Integration Of Electric Vehicles Research Articles

Development of an efficient vehicle-to-grid method for massive electric vehicle aggregation

The growing adoption of renewable energy and electric vehicles (EVs) has contributed to environmental sustainability; nevertheless, integration of these products into the power grid has become complex owing to their unpredictable nature and variable energy demands. A significant challenge lies in the realization of large-scale, coordinated control of EVs to serve as an alternative to traditional energy storage systems. This challenge is underscored by the complexity of optimization in large-scale cooperative control problems and the difficulty in reducing such problems to an easily manageable and practical level in real-world application. In response to these challenges, a practical mechanism for the integration of EVs into a vehicle-to-grid concept is proposed in this study. In this approach, the constraints involved in merging multiple EVs into a fictitious clustered energy storage unit, which are often neglected, are given renewed focus. An iterative multi-stage optimization method is introduced that includes an EV aggregation clustering model, multi-tier optimization model, and recursive framework. Here, marked efficacy for larger EV fleets is demonstrated for this method, providing optimal charging and discharging schedules for each vehicle while a high degree of precision is maintained. With the proposed technique, validated through numerous case studies using historical data, the global optimum solution is largely approximated, with a marginal deviation of 4 %. In addition, robustness of the model is demonstrated under varying pricing scenarios, with a computation time that increases in a linear manner, rather than exponentially, as the number of EVs increases. Meanwhile, compared with conventional methods, the technique proposed in this study has the capacity to fulfill the charging demands of all users with reduced charging expense, demonstrating the high precision and efficacy of the technique.

Optimal scheduling of electric vehicle aggregators for frequency regulation and cost efficiency in renewable-powered grids

The growing integration of intermittent renewable energy sources (RESs), such as wind and solar, into power grids presents significant operational challenges, particularly in maintaining grid stability and managing generation forecasting errors. This study underscores the critical role of Electric Vehicles (EVs) as a flexible load in providing ancillary services, especially for secondary frequency regulation, to address these challenges. Moreover, a novel scheduling model for EV aggregators is introduced that aims to optimize frequency regulation and cost efficiency. This model strategically manages the State of Charge (SOC) to ensure EV owner satisfaction while minimizing operational costs. Employing Mixed-Integer Linear Programming (MILP), solved via CPLEX-GAMS in both deterministic and stochastic scenarios, the proposed approach evaluates various EV integration strategies, demonstrating potential reductions in charging costs and enhancing owner participation by considering parking constraints. This research highlights the importance of innovative grid management solutions in the era of renewable energy proliferation, offering significant insights into cost-effective and reliable energy practices. The results in the deterministic show that by considering the EV parking constraint, the total charging cost will be increased by about 20%. Also, by applying the proposed strategy to the down-regulation value of 18%, the total cost of EV charging is decreased to 11,400 $ which shows about a 5% decrement in comparison with the down-regulation value of 32%.

Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles

The integration of Artificial Intelligence (AI) in Energy Storage Systems (ESS) for Electric Vehicles (EVs) has emerged as a pivotal solution to address the challenges of energy efficiency, battery degradation, and optimal power management. The capability of such systems to differ from theoretical modeling enhances their applicability across various domains. The vast amount of data available today has enabled AI to be trained and to predict the behavior of complex systems with a high degree of accuracy. As we move towards a more sustainable future, the electrification of vehicles and integrating electric systems for energy storage are becoming increasingly important and need to be addressed. The synergy of AI and ESS enhances the overall efficiency of electric vehicles and plays a crucial role in shaping a sustainable and intelligent energy ecosystem. To the best of the authors’ knowledge, AI applications in energy storage systems for the integration of electric vehicles have not been explicitly reviewed. The research investigates the importance of AI advancements in energy storage systems for electric vehicles, specifically focusing on Battery Management Systems (BMS), Power Quality (PQ) issues, predicting battery State-of-Charge (SOC) and State-of-Health (SOH), and exploring the potential for integrating Renewable Energy Sources with EV charging needs and optimizing charging cycles. This study examined all topics to identify the most commonly used methods, which were analyzed based on their characteristics and potential. Future trends were identified by exploring emerging techniques introduced in recent literature contributions published since 2017.

