Next-Generation Green Energy Technologies for Sustainable Development

The integration of Renewable Energy Sources (RES) into power systems presents challenges such as intermittency, stability issues, and power quality concerns. This work reviews key integration methods, including grid-connected, standalone, hybrid, and microgrid systems, while addressing major stability challenges like frequency fluctuations, voltage instability, and transient response. Traditional controllers such as PI, PID, and PR struggle to manage the nonlinear behavior of RES. Advanced control techniques like Sliding Mode Control (SMC), Model Predictive Control (MPC), and Fuzzy Logic Control (FLC) offer improved performance but have limitations, including chattering in SMC, high computational complexity in MPC, and tuning difficulties in FLC. To overcome these issues, Adaptive Voltage Controllers (AVC) provide a more effective solution by dynamically adjusting control parameters in real time, ensuring superior power quality, fast response, and enhanced system stability. This study highlights AVC as the optimal choice for improving renewable energy integration and performance.

Accurate electrical energy price forecasting is an essential task for the utilities and generation companies for effective energy trading in markets. It helps generation companies, utilities, and industries for proper bidding strategy and schedule production and consumption effectively in such way that risk will be minimized and profit will be maximized. In this chapter, A complete procedure for electrical energy price forecasting using an deep neural network (DNN) model is presented. To train and test DNN model, data is collected from Indian Energy Exchange (IEX) and this complete data is available at https://​data.​mendeley.​com/​datasets/​v5znbkzjd4/​1. The suggested model is verified by comparison with different machine learning models, such as SVM, Random Forest, Decision Trees, and Linear Regression. Comparatively speaking, the constructed DNN model can predict the load with a lower error of 0.0024.

Integration of photovoltaic (PV) systems into microgrids has gained significant attention due to its potential to enhance energy sustainability and resilience. However, PV microgrids face challenges such as islanding, posing safety risks and reliability concerns. This study proposes a comprehensive approach to address islanding in PV microgrids by employing advanced control strategies and optimization algorithms. The detection of islanding events is crucial for maintaining grid stability and ensuring the safety of utility workers. In this research, the Change of Frequency with respect to Change in Reactive Power (COF-CIQ) method is utilized for islanding detection due to its effectiveness in identifying islanding occurrences accurately and promptly. Furthermore, optimal control of PV microgrids is essential for maximizing energy efficiency and grid stability. This study explores the application of optimal controllers to regulate the operation of PV systems within microgrids. Specifically, the effectiveness of proportional-integral (PI) and fractional-proportional-integral (FOPI) controllers are tested in the performance of PV microgrid. To enhance the efficiency of the optimization process, three nature-inspired metaheuristic algorithms, namely Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), and Sine Cosine Algorithm (SCA), are employed for tuning the parameters of the controllers. These algorithms are known for their ability to find near-optimal solutions in complex optimization problem.

Global energy usage and climate change have prompted a shift towards sustainable energy solutions in electric distribution networks (DNs). This study employs case studies to discuss sustainable energy systems’ issues, strategies, and technological advances to improve electric DN performance. Renewable energy sources are gaining popularity as fossil fuels pollute the environment and jeopardize energy security. However, intermittent renewables like solar and wind endanger grid reliability. Thus, sustainable energy systems require advanced energy storage like batteries, pumped hydro storage, and flywheels, which can store and release energy during high and low production while maintaining grid power stability. Including renewable energy requires revaluing utility infrastructure. Utility demand-side management further improves grid stability by dynamically adjusting energy usage during peak demands. Future electric distribution grid sustainability requires electrifying transport and heating. Electric vehicle (EV) integration presents challenges and opportunities as their use rises. Controlled electric vehicle charging reduces grid demand and enables the grid-to-vehicle and vehicle-to-grid interactions, making the grid more dynamic. This work focuses on the integration benefits of sustainable energy systems, such as wind, solar, and photovoltaic energy systems, along with electric vehicle charging stations, through case studies.

