Predictive Management Algorithm for Controlling PV-Battery Off-Grid Energy System

Area of Science:

• Renewable Energy Systems
• Artificial Intelligence in Energy Management

Background:

• Off-grid hybrid energy systems are crucial for reliable power supply in remote areas.
• Accurate forecasting of renewable energy generation and battery storage is essential for efficient energy management.

Purpose of the Study:

• To introduce an energy management strategy for an off-grid hybrid energy system.
• To enhance system efficiency and reliability through intelligent load control based on energy availability forecasts.

Main Methods:

• Implementation of a Long Short-Term Memory (LSTM) network for forecasting photovoltaic (PV) and battery power.
• Development of a load management control unit integrated with a hybrid solar inverter and Battery Management System (BMS).
• System simulation and testing using Matlab/Simulink.

Main Results:

• The LSTM-based forecasting significantly increases prediction efficiency.
• The proposed energy management strategy reduced the energy deficit by approximately 53% compared to a system without load management.
• System reliability improved, with a decrease in the loss of power supply probability (LPSP) from 5% to 3%.

Conclusions:

• The developed energy management strategy effectively optimizes the operation of off-grid hybrid energy systems.
• LSTM-based forecasting and intelligent load control are key to improving energy efficiency and reliability in such systems.
• The strategy demonstrates significant improvements in reducing energy deficits and power supply interruptions.