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Enhancing demand response and energy management in multi- microgrid systems with renewable energy sources

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  • Satish kumar, S.
  • Pramila, V.
  • Rudhra, S.
  • Vinod, S.
  • Lakshmi, D.

Abstract

The integration of distributed and Renewable Energy Sources (RES) has boosted Microgrid (MG) sustainability, but managing these resources and ensuring reliability, especially during grid disconnections, remains a challenge for networked microgrids. This research presents a unique Energy Management System (EMS) for isolated networked MGs to overcome these problems, featuring Demand Response (DR) program and a new microgrid marginal pricing model to optimize operational efficiency and reduce overall costs. The proposed novel technique is the Metapath Context Convolution-based Heterogeneous Graph Neural Networks (MCCHGNN) and Improved Bald Eagle Search Optimization Algorithm (IBESOA), which is known as MCCHGNN-IBESOA technique. MCCHGNN predicts load demand, while IBESOA optimizes energy dispatch and scheduling. The proposed strategy is run on the MATLAB environment and contrasted with existing techniques such as Transient Search Optimization Algorithm (TSOA), Multi-Objective Improved Slime Mould Algorithm (MOISMA) and Enhanced Multi-Objective Optimization Sparrow Search Algorithm (EMOSSA). The proposed method achieved significant cost reductions across various micro-grid scenarios. For MG1, costs were 5500$, lower than existing methods; MG2 costs 2800$, showing a reduction; MG3 costs were 1000$, significantly less; MG4 achieved3200$; and MG5 costs were 3500$, reflecting a reduction. These results demonstrate the proposed method's superior cost efficiency across all scenarios.

Suggested Citation

  • Satish kumar, S. & Pramila, V. & Rudhra, S. & Vinod, S. & Lakshmi, D., 2025. "Enhancing demand response and energy management in multi- microgrid systems with renewable energy sources," Renewable Energy, Elsevier, vol. 253(C).
    • Handle: RePEc:eee:renene:v:253:y:2025:i:c:s0960148125011528
    DOI: 10.1016/j.renene.2025.123490
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    References listed on IDEAS

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    1. Horrillo-Quintero, Pablo & García-Triviño, Pablo & Ugalde-Loo, Carlos E. & Hosseini, Ehsan & García-Vázquez, Carlos Andrés & Tostado, Marcos & Jurado, Francisco & Fernández-Ramírez, Luis M., 2025. "Efficient energy dispatch in multi-energy microgrids with a hybrid control approach for energy management system," Energy, Elsevier, vol. 317(C).
    2. Moazzen, Farid & Hossain, M.J., 2025. "A two-layer strategy for sustainable energy management of microgrid clusters with embedded energy storage system and demand-side flexibility provision," Applied Energy, Elsevier, vol. 377(PD).
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    Cited by:

    1. Chen, Wenhao & Rong, Fei & Lin, Chuan, 2025. "A deep reinforcement learning method based on Mamba model with adaptive cross-attention for multi-energy microgrid energy management," Energy, Elsevier, vol. 340(C).
    2. Paweł Kut & Katarzyna Pietrucha-Urbanik, 2025. "Forecasting Short-Term Photovoltaic Energy Production to Optimize Self-Consumption in Home Systems Based on Real-World Meteorological Data and Machine Learning," Energies, MDPI, vol. 18(16), pages 1-31, August.

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