Optimization of the photovoltaic and wind turbine electrical power distribution and storage

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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 presents an optimized energy management concept for domestic renewable energy systems combining photovoltaic panels, wind turbines, battery storage, controllable loads, and grid interaction. Because renewable energy production and household demand are highly variable, the proposed system improves classical power distribution by introducing a modular, multi-level control architecture capable of dynamically allocating energy between loads, storage systems, and the grid. The approach includes direct DC supply for selected loads, such as hot-water systems, heating components, lighting, and other compatible devices, in order to reduce conversion losses and increase overall energy-use efficiency. The hot-water system is also considered as an additional thermal storage unit, allowing surplus renewable energy to be used locally instead of being exported to the grid at lower value. The proposed architecture is supported by error-detection and health-monitoring functions that supervise electrical and environmental parameters, identify abnormal behavior, isolate faulty modules, activate backup operating modes, and switch to classical energy management when necessary. In addition to the system-level optimization strategy, the paper investigates a neural-network-based maximum power point tracking algorithm, referred to as NN-MPPT. A real photovoltaic installation consisting of nine 465 W panels, a 4.2 kW hybrid inverter, and a 15 kWh LiFePO₄ battery storage system was used to acquire operational data over six consecutive days. A feed-forward deep neural network was trained using photovoltaic voltage, current, irradiance, and temperature data to estimate the maximum power deliverable to the storage system. The optimized model achieved satisfactory prediction performance and, when tested on unseen data, estimated an average output power approximately 12% higher than the measured device output. These results suggest that neural-network-based MPPT and intelligent energy management can improve renewable energy harvesting, storage utilization, and domestic energy efficiency. Future developments include real-time embedded implementation, time-series neural architectures, adaptive retraining, weather-aware prediction, and thermal recovery from photovoltaic panels.

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