李昭昱;艾芊;张宇帆;殷爽睿;孙东磊;李雪亮;

• 1:电力传输与功率变换控制教育部重点实验室(上海交通大学)
• 2:国网山东省电力公司经济技术研究院

摘要(Abstract)：

虚拟电厂(virtualpowerplant,VPP)作为电力市场下促进可再生能源消纳、应对能源分布不均的重要运营模式,近年来得到迅速发展。同时,国家电网逐渐普及配网量测装置,并推进通信网络建设,其与VPP的聚合及通信内核紧密融合,为VPP带来了新特点、新模式、新方法。鉴于此,探讨VPP的内涵、特性、功能及工程示范;深入分析大数据背景下VPP呈现出的新特点、新型构成主体及发展关键技术;基于海量广域实时数据,进一步研究数据驱动方法在VPP构成主体及外部环境预测、优化调控策略及电力市场分析3个角度的应用现状。

关键词(KeyWords)： 虚拟电厂;数据驱动;大数据;分布式能源

Abstract:

Keywords:

基金项目(Foundation): 国家电网公司科技项目(SGSDJY00GPJS1900179)~~

作者(Author): 李昭昱;艾芊;张宇帆;殷爽睿;孙东磊;李雪亮;

Email:

DOI: 10.13335/j.1000-3673.pst.2020.0207a

参考文献(References)：

• [1]Asmus P.Microgrids,virtual power plants and our distributed energy future[J].Electricity Journal,2010,23(10):72-82．
• [2]Pudjianto D,Ramsay C,Strbac G.Virtual power plant and system integration of distributed energy resources[J].IET Renewable Power Generation,2007,1(1):10-16．
• [3]Cappers P,Goldman C,Kathan D.Demand response in US electricity markets:empirical evidence[J].Energy,2010(35):1526-1535．
• [4]Kieny C,Berseneff B,Hadjsaid N,et a1.On the concept and the interest of virtual power plant:some results from the European project FENIX[C]//IEEE Power&Energy Society General Meeting.Calgary,Canada:IEEE,2009:1-6．
• [5]Binding C,Gantenbein D,Jansen B,et a1.Electric vehicle fleet integration in the Danish EDISON project-a virtual power plant on the island of Bornholm[C]//Power and Energy Society General Meeting.Providence,USA:IEEE,2010:1-8．
• [6]方燕琼，艾芊，范松丽．虚拟电厂研究综述[J]．供用电，2016,33(4):8-13.Fang Yanqiong,Ai Qian,Fan Songli.A review on virtual power plant[J].Distribution&Utilization,2016,33(4):8-13(in Chinese)．
• [7]夏榆杭，刘俊勇．基于分布式发电的虚拟发电厂研究综述[J]．电力自动化设备，2016,36(4):100-106.Xia Yuhang,Liu Junyong.Review of virtual power plant based on distributed generation[J].Electric Power Automation Equipment,2016,36(4):100-106(in Chinese)．
• [8]王毅，陈启鑫，张宁，等．5G通信与泛在电力物联网的融合：应用分析与研究展望[J]．电网技术，2019,43(5):1575-1585.Wang Yi,Chen Qixin,Zhang Ning,et al.Fusion of the 5Gcommunication and the ubiquitous electric internet of things:application analysis and research prospects[J].Power System Technology,2019,43(5):1575-1585(in Chinese)．
• [9]Saboori,Mohammadi,Taghe.Virtual power plant(VPP),definition,concept,components and types[C]//Power&Energy Engineering Conference.Wuhan,China:IEEE,2011:1-4．
• [10]Vardakas J S,Zorba N,Verikoukis C V.A survey on demand response programs in smart grids:pricing methods and optimization algorithms[J].IEEE Communications Surveys&Tutorials,2015,17(1):152-178．
• [11]Doostizadeh M,Ghasemi H.A day-ahead electricity pricing model based on smart metering and demand-side management[J].Energy,2012,46(1):221-230．
• [12]Abhilasha B,Md R A K,Semhar M,et al.Forecasting data center load using hidden markov model[C]//2018 North American Power Symposium(NAPS).Fargo,USA:IEEE,2018:1-5．
• [13]Liu Z,Liu I,Low S,et al.Pricing data center demand response[J].ACM SIGMETRICS Performance Evaluation Review,2014,42(1):111-123．
• [14]Awasthi S R,Chalise S,Tonkoski R.Operation of datacenter as virtual power plant[C]//2015 IEEE Energy Conversion Congress and Exposition.Montreal,Canada:IEEE,2015:3422-3429．
• [15]Shi W,Cao J,Zhang Q,et al.Edge computing:vision and challenges[J].IEEE Internet of Things Journal,2016,3(5):637-646．
• [16]胡海洋，刘润华，胡华．移动云计算环境下任务调度的多目标优化方法[J]．计算机研究与发展，2017,54(9):1909-1919.Hu Haiyang,Liu Runhua,Hu Hua.Muti-objective optimization for task scheduling in mobile cloud computing[J].Journal of Computer Research and Development,2017,54(9):1909-1919(in Chinese)．
• [17]田辉，范绍帅，吕昕晨，等．面向5G需求的移动边缘计算[J]．北京邮电大学学报，2017,40(2):5-14.TianHui,Fan Shaoshuai,LüXinchen,et al.Mobile edge computing for 5G requirement[J].Journal of Beijing University of Posts and Telecommunications,2017,40(2):5-14(in Chinese)．
