Research on multi-objective distributed robust optimization for power market dispatching and maintenance, considering the high integration rate of renewable energy sources
Abstract
The high integration of renewable energy sources significantly increases operational uncertainties in power systems, while traditional stochastic programming and robust optimization methods exhibit limitations when dealing with incomplete probability distribution information. This paper proposes a multi-objective distributed robust optimization method for power market scheduling and maintenance planning that incorporates renewable energy integration. First, Wasserstein distance is employed to construct fuzzy sets representing the probability distributions of renewable energy outputs, capturing statistical characteristics of wind and solar power generation without requiring precise prior assumptions. Second, a two-stage multi-objective optimization model is developed, encompassing conventional power generation scheduling, transmission/transformer equipment maintenance planning, and reserve capacity allocation. The objective function balances operational efficiency, renewable energy integration rates, and system robustness, while using conditional risk measures to quantify operational risks under extreme scenarios. Third, a column-generation and constraint-generation algorithm based on Nataf transformation and scenario aggregation is applied to solve the model. Using an improved IEEE 118-node system as a case study with renewable energy penetration rates of 40%, 50%, and 60%, the proposed method demonstrates significant improvements over traditional robust optimization: expected operating costs are reduced by 12.7%, wind/solar curtailment rates remain below 4.2%, and load losses under worst-case scenarios decrease by 43.6%. Optimal maintenance planning reduces forced outage rates by 21.3%, demonstrating the dual benefits of coordinated scheduling-maintenance decision-making in enhancing both operational safety and economic efficiency of high-renewable-energy systems.