考虑风险偏好的多主体虚拟电厂经济调度与收益分配策略

doi:10.3969/j.issn.2097-0706.2024.06.008

摘要/Abstract

摘要：

虚拟电厂（VPP）在新型电力系统中的地位日益凸显，而可聚合分布式能源（DER）的VPP内部风电机组、光伏发电机组的随机性和波动性负荷等不确定因素给VPP的运营决策和收益带来风险。如何建立公平、合理、透明的利益分配机制是成功维护 VPP 内DER合作关系的关键。为了激励VPP内部各主体参与市场的积极性，各主体可根据自身特点与贡献度协商分配收益，构建计及风险偏好的包含风电机组、光伏发电、可控分布式电源、储能及柔性负荷的多主体VPP内部优化调度决策模型，参与电力市场，并建立Nash-Harsanyi讨价还价解的VPP利益分配模型。算例分析以VPP最大化运行效益为目标，指导系统运营策略和降低VPP运行风险水平，验证了所提利益分配法能有效平衡各方利益，量化各DER对VPP利润的实际贡献度，提高各成员参与市场竞争的积极性。采用Nash-Harsanyi解的分配方案在VPP收益分配合理性和适用性方面优于Shapley值法方案。

关键词: 虚拟电厂, 新型电力系统, 分布式能源, 电力市场, 利益分配, 风险偏好, 运营策略

Abstract:

Virtual power plants （VPPs） are playing an increasingly prominent role in new power systems， while the operational strategies and revenues of a VPP with distributed energy resources（DERs）are affected by the fluctuated outputs of wind turbines and PV generators in the plant. A fair， reasonable and transparent benefit distribution mechanism is the key to maintaining the cooperation between different DERs in a VPP. To motive various entities in a VPP to participate in trades， the profit is allocated among different entities based on their own characteristics and contributions though consultations. An optimal scheduling and profit distribution strategy is proposed for the VPP integrating wind turbines， PV generators， controllable distributed power suppliers and flexible loads considering their risk preferences. The multi-agent VPP participates in the electricity market （EM），and its profit distribution model is built based on Nash-Hassanyi bargaining solution. A numerical analysis is carried out，aiming to maximize the operational efficiency， guide the operation and reduce the operation risk of the VPP. The results verify that the proposed benefit distribution method can effectively balance the interests of all parties， quantify the actual economic contribution of each DER to the VPP， and improve the willingness of each entity to participate in market competition. The benefit allocation mechanism using Nash-Hassanyi solution is superior to that using Shapley value method in terms of allocation rationality and applicability.

Key words: virtual power plant, new power systems, distributed energy resources, electricity market, benefit distribution, risk preference, operational strategy

中图分类号:

TK019：TM73

郁海彬, 卢闻州, 唐亮, 张煜晨, 邹翔宇, 姜玉靓, 刘嘉宝. 考虑风险偏好的多主体虚拟电厂经济调度与收益分配策略[J]. 综合智慧能源, 2024, 46(6): 66-77.

YU Haibin, LU Wenzhou, TANG Liang, ZHANG Yuchen, ZOU Xiangyu, JIANG Yuliang, LIU Jiabao. Economic dispatch and profit distribution strategy for multi-agent virtual power plants considering risk preferences[J]. Integrated Intelligent Energy, 2024, 46(6): 66-77.