• [18]王田，沈雪微，罗皓，等．基于雾计算的可信传感云研究进展[J]．通信学报，2019,40(3):170-181.Wang Tian,ShenXuewei,LuoHao,et al.Research progress of trusted sensor-cloud based on fog computing[J].Journal on Communications,2019,40(3):170-181(in Chinese)．
• [19]IMT-2020工作组．IMT-2020.5G概念白皮书[EB/OL].[2020-02-01].http://www.imt-2020.org.cn/zh/documents/1．
• [20]袁勇，王飞跃．区块链技术发展现状与展望[J]．自动化学报，2016,42(4):481-494.Yuan Yong,Wang Feiyue.Blockchain:the state of the art and future trends[J].ActaAutomaticaSinica,2016,42(4):481-494(in Chinese)．
• [21]Nguyen Q K.Blockchain-a financial technology for future sustainable development[C]//2016 3rd International Conference on Green Technology and Sustainable Development(GTSD).Taiwan:IEEE,2016:51-54．
• [22]Hinton G E,Osindero S,Teh Y W.A fast learning algorithm for deep belief nets[J].Neural Computation,2006,18(7):1527-1554．
• [23]张宇帆，艾芊，林琳，等．基于深度长短时记忆网络的区域级超短期负荷预测方法[J]．电网技术，2019,43(6):1884-1891.Zhang Yufan,Ai Qian,Lin Lin,et al.A very short-term load forecasting method based on deep LSTM RNN at zone level[J].Power System Technlogy,2019,43(6):1884-1891(in Chinese)．
• [24]Wang Y,Chen Q X,Sun M Y,et al.An ensemble forecasting method for the aggregated load with subprofiles[J].IEEE Transactions on Smart Grid,2018,9(4):3906-3908．
• [25]王德文，孙志伟．电力用户侧大数据分析与并行负荷预测[J]．中国电机工程学报，2015,33(3):527-537.Wang Dewen,Sun Zhiwei.Big data analysis and parallel load forecasting of electric power user side[J].Proceedings of the CSEE,2015,33(3):527-537(in Chinese)．
• [26]牛东晓，王建军，李莉，等．基于粗糙集和决策树的自适应神经网络短期负荷预测方法[J]．电力自动化设备，2009,29(10):30-34NiuDongxiao,Wang Jianjun,Li Li,et al.Short-term load forecasting using adaptive ANN based on rough set and decision tree[J].Electric Power Automation Equipment,2009,29(10):30-34(in Chinese)．
• [27]吴倩红，高军，侯广松，等．实现影响因素多源异构融合的短期负荷预测支持向量机算法[J]．电力系统自动化，2016,40(15):67-72,92.Wu Qianhong,Gao Jun,HouGuangsong,et al.Short-term load forecasting support vector machine algorithm based on multi-source heterogeneous fusion of load factors[J].Automation of Electric Power Systems,2016,40(15):67-72,92(in Chinese)．
• [28]林顺富，郝朝，汤晓栋，等．基于数据挖掘的楼宇短期负荷预测方法研究[J]．电力系统保护与控制，2016,457(7):89-95.Lin Shunfu,Hao Chao,Tang Xiaodong,et al.Study of short-term load forecasting method based on data mining for buildings[J].Power System Protection and Control,2016,457(7):89-95(in Chinese)．
• [29]Zhang Y F,Ai Q,Li Z Y,et al.Data augmentation strategy for small sample short‐term load forecasting of distribution transformer[J/OL].International Transactions on Electrical Energy Systems,(2019-11-11).https://publons.com/publon/10.1002/2050-7038.12209．
• [30]Chen Y,Wang Y,Kirschen D S,et al.Model-free renewable scenario generation using generative adversarial networks[J].IEEETransactions on Power Systems,2018,33(3):3265-3275．
• [31]Zhang Y,Ai Q,Xiao F,et al.Typical wind power scenario generation for multiple wind farms using conditional improved Wasserstein generative adversarial network[J].International Journal of Electrical Power&Energy Systems,2020(114):1-12．
• [32]Georgilakis P S.Market clearing price forecasting in deregulated electricity markets using adaptively trained neural networks[C]//Advances in Artificial Intelligence,4th Helenic Conference on AI.Pringer-Verlag:4th Helenic Conference on AI,2006:56-66．
• [33]东明，郭亚军，郭宏．统一价格竞价机制下发电商报价策略研究[J]．系统工程理论与实践，2004,24(4):83-87.Dong Ming,GuoYajun,Guo Hong.Research on generating entities'bidding strategies in uniform price auction rule[J].System Engineering Theory and Practice,2004,24(4):83-87(in Chinese)．
• [34]谢晓龙，叶笑冬，董亚明．梯度提升随机森林模型及其在日前出清电价预测中的应用[J]．计算机应用与软件，2018,35(9):327-333.XieXiaolong,Ye Xiaodong,Dong Yaming.Radom forest model with gradient boosting and its application to forecasting on day-ahead market clearing proce[J].Computer Applications and Software,2018,35(9):327-333(in Chinese)．
• [35]Zhang L,Liu P B.Energy clearing price prediction and confidence interval estimation with confidence interval estimation with cascaded neural networks[J].IEEE Transactions on Power Systems,2003,18(1):99-105．
