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TSO/DSO Coordination for RES Integration: A Systematic Literature Review

Author

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  • Talal Alazemi

    (Department of Electronic and Electrical Engineering, Brunel University, London UP8 3PH, UK)

  • Mohamed Darwish

    (Department of Electronic and Electrical Engineering, Brunel University, London UP8 3PH, UK)

  • Mohammed Radi

    (UK Power Networks, London SE1 6NP, UK)

Abstract

The increasing penetration of large-scale Renewable Energy Sources (RESs) has raised several challenges for power grid operation. Power management solutions supporting the integration of RESs, such as those based on energy storage technologies, are generally costly. Alternatively, promoting a more proactive role of the Distribution System Operator (DSO) to successfully manage RESs’ uncertainty, and take advantage of their flexible resources for the provision of ancillary services, can avoid installing expensive devices in the network and reduce costs. In this line, improved coordination between Transmission System Operators (TSOs) and DSOs is highly desirable. In this paper, the feasibility of solving different aspects of the integration of RESs through an improved TSO/DSO coordination is evaluated. In particular, a Systematic Literature Review (SLR) is conducted to study the most relevant TSO/DSO coordination approaches, exclusively focused on integrating distributed RESs, currently available in the literature. Their main operational, managerial, economic, and computational challenges, advantages, and disadvantages are discussed in detail to identify the most promising research trends and the most concerning research gaps to pave the way for future research toward developing a solid TSO/DSO coordination mechanism for integrating RESs efficiently. The main results of the SLR show a clear trend in implementing decentralized TSO/DSO coordination models since they provide efficient facilitation of RESs’ services, while reducing computational burden and communication complexity and, consequently, reducing operative costs. In addition, while different aspects of the TSO/DSO coordination implementation, such as reactive power and voltage regulation, operational cost minimization, operational planning, and congestion management, have been thoroughly addressed in the literature, further research is needed regarding data exchange mechanisms and RESs’ uncertainty modeling and prediction. In this line, the development of standardized communication solutions, based on the Common Grid Model Exchange Standard (CGMES) of the International Electrotechnical Commission (IEC), has shown promising interoperability results, whereas the use of learning-based approaches to predict RESs’ uncertain behavior and distribution networks’ responses, using only historical data, which relieves the need for access to commercially sensitive and proprietary network data, has also shown itself to be a promising research direction.

Suggested Citation

  • Talal Alazemi & Mohamed Darwish & Mohammed Radi, 2022. "TSO/DSO Coordination for RES Integration: A Systematic Literature Review," Energies, MDPI, vol. 15(19), pages 1-26, October.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7312-:d:933846
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    References listed on IDEAS

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    1. Yuan, Zhao & Hesamzadeh, Mohammad Reza, 2017. "Hierarchical coordination of TSO-DSO economic dispatch considering large-scale integration of distributed energy resources," Applied Energy, Elsevier, vol. 195(C), pages 600-615.
    2. Hélène Le Cadre & Ilyès Mezghani & Anthony Papavasiliou, 2019. "A game-theoretic analysis of transmission-distribution system operator coordination," LIDAM Reprints CORE 2996, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Srđan Skok & Ahmed Mutapčić & Renata Rubesa & Mario Bazina, 2020. "Transmission Power System Modeling by Using Aggregated Distributed Generation Model Based on a TSO—DSO Data Exchange Scheme," Energies, MDPI, vol. 13(15), pages 1-15, August.
    4. Le Cadre, Hélène & Mezghani, Ilyès & Papavasiliou, Anthony, 2019. "A game-theoretic analysis of transmission-distribution system operator coordination," European Journal of Operational Research, Elsevier, vol. 274(1), pages 317-339.
    5. Eid, Cherrelle & Codani, Paul & Perez, Yannick & Reneses, Javier & Hakvoort, Rudi, 2016. "Managing electric flexibility from Distributed Energy Resources: A review of incentives for market design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 237-247.
    6. Karimi, Hamid & Jadid, Shahram, 2020. "Optimal energy management for multi-microgrid considering demand response programs: A stochastic multi-objective framework," Energy, Elsevier, vol. 195(C).
    7. Liu, Jia & Cheng, Haozhong & Zeng, Pingliang & Yao, Liangzhong & Shang, Ce & Tian, Yuan, 2018. "Decentralized stochastic optimization based planning of integrated transmission and distribution networks with distributed generation penetration," Applied Energy, Elsevier, vol. 220(C), pages 800-813.
    8. David Sebastian Stock & Francesco Sala & Alberto Berizzi & Lutz Hofmann, 2018. "Optimal Control of Wind Farms for Coordinated TSO-DSO Reactive Power Management," Energies, MDPI, vol. 11(1), pages 1-25, January.
    9. Tran, Jacob & Madlener, Reinhard & Fuchs, Alexander, 2016. "Economic Optimization of Electricity Supply Security in Light of the Interplay between TSO and DSO," FCN Working Papers 21/2016, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    10. Cherrelle Eid & Paul Codani & Yannick Perez & Javier Reneses & Rudi Hakvoort, 2016. "Managing electric flexibility from Distributed Energy Resources: A review of incentives for market design," Post-Print hal-01792419, HAL.
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    1. Rebenaque, Olivier & Schmitt, Carlo & Schumann, Klemens & Dronne, Théo & Roques, Fabien, 2023. "Success of local flexibility market implementation: A review of current projects," Utilities Policy, Elsevier, vol. 80(C).
    2. Vijay, Rohit & Mathuria, Parul, 2024. "Common TSO-DSO market framework with no upfront priority to utilize DER flexibility," Energy, Elsevier, vol. 299(C).

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