Smart Grid System Research Articles

BSPM: Blockchain-based Security Protection Model for power load management terminals

Power load management terminals are essential components of the smart grid system. However, they encounter numerous security challenges owing to their widespread deployment across large areas. These nodes exist in uncontrollable physical environments and are vulnerable to malicious attacks. Researching security measures for these terminals is vital to enhancing the security of the smart grid. Blockchain technology, with its distributed storage and tamper-proof features, can effectively protect the data security of these terminals. This paper proposes a blockchain-based security protection model for power load management terminals. First, the architecture of the model is designed using blockchain technology. In addition, a dynamic trust evaluation method for terminals is established through the beta reputation system. The blockchain consensus algorithm for power load management terminals is designed based on the trust evaluation value and practical Byzantine fault tolerance. The effectiveness of the trust evaluation model is verified by simulating the terminal operating situation. The message complexity and storage requirements of the security protection model are analyzed by comparing them with results from related studies.

Maturity quantification and evaluation of R&I needs of the smart grid

The European Technology and Innovation Platform Smart Networks for Energy Transition (ETIP SNET) roadmap has proposed the development of a set of high-level use cases serving the Power Grids of the future as a decisive need calling for research, small to large-scale demonstrations and deployment of services and products leading to replication and scaling-up to realize real-world implementation. R&I Projects orchestrate these developments and thus the evolution of technologies, solutions and systems are their valuable deliverable that respond to calls for contribution. As such in this publication we use this linking chain of evolution to develop a sound methodology starting from the evaluation of project results and leading to sound quantification of research needs. Specifically, by keeping track of technologies / solutions / systems advancement, the quantification of the maturity of high-level use cases can be induced following a universal approach and thus the whole smart grid system readiness can be evaluated as a valuable input to gap analysis and identification of R&I needs enabling appropriate policy decisions.

Macroscopic state‐based reactive voltage control of virtual synchronous generator in AC microgrid

AbstractIn building a smarter and more flexible low‐carbon smart grid system, alternating current (AC) microgrids using virtual synchronous generator (VSG) technology are viewed as a key link in integrating distributed renewable energy access into the main grid. Given that renewable energy sources (such as solar, hydroenergy, and wind) do not have sufficient capacity for reactive power when not available, AC microgrids face challenges in maintaining stable operation. In order to overcome this difficulty, it is hoped that digging deeper and applying more system information can significantly improve the overall performance of the microgrid. This paper proposes a novel method based on macroscopic state dynamic modeling. This method expands the understanding of the inherent rational control mechanism within the microgrid, enabling the overall control objective of the microgrid to be expressed in a more abstract and direct manner. Additionally, by implementing additional convergence constraint conditions on the macroscopic state dynamics, such as based on some optimality criteria, a set of macroscopic state controllers can be obtained to meet specific performance indicators. Theoretical analysis combined with simulation validation demonstrate the effectiveness of this macroscopic state based control strategy. It proves that when meeting the predefined design requirements, the designed controller can enhance the transient response of microgrids in practical applications, thus supporting higher rate of renewable energy access and promoting the development of the smart grid.

Network Security Challenges and Countermeasures for Software-Defined Smart Grids: A Survey

The rise of grid modernization has been prompted by the escalating demand for power, the deteriorating state of infrastructure, and the growing concern regarding the reliability of electric utilities. The smart grid encompasses recent advancements in electronics, technology, telecommunications, and computer capabilities. Smart grid telecommunication frameworks provide bidirectional communication to facilitate grid operations. Software-defined networking (SDN) is a proposed approach for monitoring and regulating telecommunication networks, which allows for enhanced visibility, control, and security in smart grid systems. Nevertheless, the integration of telecommunications infrastructure exposes smart grid networks to potential cyberattacks. Unauthorized individuals may exploit unauthorized access to intercept communications, introduce fabricated data into system measurements, overwhelm communication channels with false data packets, or attack centralized controllers to disable network control. An ongoing, thorough examination of cyber attacks and protection strategies for smart grid networks is essential due to the ever-changing nature of these threats. Previous surveys on smart grid security lack modern methodologies and, to the best of our knowledge, most, if not all, focus on only one sort of attack or protection. This survey examines the most recent security techniques, simultaneous multi-pronged cyber attacks, and defense utilities in order to address the challenges of future SDN smart grid research. The objective is to identify future research requirements, describe the existing security challenges, and highlight emerging threats and their potential impact on the deployment of software-defined smart grid (SD-SG).

