Smart Grid System Research Articles

Cyber Attacks Detection And Mitigation Using Machine Learning In Smart Grid Systems

The increasing complexity and interconnectedness of smart grid systems have heightened their vulnerability to cyber threats, necessitating advanced solutions for securing these critical infrastructures. This study aims to explore the potential of AI-driven predictive analytics in enhancing the cybersecurity and operational efficiency of smart grids. By leveraging the systematic approach of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive review of 1,526 initial articles was conducted, which was subsequently narrowed down through rigorous screening and evaluation to a final set of 127 high-quality studies. The review reveals that machine learning models, such as neural networks and time series forecasting, significantly enhance the early detection and mitigation of cyber threats, allowing grid operators to take proactive measures to safeguard against disruptions. Additionally, the integration of AI with real-time data analytics was found to optimize load forecasting, predict equipment failures, and improve overall grid resilience, leading to reduced downtime and operational costs. However, significant challenges related to data privacy, scalability, and integration with legacy systems persist. The findings suggest that techniques like federated learning and blockchain integration could address these challenges, though further research is needed to enhance model robustness and efficiency. This review provides critical insights into the practical applications of AI in smart grid cybersecurity, highlighting both the benefits and the barriers that must be overcome to achieve widespread adoption.

Big Data in Pharmacy: Transforming Patient Care with Analytics

The integration of big data analytics in pharmacy is revolutionizing patient care by providing insights that enhance medication management and therapeutic outcomes. By leveraging vast amounts of health data, pharmacists can deliver more personalized treatment and optimize drug therapy. Despite the potential benefits, existing methods in pharmacy often suffer from data silos, lack of interoperability, and insufficient analytical capabilities, leading to suboptimal patient outcomes and inefficient medication management. These limitations hinder the effective utilization of data for real-time decision-making in patient care. To address these challenges, it propose the Patient Care Transformation using Big Data Analytics (PCT-BDA) framework, which facilitates a holistic approach to patient data integration and analysis within smart grid systems. This framework enables seamless data flow between various healthcare entities, improving collaboration and fostering a comprehensive view of patient health profiles. The proposed method emphasizes real-time analytics, predictive modeling, and machine learning algorithms to enhance decision-making processes in medication management. By utilizing big data analytics, pharmacists can better predict patient responses, identify potential drug interactions, and tailor therapy plans to individual needs. Preliminary findings from the implementation of the PCT-BDA framework indicate significant improvements in patient outcomes, including reduced medication errors, enhanced adherence to treatment protocols, and overall increased satisfaction with pharmacy services. This innovative approach highlights the transformative potential of big data analytics in optimizing patient care in the pharmacy sector.

Comparative Study of Time Series Analysis Algorithms Suitable for Short-Term Forecasting in Implementing Demand Response Based on AMI

This paper compares four time series forecasting algorithms—ARIMA, SARIMA, LSTM, and SVM—suitable for short-term load forecasting using Advanced Metering Infrastructure (AMI) data. The primary focus is on evaluating the applicability and performance of these forecasting models in predicting electricity consumption patterns, which is a critical component for implementing effective demand response (DR) strategies. The study provides a comprehensive analysis of the predictive accuracy, computational efficiency, and scalability of each algorithm using a dataset of real-time electricity consumption collected from AMI systems over a designated period. Through extensive experiments, we demonstrate that each algorithm has distinct strengths and weaknesses depending on the characteristics of the dataset. Specifically, SVM exhibited superior performance in handling nonlinear patterns and high volatility, while SARIMA effectively captured seasonal trends. LSTM showed potential in modeling complex temporal dependencies but was sensitive to hyperparameter settings and required a substantial amount of training data. This research offers practical guidelines for selecting the optimal forecasting model based on data characteristics and application needs, contributing to the development of more efficient and dynamic energy management strategies. The findings highlight the importance of integrating advanced forecasting techniques into smart grid systems to enhance the reliability and responsiveness of DR programs. This study lays a solid foundation for future research on integrating these forecasting models into real-world AMI applications to support effective demand response and grid stability.

