Economic dispatch and profit distribution strategy for multi-agent virtual power plants considering risk preferences

doi:10.3969/j.issn.2097-0706.2024.06.008

Abstract

Abstract:

Virtual power plants （VPPs） are playing an increasingly prominent role in new power systems， while the operational strategies and revenues of a VPP with distributed energy resources（DERs）are affected by the fluctuated outputs of wind turbines and PV generators in the plant. A fair， reasonable and transparent benefit distribution mechanism is the key to maintaining the cooperation between different DERs in a VPP. To motive various entities in a VPP to participate in trades， the profit is allocated among different entities based on their own characteristics and contributions though consultations. An optimal scheduling and profit distribution strategy is proposed for the VPP integrating wind turbines， PV generators， controllable distributed power suppliers and flexible loads considering their risk preferences. The multi-agent VPP participates in the electricity market （EM），and its profit distribution model is built based on Nash-Hassanyi bargaining solution. A numerical analysis is carried out，aiming to maximize the operational efficiency， guide the operation and reduce the operation risk of the VPP. The results verify that the proposed benefit distribution method can effectively balance the interests of all parties， quantify the actual economic contribution of each DER to the VPP， and improve the willingness of each entity to participate in market competition. The benefit allocation mechanism using Nash-Hassanyi solution is superior to that using Shapley value method in terms of allocation rationality and applicability.

Key words: virtual power plant, new power systems, distributed energy resources, electricity market, benefit distribution, risk preference, operational strategy

CLC Number:

TK019：TM73

YU Haibin, LU Wenzhou, TANG Liang, ZHANG Yuchen, ZOU Xiangyu, JIANG Yuliang, LIU Jiabao. Economic dispatch and profit distribution strategy for multi-agent virtual power plants considering risk preferences[J]. Integrated Intelligent Energy, 2024, 46(6): 66-77.

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收益分配方法	成员	改进策略
改进Shapley值法	DERs	评分机制、多因素改进
	WT,PV,CHP,FL和 ESS	调整权重
	WT,EV	引入投标偏差的惩罚机制
市场竞价分配机制	DERs	自适应过程和谐搜索、内部交易市场机制
市场竞价分配机制	VPPs	上层：竞价平衡;下层：多时间尺度最优

成员组合	λ=0.25	λ=0.50	λ=0.75
ES	3 691	3 691	3 691
GT	4 547	4 547	4 547
ES，GT	8 239	8 239	8 239
FL	-26 207	-21 588	-18 770
ES，FL	-21 861	-17 473	-14 397
GT，FL	-20 948	-16 935	-14 251
ES，GT，FL	-17 048	-12 921	-10 020
PV	5 186	3 448	2 340
ES，PV	9 007	7 584	6 556
GT，PV	9 916	8 451	7 796
ES，GT，PV	13 304	12 428	11 301
PV，FL	-20 915	-1 7297	-14 940
ES，PL，PV	-16 449	-15 861	-9 915
GT，PL，PV	-15 355	-14 019	-11 704
ES，GT，PL，PV	-11 640	-8 460	-6 676
WT	36 721	27 616	10 309
ES，WT	42 300	32 842	15 804
GT，WT	41 102	33 103	17 081
ES，GT，WT	47 771	37 266	20 475
FL，WT	11 274	6 966	-5 172
ES，FL，WT	17 087	12 109	-183
GT，FL，WT	16 986	11 758	544
ES，GT，FL，WT	22 552	17 239	4 360
PV，WT	43 128	32 676	13 657
ES，PV，WT	45 859	36 508	18 408
GT，PV ，WT	45 860	37 375	22 648
ES，GT，PV，WT	52 828	42 210	25 866
FL，PV，WT	16 664	10 703	-27 600
ES，FL，PV，WT	21 746	14 952	2 155
GT，FL，PV，WT	24 964	15 041	4343
ES，GT，FL，PV，WT	26 224	26 797	9 004

方法	λ	ES	GT	FL	PV	WT
独立运营	0.25	3 691	4 547	-26 207	5 186	36 721
	0.50
	0.75
Shapley值分配	0.25	37 688	5 359	-24 389	4 682	4 344
	0.50	27 556	4 226	-21 372	4 852	4 132
	0.75	12 285	3 332	-16 904	5 432	4 233
Nash-Harsanyi 讨价还价解分配	0.25	41 396	6 151	-24 440	4 579	4 106
	0.50	31 672	4 370	-25 348	4 683	4 018
	0.75	14 252	3 376	-18 776	5 358	4 186