Grid System Research Articles

Construction of inherent geometric pattern for equal-area hexagonal discrete global grid systems

ABSTRACT Discrete Global Grid Systems (DGGSs) are hierarchical frameworks that provide seamless global coverage and enable the efficient processing and analysis of heterogeneous geospatial data. As cell resolution becomes finer, similarities in cell attributes, such as cell size and shape, increase. Understanding these shared characteristics enhances our knowledge of DGGSs structures, improving their reliability and accuracy across a range of applications. However, existing methods for quantifying these characteristics are limited, often emphasizing overall analysis and visualization rather than detailed assessment. This paper introduces a geometric pattern quantification method for equal-area hexagonal DGGSs by calculating inter-level attribute similarities to identify a reference level, segmenting the spatial distribution image at this reference level, and fitting the boundaries of these sub-images to create iso-feature lines. The results show that this method improves the accuracy and efficiency of cell attribute calculations, with enhancements of at least 5.6 times for study regions and 13.8 times for sample points at lower levels. This pattern provides a robust basis for grid selection and optimization in regional applications, promoting the broader adoption of DGGSs across various fields.

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.

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.

Multi-scenario flexibility requirement analysis of high proportion of new energy access to power system

Abstract To address climate change, the proportion of renewable energy integration into the grid system is gradually increasing, leading to higher demands for flexibility. Current research typically employs methods such as dynamic system modeling, the construction of flexibility indicators, and scenario analysis to measure the flexibility requirements of the power system across different time scales. The use of frequency decomposition algorithms to explore flexibility requirements from the perspective of net load curves is relatively rare. This study utilizes net load data from the Western Inner Mongolia Power Grid to explore the distribution patterns of net load across different time scales under various seasonal and penetration scenarios using frequency decomposition algorithms. The results reveal that renewable energy output exhibits significant fluctuations and distinct diurnal variations, while net load also shows notable patterns and considerable volatility and seasonality. The analysis of flexibility requirements on typical seasonal days highlights the differences in demand distribution at various frequencies, with long-time-scale components primarily reflecting the overall trend of net load, while the medium-to-long time scale components characterize smaller, more frequent fluctuations. Additionally, the uncertainty associated with wind and solar output significantly affects the diurnal fluctuations of net load, with seasonal changes mainly represented in short-time-scale components. The study also emphasizes the impact of different penetration levels on flexibility requirements, indicating that as penetration decreases, the midnight requirement peak diminishes, suggesting differences in flexibility requirements based on renewable energy integration levels. Furthermore, the paper proposes corresponding solution technologies for “generation–grid–load–storage” across different time scales of flexibility requirements, ensuring the stable operation of the grid amidst climate change and rising electricity demand.

Simulation-Based Design for Inlet Nozzle of Vortex Tube to Enhance Energy Separation Effect

The vortex tube, also known as the Ranque–Hilsch vortex tube, is a mechanical device that separates compressed gas into hot and cold streams. It offers a reliable and cost-effective solution to a wide range of cooling applications, as it operates without moving parts, electricity, or refrigerants. Research on vortex tubes has primarily focused on understanding the mechanisms of energy separation and optimizing cooling performance by altering geometric operational parameters. In this study, a Computation Fluid Dynamics (CFD) analysis was conducted to enhance the prediction of energy separation performance and improve the overall energy efficiency of the vortex tube. First, the geometry of the experimental device was modeled to closely match its actual shape, unlike the simplified geometries commonly used in previous CFD studies. Simulations were then carried out with variation in grid systems and turbulence models, and the results demonstrated improved agreement with experimental data compared to those reported in previous studies. Finally, simulations with a modified shape of the inlet nozzle shape were performed, revealing that the energy separation effect of the vortex tube could be enhanced by approximately 15% with an increased inlet expansion ratio (ϵ) while maintaining a constant nozzle length.