Synergistic Effects of Energy Storage Systems and Demand-Side Management in Optimizing Zero-Carbon Smart Grid Systems

The urgent need to mitigate climate change and reduce reliance on fossil fuels has driven the global shift towards renewable energy sources (RESs). However, the intermittent nature of RESs poses significant challenges to the widespread adoption of Zero-Carbon Smart Grids (ZCSGs). This study proposes a synergistic framework to address this hurdle. It utilizes energy storage systems (ESSs) by comparing Vanadium redox flow batteries (VRFBs) and Lithium ion batteries (LIBs) to identify the most suitable option for ZCSGs, with precise models enabling robust performance evaluation. Moreover, an accurate demand-side management (DSM) strategy considering power elasticity to manage discrepancies between electricity load, RES generation, and ESS availability is introduced for estimating fair, dynamic tariffs. An advanced load and weather-forecasting strategy is introduced for improving grid planning and management. An advanced optimization algorithm enhances grid stability and efficiency. Simulations demonstrate significant reductions in carbon footprint, peak power demand, and reliance on fossil fuels. The study finds that VRFBs outperform LIBs in cost and security, and dynamic tariffs based on accurate DSM significantly reduce energy costs. This work explores the challenges and opportunities of this integrated approach, offering policy recommendations and future research directions for truly optimized ZCSG implementation.

Advancing system of systems engineering using intangible value logic measurements from intellectual capital thinking approach

This study explores the integration of intangible value logic measurements derived from the Intellectual Capital Thinking (ICT) approach to advance System of Systems Engineering (SoSE). Focusing on demand-side electricity management, the research illuminates the challenges and opportunities in capturing intangible value creation within engineering systems. By delving into human behaviour-driven demand intricacies and recognising intangible resources among stakeholders, the study unveils the potential for innovation in demand management strategies. Through the analysis of three interacting value logics—shop, chain, and network—the research elucidates their impact on system performance. Addressing the practical application gap, the study introduces ICT as a strategic lens to enhance performance through non-monetary values. Qualitative and quantitative exploration showcases ICT's ability to propel system dynamics, offering crucial insights for sustainable smart grid systems. By emphasising the significance of understanding intangible measures in engineering systems, this research contributes to innovation and performance improvement in SoSE.

Adaptive electronic relay for smart grid based on self-healing protection.

The protection system is crucial for grid stability and safeguarding essential components, including generators, transformers, transmission systems, and power connections. The smart grid system increases the flexibility and complexity of the power system, making fault detection and isolation the primary challenges for the protection system. This paper presents an optimal protection solution using an adaptive electronic relay to enhance reliability and enable self-healing. The proposed protection algorithm quickly detects faults and automatically isolates them from the rest of the healthy system in 25ms. The relay operation algorithm has been validated using MATLAB SIMULINK software. The results confirm the effectiveness of the proposed smart electronic relay in various sections of the smart grid system, including transformers, transmission and distribution.

Enhancing Smart Grid Stability through a Hybrid Biometric Pattern Recognition and Long Short-Term Memory Approach

The stability of power grids is critical in ensuring the consistent and efficient delivery of electricity. However, traditional predictive models often fall short in addressing grid data's intricate and ever-changing nature, making it challenging to maintain grid reliability. This paper introduces a novel hybrid approach that combines Biometric Pattern Recognition (BPR) with Long Short-Term Memory (LSTM) networks to enhance the prediction of smart grid stability. This approach employs BPR techniques to extract essential features from smart grid data by leveraging the pattern recognition capabilities typically used in biometric systems. These techniques are particularly effective in identifying and isolating the most relevant patterns within the complex datasets generated by smart grids. On the other hand, the LSTM-based model is designed to handle the temporal dependencies and nonlinear patterns characteristic of grid data. LSTMs, known for their proficiency in time-series analysis, are well-suited for capturing the sequential nature of grid data, enabling more accurate predictions over time. Integrating BPR and LSTM in this hybrid model addresses several limitations in existing predictive methods. By combining the strengths of both techniques, the model enhances the accuracy of predictions and improves the overall reliability of grid stability assessments. Extensive experiments were conducted using real-world datasets to validate the effectiveness of the proposed hybrid model. The results demonstrate a significant improvement in prediction accuracy, with the BPR-LSTM model achieving a 98.25% increase in accuracy compared to traditional prediction methods. This improvement underscores the potential of the BPR-LSTM hybrid approach to play a pivotal role in advancing the stability and reliability of smart grid systems.

Performance Comparison of Bio-Inspired Algorithms for Optimizing an ANN-Based MPPT Forecast for PV Systems.

