Multi-objective optimization scheduling based on renewable energy uncertainty scenarios for the reliability of power system operation

Addressing the power system scheduling problem within the context of economic and emission factors, and taking into account the influence of renewable energy generation on stability and security, a multi-objective optimization scheduling framework is proposed grounded in uncertainty scenarios. Utilizing wind power as an exemplar, characterized by its typical volatility and instability, generates uncertain wind power scenarios through the application of Latin hypercube sampling. To provide constraints for uncertainty problems in multi-objective optimization scheduling, and thereby achieve high-level penetration scheduling of wind power. The fuel cost, wind curtailment cost, and rotating reserve cost constitute the economic cost target while considering constraints to ensure the reliability of power system operation. Employing a multi-objective gray wolf algorithm, the proposed model is solved, introducing the Pareto front-scale control strategy. The effectiveness of the proposed model is verified by analyzing examples from distributed energy systems and comparing it with several other multi-objective optimization algorithms. It exhibits superior global search capabilities and achieves more optimal Pareto front and compromise solutions. Effectively improve the penetration rate of renewable energy, and ensure the economy and environmental protection of power system energy consumption under the target of reliable operation.