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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.

In recent years, the importance of deregulated power systems has grown significantly, resulting in positive effects on stability, reliability, innovation, and investment in new energy grid technology. The competitive landscape among energy providers and distributors has empowered consumers to not only save money on their energy bills but also incorporate sustainable energy sources into the grid. To efficiently manage electricity distribution, deregulated power systems must include a smart grid and microgrid (MG). Herein, the potential for sustainable expansion of these systems, as well as their economic and environmental implications, are examined. A comprehensive grid system that integrates smart grids and MGs can offer a complete solution, catering to the evolving energy needs of communities and businesses. The advantages of establishing such a system, including improved grid stability, reliability, and increased utilization of renewable energy sources (RES), are highlighted. Furthermore, the integration of MGs and smart grids enhances the management of distributed generation, allowing power companies to optimize system operations for profitability and efficiency. By following these suggestions, businesses and stakeholders in the power sector can enhance the efficiency and responsibility of their systems, resulting in benefits for both the economy and the environment.

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.

Profit maximization is crucial for both producers and customers in power grids, particularly in deregulated energy markets. By focusing on profit maximization measures, power grids can improve economic viability and sustainability for all stakeholders. The growing demand for electricity in modern civilization poses a significant challenge to the power grid’s ability to meet it. To address these difficulties, the power industry is attempting to integrate renewable sources into existing power facilities. Because of its unpredictable character, the presence of renewable energy in the electricity system becomes increasingly important and adds complexity to the grid. Given the intermittent nature of renewable sources, an energy storage device is required in this scenario. During the low-power use phase, the grid has ample power. Meanwhile, energy storage devices can be utilized to store extra power in the low power demand phase while maintaining the safety and stability of the power network during peak demand periods. V2G (Vehicle-to-Grid) technology is commonly employed to address the uncertainty of renewable resources while maintaining system stability. By using automobiles as transportable storage devices, V2G can make the best use of excess electricity during low-energy hours. Wind energy’s volatility causes imbalances, lowering system profitability. With wind energy integration, power system profitability can be improved by analyzing uncertainties and employing mitigation strategies. To overcome the issue, the research optimizes the use of V2G and TCSC (thyristor-controlled series compensator). V2G permits bidirectional power flow, lowering imbalance pricing (IP) while considering power demand. TCSC can increase the transient stability of transmission lines, hence reducing line faults. TCSC’s involvement in the system increases profitability by lowering congestion expenses. Strategic TCSC placement increases overall profitability. The approach strives to offer efficient, environmentally friendly power at a reasonable price while addressing the needs of both power providers and customers. This study also looks into the effects of V2G mixing on voltage stability, LMP (locational marginal price), and the effectiveness of a TCSC in a deregulated electrical system. MiPower software is used to simulate operation and find the optimal placement of the storage unit within the IEEE 14-bus system.

The depletion of conventional energy sources has led to an increase in interest in renewable energy across the globe. The usage of renewable energy has lowered economic risk in the electricity markets. This study presents an approach to utilize solar photovoltaic as a renewable energy source, fuel cells as the energy storage system, and Flexible AC Transmission networks (FACTS) to reduce system risk in deregulated networks. The difference between real and expected renewable energy data is the primary cause of disequilibrium pricing (DP) in the renewable energy-integrated system. Integration of the FCs with a Unified Power Flow Controller (UPFC) can play an important role in coping with the disequilibrium pricing, emphasizing optimizing profitability and societal welfare in a deregulated environment. The paper also evaluates the system voltage outline and LBMP (location-based marginal pricing) scenarios, both with and without the integration of solar power. Two distinct factors, i.e., Bus Sensitivity Index (BSI) and Line Congestion Factor (LCF), have been proposed to identify the key buses and lines for solar power and Unified Power Flow Controller installation in the system. The study also employs conditional-value-at-risk (CVaR) and value-at-risk (VaR) to assess the system’s risk. Using a real-time IEEE 39-bus New England system, multiple optimization algorithms including Sequential Quadratic Programming and the Slime Mould Algorithm (SMA) are employed to estimate the financial risk of the considered system. This analysis demonstrates that the risk coefficient values improve with the placement of UPFC and fuel cells in the renewable incorporated system.

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.

This paper describes an effective operating strategy for electric vehicles (EVs) in a hybrid facility that leverages renewable energy sources. The method is to enhance the profit of the wind–thermal–EV hybrid plant while maintaining the grid frequency (fPG) and energy level of the EV battery storage system. In a renewable-associated power network, renewable energy producers must submit power supply proposals to the system operator at least one day before operations begin. The market managers then combine the power plans for the next several days based on bids from both power providers and distributors. However, due to the unpredictable nature of renewable resources, the electrical system cannot exactly adhere to the predefined power supply criteria. When true and estimated renewable power generation diverges, the electrical system may experience an excess or shortage of electricity. If there is a disparity between true and estimated wind power (TWP, EWP), the EV plant operates to minimize this variation. This lowers the costs associated with the discrepancy between actual and projected wind speeds (TWS, EWS). The proposed method effectively reduces the uncertainty associated with wind generation while being economically feasible, which is especially important in a deregulated power market. This study proposes four separate energy levels for an EV battery storage system (EEV,max, EEV,opt, EEV,low, and EEV,min) to increase system profit and revenue, which is unique to this work. The optimum operating of these EV battery energy levels is determined by the present electric grid frequency and the condition of TWP and EWP. The proposed approach is tested on a modified IEEE 30 bus system and compared to an existing strategy to demonstrate its effectiveness and superiority. The entire work was completed using the optimization technique called sequential quadratic programming (SQP).

