Renewable Energy Management Research Articles

A statistical study for the impact of REMS and nuclear energy on carbon dioxide emissions reductions in G20 countries

Renewable and nuclear energy are seen as clean and eco-friendly substitutes for energy derived from fossil fuels. Which of the two is better, though, is still up for debate. In order to meet their combined global energy objectives and perhaps become carbon neutral or even carbon negative in the near future, a shift to additional renewable energy sources and nuclear energy will be necessary. One such method of improving energy usage through the implementation of a renewable energy management system (REMS) that will help attain a continuous and reliable source of energy, allowing for the proactive and effective management of renewable assets and dependable supply. This article investigates attitudes and opinions about nuclear and renewable energy production technologies by analyzing the dependence of carbon dioxide (CO2) emissions on non-renewable energy sources, renewable energy sources, and nuclear energy in the group of twenty (G20) countries, over the period from 2000 to 2021. This study employs advanced panel data econometric techniques in the presence of outliers in the G20 dataset, utilizing a robust random coefficient regression (RRCR) model via the M-estimation approach to study the impacts of non-renewable, renewable, and nuclear energy use with respect to CO2 emission mitigation in G20 countries. The overall conclusions of the econometric analysis confirm renewable and nuclear energy's importance in reducing CO2 emissions. We can conclude that the robust M-estimate approach is the best estimation technique for the G20 dataset, as compared to the classical estimation methods in the presence of outliers. The results show that for every percentage point of energy consumption that falls into the category of non-renewable energy, CO2 emissions will increase by 2.5737% for all G20 countries. Also, A 1% rise in renewable energy consumption reduces CO2 emissions by 1.0566% for all G20 countries. Similarly, a 1% increase in nuclear energy consumption will reduce CO2 emissions by 5.5703% for all G20 countries. Therefore, we can conclude that energy derived from renewable sources and nuclear energy greatly cuts CO2 emissions in all G20 countries. Total energy consumption, on the other hand, increases CO2 emissions in the long run. According to these findings, it is pivotal for the G20 countries to increase nuclear energy use and employ REMS to reduce the majority of G20 country's reliance on fossil fuels in order to minimize CO2 emissions. These findings are vital for understanding the relationship between these variables and the implementation of new or revised policies and initiatives supporting carbon neutrality at the G20 countries and national levels.

Artificial Intelligence for Sustainable Energy Transition: Optimising Renewable Energy Integration and Management

As climate change and long-term energy security drive the global energy sector towards renewable resources, powerful tools are required to optimise integration and management. A novel framework is proposed for effectively utilising Artificial Intelligence (AI) in the renewable energy landscape. AI algorithms can analyse weather patterns, historical generation data, and environmental factors to predict renewable energy output. Energy dispatch is optimised, grid integration is improved, and energy storage requirements are reduced. A system powered by artificial intelligence also significantly reduces downtime, optimises maintenance schedules, and minimises operational costs in wind turbines, solar panels, and other renewable infrastructure. AI can also optimise energy flows, reduce grid instability, and ensure efficient resource utilisation within the smart grid by dynamically managing renewable sources, energy storage systems, and demand profiles. Furthermore, AI-driven spatial analysis and resource mapping can identify optimal locations for renewable installations, considering factors like wind speed, solar irradiance, and environmental constraints. This paper presents two AI frameworks, one for solar energy and one for wind energy, to demonstrate possible applications. They both utilise comprehensive data acquisition, including real-time sensor data and external factors like weather forecasts and historical generation patterns. AI algorithms use these combined data to perform critical tasks such as predictive maintenance, minimising downtime, and maximising efficiency. Power output forecasting enables real-time adjustments based on weather, and optimal site selection maximises energy production. AI is used for proactive issue identification, accurate power output forecasting based on wind conditions, grid demand, storage capacity, dynamic load optimisation for maximum energy efficiency, and wind farm site selection. Integrating these tailored AI frameworks with solar and wind energy can achieve significant benefits such as increased efficiency, reduced operational costs, and seamless grid integration. In addition to analysing the challenges and opportunities associated with this AI integration, the paper explores infrastructure development, ethical considerations, and data acquisition. A second benefit of the research methodology is that it highlights how these tailored AI frameworks can optimise the integration of solar and wind renewable energy sources, providing valuable insights for researchers, practitioners, and policymakers who wish to use AI to create a more sustainable and efficient energy system. Keyword: Artificial Intelligence, renewable energy, climate change.

