With the large-scale integration of renewable energy, power systems are facing profound changes in operational modes that directly necessitate more flexible energy management strategies within energy-intensive textile manufacturing facilities. This paper proposes a multi-objective distributionally robust optimization method for power market scheduling and maintenance that accounts for high proportion of renewable energy accommodation. This method adopts a datadriven approach to construct a probability distribution ambiguity set considering renewable energy correlations, introduces fuzzy chance constraints to characterize photovoltaic output uncertainty, and builds a multi-objective optimization model that minimizes total cost, maximizes renewable energy accommodation, and minimizes risk. Meanwhile, considering the coupling relationship between power grid maintenance and operation, the system topology is optimized and strictly maintained throughout the entire maintenance duration. While the primary switching actions occur at the start and end times of maintenance plans to avoid frequent operations, continuous N-1 security constraints and power flow balance are enforced across the full maintenance horizon to prevent mid-term reliability violations. The improved IEEE 118-bus system is used as a test case to verify the effectiveness of the proposed method. Results show that compared with traditional methods, the proposed method reduces system operating costs while improving renewable energy accommodation rate by 8.2%, reducing wind and solar curtailment rate by 22.6%, and significantly improving system voltage levels, providing a new solution for scheduling and maintenance of power systems with high proportion of renewable energy.

high proportion of renewable energy, power market scheduling, distributionally robust optimization, fuzzy chance constraints, textile manufacturing