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.

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.

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.

Profit maximization is critical in the control of power system networks for both power providers and users. Electrical energy is freely accessible in the electrical grid during off-peak hours, with storage units helping to store excess energy and assist the electrical grid during high-demand situations. Such techniques promote grid stability and ensure safe operation. Because renewable resources are intermittent, energy storage technologies are especially significant in renewable-associated power systems. Vehicle-to-grid (V2G) technology has recently acquired popularity in preserving power grid stability in the presence of renewable resources.V2G technology employs automobiles as mobile storage devices and focuses on the efficient utilization of extra power available during off-peak hours. The goal of this work is to improve the functioning of a V2G system in a power network to reduce energy production costs while increasing system profitability. This study for deregulated power environments also depicts the influence of V2G mixing on system voltage profile and locational marginal pricing (LMP), as well as the performance of the Unified Power Flow Controller (UPFC) on system economics. The MiPower simulation program is used in the study to find the best placement of the power storage unit for the modified IEEE 14-bus system.

Electric vehicles (EVs) have emerged as a promising solution for sustainable transportation. The high energy density, long cycle life, and low self-discharge rate of lithium-ion batteries make them an ideal choice for EVs. Recently, these batteries have been prone to faster decay in life span, leading to sudden failure of the battery. To avoid uncertainty among EV users with sudden battery failures, a robust health monitoring and prediction scheme is required for the EV battery management system. In this regard, the Unscented Kalman Filter (UKF)-based technique has been developed for accurate and reliable prediction of battery health status. The UKF approximates nonlinearity using a set of sigma points and propagates them via the nonlinear function to enhance battery health estimation accuracy. Furthermore, the UKF-based health estimation scheme considers the state of charge (SOC) and internal resistance of the battery. Here, the UKF-based health prediction technique is compared with the Extended Kalman filter (EKF) scheme. The robustness of the UKF and EKF-based health prognostic techniques were studied under varying initial SOC values. Under these abrupt changing conditions, the proposed UKF technique performed effectively in terms of state of health (SOH) prediction. Accurate SOH determination can help EV users to decide when the battery needs to be replaced or if adjustments need to be made to extend its life. Ultimately, accurate and reliable battery health estimation is essential in vehicular applications and plays a pivotal role in ensuring lithium-ion battery sustainability and minimizing environmental impacts.

It is important to understand the features of an integrated renewable energy power system, especially for deregulated systems. The greatest obstacle to assimilating renewable energy generators with the existing electrical system is their unpredictability. Because wind energy is inconsistent, incorporating it into an established power system necessitates more planning. The effects of wind farm (WF) incorporation with fuel cells and a unified power flow controller (UPFC) on electric losses, voltage profile, generating price, and the economics of the system in a deregulated power market are examined in this paper. An impact analysis of integrating wind farms into controlled and uncontrolled situations is conducted. At two randomly selected locations in India, the real-time statistics of the actual wind speed (AWS) and forecasted wind speed (FWS) were merged for this study. The surplus charge rate and deficit charge rate are intended to evaluate the imbalance cost which is arising from the difference between anticipated and true wind speeds to determine the economics of the system. Customers are always trying to find electricity that is reliable, inexpensive, and efficient due to the reconfiguration of the power system. As a consequence, the security limitations of the system may be surpassed or might function beyond the safety limit, which is undesirable. In the last section, heuristic algorithms, such as sequential quadratic programming (SQP), artificial bee colony algorithms (ABC), and moth-flame optimization algorithms (MFO), are employed to analyze economic risk. In the real-time energy market, it also covers how the fuel cells and UPFC are utilized to rectify the WF integration’s deviation. Economic risk evaluation approaches include value-at-risk (VaR) and conditional value-at-risk (CVaR). A modified IEEE 30-bus test system is used throughout the whole project.

Studying a renewable energy integrated power system’s features is essential, especially for deregulated systems. The unpredictability of renewable sources is the main barrier to integrating renewable energy-producing units with the current electrical grid. Due to its unpredictable nature, integrating wind power into an existing power system requires significant consideration. In a deregulated electricity market, this paper examines the implications of wind farm (WF) integration with CAES on electric losses, voltage profile, generation costs, and system economics. Comparative research was done to determine the impact of wind farm integration on regulated and deregulated environments. Four randomly chosen locations in India were chosen for this investigation, together with real-time information on each location’s real wind speed (RWS) and predicted wind speed (PWS). Surplus charge rates and deficit charge rates were created to assess the imbalance cost arising from the discrepancy between predicted and real wind speeds to calculate the system economics. When the effect of imbalance cost is considered, the daily system profit shows a variation of about 1.9% for the locations under study. Customers are always seeking electricity that is dependable, affordable, and efficient due to the reorganization of the power system. As a result, the system security limit could be exceeded or the system might function dangerously. The final section of this paper presents an economic risk analysis using heuristic algorithms such as sequential quadratic programming (SQP), artificial bee colony algorithms (ABC), and moth flame optimization algorithms (MFO). It also discusses how the CAES is used to correct the deviation of WF integration in the real-time electricity market. Economic risk analysis tools include value-at-risk (VaR) and conditional value-at-risk (CVaR). The entire piece of work was validated using a modified IEEE 30-bus test system. This works shows that with a three-fold increase in wind generation, the risk coefficient values improves by 1%.