A techno-economic perspective on efficient hybrid renewable energy solutions in Douala, Cameroon’s grid-connected systems

Cameroon is currently grappling with a significant energy crisis, which is adversely affecting its economy due to cost, reliability, and availability constraints within the power infrastructure. While electrochemical storage presents a potential remedy, its implementation faces hurdles like high costs and technical limitations. Conversely, generator-based systems, although a viable alternative, bring their own set of issues such as noise pollution and demanding maintenance requirements. This paper meticulously assesses a novel hybrid energy system specifically engineered to meet the diverse energy needs of Douala, Cameroon. By employing advanced simulation techniques, especially the Hybrid Optimization Model for Electric Renewable (HOMER) Pro program, the study carefully examines the intricacies of load demands across distinct consumer categories while accommodating varied pricing models. The paper offers a detailed analysis of the proposed grid-connected PV/Diesel/Generator system, aiming to gauge its performance, economic feasibility, and reliability in ensuring uninterrupted energy supply. Notably, the study unveils significant potential for cost reduction per kilowatt-hour, indicating promising updated rates of $0.07/kW, $0.08/kW, and $0.06/kW for low, medium, and high usage groups, respectively. Furthermore, the research underscores the importance of overcoming operational challenges and constraints such as temperature fluctuations, equipment costs, and regulatory compliance. It also acknowledges the impact of operational nuances like maintenance and grid integration on system efficiency. As the world progresses towards renewable energy adoption and hybrid systems, this investigation lays a strong foundation for future advancements in renewable energy integration and energy management strategies. It strives to create a sustainable energy ecosystem in Cameroon and beyond, where hybrid energy systems play a pivotal role in mitigating power deficiencies and supporting sustainable development.

THE IMPACT OF ECOPORT SOCIALIZATION AND PORT WASTE MANAGEMENT SYSTEM ON GREENPORT IMPLEMENTATION

Port Waste Management System (PWMS) is a comprehensive waste management approach employed at ports to mitigate environmental harm. PWMS encompasses the collection, segregation, and disposal of various types of waste generated at ports, including solid, liquid, and hazardous waste. Through the adoption of sustainable technologies such as renewable energy and efficient water management, ports can significantly reduce their carbon footprint and ecological impact. Ecoport and Port Waste Management System advocate for sustainable practices within port operations. This study aims to assess the influence of the Ecoport Socialization Program on Greenport implementation at Tanjung Priok Port, aiming to enhance awareness regarding the importance of minimizing environmental impact and improving resource efficiency within the port sector. The findings indicate a positive and statistically significant relationship between the Port Waste Management System (X2) and Greenport Implementation (Y), with a coefficient value (Original Sample column) of 0.579 and P-Values of 0.000 < 0.05. Furthermore, the Q-Square (Q2) value for Greenport Implementation (Y) is 0.465, indicating that Ecoport Socialization (X1) and Port Waste Management System (X2) possess predictive relevance for Greenport Implementation (Y). Combined, Ecoport Socialization (X1) and Port Waste Management System (X2) account for 59.6% of the variance in Greenport Implementation (Y), with the remaining 40.4% attributable to other factors. In conclusion, this study demonstrates that Ecoport socialization and Port Waste Management System significantly contribute to explaining Greenport Implementation.

Urban energy transition in renewable energy management considering policy and law challenges and opportunities in the pursuit of clean and sustainable urban environments

This research presents a novel approach to enhance sustainable energy development based on wind turbine (WT) output power prediction through the development of a hybrid deep learning model, considering policy and law challenges associated with the problem. The proposed model integrates a Self-Supervised Generative Adversarial Network (GAN) with Attention-based Recurrent Neural Networks (RNNs). The Self-Supervised GAN contributes to improved feature representation learning, while Attention-based RNNs capture temporal dependencies for accurate WT output power forecasting considering political challenges. Additionally, a Modified Adaptive Flower Pollination Algorithm (AFPA) is introduced to optimize the selection of the GAN model structure in view of the legal and political objectives. This adaptive approach refines the architecture of the GAN, enhancing its ability to generate realistic and informative features for the subsequent Attention-based RNNs. The efficacy of the hybrid model is demonstrated through real-world applications in an urban area, emphasizing sensitivity and stability analyses. A thorough investigation into the sensitivity of key model parameters, including the number of hidden units in the Attention-based RNNs and GAN structures, is conducted to fine-tune the model's performance. Stability analysis is employed to assess the robustness of the proposed method under dynamic conditions, ensuring its reliability in practical scenarios. Results indicate that the hybrid model, guided by the Modified AFPA, outperforms existing approaches in WT output power prediction. The proposed methodology showcases the significance of combining state-of-the-art deep learning techniques with adaptive optimization algorithms for accurate and stable predictions in urban energy management applications.

