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Economic Dispatch of Power Retailers: A Bi-Level Programming Approach via Market Clearing Price

Author

Listed:
  • Hui Zhou

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Jian Ding

    (NARI Technology Corporation Limited, Nanjing 211106, China)

  • Yinlong Hu

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Zisong Ye

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Shang Shi

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Yonghui Sun

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Qiyu Zhang

    (Intelligence Network & Innovation Center, China United Network Communications Corporation Limited, Beijing 100037, China)

Abstract

For power retailers in a smart grid, it is necessary to design an economic dispatch method to maintain a balance between power supply and demand on the sale side as well as obtain better economic benefits. This study concentrates on the economic dispatch of the dominant retailer in a regional market. The dominant retailer is considered to be equipped with generator resources such as distributed photovoltaics (PV), wind turbines (WT), and microturbines (MT). As one retailer cannot exactly predict the market conditions of other retailers, the retail market is considered to be modeled as a dichotomous-market model consisting of the dominant retailer market and the other retailers market. As a result, a bi-level optimal dispatch model is proposed for the dominant power retailer based on the dichotomous-market model. In the proposed model, the outer problem aims to minimize the costs of purchases under time-of-use (TOU) price given in the market clearing process, while the inner problem is formulated to simulate the process of market clearing. Furthermore, the bi-level model is converted to a single-level model via the Karush–Kuhn–Tucker (KKT) conditions and eventually solved by employing the YALMIP toolbox with Gurobi solver. Finally, a case study is conducted to validate the effectiveness and adaptability of the proposed model, and the analysis of the variables is presented.

Suggested Citation

  • Hui Zhou & Jian Ding & Yinlong Hu & Zisong Ye & Shang Shi & Yonghui Sun & Qiyu Zhang, 2022. "Economic Dispatch of Power Retailers: A Bi-Level Programming Approach via Market Clearing Price," Energies, MDPI, vol. 15(19), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7087-:d:926351
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    References listed on IDEAS

    as
    1. Yu Hu & Miguel Armada & Maria Jesus Sanchez, 2021. "Potential utilization of Battery Energy Storage Systems (BESS) in the major European electricity markets," Papers 2112.09816, arXiv.org, revised Jun 2022.
    2. Dadashi, Mojtaba & Haghifam, Sara & Zare, Kazem & Haghifam, Mahmoud-Reza & Abapour, Mehdi, 2020. "Short-term scheduling of electricity retailers in the presence of Demand Response Aggregators: A two-stage stochastic Bi-Level programming approach," Energy, Elsevier, vol. 205(C).
    3. Reddy, S. Surender & Bijwe, P.R., 2015. "Real time economic dispatch considering renewable energy resources," Renewable Energy, Elsevier, vol. 83(C), pages 1215-1226.
    4. Rasoul Akbari & Seyede Zahra Tajalli & Abdollah Kavousi-Fard & Afshin Izadian, 2022. "Economic Operation of Utility-Connected Microgrids in a Fast and Flexible Framework Considering Non-Dispatchable Energy Sources," Energies, MDPI, vol. 15(8), pages 1-24, April.
    5. Li, Yang & Yang, Zhen & Li, Guoqing & Mu, Yunfei & Zhao, Dongbo & Chen, Chen & Shen, Bo, 2018. "Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: A bi-level programming approach via real-time pricing," Applied Energy, Elsevier, vol. 232(C), pages 54-68.
    6. RuthDominguez & Giorgia Oggioni & Yves Smeers, 2019. "Reserve procurement and flexibility services in power systems with high renewable capacity: Effects of integration on different market designs," LIDAM Reprints CORE 3019, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. Grimm, Veronika & Orlinskaya, Galina & Schewe, Lars & Schmidt, Martin & Zöttl, Gregor, 2021. "Optimal design of retailer-prosumer electricity tariffs using bilevel optimization," Omega, Elsevier, vol. 102(C).
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