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Forecast-Aided Converter-Based Control for Optimal Microgrid Operation in Industrial Energy Management System (EMS): A Case Study in Vietnam

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  • Yeong-Nam Jeon

    (Department of AI Mechanical Convergence Engineering, Donggang University, 50 Dongmun-daero, Buk-gu, Gwangju 61200, Republic of Korea)

  • Jae-ha Ko

    (Department of Electrical Engineering, Honam University, 417 Eodeung-daero, Gwangsan-gu, Gwangju 62399, Republic of Korea)

Abstract

This study proposes a forecast-aided energy management strategy tailored for industrial microgrids operating in Vietnam’s tropical climate. The core novelty lies in the implementation of a converter-based EMS that enables bidirectional DC power exchange between multiple subsystems. To improve forecast accuracy, an artificial neural network (ANN) is used to model the relationship between electric load and localized meteorological features, including temperature, dew point, humidity, and wind speed. The forecasted load data is then used to optimize charge/discharge schedules for energy storage systems (ESS) using a Particle Swarm Optimization (PSO) algorithm. The strategy is validated using real-site data from a Vietnamese industrial complex, where the proposed method demonstrates enhanced load prediction accuracy, cost-effective ESS operation, and multi-microgrid flexibility under weather variability. This integrated forecasting and control approach offers a scalable and climate-adaptive solution for EMS in emerging industrial regions.

Suggested Citation

  • Yeong-Nam Jeon & Jae-ha Ko, 2025. "Forecast-Aided Converter-Based Control for Optimal Microgrid Operation in Industrial Energy Management System (EMS): A Case Study in Vietnam," Energies, MDPI, vol. 18(12), pages 1-29, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:12:p:3202-:d:1682025
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    1. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    2. Jiakang Wang & Hui Liu & Guangji Zheng & Ye Li & Shi Yin, 2023. "Short-Term Load Forecasting Based on Outlier Correction, Decomposition, and Ensemble Reinforcement Learning," Energies, MDPI, vol. 16(11), pages 1-16, May.
    3. Heldeweg, Michiel A. & Séverine Saintier,, 2020. "Renewable energy communities as ‘socio-legal institutions’: A normative frame for energy decentralization?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    4. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.
    5. Yaqoot, Mohammed & Diwan, Parag & Kandpal, Tara C., 2016. "Review of barriers to the dissemination of decentralized renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 477-490.
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