Energy-Reserve Joint Scheduling Model of Community Microgrid Based on Improved Gray Wolf Algorithm

ZHANG Yan,WANG Shuang,SONG Chuang

Distributed Energy ›› 2023, Vol. 8 ›› Issue (1) : 19-29.

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Distributed Energy ›› 2023, Vol. 8 ›› Issue (1) : 19-29. DOI: 10.16513/j.2096-2185.DE.2308103
Basic Research

Energy-Reserve Joint Scheduling Model of Community Microgrid Based on Improved Gray Wolf Algorithm

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Abstract

With the increasing proportion of distributed energy connected to the power grid, a large number of prosumers with source load dual attributes will emerge in the community microgrid in the future. In order to fully tap the power complementary potential between prosumers in community microgrid, firstly, the cooperative game mechanism is introduced, and a energy-reserve joint scheduling model for community microgrid is constructed with the participation of multiple prosumers; secondly, the cooperation surplus of the alliance is fairly distributed by using Nash negotiation, combining with risk preference theory and a Sharpey value distribution; finally, the hybrid Gray Wolf algorithm based on Tent mapping is used to solve the constructed model efficiently. The example results show that the community microgrid participating in the joint bidding of energy-reserve joint scheduling at the same time can significantly improve its income level, and the proposed distribution method can realize the fair distribution of the cooperative surplus of all prosumers and ensure the long-term stability of the alliance.

Key words

cooperative game / community microgrid system / energy market / ancillary services market / Nash negotiation

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Yan ZHANG , Shuang WANG , Chuang SONG. Energy-Reserve Joint Scheduling Model of Community Microgrid Based on Improved Gray Wolf Algorithm[J]. Distributed Energy Resources. 2023, 8(1): 19-29 https://doi.org/10.16513/j.2096-2185.DE.2308103

