基于负荷预测的储能功率分配优化策略

Optimization strategy of energy storage power allocation based on load forecasting

针对目前电力系统调峰、调频储能电站容量有限的情况,文章提出了一种基于负荷预测的储能电站调峰、调频功率分配策略。首先建立了基于遗传算法(Genetic Algorithm,GA)优化BP神经网络的负荷预测模型,对电力系统中的负荷进行精准的预测,为储能电站参与调峰、调频提供计划调度参考;在此基础上,计及储能电站参与调峰、调频辅助服务的收益以及成本,建立储能电站参与调峰、调频功率分配经济模型,并利用粒子群算法对其进行优化求解,确定储能电站最优分配结果;最后,基于MTALAB仿真平台,验证了所提功率分配策略的有效性。

With the increasingly complex structure of power system,the load characteristics of the system have changed greatly,which brings difficulties to the peak regulation and frequency regulation of power system.With the development of energy storage technology,energy storage has become an effective means to participate in peak regulation and peak regulation ancillary services.In order to solve the problem of limited capacity in power system,this paper proposes a power allocation strategy based on load forecasting.In this paper,a load forecasting model based on Genetic algorithm optimization(GA)BP(Back Propagation Network)neural Network(bpnn)is established to forecast the load in power system accurately,which provides a reference for the planning and scheduling of energy storage power stations participating in peaking and frequency modulation,on this basis,taking into account the benefits and costs of energy storage power stations participating in peak regulation and frequency modulation auxiliary services,an economic model of energy storage power stations participating in peak regulation and frequency modulation power allocation is established and optimized by particle swarm optimization algorithm,determining the optimal distribution result of energy storage power station.Finally,based on the MTALAB simulation platform,the effectiveness of the proposed power allocation strategy is verified.