Integrating Artificial Intelligence for Enhanced Grid Stability and Renewable Energy Management in France

Integrating Artificial Intelligence for Enhanced Grid Stability and Renewable Energy Management in France

Integrating electric vehicles into hybrid microgrids: A stochastic approach to future-ready renewable energy solutions and management

As electric vehicles (EVs) continue to rise in popularity, there has been an intensified focus on the distribution of power within hybrid renewable energy systems. In this context, the significance of vehicle-to-grid (V2G) and grid-to-vehicle (G2V) models for mitigating grid pressures has been increasingly recognized. These models help mitigate grid pressures by using EVs as reliable loads. This research delves into the technical and economic aspects of a hybrid microgrid integrated with various components such as photovoltaic panels (PVs), wind turbines (WTs), battery energy storage systems (BESSs), and EV grid connections, situated at a specific latitude of 40°39.2′N and longitude of 29°13.2′E. The methodology employed is grounded in advanced stochastic metaheuristic approaches. The infrastructure accommodates two distinct loads: a primary load and another dedicated to EV charging. The cornerstone of the energy framework is renewables, particularly PV panels and wind turbines. A dynamic rule-based energy management scheme is implemented to ensure consistent fulfillment of energy demands with an emphasis on cost efficiency. In defining the dimensions of the microgrid, various algorithms including the Coati Optimization Algorithm (COA), Driving Training-Based Optimization (DTBO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Turbulent Flow of Water-based Optimization (TFWO), White Shark Optimizer (WSO), Zebra Optimization Algorithm (ZOA), and Flying Foxes Optimization (FFO) were used. This configuration was evaluated in terms of the annual system cost, present-day value, loss of power supply probability (LPSP), and leveled cost of energy (LCOE). Thanks to the strategic deployment of the TFWO algorithm, optimal results were achieved for the system, including a PV capacity of 411.0560 kW, a WT capacity of 327.0229 kW, and a BESS capacity of 561.1750 kW. With this configuration, the annual cost of the system was determined to be $4.0499 × 105, the present value was set at $4.6452 × 106, the nominal LPSP was calculated at 0.0487 %, and the LCOE was established at $0.1597/kWh. Notably, a significant portion of the energy demand, approximately 69.87 %, was met through renewables, with the remaining 30.13 % covered by traditional sources. Comprehensive sensitivity analysis of key parameters enabled forecasting of future trends. All research activities underwent thorough review, confirming the TFWO algorithm's superior efficiency and faster convergence compared to other methods. The optimization algorithms were implemented in MATLAB 2022b, and statistical evaluations performed using the R programming language.

A novel standalone hybrid renewable energy systems onboard conventional and autonomous tugboats

The recent surge in fuel costs and stringent gas emission regulations mandated by the International Maritime Organization (IMO) has prompted research into integrating renewable energy and energy management strategies (EMS) onboard ships. This study illustrates the computation, simulation, and optimization of a hybrid renewable energy system (HRES) to ensure continuous power supply for auxiliary loads and critical systems on both conventional and fully autonomous tugboats. The primary aim is to design an optimal HRES with minimal annualized cost of system (ACS) and a higher proportion of renewable energy while using an artificial bee colony (ABC) algorithm. Validation of optimization outcomes is conducted using particle swarm optimization (PSO), genetic algorithm (GA), and Hybrid Optimization of Multiple Energy Resources (HOMER) Pro. The HRES incorporates diesel generators (Gensets), photovoltaic (PV) arrays, vertical axis wind turbines (VAWT), and battery banks. The optimal HRES configuration for both conventional and fully autonomous tugboats is found to be Genset/PV/VAWT/Battery. We observe that the ABC algorithm exhibits superior convergence, reliability, cost-effectiveness, renewable energy fraction, and reduced carbon emissions compared to alternative algorithms. Results of robustness tests suggest that the shipload variation, fuel prices, temperature fluctuations, wind speed and solar irradiance along the navigation route have significant impact on the optimal HRES configuration. Ultimately, we conclude that the fully autonomous tugboat demonstrates superior performance in terms of costs, carbon dioxide emissions, and renewable energy fraction compared to its conventional counterpart.

