Optimal Scheduling Strategy for Virtual Power Plant Considering Electricity-Gas-Heat Coupling and Demand Response

doi:10.11930/j.issn.1004-9649.202309050

Abstract

Abstract:

The combined heat and power (CHP) units can not meet the maximum heating efficiency and power peak shaving demand in winter at the same time, and there exit problems of insufficient power generation output regulation ability. In view of the above problems, an optimal scheduling strategy for virtual power plant (VPP) is proposed considering electricity-gas-thermal energy coupling and demand response. Firstly, in order to improve the downward peak shaving capacity of CHP units, the P2G equipment and carbon capture technology are introduced to construct a new CHP coupling model. Secondly, in order to improve the operation flexibility of the system, considering the peak-valley time-of-use electricity price and heat price, a comprehensive demand response mechanism is established. And then, in order to reduce the generation cost of the system, the electric and thermal energy storage devices are introduced, and a VPP bi-level optimization model is established with the goal of minimizing the total cost of the system and the operation cost of the electric and thermal energy storage devices. According to the Karush-Kuhn-Tucher ( KKT ) condition of the lower-level optimization model, the bi-level model is transformed into a single level and linearized for solution. The results show that the carbon emissions, operation cost and new energy consumption rate of the proposed method are optimal, which improves the downward peak shaving capacity of the CHP units and meets the low-carbon and economic requirements of the system.

Key words: heat and power coupling, electric-thermal energy storage, demand response, bi-level optimization, virtual power plant

Shijie WANG, Tianbo FENG, Ning SUN, Ke HE, Jiawen LI, Cheng YANG, Haoyang CUI. Optimal Scheduling Strategy for Virtual Power Plant Considering Electricity-Gas-Heat Coupling and Demand Response[J]. Electric Power, 2024, 57(1): 101-114.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202309050

https://www.electricpower.com.cn/EN/Y2024/V57/I1/101

Figures/Tables 12

Fig.1 Virtual power plant system structure

Fig.2 Comparison of PCC and CHP operation characteristics

Fig.3 The operating characteristic curve of PCC model

Fig.4 The solving process of the bi-level optimization model

Fig.5 Forecast curves of wind power, photovoltaic output and multi-load demand

Fig.6 The time-of-use electric heating price curve

Fig.7 Comparison of power output of cogeneration units

Table 1 Scheduling results of different methods

项目	模式1	模式2	模式3	模式4
风电利用率/%	64.45	91.46	97.24	98.37
光伏利用率/%	65.43	74.75	100.00	100.00
碳排放量/t	1382.65	1376.09	1246.97	1230.24
VPP运行成本/元	537792.14	533556.79	435768.54	403244.09
热电机组成本/元	64309.70	98940.94	81200.49	75478.69
MT成本/元	90435.45	97438.67	105081.20	105175.28
电制冷机成本/元	16445.10	15772.40	12509.42	11715.27
碳交易成本/元	43378.30	43194.20	39135.53	38510.22
弃风弃光成本/元	309780.03	112570.50	11976.44	7055.58
购电购气成本/元	9346.84	3702.28	0	0
VPP系统收益/元	3846.18	5222.37	7752.12	9127.76

Fig.8 VPP internal unit scheduling result

Fig.9 P2G power consumption and carbon capture of CCS system

Fig.10 Optimization results of electric-thermal demand response

Fig.11 Optimal operation results of electric and thermal energy storage

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