Day-Ahead Source-Load Co-Optimization Scheduling Considering Multiple Types of Demand Response Resources

With the increasing proportion of new energy in the installed power system, the prediction deviation difficulties caused by its stochasticity and volatility bring great challenges to the safe and economic operation of the power system, and the formulation of the day-ahead unit combination scheme of the power system also faces the risk of uncertainty. Demand response, as an effective power and energy management strategy, can provide more diversified dispatching decision-making means for system operation through decision optimization of adjustable loads, and effectively mitigating the risks of power abandonment and load cutting that the system may face under a high proportion of new energy. This paper models the participation of electrolytic aluminum load and electric vehicles in demand response, and constructs a two-stage robust optimal scheduling model that takes uncertainty and multiple types of demand response resources into account to achieve the source-load co-optimization scheduling. Example analysis results show that the proposed method can give full play to the flexible regulation capability of the load side, effectively cope with the new energy uncertainty risk, and improve the capability of power system to consume new energy.