Smart Grid Integration Research Articles

Feasibility of a 40kWp Grid-Connected Solar Power Plant in Tiaret, Algeria:

This study evaluates the technical and economic feasibility of a 40kWp grid-connected solar power plant in Tiaret, Algeria. Utilizing comprehensive solar irradiance data and advanced PV system software, we designed and simulated the plant's performance under local conditions. Our analysis incorporates smart grid integration strategies and economic modeling. Results indicate an annual electricity generation of approximately 68,000 kWh, with a levelized cost of energy (LCOE) of 0.12 USD/kWh and an estimated payback period of 5 years. The plant demonstrates a performance ratio of 0.759, reflecting its efficiency under real-world conditions. These findings suggest that grid-connected solar power plants are not only technically viable but also economically attractive in Algeria. The study provides critical insights for policymakers, investors, and engineers, offering a replicable model for assessing and implementing solar projects in similar emerging markets across North Africa and beyond.

Soft-system management framework for power supply and grid integration issues

Purpose The balance between power supply and demand gets more challenging when electrical networks switch from centralized thermal power plants to distributed renewable energy sources for power generation. Such problems present a diverse set of challenges that require a solution through system and control methods. Hence, the purpose of this study is to understand the issues faced by each actor in the power sector’s supply chain, which would restrict the stability of the power supply and quality of service. Design/methodology/approach This study provides a conceptual model, soft system methodology (SSM), for power supply management or grid integration issues through the mapping of identified issues with their possible solutions. Findings This study offers an analysis that uses methods of problem structuring to construct the major issues and measure technological advancements in the energy sector. This research highlights the need to integrate energy storage systems with the grid for the effective operation of the system to manage various power supply issues. Research limitations/implications SSM is used to establish a mechanism to manage grid integration problems by comparing established problems with their potential solutions. The resulting framework would help managers, researchers, policymakers, engineers and smart grid professionals to make the required and informed decisions on the management of grid integration issues and to form strategies fostering efficient and secure energy network. Originality/value The research is based on a conceptual framework for enhancing energy efficiency and integrated smart grid technology, which would contribute to a better supply of electricity and a more environmentally sustainable future.

Mathematical Modeling and Statistical Analysis of Novel Swarm Computing Based Charging Management Framework for Electric Vehicles

This paper presents a novel swarm computing-based charging management framework for electric vehicles (EVs), designed to optimize charging operations and enhance the grid's capacity to handle EV load without compromising system reliability. We introduce a mathematical model that incorporates swarm intelligence algorithms, particularly Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), to efficiently manage the charging schedules of EVs across multiple charging stations. The framework aims to minimize energy costs, balance load during peak hours, and reduce charging time by distributing the charging load in a more organized manner.A statistical analysis is conducted to validate the effectiveness of the proposed model. Using real-time data and simulation, the study evaluates various scenarios to analyze the impact of the swarm-based approach on overall grid stability and EV charging efficiency. The results indicate significant improvements in grid management, with a reduction in peak load demands and enhanced utilization of renewable energy sources. The analysis also demonstrates the scalability and adaptability of the model to different urban settings and EV penetration rates.The paper concludes with a discussion on the potential integration of this model into existing grid systems and its implications for future smart grid and EV infrastructure developments. The proposed framework not only contributes to more sustainable energy management practices but also opens new avenues for research in the domain of intelligent transportation systems and smart grid integration.

AI-Driven Route Planning and Scheduling for Electric School Buses

This review paper explores the current state, recent advancements, challenges, and future perspectives of AI-driven approaches for route planning and scheduling of Electric School Buses (ESBs). The integration of artificial intelligence into energy management systems for electric vehicles has gained significant attention, particularly in optimizing school transportation. This study examines various AI techniques, including genetic algorithms, reinforcement learning, and game-theoretic approaches, applied to ESB management. Key focus areas include energy consumption estimation, battery capacity optimization, and Vehicle-to-Grid (V2G) strategies. The paper also addresses critical challenges such as data integration, security concerns, and operational constraints. Future perspectives highlight the potential of advanced AI techniques, smart grid integration, and personalized transportation solutions. By synthesizing current research and identifying key areas for future development, this review contributes to ongoing efforts to improve the efficiency, sustainability, and overall quality of school transportation systems through AI technologies.

