Demand flexibility plays a vital role in maintaining grid balance, reducing peak demand, and saving customers' energy bills. Given their highly shiftable load and significant contribution to a building's energy consumption, Heating, Ventilation, and Air Conditioning (HVAC) systems can provide valuable demand flexibility to the power systems by adjusting their energy consumption in response to electricity price and power system needs. To exploit this flexibility in both operation time and power, it is imperative to accurately model and aggregate the load flexibility of a large population of HVAC systems as well as designing effective control algorithms. In this paper, we tackle the curse of dimensionality issue in modeling and control by utilizing the concept of laxity to quantify the emergency level of each HVAC operation request. We further propose a two-level approach to address energy optimization for a large population of HVAC systems. The lower level involves an aggregator to aggregate HVAC load laxity information and use least-laxity-first (LLF) rule to allocate real-time power for individual HVAC systems based on the controller's total power. Due to the complex and uncertain nature of HVAC systems, we leverage a reinforcement learning (RL)-based controller to schedule the total power based on the aggregated laxity information and electricity price. We evaluate the temperature control and energy cost saving performance of a large-scale group of HVAC systems in both single-zone and multi-zone scenarios, under varying climate and electricity market conditions. The experiment results indicate that proposed approach outperforms the centralized methods in the majority of test scenarios, and performs comparably to model-based method in some scenarios.

翻译：需求灵活性在维持电网平衡、降低峰值需求以及节省用户能源开支方面发挥着关键作用。供暖、通风与空调（HVAC）系统因其负荷具有高度可转移性且对建筑能耗贡献显著，能够通过响应电价和电力系统需求调整自身能源消耗，为电力系统提供宝贵的需求灵活性。为在运行时间和功率两方面充分挖掘这种灵活性，必须精确建模并聚合大量HVAC系统的负荷灵活性，同时设计有效的控制算法。本文利用宽松度（laxity）概念量化每个HVAC运行请求的紧急程度，从而解决建模与控制中的维数灾难问题。我们进一步提出一种双层方法，以解决大量HVAC系统的能源优化问题。下层涉及聚合器，用于聚合HVAC负荷的宽松度信息，并采用最少宽松度优先（LLF）规则，根据控制器的总功率为单个HVAC系统分配实时功率。鉴于HVAC系统的复杂性和不确定性，我们基于强化学习（RL）控制器，依据聚合的宽松度信息和电价来调度总功率。我们在不同气候和电力市场条件下，从单区域和多区域场景中评估了大量HVAC系统的温度控制与能源成本节省性能。实验结果表明，所提方法在大多数测试场景中优于集中式方法，并在部分场景中表现与基于模型的方法相当。