Wind power has shown enormous potential in capacity addition and substantial use throughout the world during the last few decades. From the very last era of the 1990s, wind energy has re-emerged and framed as a major source of renewable power generation. The worldwide wind power installed capacity has been doubled in every three years over the last decades of the 20th century. This paper demonstrates the important aspects along with the promotion policies which are adopted by the Indian government for enlarging the country's own energy security by proper utilization of available renewable energy sources. In India's perspective, wind power is not only used for power generation but also to provide the energy in a cost-effective manner. The details of India's entire power generation, demand, and contributions of various renewable sources are also comprised in this study. The present scenario of wind power potential in India has been also compared with the other leading countries in the world. Keywords: Indian wind power scenario, Grid associated renewable power generation, Levelized tariff, Accelerated depreciation, Electricity act

Electrical energy is critical to the advancement of both social and economic growth. Because of its importance, the electricity industry has historically been controlled and operated by governmental entities. The power market is being deregulated, and it has been modified throughout time. Both regulated and deregulated electricity markets have benefits and pitfalls in terms of energy costs, efficiency, and environmental repercussions. In regulated markets, policy-based strategies are often used to deal with the costs of fossil fuel resources and increase the feasibility of renewable energy sources. Renewables may be incorporated into deregulated markets by a mix of regulatory and market-based approaches, as described in this paper, to increase the systems economic stability. As the demand for energy has increased substantially in recent decades, particularly in developing nations, the quantity of greenhouse gas emissions has increased fast, as have fuel prices, which are the primary motivators for programmers to use renewable energy sources more effectively. Despite its obvious benefits, renewable energy has considerable drawbacks, such as irregularity in generation, because most renewable energy supplies are climate-dependent, demanding complex design, planning, and control optimization approaches. Several optimization solutions have been used in the renewable-integrated deregulated power system. Energy storage technology has risen in relevance as the usage of renewable energy has expanded, since these devices may absorb electricity generated by renewables during off-peak demand hours and feed it back into the grid during peak demand hours. Using renewable energy and storing it for future use instead of expanding fossil fuel power can assist in reducing greenhouse gas emissions. There is a desire to maximize the societal benefit of a deregulated system by better using existing power system capacity through the implementation of an energy storage system (ESS). As a result, good ESS device placement offers innovative control capabilities in steady-state power flow regulation as well as dynamic stability management. This paper examines numerous elements of renewable integrated deregulated power systems and gives a comprehensive overview of the most current research breakthroughs in this field. The main objectives of the reviews are the maximization of system profit, maximization of social welfare and minimization of system generation cost and loss by optimal placement of energy storage devices and renewable energy systems. This study will be very helpful for the power production companies who want to build new renewable-based power plant by sighted the present status of renewable energy sources along with the details of several EES systems. The incorporation of storage devices in the renewable-incorporated deregulated system will provide maximum social benefit by supplying additional power to the thermal power plant with minimum cost.

The changeable nature of renewable sources creates difficulties in system security and stability. Therefore, it is necessary to study system risk in several power system scenarios. In a wind-integrated deregulated power network, the wind farm needs to submit the bid for its power-generating quantities a minimum of one day ahead of the operation. The wind farm submits the data based on the expected wind speed (EWS). If any mismatch occurs between real wind speed (RWS) and expected wind speed, ISO enforces the penalty/rewards to the wind farm. In a single word, this is called the power market imbalance cost, which directly distresses the system profit. Here, solar PV and battery energy storage systems are used along by the wind farm to exploit system profit by grasping the negative outcome of imbalance cost. Along with system profit, the focus has also been on system risk. The system risk has been calculated using the risk assessment factors, i.e., Value-at-Risk (VaR) and Cumulative Value-at-risk (CVaR). The work is performed on a modified IEEE 14 and modified IEEE 30 bus test system. The solar PV-battery storage system can supply the demand locally first, and then the remaining power is given to the electrical grid. By using this concept, the system risk can be minimized by the incorporation of solar PV and battery storage systems, which have been studied in this work. A comparative study has been performed using three dissimilar optimization methods, i.e., Artificial Gorilla Troops Optimizer Algorithm (AGTO), Artificial Bee Colony Algorithm (ABC), and Sequential Quadratic Programming (SQP) to examine the consequence of the presented technique. The AGTO has been used for the first time in the risk assessment and alleviation problem, which is the distinctiveness of this work.