This study compares bio-inspired optimization algorithms for enhancing an ANN-based Maximum Power Point Tracking (MPPT) forecast system under partial shading conditions in photovoltaic systems. Four algorithms-grey wolf optimizer (GWO), particle swarm optimization (PSO), squirrel search algorithm (SSA), and cuckoo search (CS)-were evaluated, with the dataset augmented by perturbations to simulate shading. The standard ANN performed poorly, with 64 neurons in Layer 1 and 32 in Layer 2 (MSE of 159.9437, MAE of 8.0781). Among the optimized approaches, GWO, with 66 neurons in Layer 1 and 100 in Layer 2, achieved the best prediction accuracy (MSE of 11.9487, MAE of 2.4552) and was computationally efficient (execution time of 1198.99 s). PSO, using 98 neurons in Layer 1 and 100 in Layer 2, minimized MAE (2.1679) but had a slightly longer execution time (1417.80 s). SSA, with the same neuron count as GWO, also performed well (MSE 12.1500, MAE 2.7003) and was the fastest (987.45 s). CS, with 84 neurons in Layer 1 and 74 in Layer 2, was less reliable (MSE 33.7767, MAE 3.8547) and slower (1904.01 s). GWO proved to be the best overall, balancing accuracy and speed. Future real-world applications of this methodology include improving energy efficiency in solar farms under variable weather conditions and optimizing the performance of residential solar panels to reduce energy costs. Further optimization developments could address more complex and larger-scale datasets in real-time, such as integrating renewable energy sources into smart grid systems for better energy distribution.

Nonlinear Analysis and Closed-Form Solution for Overhead Line Magnetic Energy Harvester Behavior

Recently, much attention has been given to the development of various energy harvesting technologies to power remote electronic sensors, data loggers, and communicators that can be installed on smart grid systems. Magnetic energy harvesting is, perhaps, the most straightforward way to capture a significant amount of power from a current-carrying overhead line. Since the harvester is expected to have a small size, the high currents of the distribution system easily saturate its magnetic core. As a result, the operation of the magnetic harvester is highly nonlinear and makes precise analytical modeling difficult. The operation of an overhead line magnetic energy harvester (OLMEH) generating significant DC power output into a constant voltage load was investigated in this paper. The analysis method was based on the Froelich equation to analytically model the nonlinearity of the core’s BH characteristic. The main findings of this piecewise nonlinear analysis include a closed-form solution that accounts for both the core and rectifiers’ nonlinearities and provides an accurate prediction of OLMEH transfer window length, output current, and harvested power. Continuous and discontinuous operational modes are identified and the mode transition boundary is obtained quantitatively. The theoretical investigation was concluded by comparison with a computer simulation and also verified by the experimental results of a laboratory prototype harvester. A good agreement was found.

Can Renewable Energy Be a Driving Factor for Economic Stability? An In-depth Study of Sector Expansion and Economic Dynamics

India has emerged as one of the world's most appealing locations for renewable energy development. It has set lofty renewable energy goals to reach 450 gigawatts (GW) capacity by 2030. These aims indicate India's determination to move to greener and more sustainable energy sources. India has been investing in R&D to promote technological innovation in renewable energy. This includes improvements to solar photovoltaic technology, wind energy, energy storage technologies, and smart grid systems. Innovation is critical for improving efficiency, lowering prices, and increasing the reliability of renewable energy sources. This paper aims to analyse the linkages between economic growth and renewable energy usage in India. For this, the Granger Causality technique is adopted, and it is found that no short-run causality exists among the economic growth and RE installed capacity. However, Industrial Production Granger Causes both GDP and Renewable Energy Capacity. When the stock price data of the last five years of top renewable energy companies was also collected, it was found that all the companies are showing an upward trend. While renewable energy is growing rapidly, especially solar and wind power, it is insufficient to meet the bulk of India's energy demands. Renewables contribute to reducing carbon emissions and diversifying the energy mix, but they still account for a smaller percentage compared to thermal power.

Artificial intelligence‐driven sustainability: Enhancing carbon capture for sustainable development goals– A review

AbstractArtificial intelligence (AI) and environmental points are equally important components within the response to local weather change. Therefore, based on the efforts of reducing carbon emissions more efficiently and effectively, this study tries to focus on AI integration with carbon capture technology. The urgency of tackling climate change means we need more advanced carbon capture, and this is an area where AI can make a huge impact in how these technologies are operated and managed. It will minimize manufacturing emissions and improve both resource efficiency as well as our planet's environmental footprint by turning waste into something of value again. Artificial intelligence could be leveraged to analyze huge data sets from carbon capture plants, searching for optimal system settings and more efficient ways of identifying patterns in the available information at a larger scale than currently possible. In addition, AI incorporated sensors and monitoring mechanisms in the supply chain can identify any operational failure at reception itself allowing for timely action to protect those areas. Artificial intelligence also helps generative design for carbon capture materials, which allows researchers to explore new types of carbon‐absorbing material, including metal–organic frameworks and polymeric materials that are important in industrial CO2, such as moisture. In addition, it increases the accuracy of reservoir simulations and controls CO2 injection systems for storage or enhanced oil recovery. Through applying AI algorithms on reservoir geology, production performance and real‐time data this study would like to facilitate the optimization of injection processes as well as minimize CO2 emissions while assuring a maximum efficiency. Artificial intelligence integrates with renewable‐based carbon capture efforts that can be employed by AI‐driven smart grid systems to improve carbon capture methods.

