基于博弈论的互联微电网多主体协同优化配置

doi:10.16180/j.cnki.issn1007-7820.2021.07.008

Abstract

Abstract:

In view of the problem of planning and configuration of interconnected microgrids, which is composed of multiple microgrids with mutual power supply, a non-cooperative game model of planning and construction of interconnected microgrids is established based on the non-cooperative synergistic optimization mechanism of game theory. In this model, microgrid belonging to different entities is taken as game participant. The optimization goal of each participant is to maximize the annual value of the profit-cost function of the whole life cycle of investment, construction and operation. The Nash equilibrium of this problem is solved using the particle swarm optimization algorithm of integer programming. The simulation results show that the optimal allocation model of interconnected microgrid proposed in this study can provide decision-making theoretical basis for the planning and construction parties in the case of the interaction of planning decisions of different microgrid owners.

Key words: micro grid planning, inter connected microgrids, non-cooper ative game, annual value, integer programming, Nash equilibrium, life cycle cost, synergistic optimization

CLC Number:

TP202

LIANG Longji,ZHANG Jing,HE Yu,ZENG Xixi,CHEN Chaokuan. Optimal Coordination Sizing Analysis of Different Entities Interconnected Microgrids Based on Game Theory[J].Electronic Science and Technology, 2021, 34(7): 43-49.

Figures/Tables 10

Figure 1.

Figure 2.

Table 1.

Table 2.

Table 3.

Table 4.

Table 5.

Table 6.

Table 7.

Table 8.

