COST ALLOCATION STRATEGY FOR DEEP PEAK REGULATION OF THERMAL POWER UNITS CONSIDERING WIND AND SOLAR POWER INTEGRATION AND LOAD ALLIANCE GAME THEORY

Li Yongqing; Xiong Junhua; Wang Tingling; Kang Yi; Li Runnan; Lu Zifan

doi:10.19912/j.0254-0096.tynxb.2024-2314

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (7) : 117-126. DOI: 10.19912/j.0254-0096.tynxb.2024-2314

Special Topics of Academic Papers at the 38th Annual Meeting of the China Association for Science and Technology

COST ALLOCATION STRATEGY FOR DEEP PEAK REGULATION OF THERMAL POWER UNITS CONSIDERING WIND AND SOLAR POWER INTEGRATION AND LOAD ALLIANCE GAME THEORY

Li Yongqing^1,2, Xiong Junhua², Wang Tingling¹, Kang Yi¹, Li Runnan¹, Lu Zifan¹

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Abstract

Traditional regional power grid scheduling often faces conflicts of interest between different power generation entities and loads due to the cost allocation of deep peak regulation for thermal power units. To address this issue,this paper proposes a deep peak regulation cost allocation strategy for thermal power units, which incorporates wind and solar power accommodation as well as a load alliance game mechanism. To enhance the participation incentives of wind, solar, and thermal power enterprises for participating in deep peak regulation, a Shapley value-based cost allocation mechanism is introduced for strategic analysis. First, an optimal scheduling model is developed, which incorporates thermal power plants, wind farms, photovoltaic power stations, and pumped storage stations, aiming to minimize the total system peak regulation cost. The model is constructed with the objective of minimizing the system-wide cost incurred by peak regulation operations. Then, the marginal peak regulation cost is calculated based on the thermal power unit market clearing results. Simulation results demonstrate that applying the Shapley value method to allocate peak-shaving costs among alliance members results in actual cost shares for user-side entities that closely align with their respective marginal peak-shaving costs. Compared to the traditional allocation method based on electricity consumption, the relative deviations are -2.97%,-3.25%, and -2.38% versus -0.78%, -26.85%, and 7.58%, respectively. The corresponding mean absolute errors （MAEs） are 2.37% and 13.88%, indicating a reduction of 11.51 percentage points. Furthermore, by varying the grid-connected capacity multiples of wind and solar generation from 0.6 to 1.6 and 0.0 to 2.0, respectively, the corresponding peak shaving effects and the cost allocation for each participating entity in deep peak regulation are analyzed. The results provide insights into developing a more equitable and rational cost-sharing strategy for wind and solar power integration and the deep peak regulation of thermal power units.

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power plants / renewable energy resources / game theory / deep peak regulation / cost benefit analysis

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Li Yongqing, Xiong Junhua, Wang Tingling, Kang Yi, Li Runnan, Lu Zifan. COST ALLOCATION STRATEGY FOR DEEP PEAK REGULATION OF THERMAL POWER UNITS CONSIDERING WIND AND SOLAR POWER INTEGRATION AND LOAD ALLIANCE GAME THEORY[J]. Acta Energiae Solaris Sinica. 2025, 46(7): 117-126 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2314

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