Gone are the days when the transition to green energy was an alternative; nowadays, it is a must against climate change. Nonetheless, the incorporation of renewable energy sources (RES) such as wind, solar, hydro, and biomass into the existing power networks of traditional grids is a major problem due to their sporadic and unpredictable nature. Smart grids, however, which are supported by Artificial Intelligence (AI) and the Internet of Things (IoT), do solve the problems and ensure the system is stable, efficient, and sustainable in terms of energy. The use of AI in predicting energy usage and in allocating resources advances the energy grid. IoT devices with sensors send live data, which is used in the decision-making process. Furthermore, developments such as Virtual Power Plants (VPPs), energy storage systems that AI powers, and the use of blockchain to create decentralized energy trading could be considered as additional innovations that allow for the creation of a resilient and smart energy infrastructure. Despite these problems, like attacks on cybersecurity, privacy of personal data, and standardization of IT equipment for data exchange, are the ones that need tackling first. AI automation in the grid, which is enabled by the creation of energy storage solutions, besides coherent policy frameworks, is the main element required for making the shift to renewable energy a seamless process. In addition to the strategies already in place, quantum computing, federated learning, and self-healing grids may be used to make energy efficiency and sustainability reach new heights. The role of AI and IoT in smart grids is studied in this chapter, which points out how they add to the optimization of renewable energy integration, grid stability, and demand-side management, and thus people could live in a world where they can get power in a sustainable, reliable, and efficient way.

Accurate load forecasting is an essential task for the utilities for proper operation and effective energy trading in markets. Distribution companies are paying huge amount in the form of penalties due to deviation in actual power consumption. This can be avoided with accurate load forecasting tools. In this chapter, a complete procedure for short-term load forecasting using an artificial neural network (ANN) model is presented. The entire data collection used to train and test the ANN model was gathered from the Indian Energy Exchange (IEX) and can be seen at https://​data.​mendeley.​com/​datasets/​jxm8d4w4cv/​1. Comparison of the suggested model with other machine learning models validates it. Comparatively speaking, the constructed ANN model can predict the load with a lower error of 0.0017.

The demand for efficient and dependable power conversion methods has increased due to the quick development of sustainable energy systems and power electronics. Interest in multilevel inverters has increased in this area because of their capacity to generate high-quality voltage waveforms with minimal harmonic distortion. A number of power electronic switches are arranged in a staircase arrangement as part of the multilayer inverter architecture that was taken into consideration for this study to produce multilevel voltage output. Pertaining to the basic principle of Neutral Point Clamped (NPC) inverter, this new model with reduced switch topology helps in reducing harmonic distortion and reducing common mode voltage, and these reductions help it to get better efficiency than existing two-level inverters, and that’s where the novelty lies. This work describes in detail the NPC multilevel inverter model design that is chosen to be completed in MATLAB software. All the theoretical aspects of NPC multilevel inverters are covered, which include switching frequency, harmonic distortion, and common mode voltage. The hardware setup for the proposed method is executed, and the results are analyzed in this research.

The load flow analysis in radial distribution systems although less research has been done than in transmission systems, is nevertheless essential for real-world systems with large R/X ratios. It provides data on voltage magnitude, phase angles, active and reactive power flow, and total power losses to develop steady-state solutions for bus overload conditions. The optimal sizing and placement of capacitors reduces power losses and increases voltage stability, improving the system’s performance. A bio-inspired optimization approach calculates load flows and measures the voltage magnitude and resulting power losses by iterating to determine each iteration’s fitness. The first algorithm evolves the population over multiple generations through processes of genetic operators. Finally, convergence on the optimal solution for each chromosome in the population of possible solutions represents a specific arrangement of the capacitor locations, and the second algorithm, inspired by the collective behavior of swarms, efficiently navigates the solution space to identify the optimal locations and sizes of capacitors. These bio-inspired methods have been tested and compared and tested on standard IEEE radial distribution systems 6, 15, 33, and 69 distribution systems and a significant reduction in power loss, and improved voltage magnitude. The results for the IEEE 33-bus radial distribution system are shown simultaneously with capacitors to improve voltage magnitude and power loss reduction in the proposed bio-inspired optimization techniques in achieving more efficient and stable radial distribution systems.

