计及分布式需求响应的多微电网系统协同优化策略

doi:10.12204/j.issn.1000-7229.2023.05.008

摘要/Abstract

摘要：

在清洁能源高渗透态势下,多微电网系统协同优化成为促进清洁能源高比例消纳的有效手段,合理的分布式需求响应机制对于进一步提升清洁能源利用水平具有重要研究价值。因此,提出一种计及需求响应的多微电网协同优化机制,建立需求响应资源自主竞价模型,实现分布式需求响应。首先,考虑到需求响应补贴合同的激励特性,构建微电网自治优化调度模型,制定电力供需计划和需求响应策略。其次,各微电网参考历史成交信息,以预期效益最大为目标制定需求响应资源竞价策略。再次,为实现经济高效的分布式需求响应,在日前阶段引入双向拍卖机制搭建需求响应资源端对端匹配框架。最后,通过算例验证了所提机制能实现需求响应资源分布式交易,提升多微电网系统整体经济性,并有效促进清洁能源消纳。

关键词: 多微电网系统, 清洁能源, 分布式需求响应, 协同优化, 双向拍卖机制

Abstract:

With the increasing penetration of clean energy into existing power systems, the collaborative optimization of multi-microgrid systems has become an effective way to promote the utilization of clean energy. A reasonable distributed demand response mechanism has research significance for improving the capacity of clean energy consumption. In this study, a multi-microgrid collaborative optimization mechanism and an autonomous bidding strategy of demand response resources that consider the distributed demand response are proposed. First, a microgrid autonomous optimal dispatch model is established to formulate power supply-demand plans and DR strategies by considering the incentive characteristics of demand response contracts. Second, referring to historical transaction information, each microgrid proposes IDR bidding strategies to maximize prospective benefits. Third, to implement an economic and efficient distributed demand response, a two-way auction mechanism is introduced in the day-ahead stage to develop a peer-to-peer matching framework for demand response resources. Finally, numerical example results verify that the proposed mechanism can realize the distributed transactions of demand response resources, improve the economy of multi-microgrid systems, and effectively promote clean energy consumption.

Key words: multi-microgrid system, clean energy, distributed demand response, collaborative optimization, two-way auction mechanism

中图分类号:

TM734

刘任, 刘洋, 许立雄, 李振伟, 李雪玲. 计及分布式需求响应的多微电网系统协同优化策略[J]. 电力建设, 2023, 44(5): 72-83.

LIU Ren, LIU Yang, XU Lixiong, LI Zhenwei, LI Xueling. Multi-microgrid System Collaborative Optimization Strategy Considering Distributed Demand Response[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(5): 72-83.

图/表 19

图1 多微电网系统协同优化架构

Fig.1 Multi-microgrid system collaborative optimization architecture

表1 分布式需求响应机制执行算法

Table 1 Distributed demand response algorithms

表2 不同方案下多微电网系统运行指标对比

Table 2 Comparison of operation indicators in different schemes 元

图2 不同方案下微电网决策结果对比

Fig.2 Comparison of decision results in different schemes

图3 微电网2内部供需平衡结果

Fig.3 Microgrid 2 of internal supply and demand self-balance results

图4 微电网3内部供需平衡结果

Fig.4 Microgrid 3 of internal supply and demand self-balance results

图5 微电网4内部供需平衡结果

Fig.5 Microgrid 4 of internal supply and demand self-balance results

图6 需求响应供应方报价策略

Fig.6 Quotation strategy in DR suppliers

图7 需求响应接收方投标策略

Fig.7 Bidding strategy in DR receiver

图8 t=13各微电网需求响应竞价曲线及成交电量

Fig.8 t=13 Demand response bidding curve and transaction electricity quantity of each microgrid

