Multi-time Scale Co-optimization Scheduling of Integrated Energy System for Uncertainty Balancing

Secure and economic operation of the integrated energy system (IES) is challenged by the high level of the uncertainty and fluctuation introduced by wind power sources. In this paper, a multi-time scale co-optimization scheduling scheme of IES is proposed, which considers tracking the wind power uncertainty to achieve accurate power balance and optimal economic operation of the whole system. First, a multi-time scale co-optimization model framework is established, and the electric power system, natural gas system and district heating system are coordinated to achieve more flexibility in each time scale. In the day-ahead stage, the optimal unit commitment is determined, furthermore, the operation scheme is adjusted on a rolling basis to track the random fluctuation of wind power in the intra-day stage. In the real-time stage, model predictive control (MPC) is used to achieve precise control, which takes the intra-day scheme as a reference to minimize operating deviations. Besides, the auto regressive moving average (ARMA) model and scenario method are employed to represent the wind power uncertainty by typical scenarios with corresponding probabilities. Finally, simulation results on an IEEE39-NGS20-DHS21 test system demonstrate the superiority of the proposed method in operational economy and wind power utilization, and also verify the effectiveness of the method to satisfy the uncertainty balancing.