Optimal Scheduling of Critical Peak Pricing Considering Wind Commitment

Demand response has been widely implemented as one of the “virtual” control mechanisms to make peak load management more efficient and economic. One of the popular demand response programs is called critical peak pricing (CPP). Relatively simple pricing schemes and convenient implementation within current energy system metering infrastructure make it well accepted by many utilities and load-serving entities (LSEs). In this paper, we investigate the optimal scheduling of CPP events from the perspective of an LSE which has wind energy to sell into the day-ahead market. The goal is to minimize the total operational cost for the whole planning horizon, taking into account the energy purchasing cost, revenue from the CPP, and wind energy sales, as well as imbalance penalties due to wind energy over- and under-commitments. We propose a multi-stage stochastic mixed integer nonlinear programming model. In addition, we perform various analyses of both the special case of a single-stage problem and the general multi-stage problem analytically and experimentally. Our analysis leads to useful operational insights and policy implications on how to manage a renewable-integrated system more efficiently.