• [36]彭谦，周晓洁，杨睿，等．泛在电力物联网环境下综合能源型售电公司参与电力市场竞争的报价策略研究[J]．电网技术，2019,43(12):4337-4343.Peng Qian,Zhou Xiaojie,Yang Rui,et al.Bidding strategy of comprehensive energy based power selling company participating in electricity market competition under ubiquitous environment of internet of things[J].Power System Technology,2019,43(12):4337-4343(in Chinese)．
• [37]涂启玉，张茂林．小波神经网络预测电价的新改进[J]．电力系统及其自动化学报，2011,23(2):157-160.TuQiyu,Zhang Maolin.Forecasting electricity price using wavelet neural networks optimized by GA[J].Proceedings of the CSU-EPSA,2011,23(2):157-160(in Chinese)．
• [38]Zhang J,Tan Z.Day-ahead electricity price forecasting using WT,CLSSVM and EGARCH model[J].International Journal of Electrical Power&Energy Systems,2013,45(1):362-368．
• [39]李秋鹏，李艳．基于小波-支持向量机的出清电价预测仿真[J]．兰州理工大学学报，2013,39(2):86-89.Li Qiupeng,Li Yan.Simulation of clear price of electricity prediction based on wavelet analysis and supporting vector machine[J].Journal of Lanzhou University of Technology,2013,39(2):86-89(in Chinese)．
• [40]LagoJ,De Ridder F,DeSchutter B,et al.Forecasting spot electricity prices:deep learning approaches and empirical comparison of traditional algorithms[J].Applied Energy,2018,221(1):374-385．
• [41]WangL,Zhang Z,Chen J.Short-term electricity price forecasting with stacked denoising autoencoders[J].IEEE Transactions on Power Systems,2017,32(4):2673-2681．
• [42]Arif A,Wang Z,Wang J,et al.Load modeling-a review[J].IEEETransactions on Smart Grid,2018,9(6):5986-5999．
• [43]Sun M,Konstantelos I,Strbac G.C-vine copula mixture model for clustering of residential electrical load pattern data[J].IEEETransactions on Power Systems,2017,32(3):2382-2393．
• [44]Teichgraeber H,Brandt A R.Clustering methods to find representative periods for the optimization of energy systems:an initial framework and comparison[J].Applied Energy,2019(239):1283-1293．
• [45]Luo Z,Hong S H,Ding Y M.A data mining-driven incentive-based demand response scheme for a virtual power plant[J].Applied Energy,2019(239):549-559．
• [46]Albert A,Rajagopal R.Smart meter driven segmentation:what your consumption says about you[J].IEEE Transactions on Power Systems,2013,28(4):4019-4030．
• [47]张自东，邱才明，张东霞，等．基于深度强化学习的微电网复合储能协调控制方法[J]．电网技术，2019,43(6):1914-1921.Zhang Zidong,QiuCaiming,Zhang Dongxia,et al.A coordinated control method for hybrid energy storage system in microgrid based on deep reinforcement learning[J].Power System Techonology,2019,43(6):1914-1921(in Chinese)．
• [48]Mocanu E,Mocanu D C,Nguyen P H,et al.On-line building energy optimization using deep reinforcement learning[J].IEEE Transactions on Smart Grid,2017,10(4):3698-3708．
• [49]Liu Y,Zhang D,Wang X.A peak regulation ancillary service optimal dispatch method of virtual power plant based on reinforcement learning[C]//2019 IEEE Innovative Smart Grid Technologies.Chengdu,China:IEEE,2019:4356-4361．
• [50]Al-Awami A T,Amleh N,Muqbel A.Optimal demand response bidding and pricing mechanism with fuzzy optimization:application for a virtual power plant[J].IEEE Transactions on Industry Applications,2017,53(5):5051-5061．
• [51]XuZ,Deng T,Song Y,et al.Data-driven pricing strategy for demand-side resource aggregators[J].IEEE Transactions on Smart Grid,2016,9(1):57-66．
• [52]Lu T,Wang Z,Wang J,et al.A data-driven stackelberg market strategy for demand response-enabled distribution systems[J].IEEETransactions on Smart Grid,2018,10(3):2345-2357．
• [53]Wang Y,Chen Q,Kang C,et al.Clustering of electricity consumption behavior dynamics toward big data applications[J].IEEE Transactions on Smart Grid,2016,7(5):2437-2447．
• [54]Kwac J,Flora J,Rajagopal R.Household energy consumption segmentation using hourly data[J].IEEE Transactions on Smart Grid,2014,5(1):420-430.