References 23

[1]	赖清平, 吴志力, 崔凯, 等. 微电网规划设计关键技术分析与展望[J]. 电力建设, 2018,39(2):18-29.
	Lai Qingping, Wu Zhili, Cui Kai, et al. Analysis and prospect of key technologies in micro grid planning and design[J]. Electric Power Construction, 2018,39(2):18-29.
[2]	赵敏, 陈颖, 沈沉, 等. 微电网群特征分析及示范工程设计[J]. 电网技术, 2015,39(6):1469-1476.
	Zhao Min, Chen Ying, Shen Chen, et al. Characteristic analysis and demonstration engineering design of micro electric network group[J]. Power System Technology, 2015,39(6):1469-1476.
[3]	赵敏, 沈沉, 刘锋, 等. 基于博弈论的多微电网系统交易模式研究[J]. 中国电机工程学报, 2015,35(4):848-857.
	Zhao Min, Shen Chen, Liu Feng, et al. A game-theoretic approach to analyzing power trading possibilities in multi-microgrids[J]. Proceedings of The Chinese Society for Electrical Engineering, 2015,35(4):848-857.
[4]	李咸善, 谢烨. 计及小水电收益的微电网优化配置策略[J]. 电力科学与工程, 2019,35(12):1-7.
	Li Xianshan, Xie Ye. Optimal allocation strategy of microgrid considering the benefit of small hydropower[J]. Electric Power Science and Engineering, 2019,35(12):1-7.
[5]	曹智平, 周力行, 张艳萍, 等. 基于供电可靠性的微电网规划[J]. 电力系统保护与控制, 2015,43(14):10-15.
	Cao Zhiping, Zhou Lixing, Zhang Yanping, et al. Micro-grid planning based on supply reliability[J]. Power System Protection and Control, 2015,43(14):10-15.
[6]	Lotfi H, Khodaei A. Static hybird AC/DC microgrid planning[C]. Minnesota:Innovative Smart Grid Technologies Conference,IEEE, 2016.
[7]	Lotfi H, Khodaei A. AC versus DC microgrid planing[J]. IEEE Transactions on Smart Grid, 2017,8(1):296-304. doi: 10.1109/TSG.2015.2457910
[8]	雷小林, 李世春, 余梦诗. 孤岛运行的微电网一次调频备用容量配置方法[J]. 电力科学与工程, 2018,34(12):18-24.
	Lei Xiaolin, Li Shichun, Yu Mengshi. A method of primary frequency reserve capacity of microgrid of isolated island[J]. Electric Power Science and Engineering, 2018,34(12):18-24.
[9]	Salinas S, Li M, Li P. Multi-objective optimal energy consumption scheduling in smart grid[J]. IEEE Transactions on Smart Grid, 2013,4(1):341-348. doi: 10.1109/TSG.2012.2214068
[10]	陈健, 赵波, 王成山, 等. 不同自平衡能力并网型微电网优化配置分析[J]. 电力系统自动化, 2014,38(21):16-18.
	Chen Jian, Zhao Bo, Wang Chengshan, et al. Optimal sizing analysis on grid-connected microgrid with different self-balancing capabilities[J]. Automation of Electric Power System, 2014,38(21):16-18.
[11]	陈健, 刘玉田, 张文, 等. 基于博弈论的配电网中多级微电网优化配置分析[J]. 电力系统自动化, 2016,40(1):45-52.
	Chen Jian, Liu Yutian, Zhang Wen, et al. Optimal sizing analysis of multilevel microgrids in distribution network based on game theory[J]. Automation of Electric Power System, 2016,40(1):45-52.
[12]	田培根, 肖曦, 丁若星, 等. 自治型微电网群多元复合储能系统容量配置方法[J]. 电力系统自动化, 2013,37(1):168-173.
	Tian Peigen, Xiao Xi, Ding Ruoxing, et al. A capacity configuring method of composite energy storage system in autonomous multi-microgrid[J]. Automation of Electric Power System, 2013,37(1):168-173.
[13]	Kroposki B, Lasseter R, Ise T, et al. Making microgrids work[J]. IEEE Power & Energy Magazine, 2008(6):40-53.
[14]	Mei S W, Wang Y Y, Liu F, et al. Game approaches for hybrid power system planning[J]. IEEE Transactions on Sustainable Energy, 2012(3):506-517.
[15]	张晋为, 李国平. 微电网多目标鲁棒优化运行[J]. 电子科技, 2019,32(3):26-30,41.
	Zhang Jinwei, Li Guoping. Economic operation of microgrid considering operational risk and customer satisfaction[J]. Electronic Science and Technology, 2019,32(3):26-30,41.
[16]	唐程辉, 张凡, 张宁, 等. 考虑可再生能源随机性和需求响应的电力系统日前经济调度[J]. 电力系统自动化, 2019,43(15):18-25,63.
	Tang Chenghui, Zhang Fan, Zhang Ning, et al. Day-ahead economic dispatch of power system considering renewable power uncertainty and demand response[J]. Automation of Electric Power Systems, 2019,43(15):18-25,63.
[17]	李建林, 郭斌琪, 牛萌, 等. 风光储系统储能容量优化配置策略[J]. 电工技术学报, 2018,33(6):1189-1196.
	Li Jianlin, Guo Binqi, Niu Meng, et al. Optimal allocation strategy of energy storage capacity of wind and solar energy storage system[J]. Journal of Electrical Technology, 2018,33(6):1189-1196.
[18]	刘春阳, 王秀丽, 刘世民, 等. 计及蓄电池使用寿命的微电网经济调度模型[J]. 电力自动化设备, 2015,35(10):29-36.
	Liu Chunyang, Wang Xiuli, Liu Shimin, et al. Economic dispatch model of microgrid considering battery life[J]. Power Automation Equipment, 2015,35(10):29-36.
[19]	王荔妍, 陈启鑫, 何冠楠, 等. 考虑电池储能寿命模型的发电计划优化[J]. 电力系统自动化, 2019,43(8):93-101.
	Wang Liyan, Chen Qixin, He Guannan, et al. Generation planning optimization considering battery energy storage life model[J]. Automation of Electric Power System, 2019,43(8):93-101.
[20]	Jenkins D P, Fletcher J, Kane D. Lifetime prediction and sizing of lead-acid batteries for microgeneration storage applications[J]. IET Renewable Power Generation, 2008,2(3):191-200. doi: 10.1049/iet-rpg:20080021
[21]	Su W, Wang J, Roh J. Stochastic energy scheduling in microgrids with intermittent renewable energy resources[J]. IEEE Transactions on Smart Grid, 2014,5(4):1876-1883. doi: 10.1109/TSG.2013.2280645
[22]	Duggal I, Venkatesh B. Short-term scheduling of thermal generators and battery storage with depth of dischargebased cost model[J]. IEEE Transactions on Power Systems, 2015,30(4):2110-2118. doi: 10.1109/TPWRS.2014.2352333
[23]	Salinas S, Li M, Li P. Multi-objective optimal energy consumption scheduling in smart grid[J]. IEEE Transactions on Smart Grid, 2013,4(1):341-348. doi: 10.1109/TSG.2012.2214068

