Photovoltaic generation variability represents a substantial operational challenge for grid-connected microgrids, requiring sophisticated forecasting and control strategies to maintain reliability and economic efficiency. A new research effort addresses this challenge by coupling long short-term memory (LSTM) neural networks with multi-objective scheduling optimization for systems combining solar generation and battery energy storage.
The study evaluates three forecasting approaches: ideal perfect forecast, conventional persistence modeling, and LSTM-based prediction. Results demonstrate meaningful performance improvements from the machine learning approach. The LSTM model reduced root mean squared error by 6% relative to persistence forecasting, increased PV self-consumption ratio from 78.1% to 84.5%, and decreased unnecessary grid injections by 82%. These gains translate directly to improved economics and reduced stress on distribution networks.
The research framework quantifies how forecast accuracy propagates through key operational metrics: self-consumption efficiency, energy costs, grid interaction profiles, and storage utilization patterns. This granular assessment reveals important trade-offs inherent in optimized microgrid operation. While improved forecasting enables better energy scheduling and reduced reliance on external grid supplies, it simultaneously increases battery cycling rates, potentially accelerating degradation and raising long-term maintenance costs.
The findings underscore that PV forecasting accuracy is not merely a technical consideration but a fundamental driver of microgrid economics and grid support capability. Operators face practical choices between maximizing short-term performance gains and managing battery longevity constraints.
Future research directions include probabilistic forecasting methods to quantify forecast uncertainty bounds, incorporation of load-side predictions to enable demand-side optimization, and development of advanced control strategies allowing microgrids to provide grid-support services. These enhancements will be essential as renewable penetration increases and microgrids assume greater responsibility for grid stability and resilience.