Effects of green policies on sustainability of local economies in Africa

This article conducts a thorough analysis of the potential impacts of environmentally friendly policies on the long-term viability of renewable energy and waste management policies on the economics of local regions within the African Continental Free Trade Area. The research examines how these policies intersect with the development of intra-African enterprise and local employment rates. The study employs a qualitative methodology, utilising an exploratory research design to investigate African economic performance metrics. It also uses qualitative data to gain a deeper understanding of the subtle implications of policies. The research evaluates the economic consequences of these policies in African contexts by utilising a range of secondary sources, such as governmental reports, peer-reviewed studies, and policy evaluations. Initial results indicate that although green policies have resulted in substantial environmental advantages, their influence on intra-African economies is intricate and diverse. The report asserts that comprehending these dynamics is crucial for policymakers, practitioners, and researchers who have an interest in fostering sustainable development and inclusive economic growth in Africa. The study suggests the adoption of environmentally friendly policies that take into account the economic conditions inside Africa and aim to achieve a local economy that is both environmentally sustainable and inclusive.

Economic operation of residential load using IOT-based renewable energy management system

Economic operation of residential load using IOT-based renewable energy management system

Energy management of renewable energy connected electric vehicles charging using Internet of things: Hybrid MRA-SDRN approach

This manuscript presents a novel hybrid technique, termed the MRA-SDRN method, for optimizing renewable energy-based electric vehicle (EV) charging within an Internet of Things (IoT) architecture. The key objective is to enhance the efficiency of PV-connected EV systems while concurrently reducing electricity costs and pollutant emissions. The MRA-SDRN approach leverages the Mud Ring Algorithm (MRA) and the Spiking Deep Residual Network (SDRN) to optimize several parameters critical for effective EV charging and renewable energy utilization. Comparative analysis with existing techniques conducted in MATLAB demonstrates notable reductions in computation time, with the MRA-SDRN method achieving a calculation time of 13.04 s, outperforming simulated annealing (SA), multi-objective particle swarm optimization and genetic algorithms (GA).Additionally, significant decreases in pollutant emissions are observed, with levels measured at 472.38 g compared to 570.48 g for GA, 528.35 g for SA, and 502.01 g for MOPSO. Regarding electricity costs, the MRA-SDRN approach achieves a competitive rate of 0.306 yuan/kWh, emphasizing its cost-effectiveness. These numerical results underscore the efficacy of the MRA-SDRN method in optimizing renewable energy management for EV charging, thereby contributing to enhanced reliability, efficiency, and sustainability in the transportation sector within the IoT framework.

The safety and environmental impacts of battery storage systems in renewable energy

The integration of battery storage systems in renewable energy infrastructure has garnered significant attention due to its potential to enhance energy reliability, efficiency, and sustainability. However, alongside these benefits, concerns persist regarding the safety and environmental impacts associated with the deployment and operation of such systems. This review explores the multifaceted aspects of safety and environmental considerations in battery storage systems within the context of renewable energy. Firstly, safety concerns encompass a range of factors, including thermal runaway, fire hazards, and chemical leakage, which pose risks to both human life and property. Mitigation strategies such as advanced battery management systems and fire suppression technologies are critical for addressing these risks effectively. Secondly, environmental impacts arise throughout the lifecycle of battery storage systems, from raw material extraction to end-of-life disposal. Key issues include resource depletion, greenhouse gas emissions, and pollution from mining activities. Sustainable practices such as responsible sourcing of materials, recycling initiatives, and the development of second-life applications are essential for minimizing environmental footprints. Furthermore, the interaction between battery storage systems and renewable energy sources introduces complexities in assessing environmental impacts. While battery storage facilitates the integration of intermittent renewables like solar and wind by providing grid stabilization and energy storage capabilities, its environmental benefits may be compromised by factors such as energy-intensive manufacturing processes and reliance on non-renewable resources. The safety and environmental impacts of battery storage systems in renewable energy demand comprehensive evaluation and management strategies to maximize benefits while minimizing risks. Collaboration among stakeholders, technological innovation, and regulatory frameworks are crucial for promoting the sustainable deployment and operation of these systems in the transition towards a cleaner and more resilient energy future.