Smart Grid Technologies and Their Role in Sustainable Energy Management

Purpose: The general objective of this study was to analyze smart grid technologies and their role in sustainable energy management. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to smart grid technologies and their role in sustainable energy management. Preliminary empirical review revealed that smart grid technologies significantly transformed energy management by enhancing sustainability, reliability, and efficiency through advanced metering, real-time analytics, and improved communication networks. These technologies enabled better integration of renewable energy sources, reduced greenhouse gas emissions, and empowered consumers with detailed energy usage information, leading to cost savings and increased energy efficiency. However, challenges such as high implementation costs, infrastructure needs, regulatory hurdles, and cybersecurity risks were identified. The study highlighted the importance of continued research, supportive policies, and public engagement to optimize smart grid deployment and achieve a more sustainable energy system. Unique Contribution to Theory, Practice and Policy: The Diffusions of Innovations Theory, Socio- Technical Systems Theory and Actor- Network Theory may be used to anchor future studies on smart grid technologies. The study recommended adopting a multidisciplinary approach to smart grid research, enhancing interoperability through standardized protocols, and investing in workforce training. It emphasized supportive regulatory frameworks, including incentives and dynamic pricing models, to promote smart grid adoption. Public awareness and engagement were deemed crucial, suggesting educational campaigns to increase consumer participation. The study highlighted the importance of international collaboration for knowledge sharing and called for continuous investment in research and development to drive technological advancements and ensure the sustainability and efficiency of smart grid systems.

Knowledge-assisted differential evolution based non-contact voltage measurement for multiconductor systems in smart grid

Multiconductor systems, such as overhead lines and multicore cables, are widely used in smart grids for their reliable power transmission capabilities. However, measuring the voltage of individual conductors in these systems has been challenging despite their promising applications. This paper proposes a novel framework to address this issue by utilizing a low-cost electric field (EF) sensor array and a knowledge-assisted differential evolution algorithm. The framework involves designing an annular EF sensor array to capture EF information around the multiconductor systems while ensuring its anti-interference capabilities through grounding and balanced electrodes. Furthermore, an optimization problem is formulated to reflect the model accuracy, and a knowledge-assisted differential evolution algorithm is proposed to optimize the voltage values and conductor positions by leveraging knowledge about the conductor positions and EF distribution. The use of this knowledge is expected to generate promising initial solutions for accelerating the optimization process. The proposed framework is experimentally validated, and the results demonstrate the robustness and accuracy of the EF sensor array and algorithm in obtaining the voltage of individual conductors in multiconductor systems.

Solar–Hydrogen-Storage Integrated Electric Vehicle Charging Stations with Demand-Side Management and Social Welfare Maximization

The reliable operation of a power system requires a real-time balance between supply and demand. However, it is difficult to achieve this balance solely by relying on supply-side regulation. Therefore, it is necessary to cooperate with effective demand-side management, which is a key strategy within smart grid systems, encouraging end-users to actively engage and optimize their electricity usage. This paper proposes a novel bi-level optimization model for integrating solar, hydrogen, and battery storage systems with charging stations (SHS-EVCSs) to maximize social welfare. The first level employs a non-cooperative game theory model for each individual EVCS to minimize capital and operational costs. The second level uses a cooperative game framework with an internal management system to optimize energy transactions among multiple EVCSs while considering EV owners’ economic interests. A Markov decision process models uncertainties in EV charging times, and Monte Carlo simulations predict charging demand. Real-time electricity pricing based on the dual theory enables demand-side management strategies like peak shaving and valley filling. Case studies demonstrate the model’s effectiveness in reducing peak loads, balancing energy utilization, and enhancing overall system efficiency and sustainability through optimized renewable integration, energy storage, EV charging coordination, social welfare maximization, and cost minimization. The proposed approach offers a promising pathway toward sustainable energy infrastructure by harmonizing renewable sources, storage technologies, EV charging demands, and societal benefits.

Research on time series prediction of hybrid intelligent systems based on deep learning

Power forecasting plays a crucial role in the operation of smart grid system, which is indispensable for making the operation plan of power system, improving economic efficiency and ensuring the quality of power supply. In order to enhance the accuracy of power load forecasting, a hybrid intelligent power load forecasting system is proposed in this paper. The system first preprocesses the raw data using Savitzky-Golay smoothing technology to eliminate noise and improve data quality. Then, a long and short term memory network with attention mechanism is used to enhance the generalization ability of the model. In addition, in order to further improve the prediction performance, an improved genetic algorithm is integrated to optimize the model parameters. Finally, a data set is used to verify the proposed prediction method. In terms of short-term forecasting ability of experiment of the testing data set, compared with LSTM model, the proposed method shows superior performance in root mean square error and mean absolute error indicators, with root mean square error reduced by 18.7 % and mean absolute error reduced by 26.2 %. In terms of long-term prediction ability of experiment of the testing data set, compared with GBRT model, the proposed method reduces root mean square error and mean absolute error by 24.8 % and 30.7 %, respectively. The experimental results show that compared with the existing benchmark algorithm, the proposed method is significantly improved in two key indexes of prediction accuracy, which proves its effectiveness and superiority in power load prediction.

