Solar energy paper index
Energy Management of Battery–Supercapacitor Hybrid Storage in PV-Integrated DC Microgrids Using Predictive Control
One-line summary
A solar energy research paper on Energy Management of Battery–Supercapacitor Hybrid Storage in PV-Integrated DC Microgrids Using Predictive Control.
Engineering notes
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Chinese explanation / 中文解读
中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。
Original abstract
This paper addresses the challenge of DC-link voltage instability and control conflicts in Hybrid Energy Storage Systems (HESS) for photovoltaic (PV)-integrated isolated DC microgrids, arising from the inherent variability of Renewable Energy Sources (RES). Existing control strategies often suffer from high computational complexity and inadequate coordination between battery and supercapacitor currents, limiting their effectiveness under dynamic operating conditions. To overcome these limitations, a HESS composed of batteries and supercapacitors is employed, leveraging their complementary characteristics: high energy density and high-power density, respectively. A predictive control strategy is proposed to optimize the current distribution between the battery and supercapacitor using DC-link voltage error and uncompensated power as control inputs. The proposed method is implemented in MATLAB/Simulink and evaluated under varying irradiance conditions (1000 W/m² to 500 W/m² at 25°C) with a 500 W load. The results demonstrate that the proposed approach achieves fast DC-link voltage recovery within approximately 0.1 s, maintains voltage deviation within ±2% of the nominal value, and reduces battery current stress by approximately 30% during transient conditions. Furthermore, the supercapacitor effectively handles rapid transient loads, significantly alleviating battery stress and improving system responsiveness. Additionally, a Bode-plot-based tuning method is employed to refine PI controller parameters, further enhancing energy management and overall system efficiency. These findings highlight the effectiveness of the proposed predictive control strategy as a computationally efficient, dynamically robust solution for the reliable, stable integration of renewable energy into isolated DC microgrids.
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