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A Distributed Demand Side Energy Management Algorithm for Smart Grid

Author

Listed:
  • Min-fan He

    (School of Mathematics and Big Data, Foshan University, Foshan 528000, China)

  • Fu-xing Zhang

    (State Grid Key Laboratory of Information & Network Security, Global Energy Interconnection Research Institute, Beijing 102211, China)

  • Yong Huang

    (School of Mathematics and Big Data, Foshan University, Foshan 528000, China)

  • Jian Chen

    (School of Mathematics and Big Data, Foshan University, Foshan 528000, China)

  • Jue Wang

    (School of Physics & information technology, Shaanxi Normal University, Xi’an, 710119 China)

  • Rui Wang

    (National University of Defense Technology, Changsha, 410073, China)

Abstract

This paper proposes a model predictive control (MPC) framework-based distributed demand side energy management method (denoted as DMPC) for users and utilities in a smart grid. The users are equipped with renewable energy resources (RESs), energy storage system (ESSs) and different types of smart loads. With the proposed method, each user finds an optimal operation routine in response to the varying electricity prices according to his/her own preference individually, for example, the power reduction of flexible loads, the start time of shift-able loads, the operation power of schedulable loads, and the charge/discharge routine of the ESSs. Moreover, in the method a penalty term is used to avoid large fluctuation of the user’s operation routines in two consecutive iteration steps. In addition, unlike traditional energy management methods which neglect the forecast errors, the proposed DMPC method can adapt the operation routine to newly updated data. The DMPC is compared with a frequently used method, namely, a day-ahead programming-based method (denoted as DDA). Simulation results demonstrate the efficiency and flexibility of the DMPC over the DDA method.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:426-:d:201772
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    References listed on IDEAS

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    2. Fahad R. Albogamy & Ghulam Hafeez & Imran Khan & Sheraz Khan & Hend I. Alkhammash & Faheem Ali & Gul Rukh, 2021. "Efficient Energy Optimization Day-Ahead Energy Forecasting in Smart Grid Considering Demand Response and Microgrids," Sustainability, MDPI, vol. 13(20), pages 1-29, October.
    3. V, Kavitha & V, Malathi & Guerrero, Josep M. & Bazmohammadi, Najmeh, 2022. "Energy management system using Mimosa Pudica optimization technique for microgrid applications," Energy, Elsevier, vol. 244(PA).
    4. 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.
    5. S. Sofana Reka & Prakash Venugopal & V. Ravi & Tomislav Dragicevic, 2023. "Privacy-Based Demand Response Modeling for Residential Consumers Using Machine Learning with a Cloud–Fog-Based Smart Grid Environment," Energies, MDPI, vol. 16(4), pages 1-16, February.
    6. Lorenzo Bartolucci & Stefano Cordiner & Vincenzo Mulone & Marina Santarelli, 2019. "Ancillary Services Provided by Hybrid Residential Renewable Energy Systems through Thermal and Electrochemical Storage Systems," Energies, MDPI, vol. 12(12), pages 1-18, June.

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