基于改进细菌群体趋药性算法的风-蓄-火联合调度

Research on Joint Dispatch of Wind-Storage-Thermal System Based on Improved Bacterial Colony Chemotaxis Algorithm

具有随机性与波动性的风电直接接入电网后,给电网的调度运行带来很大的风险。针对风电出力的随机性,结合抽水蓄能电站削峰填谷特点,利用风电场景模拟风电出力波动,编制日前发电计划,引入时前调度模型修正各机组出力,建立了含风-蓄-火联合系统的优化调度模型,并利用改进的细菌群体趋药性算法求解该模型。用IEEE-10机组标准算例进行仿真,仿真结果验证了所建调度优化模型的合理性及算法的有效性,模型可供制定含风电和抽水蓄能的电力系统发电调度方案时参考。

Wind power has the characteristics of randomness and volatility. When integrated to the grid, the wind power brings a high risk to the dispatcher. With consideration of the randomness of wind power output and the peak load shifting characteristics of pumped storage power station, a day-ahead generation plan can be made by simulating the wind power fluctuations scene. The hour-ahead scheduling model can be introduced to amend each unit output so as to establish optimal scheduling model of the wind-storage-fire joint system. Meanwhile, the improved bacterial colony chemotaxis algorithm is then introduced to solve the model. Finally, IEEE-10 crew Benchmarks simulation is applied for the analysis. The simulation results demonstrate the effectiveness and rationality of the optimal scheduling models, and can provide a theoretical reference for scheduling the dispatching scheme of the power system containing wind power and pumped storage.