Accounting for fluctuating demand in the life cycle assessments of residential electricity consumption and demand-side management strategies
Accounting for fluctuating demand in the life cycle assessments of residential electricity consumption and demand-side management strategies
复制标题
居民用电生命周期评估中需求波动的考虑及需求侧管理策略
DOI：
10.1016/j.jclepro.2019.118251
复制
发表时间：
2019
影响因子：
11.1
通讯作者：
R. Samson
中科院分区：
文献类型：
--
作者：
Julien Walzberg;Thomas Dandres;Nicolas Merveille;M. Cheriet;R. Samson
在对复杂的社会技术系统进行环境评估时，必须考虑到时间因素。对于电力系统，这些考虑允许计算与需求侧管理策略相关的环境影响，这些策略无法用静态数据进行评估，例如部分需求从一天的一个时段到另一个时段的时间变化。几个生命周期评估（LCA）的研究包括时间方面，但主要是关于系统的生产函数。然而，社会技术系统的消费方面也容易随时间波动，其错误表述可能导致更多的错误。在这项研究中，一组加拿大人的家庭的住宅电力需求建模与随机的方法。然后，三种不同的LCA方法进行了比较：使用的平均或边际电力组合和两者的组合。研究了数据的时间粒度（年平均或小时数据）对LCA结果的影响。一个简单的需求侧管理策略的案例研究说明了该方法。结果表明，一个恒定的需求的假设导致错误的环境影响评估，这可能高达136%，这取决于评估的一年期间。此外，关于电力需求性质的错误假设导致需求侧战略的次优结果：使用平均电力组合略微增加温室气体排放，而应用边际组合减少10%的排放。
The inclusion of temporal aspects in the environmental assessment of complex socio-technical systems is crucial. For power systems, such considerations allow computing the environmental impacts related to demand-side management strategies which could not be assessed with static data, such as temporal shifts of part of the demand from one period of the day to another. Several life cycle assessment (LCA) studies have included temporal aspects, but mostly regarding the system's production function. The consumption side of a socio-technical system, however, is also prone to fluctuate in time and its misrepresentation may lead to additional errors. In this study, the residential power demand of a set of Canadians' homes was modeled with a stochastic approach. Then, three different LCA approaches are compared: the use of an average or a marginal electricity mix and a combination of the two. The influence of the temporal granularity of data (yearly average or hourly data) on LCA results was also investigated. The case study of a simple demand-side management strategy illustrates the method. Results show that the assumption of a constant demand leads to errors regarding environmental impacts assessment, which may be as high as 136% depending on the period of the year assessed. Moreover, the wrong assumption regarding the nature of power demand leads to sub-optimal results for demand-side strategy: the use of an average electricity mix slightly increases greenhouse gas emissions, whereas applying a marginal mix decreases emissions by 10%.