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Intelligent electric vehicle charging: Rethinking the valley-fill

机译:智能电动汽车充电:重新思考山谷填充

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This study proposes an intelligent PEV charging scheme that significantly reduces power system cost while maintaining reliability compared to the widely discussed valley-fill method of aggregated charging in the early morning. This study considers optimal PEV integration into the New York Independent System Operator's (NYISO) day-ahead and real-time wholesale energy markets for 21 days in June, July, and August of 2006, a record-setting summer for peak load. NYISO market and load data is used to develop a statistical Locational Marginal Price (LMP) and wholesale energy cost model. This model considers the high cost of ramping generators at peak-load and the traditional cost of steady-state operation, resulting in a framework with two competing cost objectives. Results show that intelligent charging assigns roughly 80% of PEV load to valley hours to take advantage of low steady-state cost, while placing the remaining 20% equally at shoulder and peak hours to reduce ramping cost. Compared to unregulated PEV charging, intelligent charging reduces system cost by 5-16%; a 4-9% improvement over the flat valley-fill approach. Moreover, a Charge Flexibility Constraint (CFC), independent of market modeling, is constructed from a vehicle-at-home profile and the mixture of Level 1 and Level 2 charging infrastructure. The CFC is found to severely restrict the ability to charge vehicles during the morning load valley. This study further shows that adding more Level 2 chargers without regulating PEV charging will significantly increase wholesale energy cost. Utilizing the proposed intelligent PEV charging method, there is a noticeable reduction in system cost if the penetration of Level 2 chargers is increased from 70/30 to 50/50 (Level I/Level 2). However, the system benefit is drastically diminished for higher penetrations of Level 2 chargers.
机译:这项研究提出了一种智能PEV充电方案,与清晨广泛讨论的集合充电谷填充方法相比,该方案可显着降低电力系统成本并保持可靠性。这项研究考虑了在2006年6月,7月和8月的21天中,将PEV最佳地集成到纽约独立系统运营商(NYISO)的日间和实时批发能源市场中,这是创纪录的夏季高峰时段。 NYISO市场和负荷数据用于开发统计位置边际价格(LMP)和批发能源成本模型。该模型考虑了峰值负载时斜坡发电机的高成本以及稳态运行的传统成本,从而形成了一个具有两个相互竞争的成本目标的框架。结果表明,智能充电将大约80%的PEV负载分配给山谷小时,以利用较低的稳态成本,同时将其余20%的平均时间分别置于路肩和高峰时段,以降低启动成本。相较于无规则PEV充电,智能充电可将系统成本降低5-16%;比平坦的山谷填埋方法提高了4-9%。此外,独立于市场模型的充电灵活性约束(CFC)是根据车辆在家中的配置以及1级和2级充电基础设施的混合而构建的。发现CFC严重限制了在早晨负荷谷期间对车辆充电的能力。这项研究进一步表明,在不调节PEV充电的情况下增加更多的2级充电器会大大增加能源批发成本。如果将2级充电器的普及率从70/30提高到50/50(I级/ 2级),则使用建议的智能PEV充电方法,系统成本会显着降低。但是,对于2级充电器的更高的渗透率,系统的优势将大大降低。

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