Secure smart grid implementation with automatic data integrity attack location prediction and exalted energy theft detection

Smart grid systems use digital communication technology to efficiently monitor and manage electricity distribution. However, there is a need for improve the robustness of smart grid systems. Hence a novel “Secure Smart Grid Implementation with Automatic Data Integrity Attack Location Prediction and Exalted Energy Theft Detection” has been proposed. Current detection methods are not good at identifying data integrity attacks. So a novel Automatic Deterministic Attack Location Prediction Model detects data integrity attacks using a Stacked Watermarking BiGRU-based Deep Deterministic Q network, extracting system matrix and state time series variation. Moreover, Energy theft detection algorithms, which are intended to detect dishonest clients, provide erroneous findings because of linear interpolation processes and randomised near misses, which ignore important data points. Thus a novel Exalted Energy Theft Detector with Secure Encoding is introduced where Amortized Multicode GAN method secures smart grids by detecting energy theft and preventing firmware code modifications, utilising fractional multi-level quantum encoding to identify injection locations. As a result, the proposed model has an accuracy of 97.1%, recall of 96.25%, error rate is 1.36%, F1-score is 96.20%, sensitivity is 96.10%, specificity is 96.10%, MAPE is 0.48%, MSE is 0.0001 and RMSE is 0.017.

Analysis of distributed smart grid system on the national grids

Analysis of distributed smart grid system on the national grids

Systematic Survey Analysis of the Application of Artificial Intelligence Base Network on Grid Computing Techniques

A smart grid is a contemporary electrical system that supports two-way communication and utilizes the concept of demand response. In order to increase the smart grid's dependability and enhance the consistency, efficiency, and efficiency of the electrical supply, stability prediction is required. The true test for smart grid system designers and specialists will therefore be the increase of renewable energy. With the goal of integrating the electric utility infrastructure into the advanced communication era of today, both in terms of function and architecture, this program has achieved great strides toward modernizing and expanding it. In this study, researchers used the Systematic literature review method which identifies, evaluates and interprets all relevant research results related to certain research questions, certain topics, or phenomena of concern. The study review on how a smart grid applied different deep learning techniques and how renewable energy can be integrated into a system where grid control is essential for energy management. The article discusses the idea of a smart grid and how reliable it is when renewable energy sources are present. Globally, a change in electric energy is needed to reduce greenhouse gas emissions, prevent global warming, reduce pollution, and boost energy security.

Designing Energy-efficient DC Robotic Machines with Advanced Cyber Security for a Smart Grid System

In this paper, an energy-efficient direct current (DC) robotic machine (E2DCRM) is developed to revolutionize industrial automation, eliminating the rectification stage using brushless alternating direct current (BADC). E2DCRM is integrated with an advanced intelligent grid structure for enhancing the smooth and soft connection process. The emphasis on threats occurs because malicious attacks are overcome using deep learning algorithms. In this approach, a hybrid deep learning-based system is used to eliminate malicious attacks. The communication protocols might operate directly over the power lines, thus eliminating the need for rectification and enabling seamless integration with the smart grid. This research offers a comprehensive, integrated solution combining energy-efficient DC robotics with state-of-the-art cyber security measures. The proposed methodologies address current industrial limitations and set the stage for a resilient and sustainable future in innovative and safe grid technology.

A Systematic Survey on the Application of Artificial Intelligence (ai) Baseline Networks on Grid Computing Techniques - Challenges, Novelty and Prospects

A smart grid is a contemporary electrical system that supports two-way communication and utilizes the concept of demand response. To increase the smart grid's dependability and enhance the consistency, efficiency, and efficiency of the electrical supply, stability prediction is required. The true test for smart grid system designers and specialists will therefore be the increase of renewable energy. To integrate the electric utility infrastructure into the advanced communication era of today, both in terms of function and architecture, this program has made great strides toward modernizing and expanding it. The study reviews how a smart grid applied different deep learning techniques and how renewable energy can be integrated into a system where grid control is essential for energy management. The article discusses the idea of a smart grid and how reliable it is when renewable energy sources are present. Globally, a change in electric energy is needed to reduce greenhouse gas emissions, prevent global warming, reduce pollution, and boost energy security.

