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A network-based dispatch model for evaluating the spatial and temporal effects of plug-in electric vehicle charging on GHG emissions

机译:用于评估插电式电动汽车充电对温室气体排放的时空影响的基于网络的调度模型

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摘要

The well-to-wheel emissions associated with plug-in electric vehicles (PEVs) depend on the source of electricity and the current non-vehicle demand on the grid, thus must be evaluated via an integrated systems approach. We present a network-based dispatch model for the California electricity grid consisting of interconnected sub-regions to evaluate the impact of growing PEV demand on the existing power grid infrastructure system and energy resources. This model, built on a linear optimization framework, simultaneously considers spatiality and temporal dynamics of energy demand and supply. It was successfully benchmarked against historical data, and used to determine the regional impacts of several PEV charging profiles on the current electricity network. Average electricity carbon intensities for PEV charging range from 244 to 391 gCO(2)e/kW h and marginal values range from 418 to 499 gCO(2)e/kW h. (C) 2015 Elsevier Ltd. All rights reserved.
机译:插电式电动汽车(PEV)的轮毂排放量取决于电力来源以及当前电网对非车辆的需求,因此必须通过集成系统方法进行评估。我们为加利福尼亚州的电网提供了一个基于网络的调度模型,该模型由相互连接的子区域组成,以评估不断增长的PEV需求对现有电网基础设施系统和能源的影响。该模型基于线性优化框架,同时考虑了能源需求和供应的空间和时间动态。它已成功地以历史数据为基准,并用于确定几种PEV充电曲线对当前电网的区域影响。 PEV充电的平均电碳强度范围为244至391 gCO(2)e / kW h,边际值范围为418至499 gCO(2)e / kW h。 (C)2015 Elsevier Ltd.保留所有权利。

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