A novel predictive optimal control strategy for renewable penetrated interconnected power system

AbstractThe stable and efficient regulation of voltage and frequency is a critical issue associated with the functioning of modern power system network having renewable integration. This manuscript focuses on the concurrent regulation of voltage and frequency in a power network comprising multiple generating sources. Herein, along with conventional thermal and diesel plants, renewable generations from different sources have been considered such as wind and solar photovoltaic (PV) resources. Further, in a fresh attempt, the authors have modeled wind and solar PV generation using two different stochastic modeling methods concerning combined voltage and frequency loops in the regulated power system model. Furthermore, the novel Leader Harris Hawks Optimized Model Predictive Controller (MPC‐LHHO) has been applied to concurrently minimize the voltage and frequency deviations in the given power network. To address the intermittent nature of load and renewable generations, various auxiliary frequency controllers, such as the unified power flow controller (UPFC) and electric vehicle (EV) integration with the grid, have been implemented. The robustness of the proposed control method has been successfully validated through diverse scenarios of random loadings.

Communications and Data Science for the Success of Vehicle-to-Grid Technologies: Current State and Future Trends

Vehicle-to-grid (V2G) technology has emerged as a promising solution for enhancing the integration of electric vehicles (EVs) into the electric grid, offering benefits, such as distributed energy resource (DER) integration, grid stability support, and peak demand management, among others, as well as environmental advantages. This study provides a comprehensive review of V2G systems, with a specific focus on the role of the communication, as they have been identified as key enablers, as well as the challenges that V2G must face. It begins by introducing the fundamentals of V2G systems, including their architecture, operation, and a description of the benefits for different sectors. It then delves into the communication technologies and protocols in V2G systems, highlighting the key requirements in achieving reliable and efficient communication between EVs and the different agents involved. A comprehensive review of communication standards is described, as well as the main communication technologies, which are evaluated in terms of their suitability for V2G applications. Furthermore, the study discusses the challenges and environmental implications of V2G technology, emphasizing the importance of addressing strong and reliable communications to maximize its potential benefits. Finally, future research directions and potential solutions for overcoming challenges in V2G systems are outlined, offering useful insights for researchers, policymakers, and administrations as well as related industry stakeholders.

Graph‐based solution for smart grid real‐time operation and control

AbstractUnder the envisioned smart grid paradigm, there is an increasing demand for a fast, accurate, and efficient power flow solution for distribution system operation and control. Various solution techniques have been proposed, each with its own unique formulation, solution methodology, advantages, and drawbacks. Motivated by challenges associated with the integration of renewable distributed energy resources and electric vehicles into distribution systems and further by the speed and convergence limitations of existing tools, this paper presents a novel graph‐based power flow solution for smart grid's real‐time operation and control, named Flow‐AugmentationPF algorithm. The proposed method formulates a power flow problem as a network‐flow problem and solves it by using a maximum‐flow algorithm, inspired by the push‐relabel max‐flow technique. The performance of the proposed algorithm is tested and validated using several benchmark networks of different sizes, topologies, and parameters and compared against the most commonly used solution techniques and commercial software packages, namely PSS/E and PSCAD. The proposed formulation is simple, accurate, fast, yet computationally efficient, as it is based on matrix‐vector multiplication, and is also scalable, considering the formulation works as a graph‐based method, which, inherently, allows for parallel computation for added computational speed.

A multi-objective energy scheduling of the reconfigurable off-grid microgrid with electric vehicles using demand response program

Microgrids are essential in ensuring responsible energy consumption and production patterns to support and advance the United Nations’ sustainable development goals. However, due to the limited supply in the off-grid microgrids, an increase in the integration of electric vehicles (EVs) into electrical networks for their charging operation is expected to create a void between the energy supply and demand. Demand response (DR) programs provide a better solution to the energy management problem of microgrids. However, the difference in the objectives of the stakeholders of the off-grid microgrid can create an issue in providing optimal solutions to its energy scheduling problem. This paper focuses on the multi-objective energy scheduling strategy of the off-grid microgrid to manage its limited energy supply optimally using an incentive-based DR program. A fast heuristic approach is also presented in this work to reconfigure off-grid microgrid optimally during its hourly operations. Furthermore, Hong’s (2m+1) point estimation method is used in this work to consider the uncertainties in the EVs and other DR participants. The proposed work is analyzed on the 33-bus and 69-bus off-grid microgrid consisting of droop-controlled distributed generations, EV charging stations, and consumers with curtailable loads. For the day-ahead operation of the off-grid microgrids, the analysis shows that the proposed work reduces the operational cost of the two off-grid microgrids by 2.3% and 4.56%, respectively.