Hybrid intelligent algorithm aided energy consumption optimization in smart grid systems with edge computing

The rapid proliferation of smart grid systems necessitates efficient management of energy resources, particularly in the context of mobile edge computing (MEC) networks. This paper presents a novel approach to optimize the energy consumption in smart grid systems with the integration of edge computing, employing a hybrid intelligent algorithm (HIA) empowered by particle swarm optimization (PSO). The primary objective is to enhance the sustainability and operational efficiency of smart grid infrastructures by minimizing the energy consumption in the MEC networks. The proposed HIA utilizes PSO to dynamically allocate computational tasks and manage resources among edge devices based on real-time demand fluctuations. This adaptive approach aims to achieve the optimal load balancing and energy efficiency across the smart grid ecosystem. By leveraging the PSO’s ability to iteratively refine solutions and adapt to changing environmental conditions, the algorithm optimizes the energy consumption while maintaining requisite service levels and reliability. Simulation experiments and case studies validate the effectiveness of the proposed PSO-based HIA in reducing the energy consumption without compromising system other performances. The results demonstrate substantial improvements in the energy efficiency, illustrating the feasibility and benefits of employing intelligent algorithms tailored for edge computing environments within smart grid systems. This research contributes to advancing sustainable smart grid technologies by introducing a robust framework for energy optimization through hybrid intelligent algorithms.

A CNN-based approach for anomaly detection in smart grid systems

Power grids are always a major center of attraction for attackers due to their large coverage area, complex technological infrastructure, remote access facilities, connectivity, and many more. The power grids that have bidirectional power flow, information interchange, and advanced communication technologies to improve the quality of power transmission and distribution are called smart grids. DoS attacks and false data injection attacks are the two major attacks on smart grids. This paper considers a false data injection attack considering noise reduction, state estimation, system parameters, and control system generation. Firstly, we measure the noise and mitigate it using the data matrix of the devices (sensors, generators, substations, and breaker systems). Secondly, state estimation uses the CNN to track the injected attack types. Finally, a control system is designed to provide anomaly patterns to the devices as input to prevent attacks in the future. The experimental results show that the proposed method achieves a higher anomaly detection rate with a lower error rate (approximately . 1.43%). The network performance parameters are providing significant results; throughput (12 Mbps), PDR (0.005%), execution time (1.43 s), and energy consumption are reduced by 12%–15% as compared to the existing research findings.

A nonsmooth Levenberg–Marquardt method based on KKT conditions for real-time pricing in smart grid

The real-time pricing of smart grid, which is an important mean of demand side management, is an ideal method to adjust the power balance between the supply and demand in smart grid system. By transforming the social welfare maximization model into a nonsmooth system of equations based on KKT conditions and investigating the related nonsmooth-function’s generalized Jacobi, a nonsmooth Levenberg–Marquardt method with global convergence is proposed in this paper. Also, the local convergence rate is obtained under the local error bound, which is weaker than the nonsingular condition. Finally, numerical simulations show that the smaller the approximate parameter in smoothing Newton’s method, the closer the electricity price is to the value obtained by the proposed algorithm. And the price of the multi price based on the method is lower than that of the single price algorithm that does not consider user types. Meanwhile, compared with the distributed algorithm with different scales, it is found that the price is lower and the social welfare is higher, which shows that the algorithm is robust and effective.

Hybrid Control Strategy for 5G Base Station Virtual Battery-Assisted Power Grid Peak Shaving

With the rapid development of the digital new infrastructure industry, the energy demand for communication base stations in smart grid systems is escalating daily. The country is vigorously promoting the communication energy storage industry. However, the energy storage capacity of base stations is limited and widely distributed, making it difficult to effectively participate in power grid auxiliary services by only implementing the centralized control of base stations. Aiming at this issue, an interactive hybrid control mode between energy storage and the power system under the base station sleep control strategy is delved into in this paper. Grounded in the spatiotemporal traits of chemical energy storage and thermal energy storage, a virtual battery model for base stations is established and the scheduling potential of battery clusters in multiple scenarios is explored. Then, based on the time of use electricity price and user fitness indicators, with the maximum transmission signal and minimum operating cost as objective functions, a decentralized control device is used to locally and quickly regulate the communication system. Furthermore, a multi-objective joint peak shaving model for base stations is established, centrally controlling the energy storage system of the base station through a virtual battery management system. Finally, a simulation analysis was conducted on data from different types of base stations in the region, designing two distinct scheduling schemes for four regional categories. The analysis results demonstrate that the proposed model can effectively reduce the power consumption of base stations while mitigating the fluctuation of the power grid load.