The rapid growth of the electric vehicle (EV) market is driving large-scale EV charging infrastructure development, offering new opportunities for grid stabilization through vehicle-to-grid (V2G) technology. V2G has gained significant attention in recent years as a key research area. This study uses the PRISMA framework to present a bibliometric analysis of V2G research trends. A structured SCOPUS search with the keywords “vehicle-to-grid” and “electric vehicle” was conducted on June 28, 2024. After screening, 2,624 relevant documents published between 2010 and 2024 were selected for analysis using VOS viewer for visualization and Microsoft Excel for data handling. Findings reveal that China and India lead in V2G research publications, showing a steady rise since 2010, whereas the U.S. has fewer articles but a higher citation impact, indicating significant global influence. IEEE Transactions on Smart Grid holds the highest citations, while MDPI’s Energies has published the most articles in this domain. Keyword cooccurrence analysis links V2G with demand response, microgrids, grid-to-vehicle (G2V) interactions, and smart grids. This study provides a comprehensive overview of V2G research, identifying key trends, knowledge gaps, and future research directions.

Renewable energy infrastructure expansion has demonstrated the requirement for data management systems that maintain both security together with scalability, and resiliency. Centralized cloud storage systems dominate the market but struggle with multiple severe limitations, such as cyberattacks against data centers and exclusive control of data, and performance problems with diverse energy dataset types. The investigation in this chapter provides an extensive analysis of decentralized cloud storage systems that combine blockchain technology with the Inter Planetary File System (IPFS) to resolve storage issues. The research investigates DCS implementation through technical designs, along with use cases supporting the evaluation of policies to show how this infrastructure boosts renewable energy network accessibility while securing and making data transparent. The analysis investigates regulatory compliance, together with scalability issues and quantum resistance, followed by solution implementations.

Predictive-based torque control (PBTC) and predictive base current control (PBCC) are sophisticated control techniques in power electronics. The predictive base torque control (PBCC) method takes into account the PTC technique evaluates Energy torque and flux through the stator in their monetary function to manage a synchronous reluctance machine (SRM) or induction machine (IM), whereas the PBTC technique assesses the deviations between the reference value of the current as well as the monetary function's measured current. The IGBT transitioning vector lessens the difference between the references and the anticipated values. Including the limitations of the system is simple. The element of weighting is not required. Both the PBCC and PBTC approaches are the most effective direct control techniques; however, the SRM approach does not require a modulator and provides ten percent to thirty percent greater torque than an induction motor. Induction motors can only provide seventy to ninety percent of the torque generated by SRM with the same current since they only operate on a lagging power factor. When employing SRM with a fifteen-level H-bridge multilevel inverter, PBCC, and PBTC lower torque, speed, and stator current by twenty-three percent more than when using an induction motor with the same inverter. By only switching the transistors when losses in switching are required to maintain torque along with flux within their respective limits, they are kept to a minimum. To improve the performance of the multilayer inverter, the semiconductor switches the method of switching is employed. In contrast to the PBTC and PBCC approaches with a voltage source inverter that has two levels, the methods used in this chapter with a fifteen-level H-bridge multilevel inverter employing both induction machines and synchronous reluctance motor provide exceptional torque along with flux responses as well as provide strong and consistent operation. Researchers immediately responded to this new approach because of its simple methodology and strong performance in both intermittent and sustained modes.

Reliability in power distribution is essential for maintaining a continuous and stable electricity supply, ensuring economic productivity, supporting critical infrastructure, and enhancing the overall quality of life. Several factors, such as diminishing fossil fuel supplies, rising demand, and environmental concerns, are contributing to the surge in the use of distributed generations (DGs) at the consumer end. Improving the distribution system's reliability may be as simple as adjusting the repair times and failure rates of individual sections. Properly locating and sizing DGs has a significant impact on reliability, as well as other aspects of distribution systems. The introduction of DGs into the distribution system also incurs a number of expenditures linked to investments, maintenance, operation, and so on. Because of the utilities’ and consumers’ perspectives on the costs and advantages of DG installations, the issue becomes a mixed-integer, non-linear programming problem. This chapter presents an analytical framework for assessing and enhancing the reliability of distribution networks that incorporate DG operating in standby mode. We optimized the distribution system's reliability by taking into account the repair time and failure rate of each segment, several energy-based reliability indices, and consumer requirements. The objective function takes into account the additional expense of DG's anticipated energy supply, as well as the cost of adjusting for repair intervals and failure rates. Transit search optimization (TSO) successfully resolved the optimization problem, and the described methodology was implemented in an 8-bus distribution system.

