基于主从博弈的虚拟电厂实时定价需求响应机制研究

doi:10.16381/j.cnki.issn1003-207x.2023.1944

Abstract

Abstract:

Distributed energy plays an important role in mitigating climate change. Its large-scale application impacts the stability of the power system due to the intermittent property. Virtual power plants （VPPs）， which aggregate distributed energy resources， adjustable loads， and energy storage systems， can effectively regulate load consumption and promote renewable energy consumption. We studied the demand response （DR） mechanism of real-time pricing within the VPP based on the Stackelberg game model， considering two-way electricity transactions between the VPP operator and prosumers， as well as between the VPP operator and the external main grid （MG）. The study focuses on the collaborative scheduling of the MG， the VPP operator， and terminal loads. A distributed genetic algorithm is used to solve the equilibrium strategy for each stakeholder. The numerical simulation results show that：（1） The real-time pricing-based DR mechanism effectively incentivizes the VPP operator to trade with internal loads， smoothing load fluctuations and achieving significant peak-shaving and valley-filling effects. （2） From the users’perspective， both non-generating users and PV prosumers can benefit from participating in DR， while prosumers with only gas turbines have limited overall benefits. （3） The introduction of a real-time pricing DR mechanism effectively improves the benefits of VPPs and facilitates power resource scheduling. Based on these findings， the paper offers three suggestions for strengthening the future development of VPPs： First， VPPs should fully tap the controllable potential of loads， energy storage and other resources， guiding the loads to participate in peak shaving and valley filling through price signals； Second， to incentivize PV prosumers equipped with gas turbines to participate in DR， their opportunities to participate in the ancillary service market should be broadened， maximizing the income from peak balancing potential； Finally， an MG-VPP interconnection system based on artificial intelligence and big data should be developed. This system would integrate the MG’s real-time price and load forecasting information with the VPP’s functions of energy production， energy storage， and demand response.

Key words: demand response, virtual power plant, stackelberg game, real-time pricing

CLC Number:

TM73

Baichen Xie, Lu Zhang, Peng Hao. Real-time Pricing Demand Response Mechanism of Virtual Power Plant Based on Stackelberg Game[J]. Chinese Journal of Management Science, 2024, 32(12): 312-322.

Figures/Tables 9

用户	效用参数		发电成本参数				用电上下限（千瓦时）
用户	$a$	$b$	$α$	$β$	$γ$	$λ$	$e ̲ t$	$e ¯ t$
用户1	0.005	1.5	-	-	-	-	50	200
用户2	0.0015	1.3	-	-	-	0.03	100	700
用户3	0.002	1	0.0005	0.0175	105	-	50	400
用户4	0.001	1.1	0.0005	0.0175	105	0.03	200	700

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价格（元/千瓦时）	谷时段	平时段	峰时段
时段	1-8	13-17，22-24	9-12，18-21
购电价	0.26	0.4976	0.736
售电价	0.325	0.622	0.92

用户	响应前总用电量（千瓦时）	响应后总用电量（千瓦时）
用户1	3107.78	3056.72
用户2	9414.77	8550.80
用户3	4613.23	4599.17
用户4	10513.19	10497.83

用户	原效益	现效益	效益增长
用户1	672.84	1947.01	1274.17
用户2	2459.08	5548.09	3089.01
用户3	-22437.05	-1823.33	20613.72
用户4	8514.63	-213.28	-8727.91

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Real-time Pricing Demand Response Mechanism of Virtual Power Plant Based on Stackelberg Game

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