一种改进的PSO算法在含高比例风电系统中的应用

Application of an Improved PSO Algorithm in the Highproportioned Wind Power System

考虑到风电场等不确定性电源的机组调度策略,高比例风电的接入给多目标节能减排发电调度带来了严峻的挑战。为了促进可再生能源的消纳,该文在基本粒子群算法(Particle Swarm Optimization,PSO)的基础上,通过改进无标度网络生成过程中的择优与增长连接机制,提出了一种基于高聚集度的无标度网络邻域结构的粒子群算法(PSO with Highly-Clustered Scale-free Neighborhood,HCSN-PSO),并采用条件风险价值量化出风电不确定性带来的风险损失,综合考虑发电成本、弃风成本和风险损失建立了多目标机组调度策略的数学模型。最后根据修改的IEEE39节点算例进行了仿真计算,结果证明了所提模型与改进算法在解决高比例风电场的多目标机组调度策略上具有一定的有效性和实用性。

Considering the unit scheduling strategy of power supply with uncertainty such as wind power farms,the assess of high-proportioned wind power brings severe challenges to multi-target energy-conservation emission-reduction power generation dispatching.In order to promote the consumption of renewable energy,based on the basic particle swarm optimization(PSO),this paper proposes a PSO with highly-clustered scale-free neighborhood(HCSN-PSO)by improving the connection mechanism of preferential and growth in the process of scale-free network generation,uses conditional risk value to quantify the risk loss caused by the uncertainty of wind power,establishes a mathematical model of the multi-target unit scheduling strategy by comprehensively considering power generation cost,curtailment cost and risk loss,and finally carries out simulation calculation according to the modified IEEE39 node example.The results show that the proposed model and improved algorithm have certain effectiveness and practicality in solving the multi-target unit scheduling strategy of high-proportioned wind power farms.