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首页> 外文期刊>Electric power systems research >Look-ahead risk-averse power scheduling of heterogeneous electric vehicles aggregations enabling V2G and G2V systems based on information gap decision theory
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Look-ahead risk-averse power scheduling of heterogeneous electric vehicles aggregations enabling V2G and G2V systems based on information gap decision theory

机译：基于信息缺口决策理论的支持V2G和G2V系统的异构电动汽车聚合的超前风险规避功率调度

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摘要

The violent uncertainty of renewables imposes substantial challenges into the revenue adequacy and cost recovery of electricity markets. Electric vehicles (EVs) can bring up significant benefits such as mitigating the sharp fluctuations of renewables and assisting them to merge into the markets that will lead to reduce the procurement costs and carbon emissions from the transportation sector. To this end, an information gap decision theory is extended to manage the revenue risk of EV managers and harnessing the system in confronting with intense uncertainty. The proposed model can provide satisfactory solution to guarantee the predefined profit for EV managers while satisfying the requirements of distribution network. Look-ahead active and reactive powers scheduling of various EV aggregations are arranged incorporating vehicle-to-grid (V2G) and grid-to-vehicle (G2V) options in the daily travel route of EVs. The proposed multi-objective problem is formulated based on augmented epsilon-constraint technique, where the main objective is to maximize the profit of EV managers constrained by operating costs of system. An innovative hybrid algorithm based on GWO&PSO is developed to optimize the problem. Simulation results are shown to illustrate the effectiveness of proposed approach in the modified IEEE 33-bus system equipped with several smart parking lots.
机译：可再生能源的剧烈不确定性给电力市场的收入充足性和成本回收带来了巨大挑战。电动汽车（EV）可以带来显着的收益，例如减轻可再生能源的剧烈波动，并帮助其融入市场，从而降低运输成本和碳排放。为此，扩展了信息缺口决策理论，以管理电动汽车经理人的收入风险，并在面对巨大不确定性的情况下利用该系统。所提出的模型可以提供令人满意的解决方案，在满足分销网络要求的同时，为电动汽车管理者保证预定的利润。安排了各种EV聚合的前瞻有功和无功功率调度，在EV的日常行驶路线中结合了车辆到电网（V2G）和电网到车辆（G2V）选项。提出的多目标问题是基于增强的ε约束技术提出的，其主要目标是使受系统运营成本约束的EV经理的利润最大化。开发了一种基于GWO＆PSO的创新混合算法来优化该问题。仿真结果表明了该方法在改进的装有多个智能停车场的IEEE 33总线系统中的有效性。

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