Virtual Power Plant Quotation Strategy Based on Information Gap Decision Theory

doi:10.11930/j.issn.1004-9649.202308122

Abstract

Abstract:

To further enhance the regulatory potential of distributed energy resource (DER), based on the information gap decision theory (IGDT), the bidding methods for virtual power plants (VPPs) participating in demand response (DR) strategies are divided into three strategy models: balanced, conservative and aggressive, and the robust and opportunity functions are designed for each strategy to optimize different types of decisions. Meanwhile, a ε-constraint model is set with consideration of the trade-off between carbon emissions and profits. The advantages and necessity of the proposed method were verified using an IEEE18 node system as the simulation environment. The simulation results show that the conservative VPP can ensure the minimum critical profit when the future price falls into the maximum robustness range; the progressive VPP can benefit from unexpected price fluctuations and achieve expected profits.

Key words: virtual power plant, information gap decision theory, robustness, opportunity function deposition

Mengfei XIE, Gaoquan MA, Bin LIU, Zhenning PAN, Yunfeng SHANG. Virtual Power Plant Quotation Strategy Based on Information Gap Decision Theory[J]. Electric Power, 2024, 57(1): 40-50.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202308122

https://www.electricpower.com.cn/EN/Y2024/V57/I1/40

Figures/Tables 16

Fig.1 Weibull power distribution

Fig.2 Beta power distribution

Table 1 Main parameters of DER in VPP

项目	DG2	DG7	DG8	DG14
最大功率/MW	4	5	5.5	7
最小功率/MW	0	0	0	0
电价/(元·(MW·h)^–1)	280	266	245	315
斜坡速率/(MW·h^–1)	1	1.25	1.375	1.75
碳排放量(kg·(MW·h)^–1)	5.44	4.08	6.8	3.18

Fig.3 VPP topology

Fig.4 Predicted prices and robustness region with λ= 0.25

Fig.5 Generation under medium risk scenarios

Fig.6 Optimal robustness variable ξ and deviation factor from profit λ

Fig.7 Robust scheduling of some generator units in VPP under two different critical profits

Fig.8 Robust variable ξ and profit

Fig.9 Profit and θ in opportunity cases

Fig.10 Opportunity scheduling of some generator units in VPP under two different critical profits

Fig.11 Profit and $\tau $ in opportunity cases

Fig.12 Accumulated profit of VPP for new/ traditional energy and emissions

Fig.13 Pareto optimal solution for accumulated profit and emissions

Fig.14 Pareto optimal solutions for the membership functions of income, emissions, and various scenarios

Table 2 Generation power and exchange power in VPP

策略模型	发电厂的发电功率/MW						交换功率/MW			削减功率P_DL/MW	收益/元
策略模型	DG2	DG7	DG8	DG14	风电	光伏	GSP1	GSP11	GSP16	削减功率P_DL/MW	收益/元
平衡型	58.90	90.74	123.75	1.75	17.528	29.134	–565.6	350.7	239.1	1.76	93694.51
保守型（Pareto No.7）	89.99	112.50	46.80	25.32	17.528	29.134	–586.4	390.1	221.0	1.79	91256.83

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