在多建筑设施中减少碳排放和能源成本:一种协同优化方法

IF 1.6 Q4 ENERGY & FUELS IET Energy Systems Integration Pub Date : 2023-01-09 DOI:10.1049/esi2.12092
Akintonde Abbas, Badrul Chowdhury
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引用次数: 0

摘要

商业建筑仍然是最重要的能源消耗者之一。因此,任何通往零排放未来的可持续道路都需要密切关注商业建筑的减排。商业建筑的一个有趣的类别是多建筑商业设施,不同的建筑在一个特定的地理区域内配置,服务于不同的目的,包含不同的设备类型。虽然大多数现有的设施管理方法侧重于在每个建筑物的层面上最大限度地减少能源成本和排放,但这项工作考虑了一个不同的角度,即在设施内的所有建筑物中共同优化能源和排放。本文考虑了基于美国能源部(DOE)商业参考建筑数据库中单个建筑组成的多建筑设施的说明性案例研究。建筑使用的不同昼夜和季节变化也被考虑在内。模拟使用基于python的商业建筑模拟工具箱运行。结果表明,与单独优化每个建筑相比,协同优化方法确实可以提供超线性减排和能源成本节约,同时满足预定义的舒适限制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Reducing carbon emissions and energy costs in multi-building facilities: A Co-optimisation approach

Commercial buildings remain one of the most significant consumers of energy. As such, any sustainable pathway to a zero-emissions future will need to pay close attention to emissions reduction in commercial buildings. An interesting category of commercial buildings is the multi-building commercial facility with different buildings collocated within a defined geographical area, serving different purposes and containing varying equipment types. While most existing facility management approaches focus on minimising energy costs and emissions at the level of each building, this work considers a different perspective where energy and emissions are co-optimised across all buildings within the facility. Illustrative case studies based on a multi-building facility consisting of individual buildings adapted from the United States Department of Energy's (DOE) Commercial Reference Buildings database are considered. Different diurnal and seasonal variations in building usage are also considered. Simulations are run using a Python-based commercial building simulation toolbox. Results indicate that the co-optimisation approach can indeed provide superlinear emissions reductions and energy cost savings while satisfying predefined comfort limits compared to when each building is separately optimised.

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来源期刊
IET Energy Systems Integration
IET Energy Systems Integration Engineering-Engineering (miscellaneous)
CiteScore
5.90
自引率
8.30%
发文量
29
审稿时长
11 weeks
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