A data-driven framework for quantifying consumption-based monthly and hourly marginal emissions factors
A data-driven framework for quantifying consumption-based monthly and hourly marginal emissions factors
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用于量化基于消耗的每月和每小时边际排放因子的数据驱动框架
DOI：
10.1016/j.jclepro.2023.136296
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发表时间：
2023
影响因子：
11.1
通讯作者：
Sanders, Kelly T.
中科院分区：
文献类型：
--
作者：
Zohrabian, Angineh;Mayes, Stepp;Sanders, Kelly T.
向电网供电的发电厂每天和每一季节都在变化，与用电有关的排放产生了很大的随时间变化的差异，特别是在可再生发电机普及率高的电网中。此外，现代电网正在纳入更多的需求侧干预措施，激励电力终端用户暂时改变其电力消费行为，努力改变一段时间内电力消费的形态和规模。考虑到供方发电资源的动态变化，目前用于量化与电力消费边际变化相关的排放的方法是不够的。本研究引入了一种新的多元线性回归模型，该模型利用历史需求、可变可再生能源发电和二氧化碳排放数据来量化2019年和2020年的小时边际排放因子。发达的以消费为基础的二氧化碳核算方法除了包括区域内发电机的排放外，还包括净电力进口所包含的排放量。将所提出的框架应用于加州独立系统运营商（CAISO）的案例研究，揭示了研究期间大范围的小时级边际排放因子（89-503 kgCO 2/MWh）。该方法在现有文献的基础上进行了改进，提出了一种基于消费的方法，该方法非常适合于估算通过负荷需求侧变化避免的排放量，特别是在可再生能源渗透率高的电网中，如CAISO。
The fleet of power plants supplying electricity to a power grid varies diurnally and seasonally, creating large time-dependent differences in the emissions associated with consuming electricity, particularly in grids with high penetrations of renewable electricity generators. In addition, modern grids are incorporating more demand-side interventions that incentivize electricity end users to temporarily modify their electricity consuming behavior in efforts to change the shape and magnitude of electricity consumption over a period of time. Current methods for quantifying the emissions associated with marginal shifts in electricity consumption are not sufficient given the changing dynamics of supply-side generation resources. This study introduces a novel multiple linear regression model that utilizes historical demand, variable renewable generation, and CO 2 emissions data to quantify hourly marginal emissions factors for the years of 2019 and 2020. The developed consumption-based CO 2 accounting method includes the emissions embedded in net electricity imports in addition to emissions from in-region generators. The proposed framework is applied to the case study of California Independent System Operator (CAISO), revealing a wide range of hourly-level marginal emissions factors (89–503 kgCO 2/MWh) during the period of study. The proposed method improves upon the existing literature by proposing a consumption-based method that is well suited for estimating emissions avoided through demand-side changes in load, particularly in electric grids, like CAISO, with high renewable energy penetrations.
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DOI：
--
发表时间：
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期刊：
影响因子：
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