Improved binary quantum-based Elk Herd optimizer for optimal location and sizing of hybrid system in micro grid with electric vehicle charging station

In recent times, there has been increasing interest in renewable power generation and electric vehicles within the domain of smart grids. The integration of electric vehicles with hybrid systems presents several critical challenges, including increased power loss, power quality issues, and voltage deviations. To tackle these challenges, researchers have proposed various techniques. Effective management of energy systems is essential for maximizing the benefits of integrating a hybrid system with a microgrid at an electric vehicle charging station. This research specifically aims to optimize the location and sizing of such a hybrid system within the microgrid. Additionally, an improved binary quantum-based Elk Herd optimizer approach is proposed. This approach addresses for optimally managing renewable energy sources and load uncertainty. The proposed system also considers the stochastic nature of electric vehicles and operational restrictions, encompassing diverse charging control modes. The proposed technique performance is implemented in MATLAB platform and compared against existing approaches. The analysis demonstrates the effectiveness in achieving optimal location and sizing for a hybrid system with an electric vehicle charging station. Additionally, the proposed approach contributes to minimizing power loss, electricity costs, and average waiting time. Furthermore, the proposed approach reduces computing time, net present cost, and emissions are 12.5 s, 1.1×106 dollar, 2.21×108 g year−1, respectively.

Collaborative water-electricity operation optimization of a photovoltaic/pumped storage-based aquaculture energy system considering water evaporation effects

The increasing global population, economic development, and urbanization trends have led to a heightened demand for seafood, presenting a significant challenge to the growth trajectory of the food industry. As the most rapidly expanding segment within the food industry, aquaculture presents a sustainable solution to mitigate the overexploitation of marine resources and address the growing demand for food. Aquaculture's sufficient aquatic expanses offer considerable potential for PV deployment, thereby relieving the demand for limited land resources. However, existing research rarely addresses the collaborative operation of water and electricity in aquaculture systems. The effects of water evaporation from PV panel-covered water surfaces on the collaborative water-electricity operation are generally neglected. Hence, this work proposes a collaborative water-electricity operation of a photovoltaic (PV)-pumped storage-based aquaculture energy system considering the water evaporation effects. This optimization method accounts for the reduced water evaporation due to PV panel shading. Water surface PV and pumped storage are integrated into the system to fulfill electricity needs, with pumped storage serving as a dual-purpose device for water and electricity supply. Environmentally sustainable water-electricity generation and aquaculture energy system requirements are established. Water surface PV electricity generation is modeled based on climate and engineering conditions, while aquaculture pond electricity requirement includes oxygenation, feeding, and water replenishment. Finally, a collaborative water-electricity operation model is introduced to optimize operation strategies for various devices. A case study on a fish-light complementation project demonstrates that this optimization method can reduce reliance on the utility grid and overall system operational costs.

Modeling and analysis of fluid-solid coupling heat transfer and fluid flow in transonic nozzle

Solid rocket motors (SRMs) are the power units of modern aerospace transportation, and it is of great significance to predict their flow and heat transfer characteristics by numerical simulation. This numerical study is based on a self-developed code framework MHT (Multi-X Heat Transfer, where X represents the region, scale, physical field, and other aspects) and presents an effective method of analyzing fluid–solid coupling heat transfer problem in an axisymmetric supersonic nozzle. For the nozzle wall heat conduction and nozzle-in fluid flow there are two kinds of solution methods, the detached (solved separately in solid and fluid region and them coupling at the interface) and the whole field method (solve the conduction and convection simultaneously). The whole field method is more efficient but few scholars use it to solve the nozzle coupled problem. This paper introduces the numerical details of the whole field solution method, and provides a new idea for the coupling solution of the nozzle. In addition, a mixed grid system is proposed around the interface region of fluid and solid, in which the quadrilateral structured mesh is used at the fluid side, and the triangular unstructured mesh is used for the outer fluid region and the whole solid region to ensure the accuracy of the 1st-oder normal derivatives calculation. The self-developed code is validated by comparison with well-known commercial soft-ware. Transient results of temperature and velocity are presented for the instants of 1st 20 and 40 s of a practical nozzle.