Synergistic Integration of EVs and Renewable DGs in Distribution Micro-Grids

This paper proposes a multi-objective optimization framework for safe, reliable, and economic integration of electric vehicles (EVs) and renewable distributed generators (DGs) in distribution micro-grids. EV and DG coordination optimization with the use of vehicle-to-grid (V2G) technology along with system reconfiguration optimization is developed to provide collective revenues and address integrational complications that may occur by additional system loading due to EV charging and EV-DG energy exchanges. A Genetic Algorithm (GA) optimizes the EV charging/discharging in synergies with renewable DGs to maximize benefits that can be captured by their collaborative participation in electricity market and through renewable energy arbitrage. The developed EV charging/discharging optimization is implemented in a real 134-bus distribution network and is evaluated for its potential operational implications, namely, increased system losses. A system reconfiguration is then proposed to reduce the system losses by optimizing the flow of power through switching on/off the connections within the micro-grid and/or with other distribution systems. Simulation results demonstrate the efficiency of the proposed method in not only providing collective revenues, but also in enhancing the system operation by reducing the losses of the distribution grid. The collective benefits proposed by the developed optimization and validated by the simulation results facilitate transitioning to clean and eco-friendly sources of energy for generation and transportation, which in turn leads to more sustainable development of societies and communities.

Design and assessment of energy infrastructure in new decarbonized urban districts: A Spanish case study

New urban districts have great potential to reduce greenhouse gas emissions by electrifying energy demand in various sectors, such as transportation and buildings. This paper proposes a novel approach that combines different energy models to plan the infrastructure for supplying the energy demand of new, decarbonized, and highly electrified urban districts on a real-world scale. First, a model for energy management in buildings is used to plan and operate equipment for heating, cooling, and solar photovoltaic distributed generation. Second, a model that plans electric vehicle charging infrastructure determines the number and types of charging stations. Then, a large-scale distribution network planning model designs a cost-efficient electricity distribution grid to supply the district. A case study is presented for a new urban district in Madrid, Spain, to demonstrate how this approach can be applied to energy infrastructure planning in a real-world context. This case study presents different scenarios of energy efficiency performances in buildings and penetration levels of solar installations, electric vehicles, and heating and cooling systems. The results show that even in a high electrification scenario, improving energy efficiency in buildings through a district heating network based on heat pumps can lead to a lower peak electricity demand. This peak load reduction allows for integrating more electric vehicles, avoiding further investments in the electricity distribution network. In addition, the results confirm that a highly electrified scenario, which combines energy-efficient buildings with high integration of solar and electric vehicles, significantly reduces non-renewable energy consumption and greenhouse gas emissions.

An Overview of Opportunities and Challenges to Vehicle-to-Grid Integration and Bahrain Perspective

Transportation sector is among the largest consumers of fossil fuels, which also makes it a significant contributor to the greenhouse gas emissions. A great deal of effort has been made to reduce this reliance of transportation on fossil fuel for its operation. These efforts have resulted in innovations in vehicle technology which include vehicles operating on electric and hybrid energy. The advent of these technologies has showed the way towards new possibilities to further increase the efficiency of energy systems and their utilization. One such system utilizes the integration of electric vehicles with the national grid system, it is referred to as vehicle-to-grid (V2G) technology. This technology provides a cost-effective and efficient solution to address the problems associated with the high demand and variable supply of renewable energy systems. This study provides a brief overview of the research conducted in this area, consequently identifying the opportunities and challenges related to this technology. Research shows that V2G integration has environmental benefits in the form of reducing CO2 emissions and reducing reliance on traditional energy sources. In spite of this, the economic feasibility of this technology requires efforts for standardization, infrastructure expansion and smart charging strategies. This paper is expected to provide future direction of work for all stakeholders, including academia, industry, and governmental bodies. There are collaborative efforts required to standardize the vehicle and grid technology and infrastructure expansion, while the research needs to focus on optimization strategies. In the end, discussion on the implementation of this technology in Bahrain is also presented. In this context, it was found that the major challenges are due to the current reliance on fossil fuel and the trend of increase in car ownership which must be addressed before taking steps towards V2G systems.