图/表 13

图1

图2

图3

表1

图4

图5

图6

图7

图8

图9

图10

表2

表3

参考文献 42

[1]	王芸芸, 马志程, 周强, 等. 兼顾公平性的多能源合作博弈优化调度[J]. 太阳能学报, 2022, 43(10):482-492. doi: 10.19912/j.0254-0096.tynxb.2021-0408
	WANG Yunyun, MA Zhicheng, ZHOU Qiang, et al. Multi energy cooperative game optimal scheduling fairness[J]. Acta Energiae Solaris Sinica, 2022, 43(10): 482-492. doi: 10.19912/j.0254-0096.tynxb.2021-0408
[2]	程松, 周鑫, 任景, 等. 面向多级市场出清的负荷聚合商联合交易策略[J]. 电力系统保护与控制, 2022, 50(20):158-167.
	CHENG Song, ZHOU Xin, REN Jing, et al. Bidding strategy for load aggregators in a multi‑stage electricity market[J]. Power System Protection and Control, 2022, 50(20): 158-167.
[3]	杨秀, 杜楠楠, 孙改平, 等. 考虑需求响应的虚拟电厂双层优化调度[J]. 电力科学与技术学报, 2022, 37(2):137-146.
	YANG Xiu, DU Nannan, SUN Gaiping, et al. Bi‑level optimization dispatch of virtual power plants considering the demand response[J]. Journal of Electric Power Science and Technology, 2022, 37(2):137-146
[4]	李东东, 王啸林, 沈运帷, 等. 考虑多重不确定性的含需求响应及电碳交易的虚拟电厂优化调度策略[J]. 电力自动化设备, 2023, 43(5):210-217,251.
	LI Dongdong, WANG Xiaolin, SHEN Yunwei, et al. Optimal scheduling strategy of virtual power plant with demand response and electricity‑carbon trading considering multiple uncertainties[J]. Electric Power Automation Equipment, 2023, 43(5):210-217,251.
[5]	芮寿辛. 虚拟电厂参与电力市场博弈的出清策略和价值分配研究[D]. 兰州: 兰州理工大学, 2023.
	RUI Shouxin. Research on clearing strategy and value allocation of virtual power plants participating in electricity market games[D]. Lanzhou: Lanzhou University of Technology, 2023.
[6]	张文煜, 徐晓川, 沈宇, 等. 考虑可聚合资源的虚拟电厂低碳联合交易优化策略研究[J]. 现代电力, 2024, 41(1):161-172.
	ZHANG Wenyu, XU Xiaochuan, SHEN Yu, et al. Research on optimization strategy for low carbon joint transaction of virtual power plant considering aggregatable resources[J]. Modern Electric Power, 2024, 41(1):161-172.
[7]	CAMAL S, TENG F, MICHIORRI A, et al. Scenario generation of aggregated wind,photovoltaics and small hydro production for power systems applications[J]. Applied Energy, 2019, 242: 1396-1406.
[8]	刘健, 刘雨鑫, 庄涵羽. 虚拟电厂关键技术及其建设实践[J]. 综合智慧能源, 2023, 45(6):59-65. doi: 10.3969/j.issn.2097-0706.2023.06.008
	LIU Jian, LIU Yuxin, ZHUANG Hanyu. Key technologies and construction practices of virtual power plants[J]. Integrated Intelligent Energy, 2023, 45(6): 59-65 doi: 10.3969/j.issn.2097-0706.2023.06.008
[9]	ZHANG R F, HREDZAK B. Distributed dynamic clustering algorithm for formation of heterogeneous virtual power plants based on power requirements[J]. IEEE Transactions on Smart Grid, 2021, 12(1): 192-204.
[10]	LIMA R M, CONEJO A J, GIRALDI L, et al. Sample average approximation for risk‑averse problems: A virtual power plant scheduling application[J]. EURO Journal on Computational Optimization, 2021, 9(2): 1-17.
[11]	宋莉, 刘敦楠, 庞博, 等. 需求侧资源参与电力市场机制及典型案例实践综述[J]. 全球能源互联网, 2021, 4(4):401-410.
	SONG Li, LIU Dunnan, PANG Bo, et al. Mechanism of demand‑side resource participation in the electricity market and typical case practice review[J]. Global Energy Internet, 2021, 4(4):401-410.