CURRENT TRENDS IN PHOTOVOLTAIC THERMAL (PVT) SYSTEMS: A REVIEW OF TECHNOLOGIES AND SUSTAINABLE ENERGY SOLUTIONS

This systematic review explores advancements and challenges in photovoltaic thermal (PVT) systems, focusing on efficiency improvements, cooling mechanisms, material innovations, Internet of Things (IoT) integration, and cost and environmental considerations. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 45 articles were analyzed to provide a comprehensive understanding of current trends and future directions in PVT technology. Findings reveal that cooling mechanisms, particularly liquid-based and nanofluid-based systems, are essential for maintaining high PVT efficiency under diverse environmental conditions. Material advancements, including phase change materials (PCMs) and nanotechnology, have enhanced thermal management and energy storage capabilities, yet their high costs and environmental impacts remain significant barriers to broader adoption. IoT and smart grid integration have transformed PVT system functionality by enabling real-time monitoring, predictive maintenance, and energy flow adjustments within connected energy networks. However, persistent challenges—including high initial investment costs, environmental concerns related to materials, and the need for adaptable designs—highlight areas for future research. Advancements in adaptive materials, sustainable cooling solutions, and digital automation are essential for developing cost-effective, resilient PVT systems that contribute to global renewable energy goals. This review provides a foundation for future research to address the identified challenges and maximize the potential of PVT systems in sustainable energy production.

Smart Grids and Renewable Energy Integration: Challenges and Solutions

Smart Grids and Renewable Energy Integration: Challenges and Solutions

ENGINEERING CHALLENGES AND SOLUTIONS IN SMART GRID INTEGRATION WITH ELECTRIC VEHICLES

This paper explores the critical engineering challenges and innovative solutions for successfully integrating intelligent grids and electric vehicles (EVs), emphasizing the increasing need for a resilient and adaptive electrical grid as global EV adoption accelerates. It comprehensively examines the technological, infrastructural, and regulatory obstacles that must be addressed to ensure seamless integration, focusing on advanced energy management systems, grid stability amidst fluctuating demand, and incorporating renewable energy sources. The study delves into the infrastructural requirements, including the expansion of charging networks, upgrades to transmission and distribution systems, and the implementation of vehicle-to-grid (V2G) technologies, while also analyzing the necessary regulatory and policy frameworks, stressing the importance of clear standards, incentives, and public-private collaboration. The paper offers a forward-looking perspective on overcoming current challenges by reviewing recent advancements in innovative grid technology—such as high-capacity energy storage and artificial intelligence (AI) use for predictive maintenance and load balancing. It highlights the need for interdisciplinary collaboration among engineers, policymakers, and industry leaders to develop a cohesive strategy for future energy distribution while underscoring the role of AI in optimizing grid performance, predicting energy consumption patterns, and enhancing overall efficiency. Ultimately, the paper provides a comprehensive analysis of the current state of smart grid and EV integration, offering actionable insights for stakeholders and concluding with recommendations for future research and development priorities, with a strong emphasis on continued innovation and cooperation to achieve a sustainable and resilient energy future.

Evaluating the Public Health Benefits of Renewable Energy Systems: How Smart Grid Technology Can Be Used to Enhance Community Health

Integrating smart grid technologies into renewable energy systems, however, is still limited by several barriers. Technical complexity, high initial costs, intricate infrastructure and potential cyber threats are all part of the game. Solving this problem is what the proposed Enhanced Social Energy Levels Optimization Analysis (SELOA) approach aims at; it is also targeted at ensuring that there is equal participation for each person in the society during the shift to renewables hence improved justice in energy consumption and distribution. Proposed application areas for residential energy management, public health monitoring and emergency planning are hardly exhaustive. SELOA use can promote healthier communities while achieving sustainable use of energy. The method was tested through simulating SELOA on various actual situations. Thus this research has indicated that less pollution from conventional power generation as well as more efficient generation methods would lead to improved health among people in various communities. These results imply that integration of Smart Grid technology with renewable energy systems could be a major contribution towards both appropriate sustainability and public health based on our finding.

