Decision-Focused Learning for Optimal Energy Management in Microgrids

Introduction: Forecasting of load, renewable generation, and electricity prices is a key component of energy management systems in microgrids. In conventional predict-then-optimize pipelines, forecasting models are usually trained to minimize point-wise errors, and their outputs are subsequently used by an optimization module. However, forecast errors with similar statistical magnitude may have markedly different impacts on the operational cost of a microgrid. This work investigates a decision-aware forecasting framework for cost-oriented energy management in renewable microgrids.

Methods: The proposed approach integrates neural time-series predictors with a differentiable optimization layer representing the microgrid energy management problem. The optimization stage determines storage and grid-exchange decisions under power-balance and storage constraints, while the learning objective accounts for the downstream economic effect of the forecasts. The experimental analysis considers multiple operating conditions, alternative decision-oriented training objectives, and comparisons with standard accuracy-driven forecasting baselines.

Results: The experimental evidence suggests that training forecasting models with direct awareness of the energy-management objective can improve the quality of the resulting operational decisions. In particular, the proposed framework tends to obtain lower microgrid operating costs than conventional forecasting-oriented training, despite not always producing the smallest point-wise prediction errors. The analysis also indicates that the most relevant forecasting errors are not necessarily those with the largest statistical magnitude, but those that alter storage scheduling and grid trading decisions.

Conclusions: These results support the adoption of decision-focused forecasting as a practical methodology for smart grid energy management. By aligning the forecasting stage with the downstream optimization goal, the framework provides a promising basis for robust, cost-aware operation of renewable microgrids and energy communities.