This chapter explains how a transformer overcurrent operates by sending a voice message to mobile phones via Bluetooth. This article's primary goal is to address errors and notify the operator of the necessary corrections promptly. The voice announcement through Bluetooth simplifies the operator's workload. When a transformer's secondary current malfunctions, current sensors detect it and send an artificial intelligence-based audible alert through a mobile application. This method is easy to use due to its hands-free announcement feature. When a transformer experiences an overcurrent, the relay continues to trip until the fault is resolved. The necessary steps are executed promptly and accurately to ensure the complete safety of the entire system. Both serious damage and frequent blackouts are prevented by the transformer. To maintain a continuous supply, this model can also be integrated into the power system.

The widespread use of Electric Vehicles (EVs) has highlighted the vital requirement for reliable and precise fault diagnosis systems for their complex battery packs. Identifying and recognizing faults in the battery system early is imperative to ensure the safety, dependability, and performance of electric vehicles. An enhanced EV battery problem diagnosis approach is presented in this abstract in full. Modern data analytics and machine learning approaches are combined in the suggested methodology to offer a methodical and efficient way to identify battery problems. To create an accurate model of typical battery behavior, it incorporates historical data together with data from multiple sensors, including voltage, current, and temperature. It is thus possible to identify deviations from this model as possible flaws. A variety of battery issues, including as cell imbalance, thermal runaway, and deterioration, can be found by the diagnostic system. It ensures timely action to prevent potential safety issues by providing real-time monitoring and diagnosing capabilities. Furthermore, the system can continuously learn and adapt, which enables it to adjust to different EV models and battery chemistries. Real-world data from a variety of EVs and rigorous testing have been used to validate the presented approach. High accuracy in fault detection is demonstrated by the results, allowing for prompt maintenance and repair measures. An efficient fault diagnosis system is essential to maintaining the longevity and safety of electric vehicles (EVs) as their adoption grows. This study advances the field of EV battery diagnostics and marks a significant milestone in the ongoing development of dependable and sustainable electric vehicles.

An improved power quality (PQ) solar photovoltaic (PV) inverter that eliminates common-mode current leakages is presented in this research. This study examines a three-phase transformer-less solar energy converting system (SECS) that, in addition to peak active power generation from PV arrays, guarantees various power quality enhancement capabilities like grid current balancing and harmonic mitigation, and provides grid reactive power support. This approach doesn't compromise the leakage currents induced by the parasitic capacitance of the PV array to ground. Unlike traditional power quality inverters, it is robust regarding anomalies in grid voltages at distant radial trimmings. PV leakage currents are often ignored in PV inverter power superiority organizations, even though the reality is that they have an important impact on system performance by degrading power quality and endangering operating staff protection. Consequently, transformer-less PV systems must activate using leakage currents within the 300 mA range as required by the principles \(VDE-00126 and NB/T-32004\). Numerous test and simulation findings demonstrate that the suggested approach performs satisfactorily even when subjected to different grid-side anomalies. The strategy's efficacy is demonstrated by the comparison with cutting-edge methods. Under all test situations, the grid current harmonics are found to be within the \(IEEE-519\) as well as \(IEC-61727\) standards’ bounds, and the PV escape currents are kept fit surrounded by the \(VDE-00126\) standard's acceptable range.

Issues with electrical power quality have become more frequent due to the widespread use of electronic devices and limitations placed on distribution lines. Consequently, there will be a growing concentration on monitoring power quality and monitoring power disruptions. Understanding power quality problems and identifying disturbances are crucial for electric compensation along with improving electrical safety. A modified S transform based on the sigmoid function is addressed for obtaining the characteristics of various power quality signals with better time–frequency (T-F) resolution as compared to other techniques. Thus, an assessment index was established to quantify the effect of different methodologies on signal. Various power quality signals are generated, and the performance of the modified S-transform is evaluated which shows the efficiency and accuracy of the method. From the results, it can be concluded that the proposed T-F method fulfills the requirements of various power quality signals and copes with the negative impact of noise and harmonics.