Integration of Electric Vehicles and Renewable Energy in Indonesia’s Electrical Grid

As the global transition toward sustainable energy gains momentum, integrating electric vehicles (EVs), energy storage, and renewable energy sources has become a pivotal strategy. This paper analyses the interplay between EVs, energy storage, and renewable energy integration with Indonesia’s grid as a test case. A comprehensive energy system modeling approach using PLEXOS is presented, using historical data on electricity generation, hourly demand, and renewable energy, and multiple scenarios of charging patterns and EV adoption. Through a series of scenarios, we evaluate the impact of different charging strategies and EV penetration levels on generation capacity, battery storage requirements, total system cost, renewable energy penetration, and emissions reduction. The findings reveal that optimized charging patterns and higher EV adoption rates, compared to no EVs adoption, led to substantial improvements in renewable energy utilization (+4%), emissions reduction (−12.8%), and overall system cost (−9%). While EVs contribute to reduced emissions compared to conventional vehicles, non-optimized charging behavior may lead to higher total emissions when compared to scenarios without EVs. The research also found the potential of vehicle to grid (V2G) to reduce the need for battery storage compared to zero EV (−84%), to reduce emissions significantly (−23.7%), and boost penetration of renewable energy (+10%). This research offers valuable insights for policymakers, energy planners, and stakeholders seeking to leverage the synergies between EVs and renewable energy integration to pursue a sustainable energy future for Indonesia.

A Review on Highway Network System - Samruddhi Expressway

Highway networks serve as critical arteries of transportation infrastructure, facilitating the movement of goods and people across vast geographical regions. As traffic volumes continue to increase and technological advancements reshape transportation systems, there is a pressing need to optimize the efficiency, safety, and sustainability of highway networks. This abstract presents a comprehensive system approach aimed at addressing these challenges. The proposed highway network system integrates advanced technologies such as intelligent transportation systems (ITS), real-time traffic monitoring, data analytics, and machine learning algorithms to optimize traffic flow, minimize congestion, and enhance safety. By leveraging data from various sources including sensors, cameras, and connected vehicles, the system enables proactive traffic management and congestion prediction, allowing authorities to implement timely interventions and mitigate potential disruptions. Furthermore, the integration of artificial intelligence and machine learning algorithms enables predictive maintenance of infrastructure, identifying potential issues before they escalate into critical failures. This proactive approach not only reduces maintenance costs but also enhances the reliability and lifespan of highway assets. Moreover, the system prioritizes safety through the implementation of automated collision avoidance systems, adaptive speed control, and real-time incident management. By leveraging vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication technologies, the system enables vehicles to exchange critical information, such as hazardous road conditions or impending collisions, thereby reducing the risk of accidents and improving overall safety. Additionally, the system incorporates sustainability principles by optimizing traffic flow patterns to minimize fuel consumption and emissions, promoting eco-friendly transportation practices, and facilitating the integration of electric and autonomous vehicles into the existing infrastructure.

Reliability-driven time-of-use tariffs for efficient plug-in electric vehicle integration

Demand response (DR) plays a crucial role in balancing electricity supply and demand, but existing studies on price-based DR for plug-in electric vehicle (PEV) load management lack comprehensive considerations. Therefore, this study designs reliability-driven time-of-use (TOU) tariffs for the integration of PEVs, considering various factors such as random component failures, the stochastic characteristics of non-dispatchable components, and fluctuations in load patterns and uncertainties. The proposed framework employs advanced stochastic simulation models to assess generation facility reliability amidst extensive PEV deployment in three steps. First, individual components, including conventional generation capacity, renewable energy sources, and demand, as well as PEV load models, are developed. Then, a system reserve margin assessment is carried out via the generation of seasonal synthetic time series data. During this step, innovative techniques, including box-plot methods and expert rules, are used to simplify the representation of the system reserve margin at each hour. Finally, optimized TOU pricing schedules for PEVs are generated. These pricing hours are initially categorized as off-peak, mid-peak, or on-peak, streamlining scenario analysis and aligning PEV charging profiles with predefined pricing schedules for grid reliability evaluation. Case studies have shown that the proposed method can improve the reliability indices by effectively mitigating the impacts.

Smart transportation systems using learning method for urban mobility and management in modern cities

The pressing demand for an escalation in renewable energy production arises from the surge in power consumption, escalating environmental concerns, and the exhaustion of fossil fuels. Microgrids, featuring dispersed power sources, exhibit superior resilience, efficiency, and stability in contrast to conventional power grids. Noteworthy investments in eco-friendly initiatives have been made by various major corporations, particularly in institutional structures. The current research advocates for the integration of photovoltaics, energy storage, and electric vehicles (EVs) as pivotal elements in a smart grid. This study introduces an optimal energy management system (EMS) designed to efficiently allocate power from existing sources. To address the challenges within an uncertain and reliable environment, a stochastic framework leveraging the whale optimization algorithm and unscented transform has been formulated. All simulations have been executed using the digital twin of the smart grid components. Results suggest that the proposed EMS ensures uninterrupted power supply, contributing to a substantial 45% reduction in energy costs. Furthermore, the potential of EVs as storage devices is explored. When employed as energy sources, EVs led to a noteworthy 45.58% reduction in power prices, while their use as loads still yielded a significant 19.33% decrease. The study also delves into the analysis of outage effects on the continuous power supply cost.

