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Abstract This work introduces an efficient operational strategy for electric vehicles (EVs) to optimize economic outcomes in a wind‐integrated hybrid power system. The proposed method enhances the profitability of a combined wind‐thermal‐EV‐fuel cell system while maintaining grid frequency stability and managing the energy states of EV storage. Accurate wind speed forecasts are crucial, as wind farms must provide projected generation data to the market controller for coordinated scheduling with thermal units. Due to wind speed variability, discrepancies between actual and predicted values can lead to mismatches in wind power output, causing financial penalties from divergence prices. To address this, the optimal deployment of the EV storage system is designed to mitigate these financial impacts. By coordinating EV, wind, and thermal operations, the approach effectively reduces wind power unpredictability and ensures economic efficiency, a necessity in competitive power markets. Four distinct energy states of the EV battery‐ maximum, optimal, low, and minimum, are proposed to enhance cost efficiency. The EV storage mode is dynamically adjusted based on real‐time grid frequency and wind speed data. Additionally, a fuel cell is incorporated to boost economic returns further. The effectiveness of the strategy is validated using an IEEE 30‐bus test system, employing sequential quadratic programming and demonstrating notable improvements over existing methods.

Abstract Multilevel inverters (MLIs) have a significant contribution in many industrial sectors due to their improved power quality and lesser voltage stress, over the conventional three‐level inverters. However, the implementation of MLIs with an increased device count creates the scope of development in MLIs topologies. In this regard, a hybrid MLI topology is studied in this paper whose architecture is based on conventional two‐level inverters. This topology has lesser device count characteristics when compared to conventional and most of the recently presented configurations for nine‐level output voltage generation. The major issue of capacitor voltage balancing is resolved by employing an appropriate switching strategy. However, the semiconductor switches are the most vulnerable components and causes the open circuit faults frequently that creates issues in real time operation. Hence, it is important to detect the open circuit fault in switches in the least possible time. A new approach to open circuit fault detection technique based on the analysis of load voltage waveform is proposed in this paper. The wavelet transform technique has been implemented for feature extraction of load voltage. Later, the classification of the fault has been achieved by training an artificial neural network (ANN). The proposed work has been studied in MATLAB/simulation and the obtained results are verified experimentally.

Abstract The goal of any well‐established deregulated power sector is to satisfy their customer by delivering the power with maintaining the best quality at a reasonable price. This paper presents the impression of the wind farm (WF) and compressed air energy storage (CAES) hybrid systems in the congested deregulated power system. The main motto of this work is to enhance social welfare by using a market‐clearing mechanism of the WFCAES hybrid system. To determine the effect of the hybrid system, initially the market‐clearing power (MCPO) problem is measured without considering the WF‐CAES hybrid system, and then the same work has been carried out with considering the WF‐CAES hybrid system. The bus sensitivity factor (BSF) decides the optimal position of the WF‐CAES hybrid system in the congested day‐ahead electrical system. The study is followed out through generator sensitivity factor (GSF) with the application of artificial bee colony algorithm (ABC) and slime mould algorithm (SMA) to calculate optimal market clearing price (MCP) and market clearing volume (MCV). The market‐clearing power problem has been solved in a modified IEEE‐30 bus test system. The SMA algorithm is been implemented in the market‐clearing mechanism for the first time which is the novelty of this paper.