General take-up of electric vehicles (EVs) is essential to support sustainable transport, but their climate effectiveness depends on how the EV is charged. Incentivization of current conventional charging is irrelevant to the indirect impacts of fossil fuel-based electricity use, and also no pricing reflects linked carbon effects. This chapter puts forward an artificial intelligence-based low-carbon EV charging scheduling paradigm that aims at optimization by utilizing instant information of the price of electricity, the grid’s carbon content, and availability of green energy. With the integration of predictive modeling, machine learning, and reinforcement learning, the system makes dynamic adjustments in charging to mitigate emissions and expenses while alleviating grid load. A carbon emission flow model allows real-time monitoring of per-session emissions. The framework also supports peer-to-peer (P2P) green energy trading through blockchain smart contracts, permitting EV owners to buy renewable energy directly. Dynamic pricing strategies further coordinate charging with low-carbon grid hours, enhancing grid resilience and sustainability. Real-world simulations prove significant emissions and peak load reductions, and carbon trading-generated financial incentives. This AI-enabled system is a scalable carbon-aware EV charging solution despite its challenges through computational complexity and regulatory barriers. It helps in achieving net-zero goals by establishing a smart, efficient, and sustainable EV energy infrastructure.

The non-linearity in loads is common nowadays in almost all industries, residences, and commercial applications due to power electronics converters. Automation in all applications requires controllable semiconductor switches to provide precise control. The increased usage of power electronic appliances in recent years has created great challenges in maintaining power quality standards. The conventional uncontrolled power converter used for energy conversion is the main cause of producing harmonics, mainly on the source side current. The suitable device to mitigate the current harmonics is a Shunt Active Power Filter. This paper proposes a new design for a three-phase filter that aims to reduce harmonics in source current under variable load conditions to enhance total harmonic distortion to the desired level. The filter is intended to operate in parallel with the load and source. It also employs a phase-locked loop for reference current computation in the synchronous reference frame theory control algorithm. The precision pulse width modulation method is used to provide a control signal to the voltage source inverter of the filter based on the dynamic conditions of the load to achieve the required level of harmonic compensation. The results obtained from the performance analysis of the filter through MATLAB simulations show that the value of total harmonic distortion is achieved as per the IEEE 519-2014 standards. The proposed filter is implemented in hardware to validate the results obtained from the simulation.

The increasing prevalence of smart homes has opened new avenues for improving energy efficiency by applying IoT technologies and advanced data analytics. This research paper focuses on the development of a comprehensive framework that combines IoT sensors, machine learning models, and automation techniques to monitor, analyze, and optimize energy consumption in residential environments. The system collects data from various household appliances and environmental sensors, providing detailed insights into usage patterns and identifying inefficiencies. Predictive algorithms are employed to forecast energy demand, while rule-based automation adjusts appliance operations to reduce unnecessary consumption. The proposed solution demonstrates that significant energy savings, up to 25% can be achieved without compromising user comfort. In addition to reducing energy bills, the system contributes to sustainability by lowering overall energy waste. Simulation results validate the effectiveness of this approach, showing how IoT-enabled analytics can play a critical role in transitioning toward smarter, more sustainable homes. This study provides a foundation for future innovations in energy management, particularly through the integration of renewable energy sources and more advanced user behavior modeling.

Power Quality (PQ) analysis is very important in electrical power systems for their effective operation and stability. The connection of power electronic devices, renewable energy sources, and non-linear loads mainly causes the PQ deviation. In this chapter, PQ disturbances were described with their simulation results, such as transients, voltage sags, voltage swells, interruptions, voltage flickers, harmonics, notching, and combined issues. The PQ issues classification approaches and their studies frequently encounter challenges in managing the diverse conditions of present electrical systems. Artificial Intelligence (AI) techniques have dynamic capabilities in PQ classification because they automate pattern identification and correlate with non-linear trends while performing real-time equipment diagnosis. This chapter investigates different power quality problems with their origins and consequences, and also evaluates various AI-based classification approaches by comparing supervised and unsupervised learning models, deep learning methods, and hybrid analytical techniques.

