发表机构
Huazhong University of Science and Technology; Nanyang Technological University（华中科技大学; 南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对大规模空调实时调度的时间耦合与因果不确定性问题，本文提出TCA-ADP算法，通过时间因果状态变量聚合平衡效率与经济性，实现空调近优实时调度并验证了其有效性。
AI 中文摘要
空调（AC）负荷为主动配电网管理不确定性（如可再生能源发电、电价及负荷需求的不确定性）提供了极具潜力的灵活性。然而，由于空调存在大规模时间耦合约束和时间因果不确定性，其实时调度颇具挑战。为解决该问题，本文提出一种新颖的基于时间因果聚合的近似动态规划（TCA-ADP）算法以实现高效调度。首先分析状态变量聚合的时间因果要求，使其与实时序贯决策过程相匹配；随后开发增强型聚合模型，确保聚合兼具高精度与时间因果性；进一步将聚合过程重构为线性规划，以优化聚合参数并实现可处理计算。据此，TCA-ADP利用聚合后的状态变量以新方式近似价值函数，在大规模空调的庞大价值函数空间中平衡计算效率、经济性。通过使用历史数据离线训练价值函数，TCA-ADP通过并行且闭式分解高效实现大规模空调的近优实时调度。案例研究验证了TCA-ADP的有效性与可扩展性，凸显其聚合精度、不确定性处理能力及经济性与可处理性之间的权衡。
英文摘要
Air conditioner (AC) loads offer promising flexibility for active distribution networks to manage uncertainties, such as those in renewable energy generation, electricity prices, and load demand. However, real-time scheduling of ACs is challenging due to their massive temporal coupling constraints and time-causal uncertainties. To address this, a novel time-causal aggregation-based approximate dynamic programming (TCA-ADP) algorithm is proposed for efficient scheduling. The time-causality requirements for aggregating state variables are first analyzed to align with the real-time sequential decision-making process. Subsequently, an enhanced aggregation model is developed to ensure both high accuracy and adherence to time causality. The aggregation process is further reformulated as a linear program to optimize aggregation parameters and enable tractable computation. Accordingly, the TCA-ADP leverages aggregated state variables to approximate the value function as a new way, balancing computational efficiency and economy against the large value function space of massive ACs. By training the value function offline using historical data, the TCA-ADP efficiently achieves near-optimal real-time scheduling of massive ACs through parallel and closed-form disaggregation. Case studies demonstrate the effectiveness and scalability of the TCA-ADP, highlighting its aggregation accuracy, uncertainty handling, and the trade-off between economy and tractability.
CommentsPublished in: IEEE Transactions on Smart Grid (vol. 16, no. 3, pp. 2389-2403, 2025)
