摘要
为了提高电力市场需求响应效率,提高成本收益,增强电力负荷特性,本文将利用遗传神经网络建立需求响应模型,提前一天获取电力市场需求响应情况,对电费影响较大的负荷进行调整。根据电力市场需求响应情况,设计峰谷组合的电力套餐,满足不同用户在用电方面的不同需求,优化需求响应中的用电有限理性行为。通过约束基础负荷与可调负荷,提高电力市场需求响应效率。实验结果表明,应用该优化方法后,电网提高负荷率始终高于对照组,有效地缩小了电网峰谷差,提升了需求响应效率,效果显著。
In order to optimize the efficiency of electricity market demand response,improve response potential,costbenefit,and electricity load characteristics,a genetic neural network-based optimization method for electricity market demand response efficiency was studied.Using genetic neural networks to establish a demand response model,obtain the demand response situation of the electricity market one day in advance,and make adjustments to loads with significant impact on electricity bills.Based on the demand response of the electricity market,design a peak valley combination of electricity packages to meet the different needs of different users in terms of electricity consumption,and optimize the rational behavior of electricity consumption in demand response.Refine and optimize the efficiency of electricity market demand response by constraining the basic load and adjustable load.The experimental results show that after the application of the proposed optimization method,the load increase rate of the power grid is consistently higher than that of the control group,effectively reducing the peak valley difference of the power grid,improving demand response efficiency,and achieving significant optimization results.
作者
孙立元
SUN Liyuan(Measurement Center,Yunan Power Grid Co.,Ltd.,Kunming Yunnan 650200,China)
出处
《信息与电脑》
2023年第22期29-31,共3页
Information & Computer
关键词
遗传神经网络
电力市场
需求响应
genetic neural network
electricity market
demand response
optimization
efficiency
