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Optimizing decentralized energy systems: Advanced models and power management strategies

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  • Wang, Qing
  • Yu, Na
  • Yang, Zhifang

Abstract

This study presents an advanced optimization framework for decentralized energy systems (DESs) to enhance energy reliability, minimize operational costs, and reduce greenhouse gas emissions in off-grid applications. The proposed DES integrates multiple renewable energy sources, including solar photovoltaics, wind turbines, and micro-hydro systems, with diesel generators and a battery storage system (BSS). A mixed-integer nonlinear programming (MINLP) approach coordinates power dispatch among the energy sources under operational and system constraints, ensuring optimal energy balance and load satisfaction. To prevent premature degradation of the BSS due to cyclic charge-discharge (CDC) operations, the modified Rain Optimization Algorithm (ROA) is applied for degradation-aware scheduling. The model is validated using a real-world load profile from 60 households in Pretoria, South Africa, and simulations are conducted via MATLAB. Simulation results validate the effectiveness of the proposed optimization framework. The system achieved a 72.45 % renewable energy fraction, leading to a 77.8 % reduction in diesel generator emissions compared to a conventional system. Additionally, operational costs were reduced by approximately 72 %, while fuel consumption dropped by 848.85 L per day.

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  • Wang, Qing & Yu, Na & Yang, Zhifang, 2025. "Optimizing decentralized energy systems: Advanced models and power management strategies," Energy, Elsevier, vol. 335(C).
    • Handle: RePEc:eee:energy:v:335:y:2025:i:c:s036054422503659x
    DOI: 10.1016/j.energy.2025.138017
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