Cost-Oriented Scenario Reduction for Stochastic Optimization of Power System Operation With High-Penetration Renewable Energy

The growing uncertainty associated with renewable energy generation necessitates effective solutions addressing stochastic optimization of power system operations. Traditional stochastic optimization methods typically take scenario reduction as a preliminary step to alleviate computational burdens, which overlooks the connections between scenario reduction and stochastic optimization. This paper proposes a novel cost-oriented scenario reduction approach for stochastic optimal power flow (SOPF) to enhance the cost-effectiveness of operational decisions while maintaining system reliability. Firstly, an integrated scenario reduction and stochastic optimization (ISRSO) framework is established for SOPF, in which the reduced scenarios serve as a bridge between uncertainty quantification and uncertainty-aware optimization. Subsequently, a coordinated optimization model of scenario reduction and stochastic optimal power flow is formulated to jointly optimize the reduced scenarios and generation schedules, which preserves the probabilistic characteristics of the original scenarios and minimizes the operational cost of SOPF. Then, an alternating optimization algorithm is developed to convert the original nonconvex model into iterative convex problems with enhanced tractability. Simulation results on IEEE 14- bus and 118- bus test systems with realistic renewable energy generation data illustrate the statistical accuracy and cost-effectiveness of the proposed method.