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A charge-discharge optimization strategy considering the spatiotemporal distribution of electric vehicles and the operational safety of the power distribution network in the power-transportation coupling network

电动汽车 电压 汽车工程 功率(物理) 联轴节(管道) 出租车 激励 计算机科学 电气工程 工程类 运输工程 物理 经济 微观经济学 机械工程 量子力学
作者
Huan Long,Zhengyang Guo,Chengang Zhou
出处
期刊:Journal of Cleaner Production [Elsevier]
卷期号:452: 142067-142067
标识
DOI:10.1016/j.jclepro.2024.142067
摘要

An electric vehicle (EV) charge-discharge optimization (CDO) strategy that accommodates both grid-side and user-side demands is conducive to mitigating the adverse effects of disordered EV charging on the power distribution network (PDN). To tackle the issue of inaccurate estimation of the schedulable capacity of EVs in existing research, a high temporal resolution dynamic spatiotemporal distribution simulation model for EVs is developed. Furthermore, leveraging the characteristics of the power-transportation coupling network (PTCN), a sub-districted dynamic electricity pricing (DEP) is proposed to assist in improving the PDN node voltage distribution. Subsequently, an incentive coefficient is introduced to incentivize the discharge of EVs during potential peak periods. The proposed CDO strategy, considering the non-cooperative behavior of EV owners (EVOs), can provide personalized charge-discharge plans for them. The temporal sequence experiments reveal a significant decrease in the peak-to-valley disparity of the load, achieving an optimization rate of load mean square error that exceeds 80%. The sub-districted DEP exhibits a significant advantage in maintaining PDN voltage stability compared to traditional time-of-use electricity pricing (TOUEP) and DEP, while also achieving reductions in active power losses of more than 15% and 10% for PDN, respectively. Moreover, in comparison to the conventional scenario, the CDO strategy leads to a maximum reduction of up to 942.5% and 22.3% in the total economic cost for electric private cars and electric taxis respectively. Lastly, the impact of seasonal factors is discussed. Numerical results indicate effective alleviation of load peak-valley differences, load fluctuations, and voltage drop phenomena in both winter and summer. Additionally, the maximum reduction in active power losses reaches 20.1% and 21.9%, respectively, while the total economic cost for EVOs participating in the CDO strategy is reduced by up to 97.7% and 114.7%.
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