• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

电力建设 ›› 2022, Vol. 43 ›› Issue (9): 77-86.doi: 10.12204/j.issn.1000-7229.2022.09.008

• 面向新型电力系统的灵活性资源发展关键技术及应用·栏目主持 鞠立伟副教授、谭忠富教授· • 上一篇    下一篇

计及电动汽车与温控负荷需求响应的分层能源系统优化调度

任鑫芳1(), 张志朝1(), 许李天伦2(), 王诗超2(), 刘展志2(), 许方圆3()   

  1. 1.中国南方电网超高压输电公司,广州市 510670
    2.中国能源建设集团广东省电力设计研究院有限公司,广州市 510663
    3.广东工业大学自动化学院,广州市 510006
  • 收稿日期:2022-01-10 出版日期:2022-09-01 发布日期:2022-08-31
  • 通讯作者: 许李天伦 E-mail:renxinfang@ehv.csg.cn;zhangzhichao@ehv.csg.cn;10024422467@qq.com;wangshichao@gedi.com.cn;liuzhanzhi@gedi.com.cn;datuan12345@hotmail.com
  • 作者简介:任鑫芳(1983),男,硕士,高级工程师,从事电网规划及综合能源系统方面的研究工作,E-mail: renxinfang@ehv.csg.cn;
    张志朝(1980),男,硕士,高级工程师,从事电网规划及综合能源系统方面的研究工作,E-mail: zhangzhichao@ehv.csg.cn;
    王诗超(1986),女,硕士,高级工程师,从事电力系统规划方面的研究工作,E-mail: wangshichao@gedi.com.cn;
    刘展志(1993),男,硕士,工程师,从事电力系统规划方面的研究工作,E-mail: liuzhanzhi@gedi.com.cn;
    许方圆(1984),男,博士,副教授,从事电力能源分析方面的研究工作,E-mail: datuan12345@hotmail.com
  • 基金资助:
    广东省自然科学基金资助项目(2021A1515010742)

Optimal Scheduling of Hierarchical Energy Systems with Electric Vehicles and Temperature-Controlled Load Demand Response

REN Xinfang1(), ZHANG Zhichao1(), XU Litianlun2(), WANG Shichao2(), LIU Zhanzhi2(), XU Fangyuan3()   

  1. 1. China Southern Power Grid EHV Power Transmission Company, Guangzhou 510670, China
    2. China Energy Engineering Group Guangdong Electric Power Design Institute Co., Ltd., Guangzhou 510663, China
    3. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2022-01-10 Online:2022-09-01 Published:2022-08-31
  • Contact: XU Litianlun E-mail:renxinfang@ehv.csg.cn;zhangzhichao@ehv.csg.cn;10024422467@qq.com;wangshichao@gedi.com.cn;liuzhanzhi@gedi.com.cn;datuan12345@hotmail.com
  • Supported by:
    Natural Science Foundation of Guangdong Province of China(2021A1515010742)

摘要:

针对电动汽车(electric vehicle,EV)大规模接入电网对电力系统带来的影响,构建了一种基于电动汽车及温控负荷需求响应的分层能源系统管理框架。受到激励的电动汽车集群(electric vehicles, EVs)和温控负荷集群(temperature-controlled load clusters, TCLs)能够快速响应负荷聚合商的调度策略,以此减少大量柔性负荷并网对电网产生的冲击。在基于卷积神经网络和长短期记忆网络混合模型对负荷进行预测的基础上,假设负荷聚合商可通过调度可控柔性负荷来减小实际负荷与预测负荷的误差,并根据制定的负荷调度策略与电力运营商之间进行点对点(peer to peer, P2P)电力交易,运用分布式优化方法求解双方可获得的最大利益。对于P2P交易以后剩余的能源需求,建立了系统运行成本、碳排放和风能溢出的多目标优化模型,采用集中优化的二代非支配排序遗传算法(non dominated sorting genetic algorithm-II, NSGA-Ⅱ)求解该模型的帕累托前沿,并在IEEE 30节点系统进行了算例验证。仿真结果表明,在所提出的能源优化调度策略下既能满足电动汽车和温控负荷的功率需求,也给电力系统带来了良好的经济效益和环境效益。

关键词: 电动汽车, 温控负荷, 点对点电力交易, 分布式优化, 多目标优化

Abstract:

A hierarchical energy system management framework based on the demand response of electric vehicles (EVs) and temperature-controlled loads (TCLs) is developed to address the impact of large-scale EVs on the power system. Stimulated EV clusters and TCL clusters can quickly respond to the scheduling strategies of load aggregators to reduce the impact on the grid caused by the large number of flexible loads connected to the grid. Firstly, a hybrid model of convolutional neural network and long-and short-term memory network is used to predict each part of the load, and the load aggregator dispatches controllable flexible loads to maximize the fit of the predicted load profile. The load aggregator performs peer to peer (P2P) power trading with the power operator according to the current scheduling strategy and applies distributed optimization to solve the maximum benefit for both parties. For the remaining energy demand after local energy trading, a multi-objective optimization model for system operating cost, carbon emission, and wind energy spillover is considered. The Pareto frontier of this model is solved using NSGA-II with centralized optimization and verified by arithmetic cases in the IEEE 30-node system. The simulation results show that the proposed optimal energy dispatch strategy can not only meet the power requirements of EVs and TCLs, but also bring good economic and environmental benefits to the power system.

Key words: electric vehicle, temperature-controlled load, peer to peer (P2P) power transaction, distributed optimization, multi-objective optimization

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