基于粗糙集理论的风光蓄互补系统优化模型

为了解决风光波动性对系统安全调度和稳定运行的影响，以系统运行成本最小和系统污染排放量最小为目标，构建风光蓄集成互补系统. 基于粗糙集理论和模糊C均值聚类算法，分别确定多目标调度中经济目标和环境目标的权重；提出基于粒子群变异策略和计及约束边界的信息共享方法的改进粒子群优化(PSO)算法，求解多目标调度优化问题；以我国西南地区某省风光蓄集成互补系统为例开展算例仿真，验证所提模型的科学性和实用性. 研究结果表明，与单目标调度相比，多目标调度兼顾经济性和环境性，所提混合粗糙集-改进粒子群算法的收敛精度更优，提高了系统的经济效益和环境效益. 引入抽水蓄能机组，对于实现系统多能源协同互补运行具有重要的意义.

Optimization model for integrated complementary system of wind-PV-pump storage based on rough set theory

The integrated complementary system of wind - PV - pump-storage considering minimizing the operation cost and pollutant emissions was constructed in order to solve the influence of the wind power and PV generation fluctuation on system operation safety and stability. The weights of the economic targets and the environmental targets were determined respectively based on rough set theory and fuzzy C mean clustering algorithm. An improved particle swarm optimization (PSO) algorithm based on particle swarm optimization and constrained boundary information sharing was proposed in order to solve the multi-objective scheduling optimization problem. The case study was based on an integrated system in southwest China to verify the scientificity and practicability of the proposed model. Results show that the multi-objective scheduling considers both economic and environmental benefits of the system compared with single-objective scheduling. The accuracy of the proposed hybrid rough set-improved particle swarm optimization algorithm is better with an improvement in the economic and environmental benefits of the system. Introducing the pumped storage power station is significant for the cooperative and complementary operation of the multi-energy system.