An End-to-End Approach for Microgrid Probabilistic Forecasting and Robust Operation via Decision-focused Learning

• URL: http://arxiv.org/abs/2512.12755v1
• Date: Sun, 14 Dec 2025 16:36:04 GMT
• Title: An End-to-End Approach for Microgrid Probabilistic Forecasting and Robust Operation via Decision-focused Learning
• Authors: Tingwei Cao, Yan Xu,
• Abstract summary: High penetration of renewable energy sources (RES) introduces significant uncertainty and intermittency into microgrid operations.<n>This paper proposes an end-to-end decision-focused framework that jointly optimize probabilistic forecasting and robust operation for microgrids.
• Score: 4.063134117836619
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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