Cooperative Scheduling of Active Distribution Network Based on Two Layer Master Slave Game

doi:10.11930/j.issn.1004-9649.202412002

Abstract

Abstract:

To solve the problem of insufficient power demand side resources mobilization, this paper proposes an active distribution network bilevel game collaborative dispatch model based on-subject master-slave game and comprehensive demand response. A multi-interest subject differential benefit model is designed to reflect the multi-type power demand response and carbon trading situation. order to seek the optimal goal of themselves, the lower multi-subject master-slave game decision model is proposed, the leader is microgrid operators, the followers include energy storage, distributed power generation operators and load aggregators, etc. The active distribution network bilevel game collaborative dispatch model is established, and the different goal characteristics of the system are. The model is solved by application of solver and the Sparrow Search Algorithm. The simulation example shows that the proposed method can coordinate multi-type power demand response of different levels ensure the economic benefits of the main participants, and improve the overall comprehensive benefits of the system.

Key words: active distribution network, demand response, multiple stakeholders, master-slave game

WANG Shiqian, HAN Ding, WANG Nan, BAI Hongkun, SONG Dawei, HU Caihong. Cooperative Scheduling of Active Distribution Network Based on Two Layer Master Slave Game[J]. Electric Power, 2025, 58(9): 105-114.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202412002

https://www.electricpower.com.cn/EN/Y2025/V58/I9/105

Figures/Tables 8

Fig.1 Typical ADN system architecture

Fig.2 "Operator-MG-ADN" game architecture

Fig.3 Flowchart of solving two layer master-slave game

Fig.4 Improved IEEE33-node distribution network system

Fig.5 Energy transaction price curve

Fig.6 Power load optimization results after demand response

Fig.7 Scenario 5 energy optimization results

Table 1 Case study on the relationship between quantity and price of demand side resource regulation in Shanghai power grid

序号	实际成交量/ 万kW	模拟成交量/ 万kW	模拟成交量误差/%	实际申报价/ (元·(kW·h)^–1)	模拟申报价/ (元·(kW·h)^–1)	模拟申报价误差/%
1	0.66	0.66	0.00	0.24	0.24	0.00
2	1.16	1.12	3.45	0.72	0.69	4.17
3	2.32	2.22	4.31	1.20	1.18	1.67
4	2.52	2.40	4.76	1.56	1.55	0.64
5	12.98	12.88	0.77	1.68	1.69	0.60
6	21.24	21.18	0.28	1.92	1.95	1.56
7	29.38	29.62	0.82	2.16	2.26	4.63
8	36.88	36.88	0.00	2.40	2.40	0.00
出清	30.00	30.00	0.00	2.40	2.40	0.00

