Source-Load Co-Optimization Scheduling of Power Systems Considering Extreme Scenarios

With the increasing penetration of wind power in the power system, the randomness and variability of wind power bring about power balancing difficulties, which makes the stable operation of the power system and the realization of efficient wind energy consumption a major challenge. Flexible resources are introduced to participate in scheduling to increase wind power consumption through the cooperative optimization and effective integration of different resources, and a battery storage adjustable model considering battery life loss and an electrolytic aluminum load adjustable model are established. In order to reduce the impact of wind power uncertainty, a source-load co-optimization scheduling model considering Extreme Scenarios is proposed. The risk cost and comprehensive cost of wind power under a given confidence level are defined. In order to minimize the comprehensive cost, an optimization method for extreme scenarios is proposed to achieve the comprehensive optimization of the model in terms of robustness and economy. The simulation results prove that the proposed model has obvious cost advantages over the traditional scheduling model, and is effective in smoothing the peak-valley difference and improving the wind power consumption.