Market Oriented Low-Carbon Optimal Scheduling of Virtual Power Plants Considering Multiple User-Side Resources Coordination

doi:10.11930/j.issn.1004-9649.202407102

Abstract

Abstract:

To leverage the synergistic effects of diverse user-side resources, enhance the revenue of Virtual Power Plants (VPPs) participating in the joint electricity-carbon market, and mitigate risks arising from the uncertainty of renewable energy sources (such as wind and solar) and electricity prices, an optimized low-carbon dispatching method for VPPs considering the coordination of diverse user-side resources in a market environment is proposed. Firstly, based on the synergistic effects of various user-side resources, a strategy for VPPs to participate in the joint electricity-carbon market operation is formulated. Secondly, a tiered carbon trading model with incentives and penalties for VPPs is established, where different transaction interval prices are set according to carbon trading volumes, thereby achieving carbon-electricity coupling. Finally, an optimized low-carbon dispatching model for VPPs participating in the joint electricity-carbon market is constructed. In this model, Conditional Value at Risk (CVaR) is introduced to measure the relationship between market returns and risks, and the model is transformed into a Mixed Integer Linear Programming (MILP) problem for solution. Through case studies, it is demonstrated that this method can effectively harness the synergistic effects of user-side resources, address the uncertainty risks associated with renewable energy sources and electricity prices, and achieve both economic efficiency and low-carbon performance for VPPs participating in market operations.

Key words: user-side resource, virtual power plant, risk of uncertainty, electricity-carbon combined market, economic dispatch

Mingbing LI, Qiang LI, Xiyang GUAN, Haoyang ZHOU, Rui LU, Yankun FENG. Market Oriented Low-Carbon Optimal Scheduling of Virtual Power Plants Considering Multiple User-Side Resources Coordination[J]. Electric Power, 2025, 58(2): 66-76.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202407102

https://www.electricpower.com.cn/EN/Y2025/V58/I2/66

Figures/Tables 14

Fig.1 VPP participates in the operation strategy of electric carbon joint market

Fig.2 VPP participates in solving flow of electric carbon joint market model

Fig.3 The initial value of the flexible load

Table 1 CDG and energy storage related parameters

CDG：燃气轮机		储能
额定功率/MW	2	额定容量/(MW·h)	10
最小发电功率/MW	0.5	额定充放电功率/MW	2
成本系数a	0.008	荷电状态范围	0.1~0.9
成本系数b	0.0175	初始荷电状态	0.2
成本系数c	105	充放电效率	0.95

Table 2 Peak market hours and prices

项目	时段	价格/(元·(MW·h)^–1)
填谷调峰	01:00—08:00	350
削峰调峰	09:00—12:00，18:00—21:00	500

Fig.4 Day ahead market price data

Fig.5 Scenic forecast data for an area

Fig.6 Typical scene set of scenery and market electricity price

Table 3 Each scenario corresponds to the probability

场景		1		2		3		4		5
概率		0.2187		0.1732		0.2359		0.2129		0.1593

Fig.7 Benefit-risk outcomes under different risk preference coefficients

Table 4 Income comparison under each scenario

场景	VPP总收益/元	电能量市场收益/元	调峰市场收益/元	碳市场收益/元	VPP运行成本/元	碳排放量/t
1	6835.09	9856.75	0	0	3021.66	31.2
2	24555.21	7512.51	19681.63	0	2978.93	29.8
3	9294.07	9639.22	0	2486.13	2831.28	26.8
4	26870.93	7255.11	19510.82	2626.56	2521.56	24.0

Fig.8 VPP electricity market purchase and sale of electricity and electricity price

Fig.9 Power of each body in VPP

Fig.10 Results of energy storage and flexible load optimization

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