不确定性下多区域HVAC负荷的耦合感知聚合：一个两级框架
Coupling-Aware Aggregation of Multi-Zone HVAC Loads under Uncertainty: A Two-level Framework
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中文总结 AI 辅助
针对多区域HVAC负荷聚合中的耦合与不确定性问题，本文提出耦合感知两级框架，通过建筑层面降维和聚合器层面矩阵变换实现高维灵活性的内近似，案例研究验证了其准确性、可靠性与可扩展性。
中文摘要 AI 辅助
聚合建筑供暖、通风与空调（HVAC）负荷可为电力系统释放大量需求侧灵活性，但多区域耦合会产生复杂的相互依赖关系与不确定性传播，使聚合灵活性的量化变得复杂。为解决该问题，本文提出一种耦合感知的两级聚合框架：在建筑层面，采用定制的高斯消元法与坐标变换技术，将高维热动力学重构为等效的低维解析表达式，该表达式通过三点简化后续聚合器层面的工作：一是明确区域级不确定性向建筑级接口的传播，二是将建筑内部多区域耦合与建筑间聚合解耦，三是提供全维度建筑级灵活性集以实现易处理的重构；在聚合器层面，通过新开发的矩阵变换技术推广现有几何聚合方法，该技术可有效构造不同维度多面体间的内近似，在电力子空间中生成高维多区域HVAC灵活性的闭式像，随后将所得内近似重构为定制的可分离线性规划，以高效确定最优聚合参数。案例研究验证了该框架的有效性，凸显其准确性、可靠性与可扩展性。
英文摘要
Aggregating building heating, ventilation, and air-conditioning (HVAC) loads unlocks substantial demand-side flexibility for power systems. Yet multi-zone coupling creates intricate interdependencies and uncertainty propagation, complicating the quantification of aggregate flexibility. To address this issue, this paper proposes a coupling-aware two-level aggregation framework. At the building level, tailored Gaussian elimination and coordinate transformation techniques are employed to recast the high-dimensional thermal dynamics as an equivalent lower-dimensional analytical expression. This expression streamlines the subsequent aggregator-level stage by (i) clarifying the propagation of zone-level uncertainties to the building-level interface, (ii) decoupling intra-building multi-zone coupling from inter-building aggregation, and (iii) providing full-dimensional building-level flexibility sets that enable tractable reformulation. At the aggregator level, existing geometric aggregation approaches are generalized by a newly developed matrix-transformation technique. This technique effectively constructs inner approximations between polytopes of different dimensions, producing closed-form images of high-dimensional multi-zone HVAC flexibility in power subspace. The resulting inner approximation is then recast as a customized separatable linear program that efficiently determines the optimal aggregate parameters. Case studies validate the effectiveness of our framework, highlighting its accuracy, reliability, and scalability.
发表机构
- Global Energy Interconnection Group Co., Ltd.（全球能源互联网集团有限公司）
机构由 AI 辅助整理，请以论文原文为准。
