基于改进多目标粒子群算法的含风电场电力系统优化调度

由于风电具有随机性和波动性的特点，常规电力系统分析、调度及控制方式受到了新的挑战。深入研究了含风电场的电力系统优化调度问题，建立了基于多目标粒子群算法的调度模型，在该模型中考虑发电成本、污染气体排放量及风电场输出功率短期波动引起的系统运行风险，在约束条件中加入了正负旋转备用容量，从而减小了风电波动和预测误差对系统的影响程度。算法设计上，通过引入遗传算子对多目标粒子群算法搜索机组组合的能力进行改进，提高了该模型的全局寻优能力。10机系统算例结果分析表明，所提方法正确有效，且能够减少寻优过程中不可行解、解决各优化目标之间的冲突性，使所有目标函数尽可能达到最优。

Power system optimal dispatch considering wind farms based on improved multi-objective particle swarm algorithm

Wind power has the characteristics of randomness and volatility, so conventional power system analysis, dispatch and control meet new challenges. This paper studies the topic of power system optimal dispatch including wind farms in detail, establishes a dispatch model based on multi-objective particle swarm algorithm. This model considers the running risk caused by cost of power generation, emissions of polluting gases, and short-term fluctuations of wind farm output power, adds reserve positive and negative rotation capacity into constraints, thus reduces the impact of power fluctuations and prediction error on the system. The searching unit ability of multi-target particle swarm algorithm is modified by mixing genetic operators in algorithm design. The capability of global optimization of the model is sharply improved. The simulation result of ten-generator system shows that the proposed method is correct and effective, and it can not only reduce the infeasible solutions in the optimization process, but also resolve the conflict between the various optimization targets, ensuring all the objective function as optimal as possible.