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HVAC-Based Cooperative Algorithms for Demand Side Management in a Microgrid

Author

Listed:
  • Jie Ma

    (Department of Engineering, Lancaster University, Lancaster LA1 4YW, UK)

  • Xiandong Ma

    (Department of Engineering, Lancaster University, Lancaster LA1 4YW, UK)

  • Suzana Ilic

    (Lancaster Environment Center, Lancaster University, Lancaster LA1 4YW, UK)

Abstract

The high penetration of renewable power generators and various loads have brought a great challenge for dispatching energy in a microgrid system. Heating ventilation air conditioning (HVAC) system, as a household appliance with high popularity, can be considered as an effective technology to alleviate energy dispatch issues. This paper presents novel distributed algorithms based on HVAC to solve the demand side management problem, where the microgrid system with HVAC units is considered as a multi-agent system (MAS). The approach provides a desirable operating frequency signal for each HVAC based on the power mismatch value occurring on each local bus. It utilizes demand response of the HVAC units to minimize the supply-demand mismatch, thus reducing the quantity and capacity of energy storage devices potentially to be required. Compared with existing approaches focusing on the distributed algorithms under a fixed communication network, this paper addresses a consensus problem under a switching topology by using the Lyapunov argument. It is verified that a jointly strong and connected topology is a sufficient condition in order to achieve an average consensus for a time-varying topology. A number of cases are studied to evaluate the effectiveness of the algorithms by taking into account its power constraints, dynamic behaviors, anti-damage characteristics and time-varying communication topology. Modelling these system interactions has demonstrated the feasibility of the proposed microgrid system.

Suggested Citation

  • Jie Ma & Xiandong Ma & Suzana Ilic, 2019. "HVAC-Based Cooperative Algorithms for Demand Side Management in a Microgrid," Energies, MDPI, vol. 12(22), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4276-:d:285313
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    References listed on IDEAS

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    1. Manuel Castillo-Cagigal & Eduardo Matallanas & Estefanía Caamaño-Martín & Álvaro Gutiérrez Martín, 2018. "SwarmGrid: Demand-Side Management with Distributed Energy Resources Based on Multifrequency Agent Coordination," Energies, MDPI, vol. 11(9), pages 1-16, September.
    2. Nikmehr, Nima & Najafi-Ravadanegh, Sajad & Khodaei, Amin, 2017. "Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty," Applied Energy, Elsevier, vol. 198(C), pages 267-279.
    3. Min-fan He & Fu-xing Zhang & Yong Huang & Jian Chen & Jue Wang & Rui Wang, 2019. "A Distributed Demand Side Energy Management Algorithm for Smart Grid," Energies, MDPI, vol. 12(3), pages 1-19, January.
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    Cited by:

    1. Baxter Williams & Daniel Bishop & Patricio Gallardo & J. Geoffrey Chase, 2023. "Demand Side Management in Industrial, Commercial, and Residential Sectors: A Review of Constraints and Considerations," Energies, MDPI, vol. 16(13), pages 1-28, July.
    2. Daniele Ferreira & Sidelmo Silva & Waner Silva & Danilo Brandao & Gilbert Bergna & Elisabetta Tedeschi, 2022. "Overview of Consensus Protocol and Its Application to Microgrid Control," Energies, MDPI, vol. 15(22), pages 1-35, November.
    3. Edward Smith & Duane Robinson & Ashish Agalgaonkar, 2021. "Cooperative Control of Microgrids: A Review of Theoretical Frameworks, Applications and Recent Developments," Energies, MDPI, vol. 14(23), pages 1-34, December.

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