Power grid accident warning based on real-time analysis of hazard sources and weights

Abstract The power grid system has diverse structures consisting of multiple subnets, equipment, and components that are interrelated and mutually influential. At the same time, there are various types of power grid accidents, including short circuits, open circuits, overloads, grounding faults, etc. Each type of fault may have its specific cause and manifestation. This complexity and diversity make establishing a comprehensive and accurate early warning model difficult. Therefore, this study proposes an adaptive warning method based on hazard sources and weighted coefficients for power grid accidents. To transform raw data containing a large amount of redundant information and noise into data with targeted features for representation, power grid accident information feature labels are generated based on weighted coefficients. Based on this, based on the improved YOLOv3, potential risks in the power grid are identified and assessed. Identification based on implementing risk prevention and control measures defines multiple warning levels to achieve adaptive warning of power grid accidents. Test data validation shows that the proposed solution has an accuracy up to 97.85%. There is also a significant improvement in other indicators, all of which remain above 98%. Indicator validation shows that the research method meets the actual process standards. For the entire day period, the precision and recall of the research method remained stable at over 90%.

Laboratory study of suspension of sediment mixtures in turbulence generated by multiple oscillating grids

Sediment suspension is vital to the geomorphological and ecological processes in rivers. However, previous research has mainly focused on the suspension of sediments with uniform grain sizes, neglecting the complexities arising from sediment mixtures. This study investigates the suspension behaviors of both uniform and mixed-size sediment grains in an oscillating grid turbulence environment. The experimental apparatus comprised a seven-panel grid system, which oscillated at varying frequencies to generate homogeneous turbulence within a water column. The results revealed that for uniform sediments, the sediment diffusion coefficient increases nonlinearly with oscillating frequency and particle size, and for sediment mixtures, the suspension of particles is influenced not only by particle inertial effect but also by the interaction between coarse and fine particles. The presence of coarse grains leads to an enhancement in the sediment diffusion coefficient for fine grains, whereas the reverse influence is minimal. Additionally, the size fraction for the suspended sediment is influenced by various factors, including the oscillating frequency, the height of suspension, and the type of the sediment bed. This study also investigates the mechanisms of sediment-carrying capacity from an energy conservation perspective. It shows that the power required to keep sediment in suspension can be adopted as a new parameter for assessing sediment-carrying capacity. These insights are expected to have broader implications for the comprehension of suspended sediment transport in rivers.

A frequency control strategy with DC microgrid systems for EV stations

ABSTRACT Fluctuations in power supply and demand lead to frequency deviations, which have detrimental effects on the operation of electrical devices and equipment. Therefore, the implementation of effective frequency control mechanisms is essential for enhancing the strength and dependability of microgrid systems. Research aims to develop a frequency control mechanism for an electrical grid system that integrates both wind and photovoltaic (PV) energy sources. The proposed system utilises a Doubly Fed Induction Generator (DFIG) system with frequency controller for regulating frequency and balancing source and demand of power in the grid. To optimise the solar power output, a Luo converter is implemented, which offers high efficiency with improved conversion of power. Additionally, an adaptive neuro fuzzy inference system (ANFIS) maximum power point tracking (MPPT) controller is engaged to ensure optimal regulation of photovoltaic system. DC link obtained from the PV system is then utilised for powering DC loads and charging electric vehicles (EVs) effectively. In order to accomplish this, a bidirectional converter is used, which permits power flow in both ways and facilitates effective EV charging from the microgrid system. The validation of research is conducted on MATLAB software, and the results proves greater converter efficiency of 94.93%, with reduced total harmonic distortion (THD) of 1.24%.

Establishing a plastic waste map using remote sensing data in the coastal area of Thanh Hoa province (Vietnam)

Plastic pollution, particularly in oceans, is a growing environmental threat. Monitoring this pollution is challenging due to the shortage of data and tools. Remote sensing offers a promising solution by using various types of satellite and aerial images to detect and track plastic waste movement. Purpose. The study aims to establish a plastic waste map using remote sensing technology, focusing on the case study of the coastal area of Thanh Hoa province, Vietnam. Methodology. The study involves several steps, including the collection of plastic waste data (samples in the case study and indices from remote sensing images) as well as topographic and base map collection, statistics of plastic samples, grid design, calculation, interpolation, accuracy assessment, and mapping. Field surveys involved taking pictures of plastic debris along designated routes and a grid system was used to classify and calculate waste samples. Satellite images, especially Sentinel-2 MSI optical satellite imagery, are utilized to identify water areas of high plastic concentration. Findings. A map of plastic wastes in coastal areas of Thanh Hoa was created, with indices like NDWI (Normalized Difference Water Index) and FDI (Floating Debris Index) helping identify potential plastic waste signals. The resulting map reveals high concentrations of plastic waste in sea areas near the major river mouths of the Ma River basin and tourist areas, which aligns with areas of high human activity. Originality. This is the first study of plastic waste in the coastal area using remote sensing data in Thanh Hoa province, Vietnam. Practical value. This study can contribute to the effective management of marine plastic waste in Thanh Hoa while opening up opportunities for applying the method to create marine plastic waste maps throughout Vietnam and foster long-term monitoring and management.

