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An integrated tool for optimal energy scheduling and power quality improvement of a microgrid under multiple demand response schemes

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  • Thomas, Dimitrios
  • D’Hoop, Gaspard
  • Deblecker, Olivier
  • Genikomsakis, Konstantinos N.
  • Ioakimidis, Christos S.

Abstract

This paper presents an integrated tool to mitigate power quality issues in a microgrid through coordinating the operating schedule of its generating resources and loads. Such a microgrid includes renewable and conventional distributed energy resources, electric vehicles, energy storage, linear and nonlinear loads, while it serves as an example small-to-medium scale residential and commercial buildings. The proposed tool operates on a sequential, two-stage basis: at the first stage the energy management system (EMS) ensures that the microgrid’s generation resources and loads are dispatched at the minimum total system cost. In addition, it assesses the potential provision of flexibility services towards the system operator, relying on financially incentivized power signal requests. At the second stage, the power quality (PQ) framework evaluates whether the proposed optimal solution complies or not with several PQ standards applicable to the distribution level. The unique characteristic of the proposed tool is the self-triggered interaction between the EMS and the PQ framework, which identifies potential PQ violations, and restores the PQ indices to acceptable levels through an iterative process. Case studies have been performed with realistic model parameters to verify the performance of the proposed integrated tool. The obtained results demonstrate the effectiveness of the algorithm in managing voltage deviations, voltage unbalance, as well as harmonic distortions with a small additional cost for the total system.

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  • Thomas, Dimitrios & D’Hoop, Gaspard & Deblecker, Olivier & Genikomsakis, Konstantinos N. & Ioakimidis, Christos S., 2020. "An integrated tool for optimal energy scheduling and power quality improvement of a microgrid under multiple demand response schemes," Applied Energy, Elsevier, vol. 260(C).
  • Handle: RePEc:eee:appene:v:260:y:2020:i:c:s030626191932001x
    DOI: 10.1016/j.apenergy.2019.114314
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    References listed on IDEAS

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    6. Lyu, Lin & Yang, Xinran & Xiang, Yue & Liu, Junyong & Jawad, Shafqat & Deng, Runqi, 2020. "Exploring high-penetration electric vehicles impact on urban power grid based on voltage stability analysis," Energy, Elsevier, vol. 198(C).
    7. Tostado-Véliz, Marcos & Kamel, Salah & Hasanien, Hany M. & Turky, Rania A. & Jurado, Francisco, 2022. "Uncertainty-aware day-ahead scheduling of microgrids considering response fatigue: An IGDT approach," Applied Energy, Elsevier, vol. 310(C).
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    9. Afentoulis, Konstantinos D. & Bampos, Zafeirios N. & Vagropoulos, Stylianos I. & Keranidis, Stratos D. & Biskas, Pantelis N., 2022. "Smart charging business model framework for electric vehicle aggregators," Applied Energy, Elsevier, vol. 328(C).
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    11. De Mel, Ishanki & Klymenko, Oleksiy V. & Short, Michael, 2024. "Discrete optimal designs for distributed energy systems with nonconvex multiphase optimal power flow," Applied Energy, Elsevier, vol. 353(PB).
    12. Zhao, Liyuan & Yang, Ting & Li, Wei & Zomaya, Albert Y., 2022. "Deep reinforcement learning-based joint load scheduling for household multi-energy system," Applied Energy, Elsevier, vol. 324(C).
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    14. Wang, Y. & Rousis, A. Oulis & Strbac, G., 2022. "Resilience-driven optimal sizing and pre-positioning of mobile energy storage systems in decentralized networked microgrids," Applied Energy, Elsevier, vol. 305(C).

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