名称	规格	购置成本	运行维护费
光伏	9.8 kW	0.4万元/kW	10元/kW/年
风机	10 kW	1万元/台	5元/台/年
储能电池	5 kW/13.5 kW·h	2.1万元/台	5元/台/年

名称	数值	名称	数值
v_ci	4 m·s^-1	σ_e	21.5%
v_N	15 m·s^-1	V_deal	5
v_oc	25 m·s^-1	R₁	0.3
P_{PV_MAX}	70	R₂	0.3
η_c	80%	l	10
η_d	80%	b	0.05
δ	0.2	N_{WT_MAX}	50
T_calendar	10	N_{PV_MAX}	50
r	0.03	N_{BAT_MAX}	50
N₀	5 000	SOC_min	0.2
k_p	0.5	SOC_max	0.8
PR_deal	0.4	E_{ex_MAX}	3 000 kW
PR_gq	0.6	E_{sur_MAX}	5 000 kW
PR_g	1	ε	3

参与者	博弈前/万元	博弈后/万元
MG1	2.319	2.934
MG2	17.362	25.301
MG3	8.842	9.667

参与者	风力发电	光伏发电	储能电池
MG₁	5	20	8
MG₂	7	40	4
MG₃	5	13	0

激励/万元	MG₁	MG₂	MG₃
5.0	(5,20,8)	(7,40,4)	(5,13,0)
5.5	(7,11,0)	(21,27,0)	(2,0,30)
6.0	(0,30,10)	(0,40,40)	(0,15,40)
6.5	(10,30,9)	(18,40,9)	(18,10,40)
7.0	(4,40,10)	(21,40,30)	(11,10,30)

Optimal Coordination Sizing Analysis of Different Entities Interconnected Microgrids Based on Game Theory

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 23

Related Articles 1

Metrics

Comments

Recommended 10

参与者	5.0万元	5.5万元	6.0万元	6.5万元	7.0万元
MG₁	2.934 7	3.125 4	4.079 3	5.673 2	9.044 7
MG₂	25.3010	26.253 9	26.523 1	27.249 9	30.081 6
MG₃	9.666 8	10.917 2	12.141 9	13.860 3	15.840 5

储能电池购置成本/万元	MG₁	MG₂	MG₃
4.2	(22,10,0)	(0,39,0)	(0,10,1)
3.8	(0,10,0)	(9,4,4)	(3,40,1)
3.4	(4,3,0)	(2,39,10)	(0,10,3)
2.0	(5,20,9)	(4,37,10)	(5,10,4)
1.6	(10,6,7)	(8,40,30)	(2,40,10)
1.2	(28,33,10)	(6,40,40)	(12,40,40)

参与者	4.2万元	3.8万元	3.4万元	2.0万元	1.6万元	1.2万元
MG₁	3.205 7	3.607 0	8.768 1	6.789 1	4.179 3	5.251 5
MG₂	18.274 0	18.723 0	19.170 0	20.215 0	21.081 0	21.247 0
MG₃	6.867 4	3.817 3	6.837 6	6.185 6	6.840 5	4.158 3