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Energy management system for microgrids using weighted salp swarm algorithm and hybrid forecasting approach

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  • Tayab, Usman Bashir
  • Lu, Junwei
  • Yang, Fuwen
  • AlGarni, Tahani Saad
  • Kashif, Muhammad

Abstract

The concept of a microgrid (MG) has been introduced to integrate the conventional generators, different renewable energy resources and energy storage systems (ESS) to meet the specific load demand. However, the intermittent nature of renewable energy resources produces a variable output, which drives an imbalance between power generation and demand in MG. The ESS is utilized to makes a balance between power generation and demand. When several renewable energy resources and ESS are available in MG as energy resources, then an energy management system (EMS) is required that can handle the stochastic nature of renewable energy resources, schedule the power of renewable energy resources and ESS for managing the power flow among MG resources and main grid while ensuing cost-effective operation. Therefore, this paper proposed an optimum EMS that aims to minimize the overall operating cost of grid-connected MG along with the short-term forecasting of PV power and load demand. The proposed EMS consists of four modules: forecasting, scheduling, data acquisition (DAQ), and human–machine interface (HMI) modules. An improved hybrid forecasting approach that combines a 3-level stationary wavelet transform (SWT) and grey wolf optimization-based least-square support vector machine (GWO-LSSVM) is proposed in the forecasting module to achieve day-ahead forecasting of PV power and load demand. In the scheduling module, the weighted salp swarm algorithm-based scheduling is applied to achieve the optimum power flow of grid-connected MG. Then, the DAQ and HMI module is used to monitor, analyze, and modified the input variables of the forecasting and scheduling module. The MATLAB/Simulink environment is then used to simulate the proposed EMS for grid-connected MG. Finally, numerical results demonstrate the efficiency of the proposed EMS for grid-connected MG with commercial load demand over the existing competitive approaches.

Suggested Citation

  • Tayab, Usman Bashir & Lu, Junwei & Yang, Fuwen & AlGarni, Tahani Saad & Kashif, Muhammad, 2021. "Energy management system for microgrids using weighted salp swarm algorithm and hybrid forecasting approach," Renewable Energy, Elsevier, vol. 180(C), pages 467-481.
  • Handle: RePEc:eee:renene:v:180:y:2021:i:c:p:467-481
    DOI: 10.1016/j.renene.2021.08.070
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    1. Moghaddam, Amjad Anvari & Seifi, Alireza & Niknam, Taher & Alizadeh Pahlavani, Mohammad Reza, 2011. "Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source," Energy, Elsevier, vol. 36(11), pages 6490-6507.
    2. 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.
    3. Mi Li & Huan Chen & Xiaodong Wang & Ning Zhong & Shengfu Lu, 2019. "An Improved Particle Swarm Optimization Algorithm with Adaptive Inertia Weights," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(03), pages 833-866, May.
    4. Elsied, Moataz & Oukaour, Amrane & Youssef, Tarek & Gualous, Hamid & Mohammed, Osama, 2016. "An advanced real time energy management system for microgrids," Energy, Elsevier, vol. 114(C), pages 742-752.
    5. Hossain, Md Alamgir & Pota, Hemanshu Roy & Squartini, Stefano & Abdou, Ahmed Fathi, 2019. "Modified PSO algorithm for real-time energy management in grid-connected microgrids," Renewable Energy, Elsevier, vol. 136(C), pages 746-757.
    6. Bessec, Marie & Fouquau, Julien, 2018. "Short-run electricity load forecasting with combinations of stationary wavelet transforms," European Journal of Operational Research, Elsevier, vol. 264(1), pages 149-164.
    7. Roy, Kallol & Mandal, Kamal Krishna & Mandal, Atis Chandra, 2019. "Ant-Lion Optimizer algorithm and recurrent neural network for energy management of micro grid connected system," Energy, Elsevier, vol. 167(C), pages 402-416.
    8. Tayab, Usman Bashir & Roslan, Mohd Azrik Bin & Hwai, Leong Jenn & Kashif, Muhammad, 2017. "A review of droop control techniques for microgrid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 717-727.
    9. Sukumar, Shivashankar & Mokhlis, Hazlie & Mekhilef, Saad & Naidu, Kanendra & Karimi, Mazaher, 2017. "Mix-mode energy management strategy and battery sizing for economic operation of grid-tied microgrid," Energy, Elsevier, vol. 118(C), pages 1322-1333.
    10. Pascual, Julio & Barricarte, Javier & Sanchis, Pablo & Marroyo, Luis, 2015. "Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting," Applied Energy, Elsevier, vol. 158(C), pages 12-25.
    11. Faridnia, N. & Habibi, D. & Lachowicz, S. & Kavousifard, A., 2019. "Optimal scheduling in a microgrid with a tidal generation," Energy, Elsevier, vol. 171(C), pages 435-443.
    12. Eun-Kyu Lee & Wenbo Shi & Rajit Gadh & Wooseong Kim, 2016. "Design and Implementation of a Microgrid Energy Management System," Sustainability, MDPI, vol. 8(11), pages 1-19, November.
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