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Review of Methodologies for the Assessment of Feasible Operating Regions at the TSO–DSO Interface

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  • Georgios Papazoglou

    (School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece)

  • Pandelis Biskas

    (School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece)

Abstract

The Feasible Operating Region (FOR) is defined as a set of points in the PQ plane that includes all the feasible active and reactive power flows at the Transmission System Operator (TSO)–Distribution System Operator (DSO) interconnection. Recent trends in power systems worldwide increase the need of cooperation between the TSO and the DSO for flexibility provision. In the current landscape, the efficient and accurate estimation of the FOR could unlock the potential of the DSO to provide flexibility to the TSO. To that end, much existing research has tackled the problem of FOR estimation, which is a challenging problem. However, no research that adequately organizes the literature exists. This work aims to fill this gap. Three categories of FOR estimation methods were identified: Geometric, Random Sampling, and Optimization-Based methods. The basic principles behind each method are analyzed and the most significant works involving each method are presented. For the reviewed works, we focus on the types of flexibility providing units included in the FOR estimation, the examination of time dependence, and the monetization of the FOR. Finally, the strengths and weaknesses of each category of methods are compared, providing a holistic review of the available FOR estimation methods.

Suggested Citation

  • Georgios Papazoglou & Pandelis Biskas, 2022. "Review of Methodologies for the Assessment of Feasible Operating Regions at the TSO–DSO Interface," Energies, MDPI, vol. 15(14), pages 1-24, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5147-:d:863728
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    References listed on IDEAS

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    1. Tao Hong & Pierre Pinson & Yi Wang & Rafal Weron & Dazhi Yang & Hamidreza Zareipour, 2020. "Energy forecasting: A review and outlook," WORking papers in Management Science (WORMS) WORMS/20/08, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    2. Emrah Öztürk & Klaus Rheinberger & Timm Faulwasser & Karl Worthmann & Markus Preißinger, 2022. "Aggregation of Demand-Side Flexibilities: A Comparative Study of Approximation Algorithms," Energies, MDPI, vol. 15(7), pages 1-25, March.
    3. Marcel Sarstedt & Leonard Kluß & Johannes Gerster & Tobias Meldau & Lutz Hofmann, 2021. "Survey and Comparison of Optimization-Based Aggregation Methods for the Determination of the Flexibility Potentials at Vertical System Interconnections," Energies, MDPI, vol. 14(3), pages 1-27, January.
    4. Aikaterini Forouli & Emmanouil A. Bakirtzis & Georgios Papazoglou & Konstantinos Oureilidis & Vasileios Gkountis & Luisa Candido & Eloi Delgado Ferrer & Pandelis Biskas, 2021. "Assessment of Demand Side Flexibility in European Electricity Markets: A Country Level Review," Energies, MDPI, vol. 14(8), pages 1-23, April.
    5. Jin, Xiaolong & Wu, Qiuwei & Jia, Hongjie, 2020. "Local flexibility markets: Literature review on concepts, models and clearing methods," Applied Energy, Elsevier, vol. 261(C).
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    Cited by:

    1. Georgios Papazoglou & Pandelis Biskas, 2023. "Review and Comparison of Genetic Algorithm and Particle Swarm Optimization in the Optimal Power Flow Problem," Energies, MDPI, vol. 16(3), pages 1-25, January.

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