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Journal articles on the topic 'Smart grid optimization'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Liang, Haobo, Yingxiong Leng, Jinman Luo, Jie Chen, and Xiaoji Guo. "Optimization of Network Security Management and Protection Mechanism in Smart Grid." Journal of Intelligence and Knowledge Engineering 2, no. 4 (2024): 11–18. https://doi.org/10.62517/jike.202404403.

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Smart grids significantly improve the reliability, security and efficiency of power systems by integrating network and information technology with power systems. However, in a highly informatized and interconnected environment, smart grids also face increasingly complex and evolving cybersecurity threats. This paper mainly analyzes the characteristics and architecture of the smart grid, describes the common security vulnerabilities of smart grid network security from the level of network and data operation security, and puts forward an optimized protection mechanism in a targeted manner to enh

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Ahat, Murat, Soufian Ben Amor, Marc Bui, Alain Bui, Guillaume Guérard, and Coralie Petermann. "Smart Grid and Optimization." American Journal of Operations Research 03, no. 01 (2013): 196–206. http://dx.doi.org/10.4236/ajor.2013.31a019.

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Niraula, Mani, Prabesh Raj Ojha, Sunil Simkhada, and Yogesh Layalu. "Review on the Application of Machine Learning Algorithms on Smart Grid Optimization." Kathford Journal of Engineering and Management 3, no. 1 (2023): 62–71. http://dx.doi.org/10.3126/kjem.v3i1.62877.

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With the increasing challenge of distributed and renewable energy sources, maintaining the stability of the power grid is becoming increasingly difficult. By incorporating information and communication technologies, along with machine intelligence, the conventional power grid has the potential to evolve into a smart grid. The integration of machine learning equips the smart grid to make its decisions and efficiently handle generation, power outages, transmission line failures, unforeseen shifts in customer demands, overall fluctuations in renewable energy production, or any unexpected catastro

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Kushawaha, Vivek, Gaurav Gupta, and Lalit Singh. "Enhancing Energy Efficiency: Advances in Smart Grid Optimization." International Journal of Innovative Research in Engineering and Management 11, no. 2 (2024): 100–105. http://dx.doi.org/10.55524/ijirem.2024.11.2.20.

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As global energy demands continue to escalate, the imperative for more efficient energy distribution and consumption systems becomes increasingly critical. The advent of smart grid technology offers a promising solution by integrating advanced communication, control systems, and information technologies into traditional power grids. This paper explores recent advances in smart grid optimization, focusing on techniques and technologies that enhance energy efficiency. We delve into the role of artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) in optimizing ene

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Fu, G. S., X. Yin, and Y. L. Xu. "Renewable energy integration and distributed energy optimization in smart grid." Journal of Physics: Conference Series 2795, no. 1 (2024): 012004. http://dx.doi.org/10.1088/1742-6596/2795/1/012004.

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Abstract In the context of this study, we leverage a hierarchical deep reinforcement learning algorithm to address challenges in the integration of renewable energy sources within smart grid environments. The primary focus is on enhancing the efficiency of large-scale renewable energy integration while ensuring grid stability and reliability. The algorithm builds on the principles of hierarchical deep reinforcement learning, aiming to optimize energy utilization, reduce operational costs, and decrease reliance on conventional energy sources within the smart grid framework. Rigorous experimenta

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Cengiz, Mehmet Sait. "System Optimization On Smart Grid." International Journal of Electrical and Electronics Engineering 1, no. 8 (2021): 28–32. http://dx.doi.org/10.14445/23488379/ijeee-v1i8p106.

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Gondesi, Dr Anand, Seerapu Varalakshmi, and Ch Ravi Kumar. "Artificial Intelligence-Driven Optimization for Smart Grid Energy Management and Load Forecasting." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–7. https://doi.org/10.55041/ijsrem43955.

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The fast transformation of energy infrastructure has led to the adoption of clever grids, which leverage advanced technology for green energy management and cargo forecasting. synthetic Intelligence (AI)-driven optimization plays a important role in improving the performance of smart grids by means of permitting real-time energy distribution, demand forecasting, and fault detection. This examine explores the combination of AI strategies including gadget gaining knowledge of, deep mastering, and reinforcement studying in optimizing smart grid operations. The outcomes highlight how AI-powered fo

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Kiasari, Mahmoud, Mahdi Ghaffari, and Hamed H. Aly. "A Comprehensive Review of the Current Status of Smart Grid Technologies for Renewable Energies Integration and Future Trends: The Role of Machine Learning and Energy Storage Systems." Energies 17, no. 16 (2024): 4128. http://dx.doi.org/10.3390/en17164128.

