许烽,陶远超,陆翌,裘鹏,李心宇,孙浩,覃洪培
XU Feng,TAO Yuanchao,LU Yi,QIU Peng,LI Xinyu,SUN Hao,QIN Hongpei

• 1:国网浙江省电力有限公司电力科学研究院
• 2:浙江大学电气工程学院

摘要(Abstract)：

随着新型电力系统建设不断推进，以风光为代表的可再生能源装机容量进一步增加，源荷双侧的随机性和不确定性为电力系统运行带来极大的挑战。为此，提出一种基于多能源荷预测的区域电热系统储能优化调度方法，利用多能储能灵活调节能力及系统电热耦合特性提高光伏消纳水平。首先，运用改进极限学习机模型和K-means聚类算法对光伏出力及电热负荷进行预测。然后，引入条件风险价值量化光伏出力不确定性对运行成本的影响，以运行成本和条件风险价值加权之和最小为目标，建立含蓄电池、飞轮储能及蓄热罐的区域电热系统优化运行调度模型，通过多能储能响应电热系统灵活运行调节需求。最后，通过算例分析表明了所提方法的有效性。
With the continuous development of the new power system construction, the installed capacity of wind power, photovoltaic and other renewable energies has been further increased. The randomness and uncertainty of source and load bring great challenges to the operation of the power system. Therefore, an optimal dispatch method of energy storage in regional electricity-thermal system based on multi-energy source and load forecasting, which improves the accommodation level of photovoltaic by the flexible adjustment ability of multi-energy storage and electricity-thermal coupling characteristics of the system. Firstly, the photovoltaic output and electricity-thermal load are forecasted based on the improved extreme learning machine and K-means clustering algorithm. Then, the conditional value at risk is applied to quantize the impact of uncertainty of photovoltaic output on operation cost.With target of minimizing the weighting sum of operation cost and conditional value at risk, the optimized operation scheduling model of district electricity-thermal system, containing battery, flywheel energy storage and heat storage tank, is established. The regulation requirement for flexible operation of the electricity-thermal system can be achieved by multi-energy storage. Finally, the case study is conducted to demonstrate the effectiveness of the proposed method.

关键词(KeyWords)： 源荷预测;多能储能;电热系统;优化调度;条件风险价值
source and load forecasting;multi-energy storage;electricity-thermal system;optimal dispatch;conditional value at risk

Abstract:

Keywords:

基金项目(Foundation): 国网浙江省电力有限公司科技项目（5211DS200083）

作者(Author): 许烽,陶远超,陆翌,裘鹏,李心宇,孙浩,覃洪培
XU Feng,TAO Yuanchao,LU Yi,QIU Peng,LI Xinyu,SUN Hao,QIN Hongpei

