发表机构
State Key Laboratory of Advanced Electromagnetic Technology, Huazhong University of Science and Technology; Thayer School of Engineering, Dartmouth College; HyperStrong Technology Co., Ltd.（华中科技大学先进电磁技术国家重点实验室; 达特茅斯学院塔克工程学院; HyperStrong科技有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种成本信息聚合框架，通过联合学习参数化存储形式代理和内近似目标，利用下游调度成本反馈分配表示能力，并采用分布鲁棒条件风险价值方法处理电价不确定性，以降低建筑HVAC负荷聚合的调度成本。
AI 中文摘要
本文开发了一个成本信息聚合框架，该框架学习建筑供暖、通风和空调（HVAC）负荷的聚合灵活性集合，以最小化聚合商的调度成本。现有聚合方法主要使用体积导向的目标，并将灵活性聚合与下游利用视为独立阶段。因此，所得的聚合集合可能无法保留对于降低下游调度成本最有价值的灵活性。为解决此局限，我们使用参数化存储形式代理来表示聚合HVAC灵活性，并联合学习代理参数和基于下游调度成本反馈的内近似目标。这种成本信息反馈将代理的有限表示能力分配给聚合灵活性集合中成本相关的区域。为考虑电价不确定性，我们制定了一个分布鲁棒条件风险价值（DR-CVaR）下游问题，该问题同时捕捉分布模糊性和尾部风险。为高效学习，我们将DR-CVaR问题重新表述为凸二阶锥规划。我们使用随机平滑和得分函数估计器估计成本梯度，避免对大规模建筑级优化问题进行微分。使用NYISO价格数据的案例研究表明，所提出的框架相对于体积导向的聚合基准降低了调度成本，并且合适的风险和模糊性设置可以降低调度成本的样本外CVaR。
英文摘要
This paper develops a cost-informed aggregation framework that learns an aggregate flexibility set of building heating, ventilation, and air-conditioning (HVAC) loads to minimize the aggregator's dispatch cost. Existing aggregation methods mainly use volume-oriented objectives and treat flexibility aggregation and downstream utilization as separate stages. Consequently, the resulting aggregate set may fail to preserve the flexibility most valuable for reducing downstream dispatch costs. To address this limitation, we represent aggregate HVAC flexibility using a parameterized storage-form surrogate and jointly learn the surrogate parameters and the inner-approximation objective from downstream dispatch-cost feedback. This cost-informed feedback allocates the surrogate's limited representation capacity to cost-relevant regions of the aggregate flexibility set. To account for electricity-price uncertainty, we formulate a distributionally robust conditional value-at-risk (DR-CVaR) downstream problem that captures both distributional ambiguity and tail risk. For efficient learning, we reformulate the DR-CVaR problem as a convex second-order cone program. We estimate cost gradients using randomized smoothing and a score-function estimator, avoiding differentiation through large-scale building-level optimization problems. Case studies using NYISO price data show that the proposed framework reduces dispatch costs relative to a volume-oriented aggregation benchmark and that suitable risk and ambiguity settings can lower the out-of-sample CVaR of dispatch costs.
Comments11 pages, 14 figures