Evaluating EV User Behavior on Aggregator Smart Charging with ESS and Real-Time Pricing-Based Demand Response

With rising global electricity consumption, governments prioritize energy efficiency and the integration of electric vehicles (EVs) into energy markets. This study evaluates EV aggregator strategies using a smart charging method that modulates charging power rates based on user preferences. Simulations in Quito's distribution system assess various actions' impacts on aggregator costs and technical conditions. The study focuses on demand response (DR) strategies, particularly for residential areas, exploring EVs' potential as energy storage via vehicle-to-home (V2H) and vehicle-to-grid (V2G) options. It introduces a collaborative evaluation of dynamic-pricing and peak power limiting-based DR strategies, incorporating bi-directional EV and energy storage system (ESS) use. A novel mixed-integer linear programming (MILP) model for home energy management (HEM) integrates distributed renewable energy, V2H/V2G capabilities, and two-way ESS energy trading and diverse DR strategies. This comprehensive approach assesses the impact of EV owner preferences and ESS availability on reducing total electricity costs through case studies.

Optimal sizing of multi-energy microgrid with electric vehicle integration: Considering carbon emission and resilience load

To address the intermittency of renewable energy sources, global warming, and increasing load demands, this paper proposes the optimal sizing of a multi-energy microgrid (MEMG) consisting of electrical, thermal, cooling, and hydrogen networks. The system integrates multi-energy storage and EVs along with the resilience load to facilitate a robust operation scheme. The paper introduces an improved resilience backup mechanism for EVs using hybrid storage. Then, random samples for stochastic parameters are generated with Monte Carlo Simulations (MCS). To this end, a mixed-integer linear programming-based model is developed to minimize cost, emissions, and load shed. Then, a CPLEX solver is applied to solve it efficiently. The optimal MEMG with the hydrogen network reduces cost by 4% and emissions by 40%. The case study validates that hybrid storage (BES-HST-TST) can effectively reduce the electricity grid purchase to zero, making MEMG self-sufficient while yielding the least annual system cost (ASC) of 3855562$ and decreasing emissions to 8385 kg, resulting in economic savings, environmental sustainability, and increased utilization of renewables. Notably, V2G can save 0.7546% of MEMG extra cost incurred due to EVs integration and significantly reduces CO2 emissions by 4%. A novel finding is that the proposed hybrid storage backup mechanism can effectively minimize the extra cost of keeping the resilience load by retaining 31.46% of backup in hydrogen form. It mitigates risk associated with power outages while achieving the least ASC of 3858933$ and CO2 emissions of 8043 kg. These results can help realize a green, cost-effective, and efficient energy system.

Integration of electric vehicle into smart grid: a meta heuristic algorithm for energy management between V2G and G2V

Recently, Electric Vehicles (EV) have been providing fast response and substantial progress in the power generation model. Further, EVs are exploited as adaptable Energy Storage Systems (ESSs) and show a promising performance in ancillary service markets to increase the demand of Smart Grid (SG) integration. The expansion of Vehicle-to-Grid concept has created an extra power source when renewable energy sources are not available. Yet, numerous operational problems still are required to be considered for EV implementation to turn out to be extensive. Even the development of Photo-Voltaic (PV) technology creates a problem in SGs when used for EV charging. Because of this, the Energy Management System (EMS) is required to handle charging requirements and deal with the intermittent generation. Here, in this research, an Improved Honey Badger algorithm (IHBA) is proposed for integrating SGs with EV parking lot, solar panels, and dynamic loads at the Point of Common Coupling (PCC). The proposed IHBA uses a dynamic programming method to optimize the charging Grid-to-Vehicle (G2V) or discharging Vehicle-to-Grid (V2G) profiles of the EVs using the forecasts of PV generation. This algorithm considers user preferences while also lowering reliance on the grid and maximizing SG effectiveness. The study’s findings show that the Honey Badger method is efficient in resolving issues involving large search spaces. The developed method is used to optimize charging and discharging of EV which is tested in MATLAB to obtain a stable load profile. From the evaluation of obtained results, it is evident that the IHBA controller outperforms the WOA and EHO controllers in terms of total harmonic distortion voltage (3.12%), power loss (0.197 kW) and efficiency (98.47%).