DOI: 10.19585/j.zjdl.202309003

参考文献(References)：

• [1]魏泓屹，卓振宇，张宁，等.中国电力系统碳达峰·碳中和转型路径优化与影响因素分析[J].电力系统自动化，2022,46(19):1-12.WEI Hongyi,ZHUO Zhenyu,ZHANG Ning,et al.Transition path optimization and influencing factor analysis of carbon emission peak and carbon neutrality for power system of China[J].Automation of Electric Power Systems,2022,46(19):1-12.
• [2]林达，钱平，张雪松，等.考虑储能寿命特性的综合能源系统经济-灵活多目标优化运行策略[J].浙江电力，2022,41(1):26-34.LIN Da,QIAN Ping,ZHANG Xuesong,et al. Multiobjective optimal operation strategy based on economy and flexibility of integrated energy system considering energy storage life characteristics[J]. Zhejiang Electric Power,2022,41(1):26-34.
• [3]贾宏杰，王丹，徐宪东，等.区域综合能源系统若干问题研究[J].电力系统自动化，2015,39(7):198-207.JIA Hongjie,WANG Dan,XU Xiandong,et al.Research on some key problems related to integrated energy systems[J].Automation of Electric Power Systems,2015,39(7):198-207.
• [4]吴潇雨，孔维政，代红才.城市电热综合能源系统分布式鲁棒经济调度[J].电力系统及其自动化学报，2022,34(8):110-117.WU Xiaoyu,KONG Weizheng,DAI Hongcai. Distributionally robust economic dispatch of urban integrated heat and power systems[J]. Proceedings of the CSU-EPSA,2022,34(8):110-117.
• [5]尉耀稳，李跃龙，陈思超，等.多类型源储协调互动的配电网分布鲁棒优化调度[J].电力工程技术，2021,40(5):192-199.YU Yaowen,LI Yuelong,CHEN Sichao,et al.Distributionally robust optimal dispatch of distribution network considering multiple source-storage coordinated interaction[J].Electric Power Engineering Technology,2021,40(5):192-199.
• [6]马锐，李相俊，李文启，等.可再生能源供电区域电网中储能系统协同调度策略[J].发电技术，2021,42(1):31-39.MA Rui,LI Xiangjun,LI Wenqi,et al.Cooperative scheduling strategy of energy storage systems for regional grid supplied by renewable energy[J].Power Generation Technology,2021,42(1):31-39.
• [7]陈兵，徐瑞，徐春雷，等.规模化储能分区聚合有功调度控制技术研究[J].电力工程技术，2021,40(3):35-41.CHEN Bing,XU Rui,XU Chunlei,et al.Large-scale energy storage aggregation active power dispatching and control in subarea division of power grid[J]. Electric Power Engineering Technology,2021,40(3):35-41.
• [8]申洪，周勤勇，刘耀，等.碳中和背景下全球能源互联网构建的关键技术及展望[J].发电技术，2021,42(1):8-19.SHEN Hong,ZHOU Qinyong,LIU Yao,et al.Key technologies and prospects for the construction of global energy Internet under the background of carbon neutral[J].Power Generation Technology,2021,42(1):8-19.
• [9]乔志杰，马临超.适用于电力系统稳定性提升的大规模储能控制策略研究[J].高压电器，2022,58(12):75-84.QIAO Zhijie,MA Linchao.Research on large-scale energy storage control strategy for power system stability improvement[J]. High Voltage Apparatus,2022,58(12):75-84.
• [10]黎博，陈民铀，钟海旺，等.高比例可再生能源新型电力系统长期规划综述[J].中国电机工程学报，2023,43(2):555-581.LI Bo,CHEN Minyou,ZHONG Haiwang,et al.Summary of long-term planning of new power system with high proportion of renewable energy[J].Proceedings of the CSEE,2023,43(2):555-581.
• [11] WAN C,XU Z,PINSON P,et al.Probabilistic forecasting of wind power generation using extreme learning machine[J].IEEE Transactions on Power Systems,2014,29(3):1033-1044.
• [12] CHEN X,DONG Z Y,MENG K,et al.Electricity price forecasting with extreme learning machine and bootstrapping[J].IEEE Transactions on Power Systems,2012,27(4):2055-2062.
• [13]王保义，赵硕，张少敏.基于云计算和极限学习机的分布式电力负荷预测算法[J].电网技术，2014,38(2):526-531.WANG Baoyi,ZHAO Shuo,ZHANG Shaomin.A distributed load forecasting algorithm based on cloud computing and extreme learning machine[J].Power System Technology,2014,38(2):526-531.
• [14]毛力，王运涛，刘兴阳，等.基于改进极限学习机的短期电力负荷预测方法[J].电力系统保护与控制，2012,40(20):140-144.MAO Li,WANG Yuntao,LIU Xingyang,et al. Shortterm power load forecasting method based on improved extreme learning machine[J].Power System Protection and Control,2012,40(20):140-144.
• [15]饶宇飞，刘阳，李玲玲，等.基于改进鸟群算法和极限学习机模型的光伏发电系统输出功率预测研究[J].可再生能源，2020,38(10):1318-1325.RAO Yufei,LIU Yang,LI Lingling,et al.Power prediction of photovoltaic power generation system based on improved bird swarm and extreme learning machine model[J]. Renewable Energy Resources,2020,38(10):1318-1325.
• [16] ALAMANIOTIS M. Synergism of deep neural network and ELM for smart very-short-term load forecasting[C]//2019 IEEE PES Innovative Smart Grid Technologies Europe(ISGT-Europe),September 29-October 2,2019,Bucharest,Romania:1-5.
