Abstract: To address the impact of uncertainties in both the generation and demand sides of the power system on grid scheduling plans, a two-stage robust optimization model is proposed for integrated source-load uncertainty management. For the generation side, the uncertainties in wind and solar power outputs are considered. Probability density function models are established for wind and solar power, and the Latin hypercube sampling method is employed to generate scenarios and perform scenario reduction, resulting in power output intervals for wind and solar generation. For the demand side, the role of flexible loads in peak shaving and valley filling is considered, and an integrated demand response model based on smart communities is proposed. In the day-ahead stage, aiming to minimize the system operating cost and carbon trading cost while considering source-load uncertainties, a price-based demand response model is developed to determine the day-ahead scheduling plan. In the intra-day stage, based on the optimized results from the day-ahead stage, a two-stage robust optimization model is formulated with the objective of minimizing the operating cost and carbon trading cost of smart communities. The column-and-constraint generation (C&CG) algorithm is employed to convert the objective function, and the Karush-Kuhn-Tucker (KKT) conditions and Big-M constraint method are utilized to transform the max-min optimization problem into a mixed integer linear programming (MILP) model. The correctness of the proposed model and the effectiveness of the algorithm are verified through case studies.

Key words: source charge uncertainty, integrated demand response, two-stage robust optimization, intelligent community, flexible load