An End-to-End Approach for Microgrid Probabilistic Forecasting and Robust Operation via Decision-focused Learning
This addresses the problem of economic and reliable scheduling for microgrids with high renewable penetration, representing an incremental improvement by integrating forecasting and optimization more effectively.
The paper tackled the challenge of uncertainty in microgrid operations due to renewable energy by proposing an end-to-end decision-focused framework that jointly optimizes probabilistic forecasting and robust operation, reducing total and net operation costs by up to 18% compared to conventional methods.
High penetration of renewable energy sources (RES) introduces significant uncertainty and intermittency into microgrid operations, posing challenges to economic and reliable scheduling. To address this, this paper proposes an end-to-end decision-focused framework that jointly optimizes probabilistic forecasting and robust operation for microgrids. A multilayer encoder-decoder (MED) probabilistic forecasting model is integrated with a two-stage robust optimization (TSRO) model involving direct load control (DLC) through a differentiable decision pathway, enabling gradient-based feedback from operational outcomes to improve forecasting performance. Unlike conventional sequential approaches, the proposed method aligns forecasting accuracy with operational objectives by directly minimizing decision regret via a surrogate smart predict-then-optimize (SPO) loss function. This integration ensures that probabilistic forecasts are optimized for downstream decisions, enhancing both economic efficiency and robustness. Case studies on modified IEEE 33-bus and 69-bus systems demonstrate that the proposed framework achieves superior forecasting accuracy and operational performance, reducing total and net operation costs by up to 18% compared with conventional forecasting and optimization combinations. The results verify the effectiveness and scalability of the end-to-end decision-focused approach for resilient and cost-efficient microgrid management under uncertainty.