Multi-time scale scheduling for virtual power plants: Integrating the flexibility of power generation and multi-user loads while considering the capacity degradation of energy storage systems
Qiang Li,
Yongcheng Zhou,
Fanchao Wei,
Shuangxiu Li,
Zhonghao Wang,
Jiajia Li,
Guowen Zhou,
Jinfu Liu,
Peigang Yan and
Daren Yu
Applied Energy, 2024, vol. 362, issue C, No S0306261924003635
Abstract:
With the high proportion of renewable energy connected to the grid, the problem of insufficient flexibility in the power system has emerged. Renewable energy and controllable distributed resources can be aggregated and managed through virtual power plants, reducing the need for flexibility to a certain extent. Although building new energy storage systems can compensate for the lack of flexibility, it requires high initial investment costs. To address this, this paper proposes a lease mechanism for coal-fired units based on the combination of carbon credits and prices, providing the right to use coal-fired units for virtual power plants. Subsequently, different demand response strategies are utilized to control the controllable loads of various users, thereby providing certain controllable resources for the virtual power plant. Additionally, to ensure the optimal decision-making of the virtual power plant operator, a cost model accurately describing the capacity degradation state of the energy storage system is adopted. Moreover, a multi-time scale scheduling strategy of virtual power plant is implemented to fully utilize controllable resources of different time scales, effectively dealing with the power imbalance caused by multiple uncertainties. The results show that utilizing the leasing mechanism of coal-fired units and employing demand response strategies from different users can provide flexibility to the virtual power plant. The accuracy of the capacity degradation model used by the operator significantly impacts the optimality of the scheduling plan.
Keywords: Virtual power plant; Lease mechanism; Demand response; Capacity degradation; Multi-time scale scheduling (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (8)
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DOI: 10.1016/j.apenergy.2024.122980
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