AI-Driven Solar Energy Generation and Smart Grid Integration A Holistic Approach to Enhancing Renewable Energy Efficiency

This paper comprehensively analyzes AI-driven solar energy generation and smart grid integration, focusing on enhancing renewable energy efficiency. The study examines applying advanced artificial intelligence techniques in optimizing solar power production, forecasting, and grid management. Machine learning algorithms, including Support Vector Regression (SVR) and Artificial Neural Networks (ANN), are evaluated for effectiveness in solar irradiance prediction and PV system performance estimation. The integration of AI in smart grids is explored, highlighting its role in demand-side management, energy storage optimization, and grid stability control. A holistic approach to improving renewable energy efficiency is proposed, encompassing integrated AI frameworks for solar-plus-storage systems, multi-objective optimization techniques for energy management, and AI-enabled microgrids and virtual power plants. The paper also addresses the challenges and future trends in AI application to renewable energy systems, including scalability issues, regulatory considerations, and ethical implications. By leveraging big data analytics and advanced AI algorithms, this research demonstrates the potential for significant improvements in overall system efficiency, reliability, and sustainability of solar energy systems integrated with smart grids.

Electric vehicle with smart grid integration using a hybrid honey badger algorithm and artificial neural network

Electric vehicle with smart grid integration using a hybrid honey badger algorithm and artificial neural network

The evolution of green fintech: Leveraging AI and IoT for sustainable financial services and smart contract implementation

The convergence of financial technology and sustainability has given rise to green fintech, an innovative field leveraging cutting-edge technologies to address environmental challenges through financial solutions. This review explores the evolution of green fintech, focusing on the transformative roles of Artificial Intelligence (AI), Internet of Things (IoT), and smart contracts in developing sustainable financial services. Through a comprehensive analysis of recent literature and case studies, we examine how AI enhances ESG assessments, enables data-driven sustainable investment strategies, and facilitates green lending practices. We investigate IoT applications in environmental monitoring, supply chain transparency, and smart grid integration, highlighting their contributions to sustainable finance. The implementation of smart contracts for sustainability is explored, discussing their potential in green bonds, carbon credit trading, and renewable energy markets. The paper addresses key challenges facing green fintech, including data quality issues, privacy concerns, and regulatory uncertainties, proposing future directions for research and development. Our findings suggest that the integration of AI, IoT, and smart contracts in green fintech has significant potential to accelerate the transition to a sustainable global economy by embedding environmental considerations into financial decision-making at all levels. This article contributes to the growing body of literature on sustainable finance, providing insights for practitioners, policymakers, and researchers. It underscores the need for a multidisciplinary approach to overcome technological, regulatory, and socio-economic barriers, paving the way for a more sustainable and technologically advanced financial ecosystem.

Integration of smart grid technologies: Implementing advanced procurement strategies leveraging AI and machine learning

Integrating smart grid technologies represents a pivotal advancement in modernizing electrical grids and enhancing efficiency, reliability, and sustainability. This publication explores the role of advanced procurement strategies, specifically leveraging artificial intelligence (AI) and machine learning (ML), in streamlining the sourcing and integration of smart grid components. By examining current challenges, potential solutions, and the impact on national security and economic stability, this paper aims to provide a comprehensive framework for the effective implementation of smart grid technologies. The traditional electrical grid, characterized by its centralized generation and unidirectional power flow, faces numerous challenges, such as inefficiency, susceptibility to outages, and inability to integrate renewable energy sources effectively (Brown et al., 2020). The transformation to a smart grid infrastructure, which incorporates digital communication technology and advanced sensors, promises significant improvements. However, the transition is fraught with challenges, particularly in the procurement and integration of the necessary components. One of the primary challenges in smart grid integration is the complexity of supply chains. The procurement of smart grid components involves multiple stakeholders, including manufacturers, suppliers, and regulatory bodies. This complexity often leads to delays, increased costs, and inefficiencies (Smith & Wang, 2019). Additionally, the high initial costs of smart grid technologies pose a barrier to widespread adoption. Traditional procurement methods struggle to balance cost-efficiency with the need for high-quality, durable components, often resulting in budget overruns or compromised quality.