• [17]国家电网公司“电网新技术前景研究”项目咨询组，王松岑，来小康，等.大规模储能技术在电力系统中的应用前景分析[J].电力系统自动化，2013,37(1):3-8.Consulting Group of State Grid Corporation of China to Prospects of New Technologies in Power Systems,WANG Songcen,LAI Xiaokang,et al.An analysis of prospects for application of large-scale energy storage technology in power systems[J]. Automation of Electric Power Systems,2013,37(1):3-8.
• [18]熊文，刘育权，苏万煌，等.考虑多能互补的区域综合能源系统多种储能优化配置[J].电力自动化设备，2019,39(1):118-126.XIONG Wen,LIU Yuquan,SU Wanhuang,et al.Optimal configuration of multi-energy storage in regional integrated energy system considering multi-energy complementation[J].Electric Power Automation Equipment,2019,39(1):118-126.
• [19]张纯江，董杰，刘君，等.蓄电池与超级电容混合储能系统的控制策略[J].电工技术学报，2014,29(4):334-340.ZHANG Chunjiang,DONG Jie,LIU Jun,et al.A control strategy for battery-ultracapacitor hybrid energy storage system[J]. Transactions of China Electrotechnical Society,2014,29(4):334-340.
• [20]程启明，徐聪，程尹曼，等.基于混合储能技术的光储式充电站直流微网系统协调控制[J].高电压技术，2016,42(7):2073-2083.CHENG Qiming,XU Cong,CHENG Yinman,et al.Coordination control of PV charging station DC microgrid system based on hybrid energy storage technology[J]. High Voltage Engineering,2016,42(7):2073-2083.
• [21]钱韦廷，赵长飞，万灿，等.基于概率预测的混合储能平抑风电波动随机优化调控方法[J].电力系统自动化，2021,45(18):18-27.QIAN Weiting,ZHAO Changfei,WAN Can,et al.Probabilistic forecasting based stochastic optimal dispatch and control method of hybrid energy storage for smoothing wind power fluctuations[J].Automation of Electric Power Systems,2021,45(18):18-27.
• [22]许庆祥，滕伟，武鑫，等.平抑风电功率波动的飞轮储能系统容量配置方法[J].储能科学与技术，2022,11(12):3906-3914.XU Qingxiang,TENG Wei,WU Xin,et al.Capacity configuration method of flywheel storage system for suppressing power fluctuation of wind farms[J]. Energy Storage Science and Technology,2022,11(12):3906-3914.
• [23]钟雅珊，付聪，钱峰，等.考虑广义储能和条件风险价值的综合能源系统经济调度[J].电力系统保护与控制，2022,50(9):54-63.ZHONG Yashan,FU Cong,QIAN Feng,et al.Economic dispatch model of an integrated energy system considering generalized energy storage and conditional value at risk[J].Power System Protection and Control,2022,50(9):54-63.
• [24]姚艳，康家乐，汪雅静，等.考虑综合需求响应和多能储能装置的综合能源系统优化调度[J].浙江电力，2022,41(8):65-72.YAO Yan,KANG Jiale,WANG Yajing,et al. Optimal dispatching of integrated energy system considering integrated demand response and multi-energy storage devices[J].Zhejiang Electric Power,2022,41(8):65-72.
• [25]吴笑民，郭雨，郑景文，等.多能互补智慧园区能源系统优化运行方法[J].高电压技术，2022,48(7):2545-2553.WU Xiaomin,GUO Yu,ZHENG Jingwen,et al.Optimal operation method for power system of multi-energy complement smart park[J]. High Voltage Engineering,2022,48(7):2545-2553.
• [26]帅轩越，王秀丽，吴雄，等.计及电热需求响应的共享储能容量配置与动态租赁模型[J].电力系统自动化，2021,45(19):24-32.SHUAI Xuanyue,WANG Xiuli,WU Xiong,et al.Shared energy storage capacity allocation and dynamic lease model considering electricity-heat demand response[J].Automation of Electric Power Systems,2021,45(19):24-32.
• [27]许鹏程.基于重要抽样与极限学习机的电力系统快速可靠性评估[D].北京：华北电力大学，2017.XU Pengcheng.Rapid reliability assessment of power system based on selective sampling and extreme learning machine[D].Beijing:North China Electric Power University,2017.
• [28]李婉莹，董福贵.基于LMDI模型和K-means聚类的中国电力消费因素分解[J].浙江电力，2020,39(12):99-105.LI Wanying,DONG Fugui. Decomposition of electricity consumption factors in China based on LMDI model and K-means clustering[J].Zhejiang Electric Power,2020,39(12):99-105.
• [29]武梦景，万灿，宋永华，等.含多能微网群的区域电热综合能源系统分层自治优化调度[J].电力系统自动化，2021,45(12):20-29.WU Mengjing,WAN Can,SONG Yonghua,et al.Hierarchical autonomous optimal dispatching of district integrated heating and power system with multi-energy microgrids[J].Automation of Electric Power Systems,2021,45(12):20-29.
• [30] ZHAO L B,WAN C,YU P,et al.Assessment of distributed photovoltaic hosting capacity in integrated electricity and heat systems considering uncertainty[J].IET Energy Systems Integration,2021,3(3):317-326.