Abstract
With the increasing penetration of renewable energy generation (such as wind power) in the future power systems, the requirement for peak regulation capacity is becoming an important issue for the utility operators. Energy storage is one of the most effective solutions to address this issue. Under this background, this paper proposes a novel multi-objective optimization model to determine the optimal allocation capacity of energy storage in a thermal power plant for provision of peak regulation service in smart grid. To achieve this, we limit our study to a context based on the compensation mechanism performed in the auxiliary peak regulation market of northeast China. On this basis, an optimal energy storage allocation model in a thermal power plant is proposed, which aims to maximize the total economic profits obtained from peak regulation and renewable energy utilization in the system simultaneously, while considering the operational constraints of energy storage and generation units. A classical weighted summation technique has been used to deal with the bi-objective structure of the problem and a compound solution algorithm based on Generic Algorithm is employed to resolve the developed model. The effectiveness of the proposed methodology is examined based on a real-world regional power system in northeast China and the obtained results verify the effectiveness of our approach.
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Acknowledgements
This work was financially supported by Science and Technology Project of State Grid (Research and Application of Key Technologies for Source and Charge Coordination Operation of Energy Storage Fusion Thermal Power Generation Units in Auxiliary Service of Power Grid Peak Load Adjustment) (2018GWJLDKY02).
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Li, D., Wang, J., Zhang, H., Gong, C. (2020). Optimal Deployment of Energy Storage for Providing Peak Regulation Service in Smart Grid with Renewable Energy Sources. In: Xue, Y., Zheng, Y., Rahman, S. (eds) Proceedings of PURPLE MOUNTAIN FORUM 2019-International Forum on Smart Grid Protection and Control. PMF PMF 2019 2021. Lecture Notes in Electrical Engineering, vol 584. Springer, Singapore. https://doi.org/10.1007/978-981-13-9779-0_75
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DOI: https://doi.org/10.1007/978-981-13-9779-0_75
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