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Microgrid Energy Management System for Residential Microgrid Using an Ensemble Forecasting Strategy and Grey Wolf Optimization

Author

Listed:
  • Usman Bashir Tayab

    (School of Engineering and Built Environment, Griffith University, Gold Coast, QLD 4215, Australia
    Department of Electrical and Biomedical Engineering, RMIT University, Melbourne, VIC 3001, Australia)

  • Junwei Lu

    (School of Engineering and Built Environment, Griffith University, Gold Coast, QLD 4215, Australia)

  • Seyedfoad Taghizadeh

    (School of Engineering, Macquarie University, Macquarie Park, NSW 2019, Australia)

  • Ahmed Sayed M. Metwally

    (Department of Mathematics, College of Science, King Saud University, Riyadh 11451, Saudi Arabia)

  • Muhammad Kashif

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

Abstract

Microgrid (MG) is a small-scale grid that consists of multiple distributed energy resources and load demand. The microgrid energy management system (M-EMS) is the decision-making centre of the MG. An M-EMS is composed of four modules which are known as forecasting, scheduling, data acquisition, and human-machine interface. However, the forecasting and scheduling modules are considered the major modules from among the four of them. Therefore, this paper proposed an advanced microgrid energy management system (M-EMS) for grid-connected residential microgrid (MG) based on an ensemble forecasting strategy and grey wolf optimization (GWO) based scheduling strategy. In the forecasting module of M-EMS, the ensemble forecasting strategy is proposed to perform the short-term forecasting of PV power and load demand. The GWO based scheduling strategy has been proposed in scheduling module of M-EMS to minimize the operating cost of grid-connected residential MG. A small-scale experiment is conducted using Raspberry Pi 3 B+ via the python programming language to validate the effectiveness of the proposed M-EMS and real-time historical data of PV power, load demand, and weather is adopted as inputs. The performance of the proposed forecasting strategy is compared with ensemble forecasting strategy-1, particle swarm optimization based artificial neural network, and back-propagation neural network. The experimental results highlight that the proposed forecasting strategy outperforms the other strategies and achieved the lowest average value of normalized root mean square error of day-ahead prediction of PV power and load demand for the chosen day. Similarly, the performance of GWO based scheduling strategy of M-EMS is analyzed and compared for three different scenarios. Finally, the experimental results prove the outstanding performance of the proposed scheduling strategy.

Suggested Citation

  • Usman Bashir Tayab & Junwei Lu & Seyedfoad Taghizadeh & Ahmed Sayed M. Metwally & Muhammad Kashif, 2021. "Microgrid Energy Management System for Residential Microgrid Using an Ensemble Forecasting Strategy and Grey Wolf Optimization," Energies, MDPI, vol. 14(24), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8489-:d:703920
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    References listed on IDEAS

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    1. Almada, J.B. & Leão, R.P.S. & Sampaio, R.F. & Barroso, G.C., 2016. "A centralized and heuristic approach for energy management of an AC microgrid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1396-1404.
    2. Meng, Lexuan & Sanseverino, Eleonora Riva & Luna, Adriana & Dragicevic, Tomislav & Vasquez, Juan C. & Guerrero, Josep M., 2016. "Microgrid supervisory controllers and energy management systems: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1263-1273.
    3. Ziogou, Chrysovalantou & Ipsakis, Dimitris & Seferlis, Panos & Bezergianni, Stella & Papadopoulou, Simira & Voutetakis, Spyros, 2013. "Optimal production of renewable hydrogen based on an efficient energy management strategy," Energy, Elsevier, vol. 55(C), pages 58-67.
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    Cited by:

    1. João Fausto L. de Oliveira & Paulo S. G. de Mattos Neto & Hugo Valadares Siqueira & Domingos S. de O. Santos & Aranildo R. Lima & Francisco Madeiro & Douglas A. P. Dantas & Mariana de Morais Cavalcant, 2023. "Forecasting Methods for Photovoltaic Energy in the Scenario of Battery Energy Storage Systems: A Comprehensive Review," Energies, MDPI, vol. 16(18), pages 1-20, September.
    2. Gao, Xinjia & Wu, Xiaogang & Xia, Yinlong & Li, Yalun, 2024. "Life extension of a multi-unit energy storage system by optimizing the power distribution based on the degradation ratio," Energy, Elsevier, vol. 286(C).

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