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The integration of renewable energy sources (RES) into smart grids has been considered crucial for advancing towards a sustainable and resilient energy infrastructure. Their integration is vital for achieving energy sustainability among all clean energy sources, including wind, solar, and hydropower. This review paper provides a thoughtful analysis of the current status of the smart grid, focusing on integrating various RES, such as wind and solar, into the smart grid. This review highlights the significant role of RES in reducing greenhouse gas emissions and reducing traditional fossil fuel r

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Shrikant Shantaram Mopari. "Smart Detection and Mitigation of Power Quality Issues in Smart Grids Using MATLAB-Based Simulation and Optimization." Journal of Information Systems Engineering and Management 10, no. 26s (2025): 1003–22. https://doi.org/10.52783/jisem.v10i26s.4328.

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Power quality issues are becoming more prevalent in investigation of smart grids due to the integration of various distributed energy resources (DER), therefore this comprehensive study provides a smart detection and mitigation approach based on MATLAB-based smart grid simulation and optimization approaches. Power quality disturbance including voltage sags, harmonics, and transients is one of the considerable problems for the reliable operation of the grid, and its appropriate management is significant to keep the system stable and efficient. Methodology combines real-time monitoring systems t

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Oluwadayomi Akinsooto, Olorunshogo Benjamin Ogundipe, and Samuel Ikemba. "Policy frameworks for integrating machine learning in smart grid energy optimization." Engineering Science & Technology Journal 5, no. 9 (2024): 2751–78. http://dx.doi.org/10.51594/estj.v5i9.1549.

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The integration of machine learning (ML) into smart grid energy systems represents a transformative approach to optimizing energy management and distribution. As smart grids evolve to accommodate renewable energy sources and fluctuating demand patterns, machine learning algorithms enable real-time data analysis, predictive maintenance, and dynamic load balancing, ensuring grid efficiency and reliability. This review explores the development of policy frameworks that support the integration of machine learning technologies within smart grids. These frameworks aim to address challenges such as d

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Xu, Na, Zhuo Tang, Chenyi Si, Jinshan Bian, and Chaoxu Mu. "A Review of Smart Grid Evolution and Reinforcement Learning: Applications, Challenges and Future Directions." Energies 18, no. 7 (2025): 1837. https://doi.org/10.3390/en18071837.

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In the face of the rapid development of smart grid technologies, it is increasingly difficult for traditional power system management methods to support the increasingly complex operation of modern power grids. This study systematically reviews new challenges and research trends in the field of smart grid optimization, focusing on key issues such as power flow optimization, load scheduling, and reactive power compensation. By analyzing the application of reinforcement learning in the smart grid, the impact of distributed new energy’s high penetration on the stability of the system is thoroughl

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Song, Ying Wei, Jian Liu, Liao Yi Ning, Zhen Tao Han, Hong Liu, and Shi Ju Wang. "Comprehensive Assessment System and Method of Smart Distribution Grid." Advanced Materials Research 860-863 (December 2013): 1901–8. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.1901.

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According to key values and demands of smart distribution grids in China, a smart distribution grid comprehensive assessment index system and method is established. The index system is divided into the demand index set and assessment index set. As the former represents the macroscopic features while the latter represents the microscopic factors of the smart distribution grid. A causal connection exists between them. Also a hierarchical optimization model with combination weight, which is based on the DEMATEL-ANP-anti-entropy weight method and the improved Grey incidence approach, is proposed.

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Kannan, S., P. Pugazhendiran, N. Rishi Keshav, A. Arul deva, K. Suresh kumar, and T. Ananth kumar. "Energy optimization mechanism using metastable-smart grid (MSSG)." Journal of Physics: Conference Series 2466, no. 1 (2023): 012024. http://dx.doi.org/10.1088/1742-6596/2466/1/012024.