Electric vehicles (EV) have increased over the past ten years. Because the EV replaces the internal combustion engine and reduces carbon dioxide emissions. People look for EV there as a means of transport. Since electric cars were invented in 1832, most people locked themselves in buying an EV vehicle had to face some problems. Now in the 20th century, the growth of electric cars is more than electric motors. Electric vehicles consist of a battery, which is an important component. This battery must be charged when it is empty. When it comes to charging a battery, the EV station must do so there. The presence of a broken cable has implications on physical and electrical connectivity and may disrupt the charging session. EV is a key step in reducing greenhouse gas emissions and transitioning to a more sustainable mode of transportation. One critical factor that influences the growth of the EV market is the accessibility of charging infrastructure. To ensure the efficient and convenient use of EVs, the placing of EV charging stations is of very important. In the current article, we will explore the factors to consider and the best practices for placing EV charging stations (CS). Only power converter topologies go into constructing an EV charging station. A rectifier and a DC convertor are then used to connect the battery pack to an existing power grid but it can remain stable all the while. In other words, when plugging an electric car into a CS, the behaviour of a CS will change either on rectifier or EV mode if the converter works. As such, in modes the EV perceives the power converter as a nonlinear excitation source. Furthermore, the EV battery also helps in alleviating the power frequency variation and disturbance in the electrical grid system.

Artificial intelligence (AI)-optimized optimization has transformed EV charging by minimizing inefficiency, reducing costs, and using renewable energy. Traditional charging stations follow fixed schedules independent of dynamic EV demand and grid conditions. Predictive analytics, reinforcement learning, and scheduling algorithms powered by AI enable real-time decision-making that optimizes the charging slots as a function of electricity prices, renewable supply, and user requests. By charging during periods of high renewable energy generation, AI minimizes carbon footprint while injecting stability into the grid. Vehicle-to-grid (V2G) technology also makes EVs possible as rolling energy storage devices, encouraging better distribution of energy. Mobile apps with AI further increase customer satisfaction through suggestions of the best time to charge, reducing wait, and station congestion. Empirical research indicates that AI-based scheduling boosts station efficiency by 30% and reduces operating costs by 20%. Nevertheless, issues such as computational complexity, privacy of data, and integration into infrastructure continue. These problems can be alleviated through federated learning and decentralized AI designs. The future holds blockchain-based charging transactions and mixed AI models that combine deep learning and real-time optimization. In general, AI-based EV charging is critical for sustainable, efficient, and cost-effective energy management with smooth integration into smart grids and renewable energy.

One of the main components in electric vehicles that influences the performance of the electric vehicle is the lithium-ion battery used. Frequent health monitoring of the EV battery by diagnosing the faults is essential for the durability and safety of the vehicle. Nowadays, many deep learning models are developed to monitor the health of battery-based fault diagnosis. However, in order to train and test these deep learning models large amount of data is required. In this chapter, the complete procedure to develop an EV battery faults dataset using MATLAB is presented. Also, all the data analytics of the developed EV battery faults dataset are presented in this chapter. The data presented in this paper can be useful to researchers who are working on applications of artificial intelligence in electric vehicles. In this chapter, data generation based on short-circuit faults in EV batteries, over-discharge faults, and healthy conditions is discussed. Two datasets are created using MATLAB, and those datasets are the Electric Vehicle Battery Health Simple Classification (EVBHSC) dataset and the Electric Vehicle Battery Health Multiple Classification (EVBHMC) dataset.