[12]	徐慧慧, 田云飞, 缪猛, 等. 计及碳交易和需求响应的虚拟电厂低碳经济调度[J]. 智慧电力, 2023, 51(8):1-7.
	XU Huihui, TIAN Yunfei, MIAO Meng, et al. Low carbon economy dispatch of virtual power plants considering carbon trading and demand response[J]. Smart Power, 2023, 51(8): 1-7.
[13]	LUO Z, HONG S H, DING Y M. A data mining‑driven incentive‑based demand response scheme for a virtual[J]. Applied Energy, 2019, 239: 549-559.
[14]	何奇琳, 艾芊. 售电侧放开环境下含需求响应虚拟电厂的电力市场竞价策略[J]. 电力建设, 2019, 40(2): 1-10. doi: 10.3969/j.issn.1000-7229.2019.02.001
	HE Qilin, AI Qian. Bidding strategy of electricity market including virtual power plant considering demand response under retail power market deregulation[J]. Electric Power Construction, 2019, 40 (2): 1-10. doi: 10.3969/j.issn.1000-7229.2019.02.001
[15]	安麒, 王剑晓, 李庚银, 等. 基于均衡理论的虚拟电厂市场参与模式及方法[J]. 电力建设, 2020, 41(6): 1-8. doi: 10.12204/j.issn.1000-7229.2020.06.001
	AN Qi, WANG Jianxiao, LI Gengyin, et al. Scheme and method for market participation of virtual power plant based on equilibrium theory[J]. Electric Power Construction, 2020, 41 (6): 1-8. doi: 10.12204/j.issn.1000-7229.2020.06.001
[16]	赵晨, 何宇俊, 罗钢, 等. 基于泛在互联的虚拟电厂参与实时市场模型[J]. 电力建设, 2020, 41(6): 36-43. doi: 10.12204/j.issn.1000-7229.2020.06.005
	ZHAO Chen, HE Yujun, LUO Gang, et al. Modeling of virtual power plants based on ubiquitous interconnection participating in real‑time market[J]. Electric Power Construction, 2020, 41 (6): 36-43. doi: 10.12204/j.issn.1000-7229.2020.06.005
[17]	袁桂丽, 苏伟芳. 计及电动汽车不确定性的虚拟电厂参与 AGC 调频服务研究[J]. 电网技术, 2020, 44(7): 2538-2548.
	YUAN Guili, SU Weifang. Virtual power plants providing AGC FM service considering uncertainty of electric vehicles[J]. Power System Technology, 2020, 44 (7): 2538-2548.
[18]	GOUGHERI S S, JAHANGIR H, GOLKAR M A, et al. Optimal participation of a virtual power plant in electricity market considering renewable energy: A deep learning-based approach[J]. Sustainable Energy, Grids and Networks, 2021, 26:100448.
[19]	RAHMANI-DABBAGH S, SHEIKH-EL-ESLAMI M K. A profit sharing scheme for distributed energy resources integrated into a virtual power plant[J]. Applied Energy, 2016, 184: 313-328.
[20]	麻秀范, 余思雨, 朱思嘉, 等. 基于多因素改进 Shapley 的虚拟电厂利润分配[J]. 电工技术学报, 2020, 35(S2): 585-595.
	MA Xiufan, YU Siyu, ZHU Sijia, et al. Profit allocation to virtual power plant members based on improved multifactor shapley value method[J]. Transactions of China Electrotechnical Society, 2020, 35(S2):585-595.
[21]	郁海彬, 董帅, 陆增洁, 等. 新型电力系统下储能参与电力调峰调频辅助市场的竞标策略[J]. 中国电力, 2023, 56(8):48-60.
	YU Haibin, DONG Shuai, LU Zengjie, et al. Bidding strategy of energy storage participating in the auxiliary market of peak and frequency modulation in new power system[J]. Electric Power, 2023, 56(8): 48-60.
[22]	刘立洲, 龚锦霞. 基于改进Shapley算法的虚拟电厂博弈模型分析[J]. 电测与仪表, 2022, 59(12): 41-47.
	LIU Lizhou, GONG Jinxia. Game model analysis of virtual power plant based on improved shapley algorithm[J]. Electrical Measurement & Instrumentation, 2022, 59 (12):41-47.
[23]	郁海彬, 张煜晨, 刘扬洋, 等. 碳交易机制下多主体虚拟电厂参与电力市场的优化调度竞标策略[J]. 发电技术, 2023, 44(5):634-644. doi: 10.12096/j.2096-4528.pgt.23004