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Abstract The Smart Grid model uses energy from renewable sources and utilities to manage energy in a way that ensures uninterrupted power supply for users. In order to operate independently from the main power grid, a DC micro grid is a small power system that generates and uses its own DC electricity. Solar panels, fuel cells, and wind turbines are the most typical power sources for DC micro grids, with batteries used to store any extra energy. The independence of the power grid is one feature of a DC micro grid that promotes increased lifetime. Building owners have more latitude to pursue th

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Researcher. "OPTIMIZATION OF SMART GRID OPERATIONS THROUGH ADVANCED LOAD FORECASTING AND REAL-TIME MONITORING SYSTEMS." Journal of Power System Engineering (JPSE) 3, no. 1 (2025): 1–6. https://doi.org/10.5281/zenodo.14752219.

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The advent of smart grids represents a transformative shift in energy distribution and consumption, enabling efficient, reliable, and sustainable electricity management. This paper explores the optimization of smart grid operations using advanced load forecasting and real-time monitoring systems. By integrating machine learning-based predictive models and IoT-enabled monitoring frameworks, utilities can achieve precise demand prediction, mitigate grid vulnerabilities, and reduce operational inefficiencies. A comprehensive literature review highlights innovative methodologies and real-world app

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Loundou Ndonda Elsi Othniel, Jing Chen, Nguembi Ines Pamela, and Marcel Merimee Bakala Mboungou. "Optimal control algorithms for Gabon’s Smart Grid: Enhancing efficiency and sustainability." International Journal of Science and Research Archive 13, no. 1 (2024): 319–33. http://dx.doi.org/10.30574/ijsra.2024.13.1.1576.

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The adoption of smart grid technologies offers Gabon several opportunities to enhance energy efficiency and sustainability. This study investigates the use of optimum control algorithms to increase grid stability and enhance the utilization of renewable energy sources. Mathematical models and simulations are used to assess genetic algorithms in light of Gabon's unique energy situation. Systems known as electrical smart grids supply electricity to users directly from power plants in an effort to reduce costs, cut down on blackouts, and improve energy efficiency. Smart grids, or SGs, are well-kn

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Wang, Qiang, Dong Yu, Jinyu Zhou, and Chaowu Jin. "Data Storage Optimization Model Based on Improved Simulated Annealing Algorithm." Sustainability 15, no. 9 (2023): 7388. http://dx.doi.org/10.3390/su15097388.

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Since there is a longitudinal and horizontal penetration problem between multi-level data centers in the smart grid information transmission network. Based on the improved Simulated Annealing algorithm, this paper proposes a data storage optimization model for smart grids based on Hadoop architecture. Combining the characteristics of distributed storage in cloud computing, the smart grid data are equivalent to a task-oriented data set. The smart grid information platform is flattened, equal to a collection of multiple distributed data centers. The smart grid data over time were counted to deri

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Ogbogu, Chisom E., Jesse Thornburg, and Samuel O. Okozi. "Smart Grid Fault Mitigation and Cybersecurity with Wide-Area Measurement Systems: A Review." Energies 18, no. 4 (2025): 994. https://doi.org/10.3390/en18040994.

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Smart grid reliability and efficiency are critical for uninterrupted service, especially amidst growing demand and network complexity. Wide-Area Measurement Systems (WAMS) are valuable tools for mitigating faults and reducing fault-clearing time while simultaneously prioritizing cybersecurity. This review looks at smart grid WAMS implementation and its potential for cyber-physical power system (CPPS) development and compares it to traditional Supervisory Control and Data Acquisition (SCADA) infrastructure. While traditionally used in smart grids, SCADA has become insufficient in handling moder

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Yilmaz, “Muhittin, and Naren Reddy Dhansri.” "“A Smart Grid Robust Optimization Framework”." Procedia Computer Science 12 (2012): 301–6. http://dx.doi.org/10.1016/j.procs.2012.09.075.

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Researcher. "ENERGY-EFFICIENT CONTROL STRATEGY DEVELOPMENT FOR SMART GRIDS INTEGRATING DISTRIBUTED RENEWABLE ENERGY RESOURCES WITH REAL-TIME DEMAND RESPONSE OPTIMIZATION." International Journal of Control Systems Engineering (IJoCSE) 3, no. 1 (2025): 1–6. https://doi.org/10.5281/zenodo.14715816.