References

[1]
杨洪明,阳泽峰,漆敏,等. 双链式区块链架构设计及其点对点交易优化决策实现[J]. 电力系统自动化2021, 45(9): 19-27.
YANG Hongming, YANG Zefeng, QI Min, et al. Design of double-chain blockchain architecture and its implementation of peer-to-peer transaction[J]. Automation of Electric Power Systems, 2021, 45(9): 19-27.
[2]
吴小汉,张谦,粟尧嘉,等. 基于区块链的私有充电桩共享平台交易策略[J]. 发电技术2022, 43(3): 439-451.
WU Xiaohan, ZHANG Qian, SU Yaojia, et al. Sharing platform trading strategy of private charging pile based on blockchain[J]. Power Generation Technology, 2022, 43(3): 439-451.
[3]
刘杨,刘天羽. 基于区块链和动态定价模型的微电网P2P能源交易[J]. 智慧电力2022, 50(3): 30-36.
LIU Yang, LIU Tianyu. P2P energy trading in microgrid based on blockchain and dynamic pricing model[J]. Smart Power, 2022, 50(3): 30-36.
[4]
艾欣,王坤宇,胡俊杰,等. 基于交互能源机制的产消者群分布式调度方法研究[J]. 电网技术2020, 44(5): 1766-1777.
AI Xin, WANG Kunyu, HU Junjie, et al. Study on distributed scheduling approach of aggregated prosumers based on transactive energy mechanism[J]. Power System Technology, 2020, 44(5): 1766-1777.
[5]
刘迪,孙毅,李彬,等. 计及调节弹性差异化的产消群价格型需求响应机制[J]. 电网技术2020, 44(6): 2062-2070.
LIU Di, SUN Yi, LI Bin, et al. Price-based demand response mechanism of prosumer groups considering adjusting elasticity differentiation[J]. Power System Technology, 2020, 44(6): 2062-2070.
[6]
胡俊杰,王坤宇,艾欣,等. 交互能源:实现电力能源系统平衡的有效机制[J]. 中国电机工程学报2019, 39(4): 953-966.
HU Junjie, WANG Kunyu, AI Xin, et al. Transactive energy: An effective mechanism for balancing electric energy system[J]. Proceedings of the CSEE, 2019, 39(4): 953-966.
[7]
单俊嘉,李阳,胡俊杰. 基于交互能源机制的产消者能量管理方法[J]. 电力建设2019, 40(11): 31-38.
SHAN Junjia, LI Yang, HU Junjie. Application of transactive energy mechanism in prosumer energy management[J]. Electric Power Construction, 2019, 40(11): 31-38.
[8]
高红均,张凡,刘俊勇,等. 考虑多产消者差异化特征的社区微网系统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.
[9]
李彪,万灿,赵健,等. 基于实时电价的产消者综合响应模型[J]. 电力系统自动化2019, 43(7): 81-88.
LI Biao, WAN Can, ZHAO Jian, et al. Comprehensive response model of producers and consumers based on real-time electricity price[J]. Automation of Electric Power Systems, 2019, 43(7): 81-88.
[10]
任洪波,吴琼,刘家明. 耦合区域售电服务的分布式能源产消者经优化与能效评估[J]. 中国电机工程学报2018, 38(13): 3756-3766.
REN Hongbo, WU Qiong, LIU Jiaming. Economic optimization and energy assessment of distributed energy prosumer coupling local electricity retailing services[J]. Proceedings of the CSEE, 2018, 38(13): 3756-3766.
[11]
陈修鹏,李庚银,夏勇. 基于主从博弈的新型城镇配电系统产消者竞价策略[J]. 电力系统自动化2019, 43(14): 97-104.
CHEN Xiupeng, LI Gengyin, XIA Yong. Producer consumer bidding strategy of new urban distribution system based on master-slave game[J]. Automation of Electric Power Systems, 2019, 43(14): 97-104.
[12]
王瀚琳,刘洋,许立雄,等. 基于主从博弈理论的社区微电网配网能量交易模型研究[J]. 电测与仪表2021, 58(6): 68-75.
WANG Hanlin, LIU Yang, XU Lixiong, et al. Research on community micro-grid distribution network energy trading model based on leader-follower game theory[J]. Electrical Measurement & Instrumentation, 2021, 58(6): 68-75.
[13]
郝思鹏,周宇,张前,等. 基于三方非合作博弈的售电侧市场交易策略[J]. 电测与仪表2019, 56(19): 70-75.
HAO Sipeng, ZHOU Yu, ZHANG Qian, et al. Strategy of electric power market transactions based on tripartite non-cooperative game[J]. Electrical Measurement & Instrumentation, 2019, 56(19): 70-75.
[14]
LONG Chao, WU Jianzhong, ZHOU Yue, et al. Peer-to-peer energy sharing through a two-stage aggregated battery control in a community microgrid[J]. Applied Energy, 2018, 226: 261-276.
[15]
LIU N, CHENG M, YU X, et al. Energy-sharing provider for PV prosumer clusters: A hybrid approach using stochastic programming and stackelberg game[J]. IEEE Transactions on Industrial Electronics, 2018, 65(8): 6740-6750.
[16]
RAMAYYA K, SMITH M D, RAHUL T. The economics of peer-to-peer networks[J]. SSRN Electronic Journal, 2003, 9: 31-44.
[17]
周亦洲,孙国强,黄文进,等. 计及电动汽车和需求响应的多类电力市场下虚拟电厂竞标模型[J]. 电网技术2017, 41(6): 1759-1767.
ZHOU Yizhou, SUN Guoqiang, HUANG Wenjin, et al. Strategic bidding model for virtual power plant in different electricity markets considering electric vehicles and demand response[J]. Power System Technology, 2017, 41(6): 1759-1767.
[18]
刘蓉晖,赵增凯,孙改平,等. 考虑不同风险偏好的虚拟电厂优化策略及利润分配[J]. 电力自动化设备2021, 41(4): 154-161.
LIU Ronghui, ZHAO Zengkai, SUN Gaiping, et al. Optimization strategy and profit allocation of virtual power plant considering different risk preference[J]. Electric Power Automation Equipment, 2021, 41(4): 154-161.
[19]
徐青山,杨辰星,颜国庆. 计及规模化空调热平衡惯性的电力负荷日前削峰策略[J]. 电网技术2016, 40(1): 156-163.
XU Qingshan, YANG Chenxing, YAN Guoqing. Strategy of day-ahead power peak load shedding considering thermal equilibrium inertia of large-scale air conditioning loads[J]. Power System Technology, 2016, 40(1): 156-163.
[20]
宋爽,李中伟,刘勇,等. 住宅小区负荷群用电优化策略研究[J]. 电测与仪表2021, 58(8): 57-66.
SONG Shuang, LI Zhongwei, LIU Yong, et al. Study on optimization strategy of load group power consumption in residential area[J]. Electrical Measurement & Instrumentation, 2021, 58(8): 57-66.
[21]
HU J, WU J, AI X, et al. Coordinated energy management of prosumers in a distribution system considering network congestion[J]. IEEE Transactions on Smart Grid, 2021, 12(1): 468-478.
[22]
滕志军,吕金玲,郭力文,等. 一种基于Tent映射的混合灰狼优化的改进算法[J]. 哈尔滨工业大学学报2018, 50(11): 40-49.
TENG Zhijun, Jinling, GUO Liwen, et al. An improved hybrid grey wolf optimization algorithm based on Tent mapping[J]. Journal of Harbin Institute of Technology, 2018, 50(11): 40-49.
[23]
SUN G Q, QIAN W H, HUANG W J, et al. Stochastic adaptive robust dispatch for virtual power plants using the binding scenario identification approach[J]. Energies, 2019, 12: 1918.

Funding

Key Scientific Research of Colleges and Universities in Henan Province(22B880047)
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