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DSO-Aggregator Demand Response Cooperation Framework towards Reliable, Fair and Secure Flexibility Dispatch

Author

Listed:
  • Venizelos Venizelou

    (FOSS Research Centre for Sustainable Energy, PV Technology Laboratory, Department of Electrical and Computer Engineering, University of Cyprus, 1678 Nicosia, Cyprus)

  • Apostolos C. Tsolakis

    (Independent Researcher, 55535 Thessaloniki, Greece)

  • Demetres Evagorou

    (Cyprus Energy Regulatory Authority (CERA), 1305 Nicosia, Cyprus)

  • Christos Patsonakis

    (Information Technologies Institute, Centre for Research and Technology—Hellas, 57001 Thessaloniki, Greece)

  • Ioannis Koskinas

    (Information Technologies Institute, Centre for Research and Technology—Hellas, 57001 Thessaloniki, Greece)

  • Phivos Therapontos

    (Electricity Authority of Cyprus (Distribution System Operator), 1856 Nicosia, Cyprus)

  • Lampros Zyglakis

    (Information Technologies Institute, Centre for Research and Technology—Hellas, 57001 Thessaloniki, Greece)

  • Dimosthenis Ioannidis

    (Information Technologies Institute, Centre for Research and Technology—Hellas, 57001 Thessaloniki, Greece)

  • George Makrides

    (FOSS Research Centre for Sustainable Energy, PV Technology Laboratory, Department of Electrical and Computer Engineering, University of Cyprus, 1678 Nicosia, Cyprus)

  • Dimitrios Tzovaras

    (Information Technologies Institute, Centre for Research and Technology—Hellas, 57001 Thessaloniki, Greece)

  • George E. Georghiou

    (FOSS Research Centre for Sustainable Energy, PV Technology Laboratory, Department of Electrical and Computer Engineering, University of Cyprus, 1678 Nicosia, Cyprus)

Abstract

Unlocking flexibility on the demand side is a prerequisite for balancing supply and demand in distribution networks with high penetration levels of renewable energy sources that lead to high volatility in energy prices. The main means of fully gaining access to the untapped flexibility is the application of demand response (DR) schemes through aggregation. Notwithstanding, to extract the utmost of this potential, a combination of performance-, financial-, and technical-related parameters should be considered, a balance rarely identified in the state of the art. The contribution of this work lies in the introduction of a holistic DR framework that refines the DR-related strategies of the aggregator towards optimum flexibility dispatch, while facilitating its cooperation with the distribution system operator (DSO). The backbone of the proposed DR framework is a novel constrained-objective optimisation function which minimises the aggregator’s costs through optimal segmentation of customer groups based on fairness and reliability aspects, while maintaining the distribution balance of the grid. The proposed DR framework is evaluated on a modified IEEE 33-Bus radial distribution system where a real DR event is successfully executed. The flexibility of the most fair, reliable and profitable sources, identified by the developed optimisation function, is dispatched in an interoperable and secure manner without interrupting the normal operation of the distribution grid.

Suggested Citation

  • Venizelos Venizelou & Apostolos C. Tsolakis & Demetres Evagorou & Christos Patsonakis & Ioannis Koskinas & Phivos Therapontos & Lampros Zyglakis & Dimosthenis Ioannidis & George Makrides & Dimitrios T, 2023. "DSO-Aggregator Demand Response Cooperation Framework towards Reliable, Fair and Secure Flexibility Dispatch," Energies, MDPI, vol. 16(6), pages 1-21, March.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2815-:d:1100608
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    References listed on IDEAS

    as
    1. Li, Songrui & Zhang, Lihui & Nie, Lei & Wang, Jianing, 2022. "Trading strategy and benefit optimization of load aggregators in integrated energy systems considering integrated demand response: A hierarchical Stackelberg game," Energy, Elsevier, vol. 249(C).
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    Cited by:

    1. Faia, Ricardo & Lezama, Fernando & Soares, João & Pinto, Tiago & Vale, Zita, 2024. "Local electricity markets: A review on benefits, barriers, current trends and future perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    2. Chenhui Xu & Yunkai Huang, 2023. "Integrated Demand Response in Multi-Energy Microgrids: A Deep Reinforcement Learning-Based Approach," Energies, MDPI, vol. 16(12), pages 1-19, June.

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