Probabilistic modeling of renewable energy sources in smart grids: A stochastic optimization perspective

Machine learning (ML) techniques have emerged as powerful tools for optimizing renewable energy management in smart grids. This paper focuses on the application of ML algorithms to enhance the efficiency and reliability of renewable energy integration within smart grid systems. By leveraging predictive analytics, ML models can forecast energy production and consumption patterns, facilitating proactive decision-making for grid operators and energy stakeholders. The abstract explores various ML methodologies such as supervised learning, unsupervised learning, and reinforcement learning, tailored to address specific challenges in renewable energy management. Supervised learning algorithms enable accurate prediction of renewable energy generation, aiding in resource allocation and demand-response strategies. Unsupervised learning techniques facilitate anomaly detection and clustering of energy consumption patterns, contributing to grid stability and optimization. Reinforcement learning algorithms optimize control strategies, enabling autonomous decision-making in dynamic grid environments. The abstract highlights case studies and real-world implementations where ML techniques have demonstrated significant improvements in renewable energy integration, grid reliability, and cost-effectiveness. Through a synthesis of research findings and practical insights, this abstract elucidates the transformative potential of machine learning in shaping the future of smart grids and renewable energy management.

Machine learning based on reinforcement learning for smart grids: Predictive analytics in renewable energy management

This paper introduces a groundbreaking approach to energy management in smart cities, leveraging deep reinforcement learning (DRL) and secured blockchain technology. The escalating demand for energy in urban areas necessitates intelligent solutions that optimize energy usage while ensuring data security and integrity. Our proposed technique integrates DRL algorithms with blockchain technology to achieve optimal energy management while addressing security concerns. The core innovation lies in the development of a DRL framework that dynamically adapts to changing energy demands and environmental factors. By continuously learning and optimizing energy allocation strategies, our approach enhances resource utilization efficiency and reduces wastage, contributing to sustainable smart city initiatives. Additionally, the incorporation of blockchain technology ensures data immutability, transparency, and secure transactions within the energy management system. We validate the effectiveness of our method through extensive simulations and real-world experiments, demonstrating significant improvements in energy efficiency, cost savings, and security compared to traditional approaches. Our research paves the way for future advancements in smart city infrastructure, offering a scalable and robust solution for optimal energy management with heightened security measures.

Analysis of the Application and Potential of Renewable Energy in Landscape Architecture

The field of landscape architecture is constantly evolving to address sustainability and climate change. There is a rising chance to use these technology into landscape design as renewable energy sources become more prevalent. An effective technique for evaluating the possibility of incorporating renewable energy management into landscape architecture is currently required. As a result, decision-making procedures are now manual and subjective, requiring greater precision and consistency. Deep learning algorithms can be used to examine the possibilities for renewable energy management in landscape architecture, which would help to solve this problem. Deep learning is a branch of artificial intelligence that automatically extracts complicated relationships and patterns from data using multi-layer neural networks. With inputs like topography, solar radiation, and climate, the algorithm can determine where in a particular landscape renewable energy installations would be most effective.

Design of an Iterative Method for Optimizing Solar Power Systems using Quad LSTM with IoT Integration Operations

The growing necessity for sustainable energy solutions underscores the critical need for optimizing solar power systems. Existing methodologies predominantly focus on static strategies with limited adaptability to dynamic environmental and operational conditions, often resulting in suboptimal performance and inefficiency. This research introduces a comprehensive, integrated framework employing Internet of Things (IoT) technologies alongside advanced machine learning and deep learning methodologies to enhance solar power system efficiency and reliability levels. The proposed model integrates four key components: predictive maintenance using Support Vector Machines (SVM) for enhanced anomaly detection, solar power forecasting via Quad Long Short-Term Memory (QLSTM) neural networks, dynamic load balancing through Reinforcement Learning with Deep Q-learning, and the integration with smart grids employing Decentralized Multi-Agent Systems (MAS) with Auction-Based Mechanisms. Each method is selected based on its suitability to address specific challenges within the solar power domain: SVMs for their effectiveness in high-dimensional anomaly detection, QLSTMs for their superior temporal pattern recognition in forecasting, Deep Q-learning for its adaptability in dynamic load management, and MAS for efficient decentralized energy resource coordination operations. The implementation of these methodologies demonstrates significant advancements over traditional approaches. Predictive maintenance facilitated by SVMs leads to a 20% reduction in maintenance costs, while QLSTM-based forecasting achieves a 95% accuracy rate, thereby enhancing grid management and reducing revenue losses. Moreover, reinforcement learning optimizes energy utilization, decreasing wastage and system downtime by 10% and 5% respectively. Lastly, the MAS framework promotes a 20% increase in energy trading efficiency, yielding a 10% reduction in transaction costs and bolstering grid resilience levels. This work represents a significant leap forward in solar power optimization, offering a scalable, efficient, and intelligent framework that paves the way for more sustainable and reliable energy systems. The integration of IoT with machine learning and deep learning presents a paradigm shift in renewable energy management, marking a critical step toward achieving global sustainability objectives..