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This paper explores an energy-efficient control strategy for smart grids by integrating distributed renewable energy resources (DRER) with real-time demand response optimization (DR). We propose a dynamic framework that balances energy supply and demand while ensuring grid stability and reducing costs. This study identifies the latest advancements in DRER, assesses existing literature, and incorporates optimization algorithms to enhance energy efficiency and sustainability. The findings underscore the importance of intelligent energy management and its potential in advancing future smart grid

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Qian, Lin, Dong Hui Li, Xiao Zhi Wu, Guang Xin Zhu, and Jiang Hui Liu. "Performance Optimization Method on Smart Grid Information Platform." Advanced Materials Research 765-767 (September 2013): 1041–45. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1041.

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In recent years, with the development of economics, Chinese government feels obliged to build a strong smart grid, state grid company starts the construction process of SG-ERP system comprehensively. However, system performance bottleneck gradually has been spotted due to expanding of IT systems. So it has important theoretical value and strong practical significance to do research on smart grid information platform. This paper proposed a framework of performance tuning for large transaction database used in smart grid based on state grid information platform construction, this method will bre

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Biradar, Vijayalaxmi. "An Energy Efficient Requirement Optimization Model Based Power Switching for Improved Performance in Smart Grids." E3S Web of Conferences 540 (2024): 10031. http://dx.doi.org/10.1051/e3sconf/202454010031.

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Power switching in smart grid has been identified as the key issue in literature. There exist number of models to handle this problem which consider the residual energy of grids in performing power switching. However, the performances of the models are not up to the expected rate. To handle this issue, an efficient Requirement Optimization based Power Switching Model (ROPSM) is presented in this article. The model focused on optimizing the selection of power grids towards maintaining the power stability in smart grids. To perform this, the model monitors the incoming voltage level and maintain

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Kindjock, J.J., H.N. Amadi, and O.N. Igbogidi. "Analysis and Simulation of Smart Energy System Planning and Operation." Journal of Research and Advancement in Electrical Engineering 7, no. 2 (2024): 15–22. https://doi.org/10.5281/zenodo.11096997.

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<em>This research tackles Nigeria's antiquated grid limitations through a multi-pronged smart grid modernization strategy emphasizing renewable integration, analytics and automation. Smart grids converge conventional infrastructure with advanced communications networks and information systems to enable intelligent monitoring, control and optimization. However, Nigeria's current grid suffers from delayed maintenance, outdated equipment, inadequate supply and systemic corruption. A modernized smart grid would facilitate large-scale adoption of sustainable generation like solar, wind, and fuel ce

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García, Moisés Antón, Ana Isabel Martínez García, Stylianos Karatzas, Athanasios Chassiakos, and Olympia Ageli. "SGAM-Based Analysis for the Capacity Optimization of Smart Grids Utilizing e-Mobility: The Use Case of Booking a Charge Session." Energies 16, no. 5 (2023): 2489. http://dx.doi.org/10.3390/en16052489.

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The description of the functionality of a smart grid’s architectural concept, analyzing different Smart Grid (SG) scenarios without disrupting the smooth operation of the individual processes, is a major challenge. The field of smart energy grids has been increasing in complexity since there are many stakeholder entities with diverse roles. Electric Vehicles (EVs) can transform the stress on the energy grid into an opportunity to act as a flexible asset. Smart charging through an external control system can have benefits for the energy sector, both in grid management and environmental terms. A

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Ponnusamy, Sivakumar, Vanitha Gurugubelli, Rambabu G V, Adlin Sheeba, Mohit Tiwari, and Saravanan T. "Cyber-Physical Systems in Smart Grids Enhancing Energy Management and Distribution Efficiency." ITM Web of Conferences 76 (2025): 03006. https://doi.org/10.1051/itmconf/20257603006.

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This paper aims to study the interaction and correlation of Cyber-Physical Systems (CPS) with emphasis on Smart Grids (SGs) to enhance and develop energy utilization and distribution systems. This study aligns with the design of adaptive, real-time operations that respond continuously and dynamically to energy demand fluctuations and can increase grid reliability and decrease operational costs through optimization. Novel solutions to some generic challenges of the grid, such as communication burdens and cyber-security threats are the application of new optimization algorithms, machine learning

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Prof., S. R. Sapkal1 Prof. S. G. Kalantri2 Prof. S. S. Bhuskute3 Prof. C. E. Morkhade4 &. Prof. A. P. Padol5. "NEURAL NETWORK BASED OPTIMIZATION TECHNIQUE FOR SMART GRIDS." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES [NC-Rase 18] (November 16, 2018): 29–33. https://doi.org/10.5281/zenodo.1489817.