Comprehensive analysis of integrating smart grids with renewable energy sources: Technological advancements, economic impacts, and policy frameworks

This study presents a comprehensive analysis of integrating smart grids with renewable energy sources, focusing on technological advancements, economic impacts, and policy frameworks. The primary objective is to explore how smart grid technologies can efficiently incorporate renewable energy sources, thereby enhancing grid reliability, efficiency, and sustainability. Utilizing a multidisciplinary approach, the study examines successful case studies, pilot projects, and innovative practices that highlight the potential and challenges of this integration. Key findings reveal that advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), and blockchain are crucial for the real-time monitoring, predictive maintenance, and optimized management of energy systems. These technologies address the inherent variability and intermittency of renewable energy sources like solar and wind power. Case studies, including the Brooklyn Microgrid and Germany’s Energiewende, demonstrate significant improvements in energy resilience, efficiency, and consumer empowerment through decentralized energy systems. Economic analysis underscores the dual impact of cost savings from operational efficiencies and the financial challenges posed by substantial upfront investments in smart grid infrastructure. Policy frameworks play a pivotal role, with recommendations for supportive regulatory policies, increased funding for research and development, and enhanced public-private partnerships to drive innovation and consumer engagement. The study concludes that overcoming the technical, economic, and regulatory barriers requires coordinated efforts among stakeholders. Recommendations include developing consistent regulatory frameworks, fostering public-private partnerships, and implementing educational programs to encourage consumer participation in renewable energy initiatives. By addressing these challenges, the integration of smart grids with renewable energy sources can pave the way for a more sustainable, resilient, and efficient energy future. Keywords: Smart Grids, Renewable Energy Integration, Technological Advancements, Economic Impacts, Policy Frameworks, Grid Management Innovations.

Ensuring Sustainable Grid Stability through Effective EV Charging Management: A Time and Energy-Based Approach

The rise of electric vehicles (EVs) has significantly transformed transportation, offering environmental advantages by curbing greenhouse gas emissions and fossil fuel dependency. However, their increasing adoption poses challenges for power systems, especially distribution systems, due to the direct connection of EVs with them. It requires robust infrastructure development, smart grid integration, and effective charging solutions to mitigate issues like overloading and peak demand to ensure grid stability, reliability, and sustainability. To prevent local equipment overloading during peak load intervals, the management of EV charging demand is carried out in this study, considering both the time to deadline and the energy demand of EVs. Initially, EVs are prioritized based on these two factors (time and energy)—those with shorter deadlines and lower energy demands receive higher rankings. This prioritization aims to maximize the number of EVs with their energy demands met. Subsequently, energy allocation to EVs is determined by their rankings while adhering to the transformer’s capacity limits. The process begins with the highest-ranked EV and continues until the transformer nears its limit. To this end, an index is proposed to evaluate the performance of the proposed method in terms of unserved EVs during various peak load intervals. Comparative analysis against the earliest deadline first approach demonstrates the superior ability of the proposed method to fulfill the energy demand of a larger number of EVs. By ensuring sustainable energy management, the proposed method supports the widespread adoption of EVs and the transition to a cleaner, more sustainable transportation system. Comparative analysis shows that the proposed method fulfills the energy needs of up to 33% more EVs compared to the earliest deadline method, highlighting its superior performance in managing network loads.

Integration of Smart Grid and Application of Microgrid Technologies in Western Pipeline Company

Integration of Smart Grid and Application of Microgrid Technologies in Western Pipeline Company

Federated Learning-Based Prediction of Energy Consumption from Blockchain-Based Black Box Data for Electric Vehicles

In modern society, the proliferation of electric vehicles (EVs) is continuously increasing, presenting new challenges that necessitate integration with smart grids. The operational data from electric vehicles are voluminous, and the secure storage and management of these data are crucial for the efficient operation of the power grid. This paper proposes a novel system that utilizes blockchain technology to securely store and manage the black box data of electric vehicles. By leveraging the core characteristics of blockchain—immutability and transparency—the system records the operational data of electric vehicles and uses federated learning (FL) to predict their energy consumption based on these data. This approach allows the balanced management of the power grid’s load, optimization of energy supply, and maintenance of grid stability while reducing costs. Additionally, the paper implements a searchable black box data storage system using a public blockchain, which offers cost efficiency and robust anonymity, thereby enhancing convenience for electric vehicle users and strengthening the stability of the power grid. This research presents an innovative approach to the integration of electric vehicles and smart grids, exploring ways to enhance the stability and energy efficiency of the power grid. The proposed system has been validated through real data and simulations, demonstrating its effectiveness and performance in managing black box data and predicting energy consumption, thereby improving the efficiency and stability of the power grid. This system is expected to empower electric vehicle users with data ownership and provide power suppliers with more accurate energy demand predictions, promoting sustainable energy consumption and efficient power grid operations.

