Research on Optimal Rolling Scheduling with over/under-Estimated Cost of Reserve Based on Chance Constrained Programming

In order to reduce the impact of wind power output prediction error on system operation, the forecast error as a random variable is organically linked to the reserve capacity regulation based on the combination of risk theory and reliability. Firstly, a day-ahead dispatching schedule is proposed. Then the joint probability of wind power prediction error due to leading time for forecast is analyzed. The mathematical analysis method is used to derive the overestimation and underestimation costs of the up- and down-spinning reserve, which are the items in the objective function of dispatch model. According to the rolling update of renewable energy output forecasting information, an intra-day rolling optimization method with power flow constraints satisfaction is proposed. A PSO algorithm with the strategy of nonlinear decreasing inertia weight is adopted for a better convergence of the intra-day model. Finally, an improved IEEE-RTS 24-node distribution network system is used as an example for simulation analysis. The simulation results show that the proposed method can effectively control the risk of prediction error by quantifying the reserve regulation.