Forecasting of Solar Irradiation Based on the Deep Learning Model Using Complete Ensemble Empirical Mode Decomposition Technique

Since solar energy plants have been developing so quickly in recent years, accurate solar power forecasting plays a significant role in contemporary intelligent grid systems and setup a bilateral contract negotiation between suppliers and customers. This paper introduces a novel three-step method for predicting two-channel solar irradiance that combines cutting-edge techniques like Bidirectional Long Short-Term Memory, Wasserstein Generative adversarial Networks and Complete Ensemble Empirical Mode Decomposition with adaptive noise. This methodology that is being suggested involves a framework of decomposition integration which allows for the effective isolation of the solar output signal into sub-sequences that are simple and can be differentiated based on their unique frequency disparities. This is achieved after the subsequences have been separated, and then they are subjected to individual prediction models. The high subsequences are predicted using the WGAN model, while the low subsequences are forecasted using the Bi-LSTM model. This approach ensures the specific characteristics of each subsequence are captured in the prediction process. The experimental result shows that the proposed model surpasses the performance of the suboptimal model in terms of evaluation metrics: RMSE, MAPE and R2. Compared to the suboptimal model, the RMSE, MAPE of all seasons

An efficient control scheme for operational performance enhancement of vehicular fuel cell integrated power system

Recent advancements in fuel cell (FC) technology have positioned it as a promising alternative energy source across various stationary, mobile, and transportation applications. As fuel cell vehicles (FCVs) become increasingly prominent in the transportation sector, they also offer the potential to function as supplementary stationary energy providers when parked, thereby contributing to the grid. In this study, a control method to enhance the energy transfer capability of an FCV-integrated grid system is proposed. To manage energy transfer between grid and FCVs, classical strategies using fixed parameter controllers suffer from performance degradation due to the nonlinear and external parameter-dependent nature of the FC stacks. The proposed adaptive fractional-order proportional-integral strategy bears the advantage of self-tuning parameters feature for designing the control parameters of power conditioning unit. Using fractional-order control endows the system with memory and heredity, enhancing its ability to handle nonlinearity and uncertainty. Through case studies, it is demonstrated that the proposed strategy reduces vehicular FC output power variations by over 60 % while enhancing transient response by 50 % compared to classical control methods. Thus, the limitations such as low control flexibility, slow transient response, and high undershoot/overshoot rates addressed by the classical controllers are countered thanks to the developed strategy.

Optimal Planning of Renewables Energies Management in Power Energy Systems

Optimal management of renewable energy resources is a priority, especially in a global energy mix where fossil fuels are increasingly exploited. The major challenge associated with these renewable resources lies in their intermittency. Complementarity and optimal management of these resources are therefore essential. This article proposes a model for managing renewable energies in power grid systems with a storage system. The resulting model has been tested. Python 3.10 programming language was used to solve the optimization problem, using mixted integer linear programming. To test the model, a special case study was carried out in the South of Togo, representing almost 96% of the country's electrical loads. In this study, resources were first evaluated for one year, then compared according to their evolution over the years. The results showed that the country's energy potential is considerable, but unevenly distributed. The study showed that in the north and center of the country, solar energy and biomass are the main resources available. In the south, on the other hand, energy potential is based on solar, wind, hydro and biomass. The optimization results obtained for the south of the country have enabled to plan better the management of these resources over the course of the year. The results show a composition of maximum load satisfaction, with 39% from grid compared with 8% from hydro, 10% from wind, 12% from batteries systems and 31% from photovoltaic systems. The storage required for energy management is estimated at 220 kWh, with an optimal annual value for the objective cost function of around 67885.10212 USD. The model thus obtained provides a decision-making tool for the optimal management of renewable resources.

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.

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.