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Nowadays energy system is changing, due to the rise of global energy consumption and the reduction of fossil energy sources. Also current power system networks have to face a number of challenges increasing like emission of greenhouse gases, electricity demand, reduction of losses, costs of electricity and voltage deviation. From the view of global warming alleviation and reduction of energy resources, renewable energy such as solar generation, wind generation are getting attention in supply systems. Moreover all-electric apartment houses or residence such as dc keen houses are swelling. Thoug

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Liao, Li, and Chengjun Ji. "Smart Grid Dispatching Optimization for System Resilience Improvement." Complexity 2020 (November 6, 2020): 1–12. http://dx.doi.org/10.1155/2020/8884279.

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A large number of modern communication technologies and sensing technologies are incorporated into the smart grid, which makes its structure unique. The centralized optimized dispatch method of traditional power grids is difficult to achieve effective dispatch of smart grids. Based on the analysis of power generation plan and maintenance plan optimization model, this paper establishes a smart grid power generation and maintenance collaborative optimization model with distributed renewable energy. The objective function of this collaborative optimization problem is the operating cost of convent

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Haque, Abrarul, Md Naeem Hussain, Md Sumon Ali, Md Yakub Ali Khan, and Md Abdul Halim. "Technical and Economic Challenges and Future Prospects of a Smart Grid - A Case Study." Control Systems and Optimization Letters 1, no. 3 (2024): 186–93. http://dx.doi.org/10.59247/csol.v1i3.57.

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In order to improve grid efficiency, dependability, and sustainability, smart grid technology is being developed and implemented at the vanguard of updating the world's electrical infrastructure. In addition to looking at the potential implications of smart grid technology, this case study analyzes the technical and financial difficulties encountered during the implementation of a smart grid. Technical issues with energy management and system stability arise when intermittent renewable energy sources are integrated into the smart grid. To make smooth transmission, sophisticated algorithms and

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Li, Fan, Dan Wang, Dong Liu, et al. "A Comprehensive Review on Energy Storage System Optimal Planning and Benefit Evaluation Methods in Smart Grids." Sustainability 15, no. 12 (2023): 9584. http://dx.doi.org/10.3390/su15129584.

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Smart grids are the ultimate goal of power system development. With access to a high proportion of renewable energy, energy storage systems, with their energy transfer capacity, have become a key part of the smart grid construction process. This paper first summarizes the challenges brought by the high proportion of new energy generation to smart grids and reviews the classification of existing energy storage technologies in the smart grid environment and the practical application functions of energy storage in smart grids. Secondly, optimization planning and the benefit evaluation methods of

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Muhammad, Qasim Habib, Usman Shoukat Muhammad, Irfan Muhammad, et al. "Smart Meter Development Using Digital Twin Technology for Green Energy Distribution Optimization." European Journal of Theoretical and Applied Sciences 1, no. 3 (2023): 181–90. https://doi.org/10.59324/ejtas.2023.1(3).20.

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This study proposes a digital twin (DT) approach and technical framework for smart meters to solve potential implementation and development problems and adapt to the new energy revolution trend and increase smart grid network security. DT models were deployed in the cloud and edge using a smart meter DT demonstration system. This paper evaluates the DT system's communication performance in real-time smart grid application through three dimensions: remote application service for smart grid user side, P2P transaction on the user side, and user real-time request service. This study's cont

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Liu, Qian, Wen Ying Liu, and Guo Hua Gao. "PSO and OCCI-Based Grid Maintenance Scheduling Management and Optimization System." Applied Mechanics and Materials 380-384 (August 2013): 4716–19. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.4716.

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Smart grid is an inevitable choice for the future development of the grid. Smart scheduling is one of the cores to build the strong and smart grid in China. Optimization and Management System for Grid Maintenance Scheduling is an in-depth exploration to achieve dispatching digitalization, intellectualization and visualization. This paper gives a rather detailed description about the functionality and implementation of Optimization and Management System for Grid Maintenance Scheduling. With received maintenance schedules as the basic data, OCCI-based data processing techniques and PSO-based opt

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Babu,, M. A. Suresh. "Applications of Artificial Intelligence in (Machine Learning /Deep learning) Smart Grid." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42202.