图9 t=13各微电网第10轮需求响应匹配结果

Fig.9 The 10th round t=13 demand response matching results of each microgrid

图A1 风电机组预测出力

Fig.A1 WPG forecast output in each microgrid

图A2 光伏机组预测出力

Fig.A2 SPG forecast output in each microgrid

图A3 负荷预测需求量

Fig.A3 Load forecast demand in each microgrid

表A1 可控机组参数

Table A1 Operating parameters of CGs

表A2 电储能系统运行参数

Table A2 Operating parameters of ESS

表A3 分时电价和上网电价

Table A3 Time-of-use tariff and on-grid tariff

表A4 IDR合同主要参数

Table A4 Parameters of IDR contract

表A5 各微电网需求响应端对端匹配方案

Table A5 Demand response peer-to-peer matching schemes of each microgrid

参考文献 32

[1]	赵毅, 陈雨婷, 孙文瑶. 多微电网配电系统区间调度方法与市场交易策略研究[J]. 电网技术, 2022, 46(1): 47-56.
	ZHAO Yi, CHEN Yuting, SUN Wenyao. Research on region dispatch method for multi microgrid distribution system and market trading strategy[J]. Power System Technology, 2022, 46(1): 47-56.
[2]	李伟, 韩瑞迪, 孙晨家, 等. 基于用电偏好的可平移负荷参与需求响应最优激励合同与激励策略[J]. 中国电机工程学报, 2021, 41(S1): 185-193.
	LI Wei, HAN Ruidi, SUN Chenjia, et al. An optimal Incentive contract and strategy of shiftable loads participation in demand response based on user electricity preference[J]. Proceedings of the CSEE, 2021, 41(S1): 185-193.
[3]	陆永耕, 李建刚, 黄禹铭, 等. 高渗透率分布式电源控制方法[J]. 发电技术, 2021, 42(1): 103-114. doi: 10.12096/j.2096-4528.pgt.19093
	LU Yonggeng, LI Jiangang, HUANG Yuming, et al. High-permeability distributed power control method[J]. Power Generation Technology, 2021, 42(1): 103-114. doi: 10.12096/j.2096-4528.pgt.19093
[4]	HASSAN M U, REHMANI M H, CHEN J. DEAL: differentially private auction for blockchain-based microgrids energy trading[J]. IEEE Transactions on Services Computing, 2020, (2): 263-275.
[5]	ADEFARATI T, BANSAL R C. Reliability, economic and environmental analysis of a microgrid system in the presence of renewable energy resources[J]. Applied Energy, 2019, 236: 1089-1114. doi: 10.1016/j.apenergy.2018.12.050 URL
[6]	吴锦领, 楼平, 管敏渊, 等. 基于非对称纳什谈判的多微网电能共享运行优化策略[J]. 电网技术, 2022, 46(7): 2711-2723.
	WU Jinling, LOU Ping, GUAN Minyuan, et al. Operation optimization strategy of multi-microgrids energy sharing based on asymmetric Nash bargaining[J]. Power System Technology, 2022, 46(7): 2711-2723.
[7]	王韵楚, 张智, 卢峰, 等. 考虑用户行为不确定性的阶梯式需求响应激励机制[J]. 电力系统自动化, 2022, 46(20): 64-73.
	WANG Yunchu, ZHANG Zhi, LU Feng, et al. Stepwise incentive mechanism of demand response considering uncertainty of user behaviors[J]. Automation of Electric Power Systems, 2022, 46(20): 64-73.
[8]	林俐, 张玉, 顾嘉. 基于云模型的激励型区域柔性负荷响应不确定性研究[J]. 电网技术, 2020, 44(11): 4192-4201.
	LIN Li, ZHANG Yu, GU Jia. Uncertainty of regional incentive flexible load based on cloud model[J]. Power System Technology, 2020, 44(11): 4192-4201.
[9]	彭春华, 张金克, 陈露, 等. 计及差异化需求响应的微电网源荷储协调优化调度[J]. 电力自动化设备, 2020, 40(3): 1-7.
	PENG Chunhua, ZHANG Jinke, CHEN Lu, et al. Source-load-storage coordinated optimal scheduling of microgrid considering differential demand response[J]. Electric Power Automation Equipment, 2020, 40(3): 1-7.
