摘要
全球能源危机促进了新能源电网的发展。研究风电/储能系统的经济优化运行问题,使电网的综合经济效益达到最优。对风/储配电网系统结构进行了分析,总结其优化运行的关键技术,在考虑需求侧响应、实时电价、储能寿命的基础上,建立风/储配电网优化运行模型,采用改进微分进化算法进行模型求解,进而制定优化调度策略。最后以风/储配电网系统进行仿真分析,与基本微分进化算法和遗传算法进行了比较,验证了所建模型的可行性。仿真结果表明,日调度周期内,改进微分进化算法(IDE)方案中的ES容量波动较小,变化更加平稳,SOC的最大值和最小值分别为80%和39%,相比遗传算法(GA),ES寿命等效成本IDE方案更低,差额为6 476元;综合各个时段的购电成本、售电收益、寿命成本等因素,IDE方案的调度成本要比GA方案少9 325元。从负荷角度来看,需求响应将负荷差从5.15 k W降低到3.91 k W,实现了削峰填谷。从储能的角度来看,有无储能对园区的调度策略影响很大,无储能的状态下,造成电能供给不足,成本增加22 526元,导致经济效益和环境效益下降。基于IDE算法的改进经济调度策略能够更好地平衡各类成本,提高系统经济效益和ES寿命。
In recent years,the global energy shortage and environmental pollution become more and more serious.There is growing concern for the development of the power grid is facing more severe challenge.Therefore,it is important to research economic optimal operation problem of wind power/energy storage systems,and make the comprehensive economic benefits of power grid achieve the top level.At present,the trend in the global energy sector has turned to a new type of clean power source that is environmentally friendly.In China,while the scale of renewable energy development continues to expand,it has caused the problem of the mismatch between growth in power installation capacity and growth in demand-side electricity consumption.In addition,the renewable energy consumption market is in an immature stage of development,the export channel is not smooth,and there exist other factors causing that the renewable energy output is limited and the abandonment of wind/photovoltaic energy resource is serious.With the development and application of new power sources in the actual power grid,this also puts forward new requirements for the planning and operation of traditional power grids.This paper analyzes the structure of wind power/energy storage systems,and summarizes the key techniques of optimal operation which are demand-side response technology and energy storage technology.There are 2 steps in the establishment of models,and the details are as follow:First,a model of demand-side response is built,which depends on load reduction,load reduction climb rate and total reduction.The second model is the total principal balance model of wind power.In addition,the third one is the energy storage cost model for the life of energy storage equipment.It just ends up with a model which combines the second one and the third one-optimized operation model of electric distribution network for wind power/energy storage.It is constrained by power balance,transmission power between systems,and operation behavior for energy storage systems.In order to solve the established model,this paper mentions a differential evolution algorithm(DEA).After introducing the basic DEA,some steps are described in detail.The steps include the data initialization,DEA mutation operation,cross operation and selection operation.In this paper,an improved differential evolution algorithm(IDEA)is put forward.This greatly improves the economic benefit and the life of energy storage.The problem of early maturity may be solved by solving group transformation.The IDEA was used to solve the model and then the optimal scheduling strategy was developed.Finally,the electric distribution network for wind power/energy storage is simulated.In this study,IDEA was programmed by MATLAB and its results were compared with the basic DEA results.It is proved that IDEA is more beneficial than DEA to maintain population diversity and avoid convergence to local optimum effectively.To illustrate the advantage of IDEA,genetic algorithm(GA)was selected for comparison.The scheduling cost of IDEA scheme is less than that of GA scheme,so the IDEA scheme is better.
作者
田德
陈忠雷
邓英
Tian De;Chen Zhonglei;Deng Ying(State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources,North China Electric Power University,Beijing 102206,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2018年第15期200-206,共7页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家863计划项目智能电网关键技术研发
风电场
光伏电站集群控制系统研究与开发子课题(2011AA05A104)
关键词
风电
优化
模型
需求侧响应
风电/储能系统
优化调度
wind powder
optimization
models
demand side response
photovoltaic/energy storage system
optimal scheduling
