ENERGY MANAGEMENT AND STORAGE IN PHOTOVOLTAIC SYSTEMS: A REVIEW OF CURRENT RESEARCH AND DEVELOPMENTS
Keywords:
photovoltaic systems (PV), machine learning, grid stability, reactive power, energy storage, power prediction.Abstract
The article reviews current research and developments in the field of energy
management and storage in the context of photovoltaic (PV) systems. The focus is on energy storage
methods, such as electrical and thermal systems, and methods for managing reactive power, voltage,
and frequency in networks with a high share of solar generation. The review emphasizes the need for
efficient and cost-effective energy storage systems and intelligent demand management for the full
integration of PV systems into the grid and ensuring its stability.
The methods include the analysis of energy storage systems, investigation of reactive power
and frequency management, use of machine learning for power prediction and optimization of energy
system management, as well as modeling in MATLAB/Simulink. The materials consist of weather
condition data, experimental results from solar PV power plants, and technical literature on energy
management and storage.
The results include an analysis of approaches to energy storage, the development of active
power control (APC) strategies for frequency stabilization in networks with PV generation, machine
learning-based power prediction methods, an assessment of storage and management systems for
improving PV system integration, and a demonstration of improved power system prediction and
optimization through intelligent management methods.
Energy management and storage are key to the successful integration of photovoltaic systems
into power grids. The development of efficient energy storage systems and intelligent management
methods helps to smooth the variability of solar generation and ensure grid stability
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