[10]	胡蓉, 魏震波, 黄宇涵, 等. 基于交替乘子法与Shapley分配法的多微网联合经济调度[J]. 电力建设, 2021, 42(7): 28-38. doi: 10.12204/j.issn.1000-7229.2021.07.004
	HU Rong, WEI Zhenbo, HUANG Yuhan, et al. Multi-microgrid joint economic dispatch based on alternating multiplier method and shapley distribution method[J]. Electric Power Construction, 2021, 42(7): 28-38. doi: 10.12204/j.issn.1000-7229.2021.07.004
[11]	李得民, 吴在军, 赵波. 多微电网系统的合作博弈模型及其优化调度策略[J]. 中国电机工程学报, 2022, 42(14): 5140-5154.
	LI Demin, WU Zaijun, ZHAO Bo. Cooperative game model and optimal dispatch strategy of multi-microgrid system[J]. Proceedings of the CSEE, 2022, 42(14): 5140-5154.
[12]	杨昭, 艾欣. 考虑电能共享的综合能源楼宇群分布式优化调度[J]. 电网技术, 2020, 44(10): 3769-3778.
	YANG Zhao, AI Xin. Distributed optimal scheduling for integrated energy building clusters considering energy sharing[J]. Power System Technology, 2020, 44(10): 3769-3778.
[13]	WANG D, GUAN X, WU J, et al. Integrated energy exchange scheduling for multi-microgrid system with electric vehicles[J]. IEEE Transactions on Smart Grid, 2016, 7(4): 1762-1774. doi: 10.1109/TSG.2015.2438852 URL
[14]	张凡, 高红均, 吴子豪, 等. 局域能源市场多产消者P2P交易框架设计[J]. 电力自动化设备, 2022, 42(12): 17-25.
	ZHANG Fan, GAO Hongjun, WU Zihao, et al. Design of P2P trading framework for multiple prosumers in local energy market[J]. Electric Power Automation Equipment, 2022, 42(12): 17-25.
[15]	李振伟, 刘洋, 许立雄, 等. 计及信誉值和电气距离的分布式电能交易区块链模型[J]. 电力建设, 2023, 44(2): 132-144. doi: 10.12204/j.issn.1000-7229.2023.02.013
	LI Zhenwei, LIU Yang, XU Lixiong, et al. Distributed energy transaction blockchain model considering reputation value and electrical distance[J]. Electric Power Construction, 2023, 44(2): 132-144. doi: 10.12204/j.issn.1000-7229.2023.02.013
[16]	YANG J, DAI J, GOOI H B, et al. Hierarchical blockchain design for distributed control and energy trading within microgrids[J]. IEEE Transactions on Smart Grid, 2022, 13(4): 3133-3144. doi: 10.1109/TSG.2022.3153693 URL
[17]	PAUDEL A, CHAUDHARI K, LONG C, et al. Peer-to-peer energy trading in a prosumer-based community microgrid: a game-theoretic model[J]. IEEE Transactions on Industrial Electronics, 2019, 66(8), 6087-6097. doi: 10.1109/TIE.2018.2874578 URL
[18]	高红均, 张凡, 刘俊勇, 等. 考虑多产消者差异化特征的社区微电网系统P2P交易机制设计[J]. 中国电机工程学报, 2022, 42(4): 1455-1470.
	GAO Hongjun, ZHANG Fan, LIU Junyong, et al. Design of P2P transaction mechanism considering differentiation characteristics of multiple prosumers in community microgrid system[J]. Proceedings of the CSEE, 2022, 42(4): 1455-1470.
[19]	孙毅, 胡亚杰, 郑顺林, 等. 考虑用户响应特性的综合需求响应优化激励策略[J]. 中国电机工程学报, 2022, 42(4): 1402-1413.
	SUN Yi, HU Yajie, ZHENG Shunlin, et al. Integrated demand response optimization incentive strategy considering users’ response characteristics[J]. Proceedings of the CSEE, 2022, 42(4): 1402-1413.
[20]	周保荣, 黄廷城, 张勇军. 计及激励型需求响应的微电网可靠性分析[J]. 电力系统自动化, 2017, 41(13): 70-78.
	ZHOU Baorong, HUANG Tingcheng, ZHANG Yongjun. Reliability analysis on microgrid considering incentive demand response[J]. Automation of Electric Power Systems, 2017, 41(13): 70-78.
