AI 中文总结
针对多不确定性下含海量空调负荷的实时微电网运行问题，提出降维ADP方法，通过价值函数投影与分段线性近似降维，在33、123节点系统上实现低计算成本的近最优运行，具备良好可扩展性。
AI 中文摘要
本文提出一种降维近似动态规划（ADP）方法，用于解决多不确定性下含海量空调负荷的实时微电网运行问题。该运行问题被建模为多阶段马尔可夫决策过程，引入决策后价值函数以刻画当前决策对未来运行成本的影响。为应对海量空调负荷引发的维度灾难，开发了基于一致性的价值函数投影方法，将每个节点的高维状态空间映射为可处理的聚合状态空间。基于降维后的状态，进一步采用分段线性近似实现高效的价值函数训练。在33节点和123节点系统上的案例研究表明，所提方法在确定性和随机条件下均能以低计算成本、良好的可扩展性实现接近最优的运行性能。
英文摘要
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.
CommentsAccepted in Proc. 2026 IEEE PES 18th Asia-Pacific Power and Energy Engineering Conference (APPEEC), Singapore, August 24--27, 2026 (Best Paper Award Presentation)