References 26

1	王杰, 郑飞, 张鹏城, 等. 基于数据驱动的高比例新能源配电网规划模型[J]. 中国电力, 2025, 58 (3): 175- 182. DOI
	WANG Jie, ZHENG Fei, ZHANG Pengcheng, et al. Model of high-proportion new energy distribution network planning based on data-driven approach[J]. Electric Power, 2025, 58 (3): 175- 182. DOI
2	周洋, 黄德志, 李培栋, 等. 考虑平衡端点相位不对称及光伏接入的低压配电网三相潮流模型[J]. 中国电力, 2024, 57 (10): 190- 198. DOI
	ZHOU Yang, HUANG Dezhi, LI Peidong, et al. A three-phase power flow model for low-voltage distribution networks considering balanced bus phase asymmetry and photovoltaic access[J]. Electric Power, 2024, 57 (10): 190- 198. DOI
3	胡程平, 范明, 刘艾旺, 等. 考虑云储能的多区互联综合能源系统规划[J]. 发电技术, 2024, 45 (4): 641- 650. DOI
	HU Chengping, FAN Ming, LIU Aiwang, et al. Multi-area interconnected integrated energy system planning considering cloud energy storage[J]. Power Generation Technology, 2024, 45 (4): 641- 650. DOI
4	朱法华, 徐静馨. 双碳背景下中国与主要发达国家电力低碳转型比较[J]. 电力科技与环保, 2024, 40 (6): 561- 571.
	ZHU Fahua, XU Jingxin. Comparison of low-carbon transformation in electricity between China and major developed countries under the background of carbon peaking and carbon neutrality[J]. Electric Power Technology and Environmental Protection, 2024, 40 (6): 561- 571.
5	王尚斌, 苑丽伟, 方莹, 等. 计及新能源波动的分布式储能分层控制策略[J]. 山东电力技术, 2024, 51 (9): 74- 84.
	WANG Shangbin, YUAN Liwei, FANG Ying, et al. Distributed energy storage layered control strategy considering renewable energy fluctuations[J]. Shandong Electric Power, 2024, 51 (9): 74- 84.
6	朱自伟, 黄彪, 裘昕月, 等. 基于平抑风光出力波动的主动配电网优化调度[J]. 太阳能学报, 2022, 43 (5): 90- 97.
	ZHU Ziwei, HUANG Biao, QIU Xinyue, et al. Optimal dispatching of active distribution network based on suppressing wind and photovoltaic power fluctuation[J]. Acta Energiae Solaris Sinica, 2022, 43 (5): 90- 97.
7	张凯恒. 计及风电消纳的需求响应博弈模型研究[D]. 南京: 东南大学, 2020.
	ZHANG Kaiheng. Research on demand response game model considering wind power consumption[D]. Nanjing: Southeast University, 2020.
8	希望·阿不都瓦依提, 吕海鹏, 晁勤. 基于非合作博弈的风-光-氢微电网容量优化配置[J]. 电力工程技术, 2022, 41 (2): 110- 118. DOI
	XIWANG Abuduwayit, LYU Haipeng, CHAO Qin. Optimal capacity configuration of wind-photovoltaic-hydrogen microgrid based on non-cooperative game theory[J]. Electric Power Engineering Technology, 2022, 41 (2): 110- 118. DOI
9	刘晓峰, 高丙团, 李扬. 博弈论在电力需求侧的应用研究综述[J]. 电网技术, 2018, 42 (8): 2704- 2711.
	LIU Xiaofeng, GAO Bingtuan, LI Yang. Review on application of game theory in power demand side[J]. Power System Technology, 2018, 42 (8): 2704- 2711.
10	格日勒图, 张立辉, 柴剑雪. 计及微电网群合作博弈的主动配电网能量优化调度二层规划模型[J]. 可再生能源, 2020, 38 (2): 205- 211. DOI
	GE Riletu, ZHANG Lihui, CHAI Jianxue. A bilevel programming model for energy dispatching optimization of active distribution networks considering microgrid group cooperation game[J]. Renewable Energy Resources, 2020, 38 (2): 205- 211. DOI
11	陈昊宇, 黄顺杰, 樊志华, 等. 基于博弈的多微网需求响应[J]. 南方电网技术, 2017, 11 (2): 34- 40.
	CHEN Haoyu, HUANG Shunjie, FAN Zhihua, et al. Demand response of multi-microgrid based on game theory[J]. Southern Power System Technology, 2017, 11 (2): 34- 40.
12	王甜婧, 许阔, 朱永强. 主动配电网的源-网-荷多层博弈经济调度策略[J]. 电力系统保护与控制, 2018, 46 (4): 10- 19. DOI
	WANG Tianjing, XU Kuo, ZHU Yongqiang. Economic dispatch strategy of active distribution network based on source-network-load multi-layer game[J]. Power System Protection and Control, 2018, 46 (4): 10- 19. DOI
13	王俐英, 曾鸣, 赵嘉欣, 等. 计及电力需求响应的多能源协同系统优化运行研究[J]. 电力工程技术, 2021, 40 (1): 2- 9. DOI
	WANG Liying, ZENG Ming, ZHAO Jiaxin, et al. Optimal operation of multi-energy collaborative system considering electricity demand response[J]. Electric Power Engineering Technology, 2021, 40 (1): 2- 9. DOI
14	王凌云, 安晓, 杨波, 等. 考虑负荷聚合商参与下的微网双层两阶段优化调度[J]. 三峡大学学报(自然科学版), 2021, 43 (2): 86- 92.