[21]	吴涵, 刘洋, 杨祺铭, 等. 计及需求响应的分布鲁棒博弈区域综合能源系统运行优化策略[J]. 电力建设, 2022, 43(4): 108-118. doi: 10.12204/j.issn.1000-7229.2022.04.012
	WU Han, LIU Yang, YANG Qiming, et al. Optimal RIES operation strategy based on distributionally robust game considering demand response[J]. Electric Power Construction, 2022, 43(4): 108-118. doi: 10.12204/j.issn.1000-7229.2022.04.012
[22]	ZHENG S, SUN Y, LI B, et al. Incentive-based integrated demand response for multiple energy carriers considering behavioral coupling effect of consumers[J]. IEEE Transactions on Smart Grid, 2020, 11(4): 3231-3245. doi: 10.1109/TSG.5165411 URL
[23]	张全明, 崔晓昱, 张笑弟, 等. 计及用户不确定性的多时段耦合需求响应激励优化策略[J]. 中国电机工程学报, 2022, 42(24): 8844-8854.
	ZHANG Quanming, CUI Xiaoyu, ZHANG Xiaodi, et al. Incentive optimization strategy of multi period coupling demand response considering user uncertainty[J]. Proceedings of the CSEE, 2022, 42(24): 8844-8854.
[24]	徐弘升, 陆继翔, 杨志宏, 等. 基于深度强化学习的激励型需求响应决策优化模型[J]. 电力系统自动化, 2021, 45(14): 97-103.
	XU Hongsheng, LU Jixiang, YANG Zhihong, et al. Decision optimization model of incentive demand response based on deep reinforcement learning[J]. Automation of Electric Power Systems, 2021, 45(14): 97-103.
[25]	YU M, HONG S H, DING Y, et al. An incentive-based demand response (DR) model considering composited dr resources[J]. IEEE Transactions on Industrial Electronics, 2019, 66(2), 1488-1498. doi: 10.1109/TIE.41 URL
[26]	段秦刚, 陈永椿, 王一, 等. 电力现货市场下激励型需求响应交易机制及出清模型[J]. 电力建设, 2021, 42(6): 145-156. doi: 10.12204/j.issn.1000-7229.2021.06.015
	DUAN Qingang, CHEN Yongchun, WANG Yi, et al. Trading mechanism and clearing model of incentive demand response in electricity spot market[J]. Electric Power Construction, 2021, 42(6): 145-156. doi: 10.12204/j.issn.1000-7229.2021.06.015
[27]	REZAEI N, PEZHMANI Y, KHAZALI A. Economic-environmental risk-averse optimal heat and power energy management of a grid-connected multi microgrid system considering demand response and bidding strategy[J]. Energy, 2022, 240:122844 - 122844. doi: 10.1016/j.energy.2021.122844 URL
[28]	JALALI M, ZARE K, SEYEDI H. Strategic decision-making of distribution network operator with multi-microgrids considering demand response program[J]. Energy, 2017, 141: 1059-1071. doi: 10.1016/j.energy.2017.09.145 URL
[29]	齐先军, 程桥, 吴红斌, 等. 激励型需求响应对配电网运行可靠性的影响[J]. 电工技术学报, 2018, 33(22): 5319-5326.
	QI Xianjun, CHENG Qiao, WU Hongbin, et al. Impact of incentive-based demand response on operational reliability of distribution network[J]. Transactions of China Electrotechnical Society, 2018, 33(22): 5319-5326.
[30]	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
[31]	GJERSTAD S, DICKHAUT J. Price formation in double auctions[J]. Games and Economic Behavior, 1998, 22(1):1-29. doi: 10.1006/game.1997.0576 URL
[32]	WANG Z, YU X, MU Y, et al. A distributed peer-to-peer energy transaction method for diversified prosumers in urban community microgrid system[J]. Applied Energy, 2020, 260: 114327. doi: 10.1016/j.apenergy.2019.114327 URL