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In recent years, battery degradation has become a critical concern in various industries, including electric vehicles, renewable energy systems, and portable electronics. To address this issue, data-driven techniques have emerged as a promising approach for lithium-ion battery (LIB) degradation analysis and estimation. This paper focuses on the application of incremental capacity curves (ICCs) in battery degradation analysis using data-driven techniques. The incremental capacity curve is a powerful tool that provides valuable insights into the capacity degradation of a battery. By analyzing the changes in the ICC over time, it is possible to identify and quantify battery degradation phenomena such as capacity fade, impedance growth, and aging effects. However, manually analyzing ICCs can be time-consuming and subjective, leading to potential errors and inconsistencies. To overcome these challenges, hyperparameter-tuned Long Short-Term Memory (LSTM) techniques are employed to automate the analysis of ICCs and extract meaningful degradation information. These techniques leverage statistical models to process large volumes of ICC data and identify degradation patterns. By training these models on historical data, they can accurately predict battery degradation and estimate the remaining useful life (RUL) of a battery. Further, to enhance the performance of estimation of RUL of the battery. A hyperparameter-tuned LSTM technique has been proposed. The proposed technique has been compared with well-known techniques (i.e. Fully Connected Neural Network (FNN), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN)). The results depict that the proposed robust LSTM technique outperforms well in terms of computational cost and speed. To demonstrate the efficiency of the proposed technique, error analysis has been carried out. The simulation and experimental results depict that the proposed hyperparameter-tuned LSTM model results in very low error indices such as RMSE, MEA, and MAPE as 0.0246, 0.0159 and 1.03 compared with models such as FNN, ANN and CNN. The proposed hyperparameter-tuned LSTM technique depicts a lesser error. By leveraging machine learning and statistical models. The results of this study contribute to the advancement of battery management systems and the optimization of battery usage in various applications.

The suggested hybrid power plant combines conventional and renewable energy sources along with energy storage devices such as wind, pumped hydro storage (PHS), thermal, and solar-powered electric vehicles (EVs). Its major goal is to improve the electricity network’s revenue and profitability while maintaining grid frequency stability (fG). To achieve this purpose, the approach considers the projected wind velocity (WVProj) in a deregulated market and commits wind farms to meet energy demand appropriately. The functioning of PHS and solar-connected EV power components is closely monitored to reduce the detrimental impact of power system imbalances caused by discrepancies in true (WPTrue) and projected (WPProj) wind production. This operational strategy aims to reduce the unpredictability associated with renewable energy sources cost-effectively. Highlighting the resilience of this technique, the approach incorporates four energy stages of the PHS upper basin (EPHS,max, EPHS,opt, EPHS,low, and EPHS,min) and four energy stages of the EV battery (EEVB,max, EEVB,opt, EEVB,low, and EEVB,min). By considering these energy levels, the approach illustrates the strategy’s long-term effectiveness. Several optimization methods are used for implementation and comparison, including Sequential Quadratic Programming (SQP), BAT algorithm, Particle Swarm Optimization (PSO) Algorithm, Genetic Algorithm (GA), and Cuckoo Search Algorithm (CSA). The efficacy of the suggested approach is assessed by analyzing and evaluating the IEEE 14-bus power network. The proposed technique deviates from conventional methods by employing the PHS plant to overcome uncertainties related to wind power, guaranteeing that committed generation patterns are reached with the support of solar power and EV-battery storage. Hourly simulations demonstrate that the proposed approach significantly enhances the use of maximum reservoir constraints, which improves system stability and security. The study demonstrates that the proposed two-phase operating technique is successful at improving revenue and profit while stressing stability and security inside the hybrid system. It also provides a feasible option for effective energy management.

The usage of renewable energy sources in the power sector has increased day by day to preserve non-renewable resources from extinction. The growth of earnings in the electricity market has also become a critical part as the electricity generated from the renewable integrated system is fluctuating in nature. For a renewable-associated system, energy is abundant during low-demand hours. These excess units are available due to the presence of renewable energy sources like solar, wind, etc. So, a better way to manage this situation is to store the excess energy in the storage devices and use that energy in high-demand hours to enhance the system’s economic profit. This paper presents a comparative study of system economic parameters between regulated and deregulated renewable-associated systems. Solar power has been considered here as a renewable energy source due to its high efficiency and easy availability nature. This work also focused on maximizing the system profit in a deregulated system by the placement of a Thyristor Controlled Series Compensator (TCSC) along with the fuel cell in the presence of a disequilibrium price (DP). The disequilibrium price has occurred due to the disparity of real and expected renewable data which controls the entire renewable-associated system. To achieve maximum profit, it is imperative to reduce generation expenses to a minimum. A flexible alternating current transmission system (FACTS) is used to enhance and increase the system’s power transfer capability and controllability. The system voltage profile and the scenarios of location-based marginal price (LBMP) with and without consideration of TCSC have also been deployed in this work. The study has been performed with different optimization techniques such as Sequential Quadratic Programming (SQP) and Artificial Bee Colony Algorithms (ABC) to analyze the economic risk of the system with a modified IEEE 14-bus system.