	YU Haibin, ZHANG Yuchen, LIU Yangyang, et al. Optimization scheduling bidding strategy for multi subject virtual power plants participating in the electricity market under carbon trading mechanism[J]. Power Generation Technology, 2023, 44 (5): 634-644. doi: 10.12096/j.2096-4528.pgt.23004
[24]	崔金栋, 汪羽晴. 云储能模式下用户侧储能协调优化调度机制研究[J]. 综合智慧能源, 2023, 45(9):18-25. doi: 10.3969/j.issn.2097-0706.2023.09.003
	CUI Jindong, WANG Yuqing. Research on user‑side energy storage coordinated and optimized scheduling mechanism under cloud energy storage mode[J]. Integrated Intelligent Energy, 2023, 45 (9): 18-25 doi: 10.3969/j.issn.2097-0706.2023.09.003
[25]	闫鹏, 曾四鸣, 李铁成, 等. 基于改进量子遗传算法的虚拟电厂在多时间尺度下参与AGC优化调度[J]. 电网与清洁能源, 2023, 39(3):23-32.
	YAN Peng, ZENG Siming, LI Tiecheng, et al. Optimal scheduling of virtual power plant participating in AGC based on improved quantum genetic algorithm on multi‑time scale[J]. Advances of Power System & Hydroelectric Engineering, 2023, 39(3): 23-32.
[26]	朱永胜, 苗阳, 谢晓峰, 等. 新能源微电网多层级车网协同优化策略[J]. 智慧电力, 2023, 51(2):61-68.
	ZHU Yongsheng, MIAO Yang, XIE Xiaofeng, et al. Multi‑level vehicle‑grid collaborative optimization strategy for new energy microgrid[J]. Smart Power, 2023, 51(2):61-68.
[27]	张立辉, 戴谷禹, 聂青云, 等. 碳交易机制下计及用电行为的虚拟电厂经济调度模型[J]. 电力系统保护与控制, 2020, 48(24):154-163.
	ZHANG Lihui, DAI Guyu, NIE Qingyun, et al. Economic dispatch model of virtual power plant considering electricity consumption under a carbon trading mechanism[J]. Power System Protection and Control, 2020, 48(24):154-163.
[28]	李旭东, 艾欣, 胡俊杰, 等. 计及碳交易机制的核-火-虚拟电厂三阶段联合调峰策略研究[J]. 电网技术, 2019, 43(7):2460-2470.
	LI Xudong, AI Xin, HU Junjie, et al. Three‑stage combined peak regulation strategy for nuclear-thermal-virtual power plant considering carbon trading mechanism[J]. Power System Technology, 2019, 43(7):2460-2470.
[29]	应志玮, 余涛, 黄宇鹏, 等. 上海虚拟电厂运营市场出清的研究与实现[J]. 电力学报, 2020, 35(2):129-134.
	YING Zhiwei, YU Tao, HUANG Yupeng, et al. Research on clearing the operation market of Shanghai virtual power plant[J]. Journal of Electric Power, 2020, 35(2):129-134.
[30]	杨梓俊, 荆江平, 邓星, 等. 虚拟电厂参与江苏电网辅助服务市场的探讨[J. 电力需求侧管理, 2021, 23(4):90-95.
	YANG Zijun, JING Jiangping, DENG Xing, et al. Discussion on virtual power plant participating in ancillary service market of Jiangsu power grid[J]. Power Demand Side Management, 2021, 23(4):90-95.
[31]	谈金晶, 李扬. 多能源协同的交易模式研究综述[J]. 中国电机工程学报, 2019, 39(22):6483-6497.
	TAN Jinjing, LI Yang. Review on transaction mode in multi‑energy collaborative market[J]. Proceedings of the CSEE, 2019, 39(22):6483-6497.
[32]	田立亭, 程林, 郭剑波, 等. 虚拟电厂对分布式能源的管理和互动机制研究综述[J]. 电网技术, 2020, 44(6):2097-2108.
	TIAN Liting, CHENG Lin, GUO Jianbo, et al. A review on the study of management and interaction mechanism for distributed energy in virtual power plants[J]. Power System Technology, 2020, 44(6):2097-2108.
[33]	雷旭, 马鹏飞, 宋智帅, 等. 计及风电预测误差的柔性负荷日内调度模型[J]. 发电技术, 2022, 43(3):485-491. doi: 10.12096/j.2096-4528.pgt.20083
