Abstract
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.
Highlights
The conventional energy resources are being substituted by renewable energy sources (RESs), such as solar, wind, tidal and many others
The proposed work focuses on the power enhancement and harmonic reduction for the photovoltaic and wind energy (PV-WE)-energy storage system (ESS)-electric vehicles (EVs) system connected to the grid
The first is based on Fuzzy Logic Controller (FLC), and the second one is based on the artificial neural network (ANN) approach
Summary
The conventional energy resources are being substituted by RESs, such as solar, wind, tidal and many others. The utilization of RESs is increasing tremendously because these are clean, easy to access, and abundant in nature. A favourable scenario such as the reduction in global warming and less emission of carbon dioxide has been observed when RESs are implemented. RESs prevails as better alternatives to conventional resources. The increasing load demand can be fulfilled by introducing RESs in existing power grid [1]. In spite of having numerous advantages, the solar and wind energy resources are highly sensitive to the weather and geographical location, which are the prime drawbacks of using RESs. In spite of having numerous advantages, the solar and wind energy resources are highly sensitive to the weather and geographical location, which are the prime drawbacks of using RESs
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