基于机会约束规划的风电预测功率分级处理  

Classified Treatment of Wind Power Predictive Power Based on Chance Constrained Programming

作　　者：王成福[1] 梁军[1] 张利[1] 牛远方[2] 贠志皓[1] 韩学山[1] 

机构地区：[1]山东大学电气工程学院,山东省济南市250061 [2]山东电力工程咨询院,山东省济南市250013

出　　处：《电力系统自动化》2011年第17期14-19,共6页Automation of Electric Power Systems

基　　金：山东省自然科学基金资助项目(ZR2010EM055)~~

摘　　要：提高现有风电功率预测精度往往难度大、经济性差,而电网调度、风电控制则需要准确的功率曲线。为此,从分析功率预测结果角度提出风电功率分级处理思想,以减少预测误差对相关决策的影响。该分级思想以预测功率为基础,考虑预测误差分布影响,利用机会约束规划方法建立基于预测功率可信度水平的分级模型,将预测功率划分为基荷出力、次级出力及高频出力3个部分。分级处理可在保证最大化利用风功率前提下,区分预测数据中不同可信度水平分量,以此为电网调度、风电功率控制提供决策依据,从而降低决策风险。结合策略迭代、粒子群算法对分级模型进行求解,以某风电场24h数据为例进行模拟分级,所得结果验证了分级思想的可行性、有效性。Improving the accuracy of current wind power prediction is very difficult and uneconomic,but power system dispatching and wind power control require accurate power curve.A novel method for classified treatment of wind farms active power using forecasting results is presented,which can reduce the influence of prediction error on the decision.The new method is based on predictive power.Considering the influence of predictive error distribution,a classified mode based on the reliability of predictive power is founded using chance constrained programming method,and predictive power is classified into three types： base load output,suboptimal output and high-frequency output.With the prerequisite of ensuring maximum use of wind power output,the classified treatment can distinguish different reliability components,supply decision basis for power dispatching and wind power control,and reduce the decision risk effectively.The policy iteration and particle swarm algorithm are used to solve the model,and the classified treatment is simulated by 24 h data of actual wind farms.The results verify the feasibility and effectiveness of the method.

关 键 词：风力发电 预测功率 分级处理 机会约束规划 粒子群算法 

分 类 号：TM614[电气工程—电力系统及自动化] TM715