基于粒子群算法的电力负荷调度决策方法  

Power Load Dispatching Decision Method Based on Particle Swarm Optimization

作　　者：景欣 杨力 JING Xin;YANG Li(Electric Power Dispatching Center,State Grid Gansu Electric Power Company,Lanzhou 730030 China)

机构地区：[1]国网甘肃省电力公司电力调度中心,甘肃兰州730030

出　　处：《自动化技术与应用》2024年第10期35-38,共4页Techniques of Automation and Applications

基　　金：甘肃省高等学校自然科学研究面上项目(18GSX520039)。

摘　　要：新能源并网条件下,电力负荷调度效率低下,为此,基于粒子群算法设计电力负荷调度决策方法。结合粒子群算法建立电力负荷预测模型,在惯性权重因子的基础上补全所有缺失数据,结合不同的单元模型,设定激活函数,获取电力负荷的预测值,综合用电负荷和电力系统运行成本,制定电力负荷的决策变量,设置目标函数与约束条件,得到电力负荷调度决策结果。实验结果表明,该方法结合实际的电力负荷对火力发电以及蓄电池进行优化调度,运行成本由原本的13 618元降低至10 615元,可见该调度决策方法有效降低电力系统运行成本。Under the condition of new energy grid connection,the power load dispatching efficiency is low.Therefore,the power load dis-patching decision-making method is designed based on particle swarm optimization algorithm.The power load forecasting model is established by combining particle swarm optimization algorithm.All missing data is supplemented on the basis of inertia weight factor.Combined with different unit models,the activation function is set to obtain the predicted value of power load.The power load and power system operation cost are integrated to formulate the decision variables of power load,set the objective function and constraint conditions,and obtain the power load dispatching decision results.The experimental results show that the operation cost of thermal power generation and storage battery is reduced from 13618 yuan to 10615 yuan by using this method to optimize the dispatching of thermal power generation and storage battery in combination with actual power load,which shows that this dispatching decision-making method can effectively reduce the operation cost of power system.

关 键 词：粒子群算法 电力负荷 调度 智能电网 预测 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]