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Effective and Local Constraint-Aware Load Shifting for Microgrid-Based Energy Communities

Author

Listed:
  • Dimitra G. Kyriakou

    (School of Electrical and Computer Engineering, Technical University of Crete, 73100 Chania, Greece)

  • Fotios D. Kanellos

    (School of Electrical and Computer Engineering, Technical University of Crete, 73100 Chania, Greece)

  • George J. Tsekouras

    (Department of Electrical and Electronics Engineering, University of West Attica, 250 Thivon Str., 12241 Athens, Greece)

  • Konstantinos A. Moungos

    (School of Electrical and Computer Engineering, Technical University of Crete, 73100 Chania, Greece)

Abstract

The rising energy demand, coupled with increased integration of distributed energy resources (DERs) and fluctuating renewable generation, underscores the need for effective load management within energy communities. This paper addresses these challenges by implementing effective, constraint-aware load shifting within microgrid-based energy communities. Specifically, the goal of this study is to flatten the electrical load profile of a High-Voltage (HV)/Medium-Voltage (MV) power transformer. The load of a central power transformer includes (a) the diverse, fluctuating electrical and thermal demands of buildings within the energy community and (b) the load of the area supplied by the substation excluding the energy community loads. To achieve a flattened load profile, we apply time shifting to both electrical and heating, ventilation, and air conditioning (HVAC) loads of the energy community, allowing for a redistribution of energy consumption over time. This approach entails shifting non-critical loads, particularly those related to HVAC and other building operations, to off-peak periods. The methodology considers critical operational constraints, such as maintaining occupant thermal comfort, ensuring compliance with building codes, and adhering to technical specifications of HVAC and electrical systems and microgrid organized energy communities. Detailed simulations were conducted to prove the effectiveness of this constraint-aware load-shifting approach.

Suggested Citation

  • Dimitra G. Kyriakou & Fotios D. Kanellos & George J. Tsekouras & Konstantinos A. Moungos, 2025. "Effective and Local Constraint-Aware Load Shifting for Microgrid-Based Energy Communities," Energies, MDPI, vol. 18(2), pages 1-24, January.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:343-:d:1566940
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    References listed on IDEAS

    as
    1. Alibabaei, Nima & Fung, Alan S. & Raahemifar, Kaamran & Moghimi, Arash, 2017. "Effects of intelligent strategy planning models on residential HVAC system energy demand and cost during the heating and cooling seasons," Applied Energy, Elsevier, vol. 185(P1), pages 29-43.
    2. Cui, Qiong & Ma, Peipei & Huang, Lei & Shu, Jie & Luv, Jie & Lu, Lin, 2020. "Effect of device models on the multiobjective optimal operation of CCHP microgrids considering shiftable loads," Applied Energy, Elsevier, vol. 275(C).
    3. Lu, Xinhui & Liu, Zhaoxi & Ma, Li & Wang, Lingfeng & Zhou, Kaile & Feng, Nanping, 2020. "A robust optimization approach for optimal load dispatch of community energy hub," Applied Energy, Elsevier, vol. 259(C).
    4. Rajeev, T. & Ashok, S., 2015. "Dynamic load-shifting program based on a cloud computing framework to support the integration of renewable energy sources," Applied Energy, Elsevier, vol. 146(C), pages 141-149.
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