LV Microgrid With V2G Technologies Applied to Electric Load Flexibility: A Case Study of an University Department

IF 3.8 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Riccardo Loggia;Andrea Golino;Lorenzo Frattale Mascioli;Cristina Moscatiello;Roberto Menichelli;Maria Carmen Falvo;Luigi Martirano
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Abstract

The integration of electric vehicles (EVs) into microgrids via vehicle-to-grid technology offers a significant opportunity to provide demand-side flexibility. This article presents an experimental analysis of two load demand flexibility (LDF) strategies: LDF-FLAT and LDF-ZERO, implemented within a real-world university microgrid at Sapienza University of Rome, featuring 12.4 kW of PV generation and 6.5 kWh of stationary storage. Experimental tests demonstrate the system's effectiveness in transforming building consumption into a dispatchable resource. Specifically, the LDF-FLAT algorithm successfully flattened consumption peaks from ∼40 kW to a constant target of 15 kW. In the LDF-ZERO scenario, the system achieved near-total grid independence, reducing the grid dependence ratio to 1.8% under sunny conditions and 2.6% under cloudy conditions. The vehicle to grid power ratio reached 97.2%, proving the EV's capability to act as the primary flexibility resource. Furthermore, the control logic ensures battery health by limiting the depth of discharge to 80%, potentially extending the battery life by 300 cycles. These quantitative results validate the reliability and efficiency of the proposed management system in meeting distribution system operator flexibility requirements.
基于V2G技术的低压微电网在电力负荷柔性中的应用——以某大学系为例
通过车辆到电网技术将电动汽车(ev)集成到微电网中,为提供需求侧灵活性提供了重要机会。本文介绍了两种负载需求灵活性(LDF)策略的实验分析:LDF- flat和LDF- zero,在罗马Sapienza大学的现实世界大学微电网中实施,具有12.4千瓦的光伏发电和6.5千瓦时的固定存储。实验验证了该系统将建筑消耗转化为可调度资源的有效性。具体来说,LDF-FLAT算法成功地将消耗峰值从~ 40 kW降至15 kW的恒定目标。在LDF-ZERO方案中,系统实现了近乎完全的电网独立,晴天条件下的电网依赖率降至1.8%,多云条件下降至2.6%。车辆与电网的功率比达到97.2%,证明了电动汽车作为主要柔性资源的能力。此外,控制逻辑通过将放电深度限制在80%来确保电池健康,可能将电池寿命延长300次。这些量化结果验证了所提出的管理系统在满足配电系统运营商灵活性要求方面的可靠性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
13.50
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