Interval optimization of the day-ahead clearing schedule considering the real-time imbalance power with wind power integration

Large-scale wind power penetration impacts the electricity market in many aspects, especially the day-ahead clearing schedule, due to the uncertainty and low predictability of wind power. Based on forecast intervals of wind power and absorption of wind power with a preference, the day-ahead clearing model of energy and reserve is built and contains 2 objective functions: the minimum clearing cost and real-time imbalance power. The constraints for the clearing schedule and for meeting the uncertainty of wind power in forecast intervals are included. To supply market operators with upper and lower boundary information, the interval optimization model is converted into double-layer nonlinear pessimistic and optimistic models. The improved multiobjective quantum particle swarm optimization (MOQPSO) with a “straight Pareto front” is developed to approximate the real, smooth, and uniform Pareto front. The day-ahead clearing schedule and corresponding reserve capacity are analyzed by testing IEEE30-bus system. The computational times of the improved MOQPSO and general MOQPSO are nearly the same, but the Pareto front is remarkably reformative. Furthermore, the upward/downward energy price in the real-time market can considerably impact the day-ahead clearing schedule.