The objective of reducing the size and cost of the grid-connected photovoltaic system has led to advancements in the field of transformerless grid-connected inverters and gained high popularity in recent years. However, in such systems, the major limitation lies in realizing maximum power from individual modules. In this regard, this paper proposes a modular transformerless grid-connected photovoltaic multilevel inverter that realizes the individual maximum power point (MPP) of each module under different operating scenarios. The presented configuration is simple and modular, providing flexibility to increase the number of inputs with less component count. A single-phase synchronous reference frame PI (SRF-PI) controller has been designed and realized for the proposed system. The systematic procedure for designing the controller has been detailed. The converter and controller allow the system to realize individual MPP of the modules and simultaneously achieve the load demand maintaining the desired grid voltage. This has been verified under different operating scenarios in the MATLAB/Simulink environment. The same has been validated on a 300W laboratory prototype under source and load intermittencies. The proposed configuration has been compared with the similar works reported in the literature and it has been observed that it employs only 18 components. Also, the efficiency of the converter has been observed in the range of 89-95%. Further, the common-mode voltage and the leakage current have been measured to verify their suitability for grid-connected systems as per German VDE 0126-1-1 standards.

The incorporation of renewable energy into the existing electrical system is vital in a competitive electrical system. The unpredictable nature of renewable sources is the main obstacle to energy source integration. Since wind energy is unpredictable, integrating it into an existing thermal system requires some additional operating procedures to maintain the economic and functioning sustainability of the system. In a competitive power network, renewable energy uncertainty creates an imbalance cost (IC) which directly affects the system economy. This study investigates system generation costs, voltage profiles, and electric losses in a deregulated power market incorporating wind farms (WF) & fuel cells (FC). The fuel cell has been used here as a reserve generating unit to mitigate the deficit of power in the renewable incorporated system. To check the efficacy of the presented method, two locations in India are chosen at random. To assess the imbalance cost caused by the discrepancy between forecasted (FWS) and actual wind speeds (AWS), several charge rates (i.e. surplus and deficit) were established. The electrical system has been restructured, so consumers are continually looking for efficient and stable economic power which is only possible by reducing the system risk. This paper outlines a strategy for the optimal operation of a Thyristor-Controlled Series Compensator (TCSC) and fuel cell in a wind-integrated system to maximize system profit and minimize the system risk. In this work, different algorithms like Sequential Quadratic Programming (SQP), Artificial Bee Colony Algorithms (ABC), and Moth Flame Optimization Algorithms (MFO) are used to analyze the economic and functional risk of the system. Additionally, it explains how the fuel cell system is employed to offset the wind farm integration’s deviation in the real-time power market. Value-at-Risk (VaR) and conditional Value-at-Risk (cVaR) have been used for risk analysis. A modified IEEE 14-bus test system is considered to validate the entire study whereas any small, large as well as hybrid systems can be considered to perform this methodology.

PoolCo electricity trading is one of the most capable bidding practices for executing a centralized energy market model. In the PoolCo market model, each seller and buyer submit their bid price and bid quantity to the independent market operator, which they are ready to sell and buy from the market respectively. The market operator regulates the equilibrium market price and volume by considering the acquiesced bid price and bid quantity to settle the market. To maximize the social welfare of market participants, the optimal bidding strategy of a wind farm integrated system is represented as a centralized power market model. Initially, the bid price and bid quantity for consumers and suppliers have been calculated using the Monte-Carlo simulation (MCS) approach. Secondly, a wind farm is incorporated into the system with the help of locational marginal price (LMP). The market operator determines market clearing price (MCP) and market clearing volume (MCV) based on the submitted bid price and bid quantity of suppliers and buyers in order to find the eligible buyers and suppliers. After obtaining MCP and MCV, the market operator reschedules the supplier’s bid quantity with the help of an artificial gorilla troops optimizer (AGTO) algorithm to maximize social welfare by pleasing the system constraints. The AGTO algorithm is used here for the first time to solve the market-clearing power simulation (MCPS) problem with the integration of wind farm. To show the feasibility and effectiveness of the proposed bidding strategy, modified IEEE 14-bus and modified IEEE 30-bus test systems are used here along with a wind farm of 5 MW and 30 MW rated capacity, respectively. Results obtained by using the AGTO algorithm have been compared with those obtained by other optimization algorithms like honey badger algorithm (HBA), artificial bee colony (ABC), particle swarm optimizer (PSO), and slime mould optimizer (SMO) algorithms.