Review on smart grids and renewable integration: An artificial intelligence-powered perspective

Micro-grids have been rising as a component of the smart grid (SG), power management systems and integrating renewable energy sources (RES). The integration of various distributed components such as modern measuring facilities, connectivity and electric transportation, are causing an exciting change in the grid around the world. The purpose of this transformation is to advance the subsequent power systems’ dependability, renewable energy management and security. In this study, we offer a systematic review of innovative (artificial intelligence AI) approaches for supporting diverse demands in a distributed SG. In particular, we examine the way that artificial approaches can help with the integration of RES, SG and power management. We also address how AI can contribute to enhancing the general social welfare of the SG, considering it encompasses a variety of consumers, including energy producers, industries and consumption. Lastly, we provide additional studies for the significant coordination, along with the integration of autonomous separated devices that highlight several unresolved issues and future directions, to construct an incredible SG.

Sustainable energy transition in cities: A deep statistical prediction model for renewable energy sources management for low-carbon urban development

This study proposes a novel approach to sustainable energy transition in urban environments, focusing on the prediction and management of renewable energy outputs for low-carbon urban development. We introduce a new intelligent prediction model based on Gated Recurrent Unit (GRU) Networks to forecast the output power of both tidal and biomass units. The GRU model, known for its capability in capturing sequential dependencies, is employed to enhance the accuracy of renewable energy predictions in the context of urban sustainability. To address the interpretability challenges inherent in complex deep learning models, we leverage LIME (Local Interpretable Model-agnostic Explanations). By integrating LIME into our GRU-based prediction model, we enhance transparency and understandability. This approach facilitates the identification of key factors influencing predictions, providing stakeholders and decision-makers with valuable insights for effective sustainable energy management. The proposed hybrid model is applied to the digital model of real tidal and biomass sites in China, allowing for a statistical examination of renewable integration and energy management strategies. The results offer a comprehensive understanding of the dynamics between renewable energy sources and urban development, paving the way for informed decision-making in the pursuit of low-carbon and resilient cities. This research contributes to the broader discourse on sustainable urban development by offering a robust statistical framework for renewable energy integration and management in urban environments.

Towards informed decision-making: A comprehensive statistical study of economic and financing models for sustainable energy transition in urban environments

This paper presents a comprehensive approach to optimizing renewable energy allocation in urban environments. Our methodology integrates statistical economic modeling and decision-making techniques to address the complex challenges associated with renewable energy management. By employing stochastic programming and robust optimization, we optimize renewable energy allocation to minimize operational costs, reduce emissions, and enhance system reliability. Our analysis demonstrates significant reductions in operational costs (15.2%) and emissions (20.5% for NOx and 18.9% for CO2) compared to baseline scenarios. Moreover, our approach improves system reliability by reducing load shedding (25.1%) and enhancing overall system reliability (12.3%). These findings underscore the effectiveness of our proposed strategy in fostering economic, environmental, and operational sustainability in urban energy environments.

Energy Management Strategies of Grid Connected Renewable Source for EV Charging Station

The growing popularity of electric vehicles (EVs) has made the need for effective charging infrastructure necessary. Combining renewable energy sources with EV charging stations offers a viable way to lessen transportation's negative environmental effects and cut back on fossil fuel use. This study looks into different grid-connected renewable energy source energy management techniques for EV charging stations. This study aims to optimize energy utilization, lower operating costs, and improve the sustainability of EV charging infrastructure by utilizing renewable energy generation, such as solar and wind power, in conjunction with intelligent charging algorithms. The study examines the body of research, evaluates various energy-management strategies, and suggests creative solutions to the problems arising from incorporating renewable energy sources into EV charging stations. The report also addresses the possible advantages, practical issues, and financial effects of putting these strategies into practice. This research advances our understanding of energy management systems for grid-connected renewable sources in the context of EV charging infrastructure through thorough analysis and simulation studies.