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Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), plays a significant role in enhancing the efficiency, reliability, and sustainability of smart grids. One of its key applications is load forecasting and demand response, where Machine Learning models predict electricity demand based on historical consumption patterns, weather conditions, and economic factors - helping in real-time energy optimization. AI also enables renewable energy integration, grid fault detection and maintenance, energy theft detection, voltage and frequency stability control, AI pred

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Eisenmann, Adrian, Tim Streubel, and Krzysztof Rudion. "Power Quality Mitigation via Smart Demand-Side Management Based on a Genetic Algorithm." Energies 15, no. 4 (2022): 1492. http://dx.doi.org/10.3390/en15041492.

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In modern electrical grids, the number of nonlinear grid elements and actively controlled loads is rising. Maintaining the power quality will therefore become a challenging task. This paper presents a power quality mitigation method via smart demand-side management. The mitigation method is based on a genetic algorithm guided optimization for smart operational planning of the grid elements. The algorithm inherits the possibility to solve multiple, even competing, objectives. The objective function uses and translates the fitness functions of the genetic algorithm into a minimization or maximiz

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Ding, Jianguo, Attia Qammar, Zhimin Zhang, Ahmad Karim, and Huansheng Ning. "Cyber Threats to Smart Grids: Review, Taxonomy, Potential Solutions, and Future Directions." Energies 15, no. 18 (2022): 6799. http://dx.doi.org/10.3390/en15186799.

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Smart Grids (SGs) are governed by advanced computing, control technologies, and networking infrastructure. However, compromised cybersecurity of the smart grid not only affects the security of existing energy systems but also directly impacts national security. The increasing number of cyberattacks against the smart grid urgently necessitates more robust security protection technologies to maintain the security of the grid system and its operations. The purpose of this review paper is to provide a thorough understanding of the incumbent cyberattacks’ influence on the entire smart grid ecosyste

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Wisdom Samuel Udo, Jephta Mensah Kwakye, Darlington Eze Ekechukwu, and Olorunshogo Benjamin Ogundipe. "SMART GRID INNOVATION: MACHINE LEARNING FOR REAL-TIME ENERGY MANAGEMENT AND LOAD BALANCING." Engineering Science & Technology Journal 4, no. 6 (2023): 603–16. http://dx.doi.org/10.51594/estj.v4i6.1395.

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The integration of machine learning into smart grid technology represents a significant advancement in real-time energy management and load balancing. Smart grids, which enhance traditional power grids with digital communication and automation, face challenges such as fluctuating energy demands and the need for efficient load distribution. Machine learning (ML) offers transformative solutions by leveraging algorithms to analyze vast amounts of data, forecast energy consumption, and optimize load balancing. This paper explores the application of ML techniques in smart grids, focusing on load fo

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Vassunova, Yuliya Yu. "SMART GRIDS OPTIMIZATION WITH RENEWABLE ENERGY SOURCES INTEGRATION." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 9/6, no. 150 (2024): 146–56. http://dx.doi.org/10.36871/ek.up.p.r.2024.09.06.017.

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The article considers the issues of smart grid optimization with renewable energy sources (RES) integration. Particular attention is paid to solving problems associated with the instability of RES generation and the need to ensure the reliability of the power system. Various approaches to demand management, the use of energy storage systems and distributed energy resources are analyzed. The challenges of cybersecurity and grid sustainability in the context of RES integration are considered. The article also provides case studies and simulation results demonstrating the effectiveness of the pro

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Ma, Yue. "Analysis of the Potential Carbon Emission Reduction Through Interaction Between New Energy Vehicles and Smart Grids." Advances in Economics, Management and Political Sciences 116, no. 1 (2024): 48–53. http://dx.doi.org/10.54254/2754-1169/116/20242364.

