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Privacy-preserving mechanism for collaborative operation of high-renewable power systems and industrial energy hubs

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  • Oskouei, Morteza Zare
  • Mohammadi-Ivatloo, Behnam
  • Abapour, Mehdi
  • Shafiee, Mahmood
  • Anvari-Moghaddam, Amjad

Abstract

Nowadays, achieving operational solutions to boost the flexibility of bulk power systems has become one of the major challenges in both industry and academia. The outcomes of recent studies demonstrate that the deployment of large-scale energy hubs can help enhance the flexibility of power systems. However, centralized management of networked energy hubs may not be compatible with the power system operator when they are managed by private owners. Motivated by this observation, a privacy-preserving decision-making structure is proposed in this paper for the collaborative operation of private industrial energy hubs and renewable power system by considering the high penetration of renewable energy sources. The proposed structure is drawn up based on the decentralized two-stage robust–stochastic approach and solved using the Benders decomposition algorithm by relying on the private ownership of various entities. The main objectives of this study lie in (1) decreasing renewable power curtailment and (2) minimizing the total operation costs of the private entities. To achieve these objectives, the effects of the multi-energy demand response program and energy conversion facilities are investigated in the context of the developed model. The competency and robustness of the proposed collaborative decision-making structure are examined on the IEEE 30-bus test system using GAMS and DIgSILENT PowerFactory software. Results show that if industrial energy hubs are successfully deployed in industrial parks, the total operation cost of the renewable power system decreases by up to 16.33%, renewable power curtailment reduces by 92.9%, and flexibility of the renewable power system enhances by increasing spinning reserve.

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  • Oskouei, Morteza Zare & Mohammadi-Ivatloo, Behnam & Abapour, Mehdi & Shafiee, Mahmood & Anvari-Moghaddam, Amjad, 2021. "Privacy-preserving mechanism for collaborative operation of high-renewable power systems and industrial energy hubs," Applied Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:appene:v:283:y:2021:i:c:s0306261920317190
    DOI: 10.1016/j.apenergy.2020.116338
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    8. Akbari, Ehsan & Mousavi Shabestari, Seyed Farzin & Pirouzi, Sasan & Jadidoleslam, Morteza, 2023. "Network flexibility regulation by renewable energy hubs using flexibility pricing-based energy management," Renewable Energy, Elsevier, vol. 206(C), pages 295-308.
    9. Fan, Linyuan & Ji, Dandan & Lin, Geng & Lin, Peng & Liu, Lixi, 2023. "Information gap-based multi-objective optimization of a virtual energy hub plant considering a developed demand response model," Energy, Elsevier, vol. 276(C).
    10. Stennikov, Valery & Barakhtenko, Evgeny & Mayorov, Gleb & Sokolov, Dmitry & Zhou, Bin, 2022. "Coordinated management of centralized and distributed generation in an integrated energy system using a multi-agent approach," Applied Energy, Elsevier, vol. 309(C).
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