	LEI Xu, MA Pengfei, SONG Zhishuai, et al. A flexible intraday load dispatch model considering wind power prediction errors[J]. Power Generation Technology, 2022, 43(3):485-491. doi: 10.12096/j.2096-4528.pgt.20083
[34]	王帅, 帅轩越, 王智冬, 等. 基于纳什议价方法的虚拟电厂分布式多运营主体电能交易机制[J]. 电力建设, 2022, 43(3):141-148. doi: 10.12204/j.issn.1000-7229.2022.03.015
	WANG Shuai, SHUAI Xuanyue, WANG Zhidong, et al. Distributed electricity trading mechanism of multi‑operator virtual power plant based on Nash bargaining method[J]. Electric Power Construction, 2022, 43(3):141-148. doi: 10.12204/j.issn.1000-7229.2022.03.015
[35]	匡熠, 王秀丽, 王建学, 等. 基于Stackelberg博弈的虚拟电厂能源共享机制[J]. 电网技术, 2020, 44(12):4556-4564.
	KUANG Yi, WANG Xiuli, WANG Jianxue, et al. Virtual power plant energy sharing mechanism based on Stackelberg game[J]. Power System Technology, 2020, 44(12): 4556-4564.
[36]	郁海彬, 高亦凌, 陆增洁, 等. 计及需求响应的风-火-储-碳捕集多源参与深度调峰市场的低碳经济调度[J]. 综合智慧能源, 2023, 45(8):80-89. doi: 10.3969/j.issn.2097-0706.2023.08.010
	YU Haibin, GAO Yiling, LU Zengjie, et al. Low‑carbon economic scheduling of deep peak regulating market with the participation of wind power,thermal power,storage and carbon capture units considering demand response[J]. Integrated Intelligent Energy, 2023, 45(8):80-89. doi: 10.3969/j.issn.2097-0706.2023.08.010
[37]	秦玉杰. 分布式电源参与电力辅助服务的机制研究[D]. 淄博: 山东理工大学, 2020.
	QIN Yujie. Research on the mechanism of distributed generation participating in power auxiliary service[D]. Zibo: Shandong University of Technology, 2020.
[38]	史沛然, 李彦宾, 江长明, 等. 第三方独立主体参与华北电力调峰市场规则设计与实践[J]. 电力系统自动化, 2021, 45(5):168-174.
	SHI Peiran, LI Yanbin, JIANG Changming, et al. Rule design and practice for third‑party independent entities participating in electric power peak regulation auxiliary service market of north China[J]. Automation of Electric Power Systems, 2021, 45(5):168-174.
[39]	宁剑, 江长明, 张哲, 等. 可调节负荷资源参与电网调控的思考与技术实践[J]. 电力系统自动化, 2020, 44(17):1-8.
	NING Jian, JIANG Changming, ZHANG Zhe, et al. Thinking and technical practice of adjustable load resources participating in dispatching and control of power grid[J]. Automation of Electric Power Systems, 2020, 44 (17):1-8.
[40]	李博嵩, 王旭, 蒋传文, 等. 广泛负荷聚集商市场策略建模及风险效益分析[J]. 电力系统自动化, 2018, 42(16):119-126.
	LI Bosong, WANG Xu, JIANG Chuanwen, et al. Market strategy modeling and risk profit analysis of demand‑side resource aggregator[J]. Automation of Electric Power Systems, 2018, 42(16):119-126.
[41]	李翔宇, 赵冬梅. 计及可调资源动态特性的虚拟电厂多级优化配置[J]. 电力系统自动化, 2020, 44(13):17-24.
	LI Xiangyu, ZHAO Dongmei. Multi‑level optimal configuration of virtual power plant considering dynamic characteristics of adjustable resources[J]. Automation of Electric Power Systems, 2020, 44 (13):17-24.
[42]	杨雪, 金孝俊, 王海洋, 等. 基于区块链的绿证和碳交易市场联合激励机制[J]. 电力建设, 2022, 43(6):24-33. doi: 10.12204/j.issn.1000-7229.2022.06.003
	YANG Xue, JIN Xiaojun, WANG Haiyang, et al. Blockchain‑based joint incentive mechanism for tradable green certificate and carbon trading market based on blockchain[J]. Electric Power Construction, 2022, 43(6):24-33. doi: 10.12204/j.issn.1000-7229.2022.06.003