	WANG Lingyun, AN Xiao, YANG Bo, et al. Double level two-stage optimal scheduling of microgrid with the participation of load aggregator[J]. Journal of China Three Gorges University (Natural Sciences), 2021, 43 (2): 86- 92.
15	GOSWAMI S K, BASU S K. A new algorithm for the reconfiguration of distribution feeders for loss minimization[J]. IEEE Transactions on Power Delivery, 1992, 7 (3): 1484- 1491. DOI
16	王树东, 杜巍, 林莉, 等. 基于合作博弈的需求侧响应下光储微电网优化配置[J]. 电力系统保护与控制, 2018, 46 (1): 129- 137. DOI
	WANG Shudong, DU Wei, LIN Li, et al. Optimal allocation of photovoltaic energy storage microgrid under the demand side response based on cooperative game[J]. Power System Protection and Control, 2018, 46 (1): 129- 137. DOI
17	刘涤尘, 马恒瑞, 王波, 等. 含冷热电联供及储能的区域综合能源系统运行优化[J]. 电力系统自动化, 2018, 42 (4): 113- 120, 141.
	LIU Dichen, MA Hengrui, WANG Bo, et al. Operation optimization of regional integrated energy system with CCHP and energy storage system[J]. Automation of Electric Power Systems, 2018, 42 (4): 113- 120, 141.
18	崔杨, 曾鹏, 王铮, 等. 计及电价型需求侧响应含碳捕集设备的电–气–热综合能源系统低碳经济调度[J]. 电网技术, 2021, 45 (2): 447- 461.
	CUI Yang, ZENG Peng, WANG Zheng, et al. Low-carbon economic dispatch of electricity-gas-heat integrated energy system with carbon capture equipment considering price-based demand response[J]. Power System Technology, 2021, 45 (2): 447- 461.
19	程杉, 黄天力, 魏荣宗. 含冰蓄冷空调的冷热电联供型微网多时间尺度优化调度[J]. 电力系统自动化, 2019, 43 (5): 30- 38.
	CHENG Shan, HUANG Tianli, WEI Rongzong. Multi-time-scale optimal scheduling of CCHP microgrid with ice-storage air-conditioning[J]. Automation of Electric Power Systems, 2019, 43 (5): 30- 38.
20	马文祚, 夏周武. 考虑新能源不确定性的电网运营商多时间尺度鲁棒交易[J]. 山东电力技术, 2024, 51 (8): 49- 58.
	MA Wenzuo, XIA Zhouwu. Multi-time scale robust trading of power grid operators considering new energy uncertainty[J]. Shandong Electric Power, 2024, 51 (8): 49- 58.
21	吴若冰, 张振超. 考虑新能源接入下的配电网线损综合检测方法[J]. 电测与仪表, 2024, 61 (9): 145- 150.
	WU Ruobing, ZHANG Zhenchao. A comprehensive detection method for distribution network line loss considering the integration of new energy source[J]. Electrical Measurement & Instrumentation, 2024, 61 (9): 145- 150.
22	马成廉, 赵宇, 宋萌清, 等. 基于新能源发电品质量化与负荷时序匹配的功率品质划分模型研究[J]. 东北电力大学学报, 2024, 44 (2): 42- 50, 78.
	MA Chenglian, ZHAO Yu, SONG Mengqing, et al. Research on power quality segmentation model based on new EnergyPower generation quality quantification and load timing matching[J]. Journal of Northeast Electric Power University, 2024, 44 (2): 42- 50, 78.
23	弭辙, 胡健祖, 郭珍妮, 等. 新型电力系统体系下新能源发展态势及市场化消纳研究[J]. 山东电力技术, 2023, 50 (10): 1- 8.
	MI Zhe, HU Jianzu, GUO Zhenni, et al. Research on the development situation and market-oriented consumption of renewable energy under new power system architecture[J]. Shandong Electric Power, 2023, 50 (10): 1- 8.
24	黄源烽, 郝飞, 滕井玉, 等. 多能互补能源基地多目标多时间尺度优化调度研究[J]. 内蒙古电力技术, 2024, 42 (4): 32- 41.
	HUANG Yuanfeng, HAO Fei, TENG Jingyu, et al. Research on multi-objective and multi-time-scale optimized scheduling of multi-energy complementary energy bases[J]. Inner Mongolia Electric Power, 2024, 42 (4): 32- 41.
25	荀汉龙, 刘杨, 金旭冉, 等. 考虑新能源场站闲置储能的储能电站两阶段配置优化研究[J]. 浙江电力, 2024, 43 (9): 49- 57.
	XUN Hanlong, LIU Yang, JIN Xuran, et al. Research on two-stage configuration optimization of energy storage power station considering idle energy storage in new energy stations[J]. Zhejiang Electric Power, 2024, 43 (9): 49- 57.
26	许洪华, 邵桂萍, 鄂春良, 等. 我国未来能源系统及能源转型现实路径研究[J]. 发电技术, 2023, 44 (4): 484- 491. DOI
	XU Honghua, SHAO Guiping, E Chunliang, et al. Research on China's future energy system and the realistic path of energy transformation[J]. Power Generation Technology, 2023, 44 (4): 484- 491. DOI

