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arXiv · 2609.01972

Dimension-Reduced ADP for Real-Time Microgrid Operation with Massive Air-Conditioning Loads under Multiple Uncertainties

Jingguan Liu·Xiaomeng Ai·Shichang Cui·Jiakun Fang·Jinyu Wen
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Abstract

This paper proposes a dimension-reduced approximate dynamic programming (ADP) method for real-time microgrid operation with massive air-conditioning loads under multiple uncertainties. The operation problem is formulated as a multi-stage Markov decision process, and a post-decision value function is introduced to characterize the impact of current decisions on future operating costs. To address the curse of dimensionality caused by massive air-conditioning loads, a consistency-based value function projection is developed to map the high-dimensional state space at each node into a tractable aggregated state space. Based on the reduced states, piecewise linear approximation is further employed for efficient value function training. Case studies on 33-bus and 123-bus systems show that the proposed method achieves near-optimal operation performance with low computational cost and good scalability under both deterministic and stochastic conditions.

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Jingguan Liu, Xiaomeng Ai, Shichang Cui, Jiakun Fang, Jinyu Wen. 2026-09-02. Dimension-Reduced ADP for Real-Time Microgrid Operation with Massive Air-Conditioning Loads under Multiple Uncertainties. https://arxiv.org/abs/2609.01972

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