A continuously rising load demand places an increasing pressure and voltage decrease on the current power distribution network. Due to previously unheard-of issues, including a supply–demand gap, growing costs, and global warming, the power supply sector urgently needs reform. This, in turn, highlights the significance of a smart grid. The smart grid includes generating integration at the distribution level as one of its features. If sized and located properly, distributed generation (DG) may significantly reduce power losses. This chapter describes the application of a genetic algorithm to reduce distribution losses in a feeder by maximizing the size and placement of DG at an existing radial distribution system that represents load with wind production linked to the substation. The performance of a Genetic Algorithm (GA) depends on several factors that must be accurately calibrated. The work in this article is an effort to address this connecting issue. With a voltage-dependent load model, the real radial distribution system is taken into consideration. In this chapter, the ideal placement and size of the DG are determined by experimenting with different GA operator combinations while maintaining constant values for factors like population, crossover percentage, and generation. To examine the impact on active and reactive power loss, the best placement and size are implemented for the least amount of loss. In the first instance, the available generation is employed, and GA determines the ideal position; in the second instance, both the optimal placement and size are implemented. The test findings show a 56.49% decrease in loss if the existing DG is linked at the ideal location as per GA and a 91.47% reduction in loss if location and size are compared to the existing DG, respectively. The tail-end voltage and power losses are both significantly improved using the evolutionary algorithm GA.

Electric vehicles (EVs) have the potential to transform the automotive industry in the next years, setting the framework for a more environmentally conscious society. In Vision 2030, the Indian government intends to electrify public transportation and increase the number of electric passenger vehicles. To comply with the challenging AIS-123 standard established by the Central Motor Car Rules (CMVR) for EV retrofits, EV conversions must thoroughly preserve and replace crucial components from gasoline-powered vehicles with the standard. The primary goal of the research work focuses on fine-tuning battery parameter characteristics for PMSM drive retrofitting electric three-wheelers. The proposed electric retrofit kit offers an affordable and practical solution for auto rickshaw drivers to transition from gasoline-powered vehicles to electric mobility with an enhanced optimization technique. Featuring a 6-kWh battery with a 95 km range per charge, the kit is designed for the daily operational needs of auto rickshaws. Priced at Rs. 79,800, the kit enables conversion of existing rickshaws into electric vehicles, reducing fuel dependency and avoiding high upfront costs of new electric vehicles. It delivers significant financial savings through reduced fuel and maintenance costs, improving profit margins. Rigorous testing ensures long-term reliability, making the retrofit kit a sustainable, cost-effective, and environmentally friendly solution.

The convergence of blockchain, Internet of Things (IoT), and AI in microgrid networks is revolutionizing the energy sector by increasing efficiency, security, and reliability. IoT provides effortless communication among distributed energy resources, sensors, and smart meters, enabling real-time data sharing, predictive analytics, and self-decision-making. With blockchain, IoT, and AI, microgrids can be made more resilient, self-sufficient, and efficient. The existing microgrid data aggregation techniques are inaccurate and vulnerable to different types of cyber attacks. Demand response prediction, maximum energy generation, optimal energy consumption, and grid self-diagnosis have been made possible by smart energy management systems in recent years. The blockchain-based smart meters can communicate with the grid as digital wallets that enable prosumers to easily purchase and sell energy in addition to serving as measurement tools. It also offers a decentralized, tamper-proof ledger for secure energy transactions and P2P energy trading. AI-based smart grids can make energy distribution adaptable with the help of present data, minimizing energy loss and making optimal use of energy at peak locations and moments. It allows them to detect opportunities for energy savings and make automatic adjustments to reduce consumption. These systems result in significant cost savings and help in the realization of wider sustainability goals.

The mass adoption of electric vehicles (EVs) presents an opportunity as well as a challenge for global energy systems. EVs result in reduced carbon emissions and the consumption of less fossil fuel, but in massive numbers, their integration into the power grid has the potential to frighten governments about energy demand surges and grid instability. Vehicle-to-grid (V2G) technology presents a game-changing solution by enabling the bidirectional exchange of energy between EVs and the power grid. This chapter addresses the concept of V2G, where EVs act as moving energy storage units, feeding electricity into the grid when demand is high and taking power when demand is low. V2G technology has some advantages, including grid balancing, enhanced integration of renewable energy, and lowering the cost to EV users. The role of smart charging infrastructure, artificial intelligence (AI), Internet of Things (IoT), and blockchain in optimizing V2G systems is also being touched upon. However, issues such as the degradation of batteries, regulatory barriers, and infrastructure limitations must be addressed to realize mass adoption. With the globe shifting toward a sustainable future, the use of V2G can enhance energy efficiency remarkably. Additionally, V2G technology can transform the energy industry by developing new economic models where EV owners can earn money by selling saved energy back to the grid.