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Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities

Author

Listed:
  • Jing Wang

    (Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, 1111 Engineering Dr, Boulder, CO 80309, USA)

  • Kaitlyn Garifi

    (Department of Electrical, Computer and Energy Engineering, University of Colorado Boulder, 425 UCB #1B55, Boulder, CO 80309, USA)

  • Kyri Baker

    (Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, 1111 Engineering Dr, Boulder, CO 80309, USA
    Renewable and Sustainable Energy Institute, 027 UCB Suite N321, Boulder, CO 80309, USA)

  • Wangda Zuo

    (Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, 1111 Engineering Dr, Boulder, CO 80309, USA
    National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USA)

  • Yingchen Zhang

    (National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USA)

  • Sen Huang

    (Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99354, USA)

  • Draguna Vrabie

    (Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99354, USA)

Abstract

This paper presents a methodology for enhancing community resilience through optimal renewable resource allocation and load scheduling in order to minimize unserved load and thermal discomfort. The proposed control architecture distributes the computational effort and is easier to be scaled up than traditional centralized control. The decentralized control architecture consists of two layers: The community operator layer (COL) allocates the limited amount of renewable energy resource according to the power flexibility of each building. The building agent layer (BAL) addresses the optimal load scheduling problem for each building with the allowable load determined by the COL. Both layers are formulated as a model predictive control (MPC) based optimization. Simulation scenarios are designed to compare different combinations of building weighting methods and objective functions to provide guidance for real-world deployment by community and microgrid operators. The results indicate that the impact of power flexibility is more prominent than the weighting factor to the resource allocation process. Allocation based purely on occupancy status could lead to an increase of PV curtailment. Further, it is necessary for the building agent to have multi-objective optimization to minimize unserved load ratio and maximize comfort simultaneously.

Suggested Citation

  • Jing Wang & Kaitlyn Garifi & Kyri Baker & Wangda Zuo & Yingchen Zhang & Sen Huang & Draguna Vrabie, 2020. "Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities," Energies, MDPI, vol. 13(21), pages 1-29, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5683-:d:437520
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    References listed on IDEAS

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    Cited by:

    1. Bernadette Fina & Miriam Schwebler & Carolin Monsberger, 2022. "Different Technologies’ Impacts on the Economic Viability, Energy Flows and Emissions of Energy Communities," Sustainability, MDPI, vol. 14(9), pages 1-20, April.
    2. Trinadh Pamulapati & Muhammed Cavus & Ishioma Odigwe & Adib Allahham & Sara Walker & Damian Giaouris, 2022. "A Review of Microgrid Energy Management Strategies from the Energy Trilemma Perspective," Energies, MDPI, vol. 16(1), pages 1-34, December.
    3. Ana Ogando-Martínez & Xela García-Santiago & Saúl Díaz García & Fernando Echevarría Camarero & Gonzalo Blázquez Gil & Pablo Carrasco Ortega, 2023. "Optimization of Energy Allocation Strategies in Spanish Collective Self-Consumption Photovoltaic Systems," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    4. Yeon-Ju Choi & Byeong-Chan Oh & Moses Amoasi Acquah & Dong-Min Kim & Sung-Yul Kim, 2021. "Optimal Operation of a Hybrid Power System as an Island Microgrid in South-Korea," Sustainability, MDPI, vol. 13(9), pages 1-18, April.
    5. Fernando V. Cerna & Mahdi Pourakbari-Kasmaei & Luizalba S. S. Pinheiro & Ehsan Naderi & Matti Lehtonen & Javier Contreras, 2021. "Intelligent Energy Management in a Prosumer Community Considering the Load Factor Enhancement," Energies, MDPI, vol. 14(12), pages 1-24, June.

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