A Proposed Hybrid Machine Learning Model Based on Feature Selection Technique for Tidal Power Forecasting and Its Integration

Renewable energy resources are playing a crucial role in minimizing fossil fuel emissions. Integrating machine learning techniques with tidal power forecasting could greatly enhance the accuracy and reliability of predictions, which is crucial for efficient energy production and management. A hybrid approach combining different methods often yields better results than relying on individual techniques. The accuracy of tidal current power is very important, especially for smart grid applications. This work proposes hybrid adaptive neuro-fuzzy inference system (ANFIS) with the Kalman filter (KF) and a neuro-wavelet (WNN) for tidal current speed, direction, and power forecasting. The turbine used in this study is driven by a direct drive permanent magnet synchronous generator (DDPMSG). The predictions of individual and hybrid models including the ANFIS, the Kalman filter, and the WNN for tidal current speed and the power it generates are compared with another dataset as a way of validation which is the tidal currents direction. Also, other published work results in the literature are compared to the proposed work. Different hybrid models are proposed for smart grid integration. The results of this work indicate that the hybrid model of the WNN and the ANFIS for tidal current power or speed forecasting has the highest performance compared to all other models.

Continuous identity authentication protocol against quantum attacks in satellite integrated smart grid

AbstractTo address the issue of low efficiency caused by the repeated use of quantum attack resistant static identity authentication methods in a satellite integrated smart grid, this paper proposes a quantum attack resistant continuous identity authentication protocol. First, in the initial authentication stage, in order to reduce computational complexity, the key encryption mechanism in the CRYSTALS‐Kyber algorithm was improved and combined with the NTRU message recovery digital signature scheme to construct a lattice based explicit AKE (Kyber NTRU. AKE), which achieved mutual authentication and negotiated shared tokens. Second, in the continuous authentication stage, incorporating quantum attack resistant tokens into the current algorithm to improve authentication efficiency. The formal analysis results indicate that compared to the weakly forward secure Kyber.AKE in the CRYSTALS‐Kyber algorithm, Kyber‐NTRU.AKE achieves complete forward secrecy, while the non‐formal analysis results demonstrate the security of the continuous authentication phase. Through theoretical analysis and efficiency comparison with Cyber.AKE, the analysis shows that the Cyber‐NTRU.AKE has higher computational and communication efficiency than Cyber.AKE.

Demand side management using ant colony optimization algorithm in renewable energy integrated smart grid

The increasing demand for electrical energy is a result of advancing technologies and changing lifestyles worldwide. Meeting this escalating energy need poses a substantial challenge, especially the difficulty in constructing new conventional power plants due to limited fossil fuel resources. To address this, demand-side management (DSM) in smart grid (SG), integrated with solar photovoltaic energy (SPE) have emerged as a crucial tool for effectively managing electricity demand, ensuring flexibility and reliability. DSM achieves optimal electricity utilization by rescheduling the operation schedules of consumer appliances and carefully adjusting their demand profiles. Integrating DSM into a smart grid framework is highly advantageous for the power industry’s pursuit of sustainable energy goals. While various heuristic-based optimization techniques have been employed for DSM, the focus on SPE has been limited to small-scale residential loads. This study utilizes the Ant Colony Optimization (ACO) algorithm to tackle a day ahead DSM minimization problem, considering SPE in areas with large number of appliances. The DSM minimization problem falls into the category of discrete combinatorial problems, making it well-suited for ACO optimization. The self-healing, self-protection, and self-organizing attributes of ACO make it particularly effective for DSM solutions. Residential, commercial, and industrial loads, with and without SPE integration, are considered to demonstrate the efficacy of the proposed ACO algorithm. Simulation results are compared with other studies in the literature, including Evolutionary Algorithm (EA), Moth Flame Optimization (MFO), and Bacterial Foraging Optimization (BFO), in terms of reducing consumer’s cost of energy (CCE) and utility peak load (UPL). The findings indicate that the proposed ACO algorithm outperforms the other algorithms considered in the current context.

Improved Maximum Power Point Tracking Control for D-PMSG Systems: Fuzzy Gradient Step Approach

Improved Maximum Power Point Tracking Control for D-PMSG Systems: Fuzzy Gradient Step Approach