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Abstract: This paper analyzes the potential for carbon emission reduction through the interaction between new energy vehicles and smart grids, discussing how the optimization of energy efficiency and balancing of grid loads can be achieved through Vehicle-to-Grid (V2G) technology. The interaction between new energy vehicles and smart grids can enhance energy utilization efficiency, optimize grid management, and reduce reliance on fossil fuels by leveraging renewable energy sources, thereby achieving the goal of carbon emission reduction. However, this process faces multiple challenges includin

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Wen, Xin, Qi Shen, Wenxuan Zheng, and Haodong Zhang. "AI-Driven Solar Energy Generation and Smart Grid Integration A Holistic Approach to Enhancing Renewable Energy Efficiency." International Journal of Innovative Research in Engineering and Management 11, no. 4 (2024): 55–66. http://dx.doi.org/10.55524/ijirem.2024.11.4.8.

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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

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Habault, Guillaume, Maxime Lefrancois, Francois Lemercier, Nicolas Montavont, Periklis Chatzimisios, and Georgios Z. Papadopoulos. "Monitoring Traffic Optimization in a Smart Grid." IEEE Transactions on Industrial Informatics 13, no. 6 (2017): 3246–55. http://dx.doi.org/10.1109/tii.2017.2742584.

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Touzene, A., S. Al Yahyai, and A. Oukil. "Smart Grid Resources Optimization Using Heuristic Approach." IOP Conference Series: Earth and Environmental Science 168 (June 2018): 012015. http://dx.doi.org/10.1088/1755-1315/168/1/012015.

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., Gundi Anny Mary. "SMART GRID COST OPTIMIZATION USING GENETIC ALGORITHM." International Journal of Research in Engineering and Technology 03, no. 19 (2014): 282–87. http://dx.doi.org/10.15623/ijret.2014.0319051.

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Marwaha, Deepika, and Dr Rajeev Yadav. "Optimization for energy management in smart grid." International Journal of Advanced Academic Studies 4, no. 4 (2022): 01–04. http://dx.doi.org/10.33545/27068919.2022.v4.i4a.856.

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Habib, Muhammad Qasim, Muhammad Usman Shoukat, Muhammad Irfan, et al. "Smart Meter Development Using Digital Twin Technology for Green Energy Distribution Optimization." European Journal of Theoretical and Applied Sciences 1, no. 3 (2023): 181–90. http://dx.doi.org/10.59324/ejtas.2023.1(3).20.

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This study proposes a digital twin (DT) approach and technical framework for smart meters to solve potential implementation and development problems and adapt to the new energy revolution trend and increase smart grid network security. DT models were deployed in the cloud and edge using a smart meter DT demonstration system. This paper evaluates the DT system's communication performance in real-time smart grid application through three dimensions: remote application service for smart grid user side, P2P transaction on the user side, and user real-time request service. This study's container-ba

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Rajendran, Gowthamraj, Reiko Raute, and Cedric Caruana. "A Comprehensive Review of Solar PV Integration with Smart-Grids: Challenges, Standards, and Grid Codes." Energies 18, no. 9 (2025): 2221. https://doi.org/10.3390/en18092221.

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Promoting a sustainable and low-carbon energy future through the integration of renewable energy is essential, yet it presents significant challenges due to the intermittent nature of resources such as solar and wind. This paper examines the technological and economic dimensions of AC, DC, and smart grids, concentrating on the optimization of costs, efficiency, stability, and scalability. Smart grids, enhanced by AI, IoT, and blockchain technologies, play a vital role in energy management optimization, predictive maintenance, and secure energy transactions. Furthermore, the incorporation of re

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Li, Shan Shan, Hong Dan Li, Hui Quan Zou, Yao Qiang Liu, and Xiang He. "Development and Application of Smart Distribution Grid Self-Healing Control System." Advanced Materials Research 960-961 (June 2014): 828–31. http://dx.doi.org/10.4028/www.scientific.net/amr.960-961.828.

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Self-healing is an important feature of smart distribution grid. Self-healing control for smart distribution grid covers risk prevention and operation optimization before accidents, in addition, it covers fault location, fault isolation and power restoration. The research of self-healing control theory, risk assessment, fault diagnosis and safety warning for smart distribution grid provides technical supports to help build a safe, reliable, efficient, high-quality, flexible and compliant smart distribution grid.

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Cheng, Fei, Sai Yu Shi, and Mohamed Nayel. "Using Smart System to Improve Electric Power Grid." Applied Mechanics and Materials 291-294 (February 2013): 2096–101. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.2096.

