Risk-aware scheduling and dispatch of flexibility events in buildings

IF 4.8 2区 工程技术 Q2 ENERGY & FUELS
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Abstract

Residential and commercial buildings, equipped with systems such as heat pumps (HPs), hot water tanks, or stationary energy storage, have a large potential to offer their consumption flexibility as grid services. In this work, we leverage this flexibility to react to consumption requests related to maximizing self-consumption and reducing peak loads. We employ a data-driven virtual storage modeling approach for flexibility prediction in the form of flexibility envelopes for individual buildings. The risk-awareness of this prediction is inherited by the proposed scheduling algorithm. A Mixed-integer Linear Program (MILP) is formulated to schedule the activation of a pool of buildings in order to best respond to an external aggregated consumption request. This aggregated request is then dispatched to the active individual buildings, based on the previously determined schedule. The effectiveness of the approach is demonstrated by coordinating up to 500 simulated buildings using the Energym Python library and observing about 1.5 times peak power reduction in comparison with a baseline approach while maintaining comfort more robustly. We demonstrate the scalability of the approach by solving problems with 2000 buildings in about 21 s, with solving times being approximately linear in the number of considered assets.

楼宇灵活性事件的风险感知调度和派遣
配备了热泵(HP)、热水箱或固定储能等系统的住宅和商业建筑在提供电网服务的消费灵活性方面潜力巨大。在这项工作中,我们利用这种灵活性来响应与最大化自我消费和减少峰值负荷相关的消费请求。我们采用数据驱动的虚拟存储建模方法,以单个建筑物的灵活性包络线的形式进行灵活性预测。这种预测的风险意识由所提出的调度算法继承。我们制定了一个混合整数线性规划(MILP)来调度楼宇池的启动,以便以最佳方式响应外部聚合消费请求。然后,根据先前确定的计划,将该汇总请求分派给活跃的单个楼宇。通过使用 Energym Python 库协调多达 500 栋模拟楼宇,证明了该方法的有效性,与基线方法相比,峰值功率降低了约 1.5 倍,同时更稳健地保持了舒适度。我们在大约 21 秒内解决了 2000 栋建筑物的问题,证明了该方法的可扩展性,解决时间与所考虑的资产数量近似线性关系。
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来源期刊
Sustainable Energy Grids & Networks
Sustainable Energy Grids & Networks Energy-Energy Engineering and Power Technology
CiteScore
7.90
自引率
13.00%
发文量
206
审稿时长
49 days
期刊介绍: Sustainable Energy, Grids and Networks (SEGAN)is an international peer-reviewed publication for theoretical and applied research dealing with energy, information grids and power networks, including smart grids from super to micro grid scales. SEGAN welcomes papers describing fundamental advances in mathematical, statistical or computational methods with application to power and energy systems, as well as papers on applications, computation and modeling in the areas of electrical and energy systems with coupled information and communication technologies.
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