资源调度中电动汽车作为应急储备的成本排放分析

K. Reddy, L. Panwar, R. Kumar
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引用次数: 0

摘要

无排放电动汽车是解决全球变暖问题的潜在实体之一。除了绿色交通,电动汽车还可以用来协商一些网络约束所带来的电力公用事业成本,如负载需求平衡、可靠性标准等。针对现代化电网的可靠性保障问题,提出了一种利用电动汽车作为应急储备能力的多收益模型。此外,通过成本-排放分析,考察了电动车储备采购与环境约束的可变性。进一步,对经济环境平衡的阈值进行了界定和评价。提出的储备采购概念然后在一个测试系统上模拟了传统和可再生能源技术(ret)。为了列举成本排放分析,提出了各种调度方案。仿真结果表明,电动汽车在参与储备市场时具有环境和经济两方面的潜力。我们还观察到,除了成本和排放外,电网的技术性能也发生了变化。此外,独立系统运营商(ISO)采购的rett和EV的数量受到排放最小化目标重要性(权重因子)的影响。结果表明,除了电动汽车采购量外,不同成本组成的份额比例也受到调度方案和能源市场价格的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cost-emission analysis of electric vehicle as contingency reserve in resource scheduling
Emission free electric vehicle is one of potential entity through which global warming issues can be resolved. Besides greener transportation, EV can also be used to negotiate electricity utility costs imposed by several network constraints like load-demand balance, reliability criteria etc. Thriving on reliability assurance in modernized electric grids, a multi-beneficiary model exploiting EV capability of being a contingency reserve is developed in this paper. Additionally, variability of EV reserve procurement in conjunction with environmental constraints is examined through cost-emission analysis. Further, the threshold limit for economic and environmental balance is defined and evaluated. The proposed reserve procurement concept is then simulated on a test system with both conventional and renewable energy technologies (RETs). For enumerating cost emission analysis, various dispatch scenarios are put forward. The simulation results shows the potential of EV in both environmental and economic perspectives when participated in reserve market. It is also observed that, apart from cost and emission, technical performance of electric network is also altered. Also, the amount of RETs and EV procured by independent system operator (ISO) is influenced by the importance (weight factor) given to emission minimization objective. The results indicate that, apart from amount of EV procured, percentage share of different cost components are also influenced by both scheduling scenario and energy market prices.
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