[1]	GAO Fangjie, SUN Yujie, LI Yi, LE Ying, ZHANG Jiguang, XU Chuanbo, LIU Dunnan. Robust Optimization Scheduling of Island Multi-energy Microgrid Considering Offshore Wind Power to Hydrogen [J]. Electric Power, 2025, 58(7): 68-79.
[2]	ZHANG Bohang, QI Jun, XIE Luyao, ZHANG Youbing, ZHANG Boyang. Distributed Model Predictive Frequency Control of Interconnected Power Systems Considering Demand Response [J]. Electric Power, 2025, 58(7): 105-114.
[3]	ZHANG Jie, HUA Yufei, WANG Chen. A Demand Side Adjustment Capacity Sharing Model Based on Cooperative Game [J]. Electric Power, 2025, 58(6): 45-55.
[4]	WEI Chunhui, SHAN Linsen, HU Dadong, GAO Qianheng, ZHANG Xinsong, XUE Xiaocen. Optimal Scheduling Strategy of Park-level Virtual Power Plant for Demand Response [J]. Electric Power, 2025, 58(6): 112-121.
[5]	XU Shijie, HU Bangjie, ZHAO Liang, WANG Pei. Research on Optimal Dispatch with Source-Load Coordination for Micro-energy Grid Based on Energy-Carbon Coupling Model [J]. Electric Power, 2025, 58(4): 1-12.
[6]	XIANG Shilin, XIANG Yue, WANG Yanliang, LU Yu. Optimization Strategy for Spatiotemporal Cooperative Operation of Multiple Data Centers Considering Load Response Characteristics [J]. Electric Power, 2025, 58(4): 170-181.
[7]	Wenjun XU, Gang MA, Yunting YAO, Yuxiang MENG, Weikang LI. Multi-energy Optimal Scheduling of Industrial Parks Considering Green Certificate - Carbon Trading Mechanism and Hydrogen Compressed Natural Gas [J]. Electric Power, 2025, 58(2): 154-163.
[8]	BAO Hongyan, LIU Jicheng, LIU Ziyi, SUN Jiakang. A Two-layer Optimization Model for Active Distribution Networks Considering Electric-Hydrogen Hybrid Energy Storage Capacity Allocation [J]. Electric Power, 2025, 58(10): 27-38.
[9]	TAN Qinliang, ZENG Jiabin, LV Hanyu, HE Jiaming, SHI Chaofan, DING Yihong. Optimization of Distribution-side Distributed Photovoltaic Market Trading Strategies Considering Source-Grid-Load-Storage Coordination [J]. Electric Power, 2025, 58(10): 39-49.
[10]	YANG Xiong, LI Juan, JIANG Yunlong, LI Hongmei, XU Wanyun, LI Xiaolu, GUO Ruipeng. An Optimal Scheduling Method for Demand-Side Resource Clustering in Distribution Networks Based on Customer Directrix Load Reconfiguration [J]. Electric Power, 2025, 58(10): 163-170.
[11]	Lingling TAN, Wei TANG, Dongqing CHU, Jingrui LI, Yumin ZHANG, Xingquan JI. Optimal Dispatching of Electric-Heat-Hydrogen Integrated Energy System Based on Stackelberg Game [J]. Electric Power, 2024, 57(9): 136-145.
[12]	Pengfei FAN, Baoqin LI, Jiangwei HOU, Rong LI, Chongming SONG, Kaijun LIN. Economic Capacity Assessment of Renewables in Distribution Networks [J]. Electric Power, 2024, 57(7): 196-202.
[13]	Jiawu WANG, Dianyun ZHAO, Changfeng LIU, Kang CHEN, Yumin ZHANG. Analytical Target Cascading Based Active Distribution Network Level Multi-agent Autonomous Collaborative Optimization [J]. Electric Power, 2024, 57(7): 214-226.
[14]	Fengliang XU, Keqian WANG, Wenhao WANG, Peng WANG, Wenye WANG, Shuai ZHANG, Fengzhan ZHAO. Optimal Allocation of Hybrid Energy Storage in Low-Voltage Distribution Networks with Incentive-based Demand Response [J]. Electric Power, 2024, 57(6): 90-101.
[15]	Cailing ZHANG, Shuang WANG, Shuna GE, Deng PAN, Yan ZHANG, Wei HAN, Wenyan DUAN. Optimal Scheduling of Integrated Energy Systems Considering Flexible Demand Response and Carbon Emission-Green Certificate Joint Trading [J]. Electric Power, 2024, 57(5): 14-25.