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Currently, the electricity infrastructure has remained for a long time, while the demand of electricity is increasing. Smart systems could be employed to improve the performance of electric power grid and reduce the . Especially, the communication technology and optimization technology of smart systems are able to enhance the reliability, stabilization and efficiency of the grid. In this paper, several different communication and optimization technologies are compared and analysed. Finally, the summary is obtained of the utilization of smart systems in order to improve the electric power grid.

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Researcher. "OPTIMIZATION OF POWER MANAGEMENT STRATEGIES IN SMART GRIDS INCORPORATING RENEWABLE ENERGY SOURCES AND ADVANCED COMMUNICATION PROTOCOLS." International Journal of Electrical and Electronic Engineering and Telecommunications (IJEEET) 2, no. 1 (2025): 1–6. https://doi.org/10.5281/zenodo.14752247.

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The rapid integration of renewable energy sources into smart grids presents new challenges and opportunities for optimizing power management strategies. This paper investigates the role of advanced communication protocols in enhancing grid efficiency, stability, and resilience. By reviewing current methodologies and incorporating data-driven approaches, we propose a comprehensive framework for optimizing power management strategies in smart grids. Our findings demonstrate the effectiveness of adaptive strategies in reducing energy losses, improving grid reliability, and accommodating the varia

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Mehdary, Adil, Abdellah Chehri, Abdeslam Jakimi, and Rachid Saadane. "Hyperparameter Optimization with Genetic Algorithms and XGBoost: A Step Forward in Smart Grid Fraud Detection." Sensors 24, no. 4 (2024): 1230. http://dx.doi.org/10.3390/s24041230.

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This study provides a comprehensive analysis of the combination of Genetic Algorithms (GA) and XGBoost, a well-known machine-learning model. The primary emphasis lies in hyperparameter optimization for fraud detection in smart grid applications. The empirical findings demonstrate a noteworthy enhancement in the model’s performance metrics following optimization, particularly emphasizing a substantial increase in accuracy from 0.82 to 0.978. The precision, recall, and AUROC metrics demonstrate a clear improvement, indicating the effectiveness of optimizing the XGBoost model for fraud detection.

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Hsu, Chao-Chung, Bi-Hai Jiang, and Chun-Cheng Lin. "A Survey on Recent Applications of Artificial Intelligence and Optimization for Smart Grids in Smart Manufacturing." Energies 16, no. 22 (2023): 7660. http://dx.doi.org/10.3390/en16227660.

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To enable highly automated manufacturing and net-zero carbon emissions, manufacturers have invested heavily in smart manufacturing. Sustainable and smart manufacturing involves improving the efficiency and environmental sustainability of various manufacturing operations such as resource allocation, data collecting and monitoring, and process control. Recently, a lot of artificial intelligence and optimization applications based on smart grid systems have improved the energy usage efficiency in various manufacturing operations. Therefore, this survey collects recent works on applications of art

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Chhaya, Lipi K., Paawan Sharma, Adesh Kumar, and Govind Bhagwatikar. "Cross Layer Optimization and Simulation of Smart Grid Home Area Network." Modelling and Simulation in Engineering 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/3561739.

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An electrical “Grid” is a network that carries electricity from power plants to customer premises. Smart Grid is an assimilation of electrical and communication infrastructure. Smart Grid is characterized by bidirectional flow of electricity and information. Smart Grid is a complex network with hierarchical architecture. Realization of complete Smart Grid architecture necessitates diverse set of communication standards and protocols. Communication network protocols are engineered and established on the basis of layered approach. Each layer is designed to produce an explicit functionality in as

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Denimal, Emmanuel, Ambroise Marin, Stéphane Guyot, Ludovic Journaux, and Paul Molin. "Reliable Detection and Smart Deletion of Malassez Counting Chamber Grid in Microscopic White Light Images for Microbiological Applications." Microscopy and Microanalysis 21, no. 4 (2015): 886–92. http://dx.doi.org/10.1017/s1431927615000665.

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AbstractIn biology, hemocytometers such as Malassez slides are widely used and are effective tools for counting cells manually. In a previous work, a robust algorithm was developed for grid extraction in Malassez slide images. This algorithm was evaluated on a set of 135 images and grids were accurately detected in most cases, but there remained failures for the most difficult images. In this work, we present an optimization of this algorithm that allows for 100% grid detection and a 25% improvement in grid positioning accuracy. These improvements make the algorithm fully reliable for grid det

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