studies based on the Singapore electricity market. Numerical studies demonstrate the outperformance of DPMM for the uncertain rpresentation, and verify that the proposed MCM could effectively reduce the economic risk while maximizing the social welfare