基于改进灰狼算法的社区微网能量备用联合调度模型

张岩; 王爽; 宋闯

doi:10.16513/j.2096-2185.DE.2308103

PDF(1558 KB)

分布式能源 ›› 2023, Vol. 8 ›› Issue (1) : 19-29. DOI: 10.16513/j.2096-2185.DE.2308103

学术研究

基于改进灰狼算法的社区微网能量备用联合调度模型

张岩 ¹ ,
王爽 ¹ ,
宋闯 ²

作者信息 +

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

Author information +

文章历史 +

摘要

随着分布式能源接入电网比例的不断提高，未来社区微网中将涌现大量源荷双重属性的产消者。为充分挖掘社区微网内部各产消者之间的电能互补潜力，首先引入合作博弈机制，构建考虑多产消者参与下的社区微网能量备用联合调度模型；其次，采用以纳什谈判为主，结合风险偏好理论以及夏普利值的分配方法，对联盟的合作剩余进行公平分配；最后，采用基于Tent映射的混合灰狼算法实现所构模型的高效求解。算例结果表明：社区微网同时参与能量备用联合调度能够显著提高其收益水平，所提分配方法能够实现各产消者合作剩余的公平分配，保证联盟的长久稳定。

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.

导出引用

张岩, 王爽, 宋闯. 基于改进灰狼算法的社区微网能量备用联合调度模型[J]. 分布式能源. 2023, 8(1): 19-29 https://doi.org/10.16513/j.2096-2185.DE.2308103

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

中图分类号： TK01;TM73

参考文献

列表( 原文顺序 | 文献年度倒序 | 文中引用次数倒序 ) 可视化分析

[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. 本文引用 [1]

[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. 本文引用 [1]

[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. 本文引用 [1]

[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. 本文引用 [1]

[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. 本文引用 [1]

[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. 本文引用 [1]

[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. 本文引用 [1]

[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.

本文引用 [1]

[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. 本文引用 [1]

[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. 本文引用 [1]

[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. 本文引用 [1]

[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. 本文引用 [1]

[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. 本文引用 [1]

[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. 本文引用 [1]

[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. 本文引用 [1]

[16]	RAMAYYA K, SMITH M D, RAHUL T. The economics of peer-to-peer networks[J]. SSRN Electronic Journal, 2003, 9: 31-44. 本文引用 [1]

[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.

本文引用 [2]

[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. 本文引用 [1]

[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. 本文引用 [1]

[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. 本文引用 [1]

[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. 本文引用 [1]

[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, LÜ 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. 本文引用 [2]

[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. 本文引用 [1]