Unified Framework for the Analysis of the Effect of Control Strategies on On-Load Tap-Changer’s Automatic Voltage Controller

With the advent of new loads and generation on the low voltage grid, voltage fluctuation has increased, especially in active distribution grids with a high penetration of distributed resources and a large deployment of electric vehicles. The coordination of different technologies has emerged as the best way for voltage regulation, among others, smart inverters, open soft points or transformers with on-load regulation capability. This paper proposes a novel way to model the control strategies for the automatic voltage controller of On-Load Tap-Changer transformers. The purpose is to standardize and simplify the way these strategies are represented, in order to facilitate (i) their selection by Distribution System Operators, (ii) their future integration with other systems, and (iii) to increase the ability to anticipate On-Load Tap-Changer behavior. The proposal has been validated using real data, obtaining an accuracy of 99.15% in the tap changer positions. A unified framework is also introduced, which allows the proposed functional representation to be combined with the On-Load Tap-Changer controller behavior estimation. A experimental validation has been carried out where more than 150 000 strategies have been simulated, finally determining the one that best fits the objectives. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Historically, on-load tap-changing transformers have been used in high-voltage substations. However, with the integration of electric vehicles and the penetration of distributed energy resources, the need to implement this type of solution in low-voltage substations has grown. However, the characteristics and requirements are not the same, for example, given their nature, low voltage grids are more unbalanced and are often regulated to ensure the quality of supply. Therefore, inheriting the control strategies of traditional on-load tap changers may pose a serious risk. This paper proposes a novel unified framework that facilitates the modeling and simulation of almost any control strategy for distribution transformers with on-load tap changers. This will allow the choice of control parameters that minimize voltage deviation from the voltage setpoint and maximize device lifetime. Our proposal can be considered as a solution to the uncertainty of which control parameters to use. It can be performed before commissioning, based on historical data, or a posteriori, based on the collected data.

Blockchain technology in the renewable energy sector: A co-word analysis of academic discourse

The transformative potential of blockchain technology in the renewable energy sector is increasingly gaining recognition for its capacity to enhance energy efficiency, enable decentralized trading, and ensure transaction transparency. However, despite its growing importance, there exists a significant knowledge gap in the holistic understanding of its integration and impact within this sector. Addressing this gap, the current study employs a pioneering approach, marking it as the first comprehensive bibliometric analysis in this field. We have systematically examined 390 journal articles from the Web of Science database, covering the period from 2017 through the end of February 2024, to map the current landscape and thematic trajectories of blockchain technology in renewable energy. The findings highlight several critical thematic areas, including blockchain's integration with smart grids, its role in electric vehicle integration, and its application in sustainable urban energy systems. These themes not only illustrate the diverse applications of blockchain but also its substantial potential to revolutionize energy systems. This study not only fills a crucial gap in existing literature but also sets a precedent for future interdisciplinary research in this domain, bridging theoretical insights with practical applications to fully harness the potential of blockchain in the renewable energy sector.

Demand side management using optimization strategies for efficient electric vehicle load management in modern power grids.

The Electric Vehicle (EV) landscape has witnessed unprecedented growth in recent years. The integration of EVs into the grid has increased the demand for power while maintaining the grid's balance and efficiency. Demand Side Management (DSM) plays a pivotal role in this system, ensuring that the grid can accommodate the additional load demand without compromising stability or necessitating costly infrastructure upgrades. In this work, a DSM algorithm has been developed with appropriate objective functions and necessary constraints, including the EV load, distributed generation from Solar Photo Voltaic (PV), and Battery Energy Storage Systems. The objective functions are constructed using various optimization strategies, such as the Bat Optimization Algorithm (BOA), African Vulture Optimization (AVOA), Cuckoo Search Algorithm, Chaotic Harris Hawk Optimization (CHHO), Chaotic-based Interactive Autodidact School (CIAS) algorithm, and Slime Mould Algorithm (SMA). This algorithm-based DSM method is simulated using MATLAB/Simulink in different cases and loads, such as residential and Information Technology (IT) sector loads. The results show that the peak load has been reduced from 4.5 MW to 2.6 MW, and the minimum load has been raised from 0.5 MW to 1.2 MW, successfully reducing the gap between peak and low points. Additionally, the performance of each algorithm was compared in terms of the difference between peak and valley points, computation time, and convergence rate to achieve the best fitness value.