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Transmission-constrained optimal allocation of price-maker wind-storage units in electricity markets

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  • Chabok, Hossein
  • Aghaei, Jamshid
  • Sheikh, Morteza
  • Roustaei, Mahmoud
  • Zare, Mohsen
  • Niknam, Taher
  • Lehtonen, Matti
  • Shafi-khah, Miadreza
  • Catalão, João P.S.

Abstract

This paper proposes an optimal allocation of a Wind-Storage Unit (WSU). Since transmission lines congestion varies according to the size, the location, and the operation of a generation unit in power systems, we assess the optimal location of a unit as a function of its variable operating condition. An independently operated wind-storage unit is assumed as a price-maker that seeks to maximize its market payoff without any prior information on optimally locating the wind and storage units. The main problem is provided as a tri-level optimization problem in which the first level is the WSU profit maximization, the second level is the power system operation cost minimization from the perspective of the independent system operator (ISO), and the third level is the maximization of the robustness of the system by using an appropriate transmission switching interval robust based chance constrained (TSIRC) method in order to minimize the operation cost of the system and transmission lines congestion problem. The tri-level model is converted to a bi-level optimization model by using Karush-Kuhn-Tucker (KKT) conditions provided as a Mathematical Programming with Equilibrium Constraint (MPEC). An effective binary particle swarm optimization algorithm (BPSO) is used in order to find the optimal location of the wind and storage units. Unscented Transform (UT) as a key element is suggested to model the uncertainties associated with the output power of the wind turbines. The proposed method is tested on an IEEE 24-bus test system and the results reveal the validity of this work.

Suggested Citation

  • Chabok, Hossein & Aghaei, Jamshid & Sheikh, Morteza & Roustaei, Mahmoud & Zare, Mohsen & Niknam, Taher & Lehtonen, Matti & Shafi-khah, Miadreza & Catalão, João P.S., 2022. "Transmission-constrained optimal allocation of price-maker wind-storage units in electricity markets," Applied Energy, Elsevier, vol. 310(C).
    • Handle: RePEc:eee:appene:v:310:y:2022:i:c:s0306261922000289
    DOI: 10.1016/j.apenergy.2022.118542
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    References listed on IDEAS

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    1. Ogunjuyigbe, A.S.O. & Ayodele, T.R. & Akinola, O.A., 2016. "Optimal allocation and sizing of PV/Wind/Split-diesel/Battery hybrid energy system for minimizing life cycle cost, carbon emission and dump energy of remote residential building," Applied Energy, Elsevier, vol. 171(C), pages 153-171.
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    3. Zhao, Haoran & Wu, Qiuwei & Hu, Shuju & Xu, Honghua & Rasmussen, Claus Nygaard, 2015. "Review of energy storage system for wind power integration support," Applied Energy, Elsevier, vol. 137(C), pages 545-553.
    4. Roustaei, M. & Niknam, T. & Salari, S. & Chabok, H. & Sheikh, M. & Kavousi-Fard, A. & Aghaei, J., 2020. "A scenario-based approach for the design of Smart Energy and Water Hub," Energy, Elsevier, vol. 195(C).
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    Cited by:

    1. Feng, Wenxiu, 2024. "Predictive day-ahead offering for renewable generators in uncertain spot and balancing markets," DES - Working Papers. Statistics and Econometrics. WS 44216, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Hailun Wang & Yang Li & Feng Wu & Shengming He & Renshan Ding, 2024. "Capacity Optimization of Pumped–Hydro–Wind–Photovoltaic Hybrid System Based on Normal Boundary Intersection Method," Sustainability, MDPI, vol. 16(17), pages 1-26, August.
    3. Zhou, Siyu & Han, Yang & Chen, Shuheng & Yang, Ping & Mahmoud, Karar & Darwish, Mohamed M.F. & Matti, Lehtonen & Zalhaf, Amr S., 2023. "A multiple uncertainty-based Bi-level expansion planning paradigm for distribution networks complying with energy storage system functionalities," Energy, Elsevier, vol. 275(C).
    4. Abdulaziz Almalaq & Saleh Albadran & Mohamed A. Mohamed, 2023. "An Adoptive Miner-Misuse Based Online Anomaly Detection Approach in the Power System: An Optimum Reinforcement Learning Method," Mathematics, MDPI, vol. 11(4), pages 1-22, February.
    5. Abdulaziz Almalaq & Saleh Albadran & Amer Alghadhban & Tao Jin & Mohamed A. Mohamed, 2022. "An Effective Hybrid-Energy Framework for Grid Vulnerability Alleviation under Cyber-Stealthy Intrusions," Mathematics, MDPI, vol. 10(14), pages 1-20, July.
    6. Zhou, Siyu & Han, Yang & Mahmoud, Karar & Darwish, Mohamed M.F. & Lehtonen, Matti & Yang, Ping & Zalhaf, Amr S., 2023. "A novel unified planning model for distributed generation and electric vehicle charging station considering multi-uncertainties and battery degradation," Applied Energy, Elsevier, vol. 348(C).

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