{"title":"分布式能源建筑电气化运行控制与碳减排系统设计集成优化","authors":"Shiyu Yang , H. Oliver Gao , Fengqi You","doi":"10.1016/j.adapen.2023.100144","DOIUrl":null,"url":null,"abstract":"<div><p>Building electrification with distributed energy resources (DERs) is a promising strategy to decarbonize the building sector. Considering the inter-dependencies between operations control and systems design, integrating technology operations control optimization with DERs investment optimization can cost-effectively enhance such building decarbonization opportunities. This study proposes a multi-timescale integrated optimization framework to simultaneously optimize the design and control of DERs and electrification technologies for buildings. A novel building operational performance prediction model based on deep learning is developed to approximate and replace the computationally expensive control optimization. This helps resolve the challenging, computationally intractable multi-timescale integrated design and control optimization problem. Applying the proposed framework to a residential building, our results demonstrate its effectiveness in cost-efficient carbon emissions reduction. With integrated design and control optimization for DERs and electric building energy systems, the proposed framework reduces operational carbon emissions by 80% and total costs by 2.7% compared to a base case, which uses typical conventional building energy systems without DERs and control/design optimization. Separate optimization of operations control and system design cannot achieve such performance. Further scenario analyses indicate that as power grids become cleaner, the reliance on DERs can be alleviated but remain important in building carbon emission reduction under 2050 power grid scenario. 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引用次数: 2
摘要
利用分布式能源资源(DERs)实现楼宇电气化是楼宇领域去碳化的一项前景广阔的战略。考虑到运营控制与系统设计之间的相互依存关系,将技术运营控制优化与 DERs 投资优化相结合,可以经济有效地提高建筑行业的去碳化机会。本研究提出了一个多时间尺度的集成优化框架,可同时优化 DERs 和建筑电气化技术的设计和控制。研究开发了一种基于深度学习的新型建筑运行性能预测模型,以近似并取代计算成本高昂的控制优化。这有助于解决具有挑战性、难以计算的多时间尺度综合设计和控制优化问题。我们将所提出的框架应用于一栋住宅楼,结果证明了它在经济高效地减少碳排放方面的有效性。通过对 DERs 和电动建筑能源系统进行集成设计和控制优化,与使用典型传统建筑能源系统(无 DERs 和控制/设计优化)的基本情况相比,所提出的框架减少了 80% 的运行碳排放和 2.7% 的总成本。单独优化运行控制和系统设计无法实现这样的性能。进一步的情景分析表明,随着电网变得更加清洁,对 DER 的依赖可以减轻,但在 2050 年电网情景下,DER 在建筑碳减排中仍然非常重要。总之,我们的研究结果表明,在减少建筑运行碳排放的同时,还能减少净电力负荷:与基本情况相比,所提出的框架有助于减少 80% 的碳排放,同时将净电力负荷从 44.1 千瓦时/平方米/年降低到 19.3 千瓦时/平方米/年。
Integrated optimization in operations control and systems design for carbon emission reduction in building electrification with distributed energy resources
Building electrification with distributed energy resources (DERs) is a promising strategy to decarbonize the building sector. Considering the inter-dependencies between operations control and systems design, integrating technology operations control optimization with DERs investment optimization can cost-effectively enhance such building decarbonization opportunities. This study proposes a multi-timescale integrated optimization framework to simultaneously optimize the design and control of DERs and electrification technologies for buildings. A novel building operational performance prediction model based on deep learning is developed to approximate and replace the computationally expensive control optimization. This helps resolve the challenging, computationally intractable multi-timescale integrated design and control optimization problem. Applying the proposed framework to a residential building, our results demonstrate its effectiveness in cost-efficient carbon emissions reduction. With integrated design and control optimization for DERs and electric building energy systems, the proposed framework reduces operational carbon emissions by 80% and total costs by 2.7% compared to a base case, which uses typical conventional building energy systems without DERs and control/design optimization. Separate optimization of operations control and system design cannot achieve such performance. Further scenario analyses indicate that as power grids become cleaner, the reliance on DERs can be alleviated but remain important in building carbon emission reduction under 2050 power grid scenario. Overall, as our results demonstrate, it is possible to reduce building operational carbon emissions simultaneously with net electrical load: compared to the base case, the proposed framework helps reduce the carbon emission by 80% while driving down the net electrical load from 44.1 to 19.3 kWh/m2/year.