收益分配方法	成员	改进策略
改进Shapley值法	DERs	评分机制、多因素改进
	WT,PV,CHP,FL和 ESS	调整权重
	WT,EV	引入投标偏差的惩罚机制
市场竞价分配机制	DERs	自适应过程和谐搜索、内部交易市场机制
市场竞价分配机制	VPPs	上层：竞价平衡;下层：多时间尺度最优

成员组合	λ=0.25	λ=0.50	λ=0.75
ES	3 691	3 691	3 691
GT	4 547	4 547	4 547
ES，GT	8 239	8 239	8 239
FL	-26 207	-21 588	-18 770
ES，FL	-21 861	-17 473	-14 397
GT，FL	-20 948	-16 935	-14 251
ES，GT，FL	-17 048	-12 921	-10 020
PV	5 186	3 448	2 340
ES，PV	9 007	7 584	6 556
GT，PV	9 916	8 451	7 796
ES，GT，PV	13 304	12 428	11 301
PV，FL	-20 915	-1 7297	-14 940
ES，PL，PV	-16 449	-15 861	-9 915
GT，PL，PV	-15 355	-14 019	-11 704
ES，GT，PL，PV	-11 640	-8 460	-6 676
WT	36 721	27 616	10 309
ES，WT	42 300	32 842	15 804
GT，WT	41 102	33 103	17 081
ES，GT，WT	47 771	37 266	20 475
FL，WT	11 274	6 966	-5 172
ES，FL，WT	17 087	12 109	-183
GT，FL，WT	16 986	11 758	544
ES，GT，FL，WT	22 552	17 239	4 360
PV，WT	43 128	32 676	13 657
ES，PV，WT	45 859	36 508	18 408
GT，PV ，WT	45 860	37 375	22 648
ES，GT，PV，WT	52 828	42 210	25 866
FL，PV，WT	16 664	10 703	-27 600
ES，FL，PV，WT	21 746	14 952	2 155
GT，FL，PV，WT	24 964	15 041	4343
ES，GT，FL，PV，WT	26 224	26 797	9 004

方法	λ	ES	GT	FL	PV	WT
独立运营	0.25	3 691	4 547	-26 207	5 186	36 721
	0.50
	0.75
Shapley值分配	0.25	37 688	5 359	-24 389	4 682	4 344
	0.50	27 556	4 226	-21 372	4 852	4 132
	0.75	12 285	3 332	-16 904	5 432	4 233
Nash-Harsanyi 讨价还价解分配	0.25	41 396	6 151	-24 440	4 579	4 106
	0.50	31 672	4 370	-25 348	4 683	4 018
	0.75	14 252	3 376	-18 776	5 358	4 186