The proposed work focuses on the power enhancement of grid-connected solar photovoltaic and wind energy (PV-WE) system integrated with an energy storage system (ESS) and electric vehicles (EVs). The research works available in the literature emphasize only on PV, PV-ESS, WE, and WE-ESS. The enhancement techniques such as Unified Power Flow Controller (UPFC), Generalized UPFC (GUPFC), and Static Var Compensator (SVC) and Artificial Intelligence (AI)-based techniques including Fuzzy Logic Controller (FLC)-UPFC, and Unified Power Quality Conditioner (UPQC)-FLC have been perceived in the existing literature for power enhancement. Further, the EVs are emerging as an integral domain of the power grid but because of the uncertainties and limitations involved in renewable energy sources (RESs) and ESS, the EVs preference towards the RES is shifted away. Therefore, it is required to focus on improving the power quality of the PV-WE-ESS-EV system connected with the grid, which is yet to be explored and validated with the available technique for enhancing power quality. Furthermore, in the case of the bidirectional power flow from vehicle-to-grid (V2G) and grid-to-vehicle (G2V), optimal controlling is crucial for which an electric vehicle aggregator (EVA) is designed. The designed EVA is proposed for the PV-WE-ESS-EV system so as to obtain the benefits such as uninterruptible power supply, effective the load demand satisfaction, and efficient utilization of the electrical power. The power flow from source to load and from one source to another source is controlled with the support of FLC. The FLC decides the economic utilization of power during peak load and off-peak load. The reduced power quality at the load side is observed as a result of varying loads in the random fashion and this issue is sorted out by using UPQC in this proposed study. From the results, it can be observed that the maximum power is achieved in the case of PV and WE systems with the help of the FLC-based maximum power point tracking (MPPT) technique. Furthermore, the artificial neural network (ANN)-based technique is utilized for the development of the MPPT algorithm which in turn is employed for the validation of the proposed technique. The outputs of both the techniques are compared to select the best-performing technique. A key observation from the results and analysis indicates that the power output from FLC-based MPPT is better than that of ANN-based MPPT. Thus, the proper and economical utilization of power is achieved with the help of FLC and UPQC. It can be inferred that the EVs can play a vital role in imparting the flexibility in terms of power consumption and grid stabilization during peak load and off-peak load durations provided that the proper control techniques and grid integration are well-established.

中文翻译：

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使用 UPQC-FLC-EVA 技术的可再生能源发电系统电网稳定的功率增强

拟议的工作重点是与储能系统 (ESS) 和电动汽车 (EV) 集成的并网太阳能光伏和风能 (PV-WE) 系统的功率增强。文献中可用的研究工作仅强调 PV、PV-ESS、WE 和 WE-ESS。统一潮流控制器 (UPFC)、广义 UPFC (GUPFC) 和静态无功补偿器 (SVC) 等增强技术和基于人工智能 (AI) 的技术，包括模糊逻辑控制器 (FLC)-UPFC 和统一电能质量调节器(UPQC)-FLC 在现有文献中已被认为用于功率增强。此外，电动汽车正在成为电网的一个组成部分，但由于可再生能源 (RES) 和 ESS 所涉及的不确定性和局限性，电动汽车对可再生能源的偏好正在转移。因此，需要重点提高光伏-WE-ESS-EV系统并网的电能质量，目前尚待探索和验证的现有技术提高电能质量。此外，在车辆到电网 (V2G) 和电网到车辆 (G2V) 的双向功率流的情况下，优化控制对于设计电动汽车聚合器 (EVA) 至关重要。为PV-WE-ESS-EV系统提出设计的EVA，以获得不间断供电、有效满足负载需求和高效利用电能等优点。在 FLC 的支持下，可以控制从电源到负载以及从一个电源到另一个电源的功率流。FLC 决定高峰负荷和非高峰负荷期间电力的经济利用。由于以随机方式改变负载，观察到负载侧的电能质量降低，并且在本研究中使用 UPQC 解决了这个问题。从结果可以看出，在基于 FLC 的最大功率点跟踪 (MPPT) 技术的帮助下，PV 和 WE 系统实现了最大功率。此外，基于人工神经网络 (ANN) 的技术被用于 MPPT 算法的开发，而 MPPT 算法又被用于验证所提出的技术。比较两种技术的输出，以选择性能最佳的技术。结果和分析的一个关键观察表明，基于 FLC 的 MPPT 的功率输出优于基于 ANN 的 MPPT。因此，在 FLC 和 UPQC 的帮助下，实现了电力的适当和经济利用。可以推断，如果适当的控制技术和电网整合得到完善，电动汽车可以在峰值负载和非峰值负载持续时间期间赋予电力消耗和电网稳定性方面的灵活性方面发挥至关重要的作用。