Cyber‐attack and defense method for load frequency control in smart grid systems with electric vehicles

AbstractThe latest advancements in Vehicle‐to‐Grid (V2G) technology enable Electric Vehicles (EVs) to contribute to Frequency Regulation (FR), mitigating the impact of uncertain power fluctuations in renewable resources. However, diverse cyber‐attacks pose threats to the Load Frequency Control (LFC) system and smart grid infrastructure, compromising their accuracy and reliability. This research paper focuses on the effects of cyber‐attacks on frequency regulation in a smart grid system that relies on a communication network. The study proposes a two‐area system and a modified IEEE‐39 bus three‐area system, incorporating intermittent solar photovoltaic and wind turbine sources, traditional conventional sources, and electric vehicles. To ensure frequency stabilization and tie‐line power, the researchers introduce a cascade FOPIDN‐(1+TD) controller. Its parameters are optimized using a novel Quasi Opposition Arithmetic Optimization Algorithm (QOAOA). The proposed approach's dynamic response is compared with other commonly used controllers, demonstrating its effectiveness in maintaining stability. The article focuses on the LFC system and presents a novel approach involving a cyber‐physical model. It introduces a method for detecting and defending against cyber‐attacks using deep learning, specifically utilizing the Long‐Short‐Term‐Memory (LSTM) network. The effectiveness of the proposed defense strategy is demonstrated through experiments conducted on both a two‐area interconnected power system and the IEEE‐39 bus system. The outcomes indicate that the suggested defense mechanism effectively maintains acceptable levels of power system frequency and tie‐line power during cyber‐attacks. Additionally, the validity of the controller's performance is verified through real‐time analysis using Hardware‐In‐The‐Loop (HIL) simulation with the OPAL‐RT platform.

Analysis of Real-time Processing Approaches for Large Data Volumes in Metering Infrastructure

Smart grid systems and communication technologies, such as Advanced Metering Infrastructure (AMI), have revolutionized utility service management and monitoring. AMI leverages smart meters equipped with advanced communication capabilities, facilitating bidirectional communication between utilities and consumers. The increasing deployment of smart meters and the adoption of sub-hourly data collection requirements by utility companies highlight significant data volume growth. Thus, there is a need for efficient real-time data processing solutions as existing approaches may not meet previously established Service-Level Agreements (SLAs) concerning performance, accuracy, and scalability metrics. This research aims to comprehensively review the latest publications relevant to distributed real-time data processing methods for smart grid applications and outline problems for further research. Specifically, the study delves into the effectiveness and application of reviewed approaches in managing the constant stream of data from smart meters and IoT devices within the smart grid context. By analysing existing methodologies and advancements, this study seeks to identify challenges and opportunities in real-time data processing for smart grid infrastructures, focusing on addressing the complexities of processing, managing, and storing large volumes of real-time data. The literature review revealed two primary applications of real-time data processing: optimization of data streaming performance and data analysis. The review encompasses various studies, each presenting distinct methodologies and technologies applied to address the challenges of processing large volumes of real- time data from smart meters and IoT devices. Future research should address the challenges and limitations discovered in this study.

Anomaly detection based on LSTM and autoencoders using federated learning in smart electric grid

In smart electric grid systems, various sensors and Internet of Things (IoT) devices are used to collect electrical data at substations. In a traditional system, a multitude of energy-related data from substations needs to be migrated to central storage, such as Cloud or edge devices, for knowledge extraction that might impose severe data misuse, data manipulation, or privacy leakage. This motivates to propose anomaly detection system to detect threats and Federated Learning to resolve the issues of data silos and privacy of data. In this article, we present a framework to identify anomalies in industrial data that are gathered from the remote terminal devices deployed at the substations in the smart electric grid system. The anomaly detection system is based on Long Short-Term Memory (LSTM) and autoencoders that employs Mean Standard Deviation (MSD) and Median Absolute Deviation (MAD) approaches for detecting anomalies. We deploy Federated Learning (FL) to preserve the privacy of the data generated by the substations. FL enables energy providers to train shared AI models cooperatively without disclosing the data to the server. In order to further enhance the security and privacy properties of the proposed framework, we implemented homomorphic encryption based on the Paillier algorithm for preserving data privacy. The proposed security model performs better with MSD approach using HE-128 bit key providing 97% F1-score and 98% accuracy for K=5 with low computation overhead as compared with HE-256 bit key.

Markov game based on reinforcement learning solution against cyber–physical attacks in smart grid

The rapid expansion of information networks has amplified vulnerabilities in cyberspace, particularly within the power grid and communication layers of smart grid systems. Safeguarding smart grid security has become imperative for users concerned about both online and offline privacy. Detecting attacks in smart grids is crucial for ensuring the safety and continuity of the electrical network. This paper introduces a novel stealth attack-defense game that examines the impact of these invasions on the availability, confidentiality, and integrity of phasor measurement units. The proposed system considers both attack and defense perspectives. Attackers focus on key parameters to covertly infiltrate system layers while defending the smart grid system requires advanced knowledge of data flow and critical insights to establish robust protections. To address this cybersecurity challenge, we propose a game approach based on a partially observed Markov game with an improved Shapley Q-value and a multi-agent reinforcement learning framework, implemented in cooperative, communicative, and dynamic environments. The evaluation results demonstrate the robustness of the proposed approach against coordinated attacks in smart grid environments. Additionally, the results emphasize the significance of attack distribution as a crucial trace for identifying the attack source. Our approach shows promise for addressing various types of attacks against smart grid systems.

Analysis and development of smart production and distribution line system in smart grid based on optimization techniques involving digital twin

The term Digital Twin (DT) is defined as the virtual demonstration of an object that is represented through real-time datasets. DT is done through artificial intelligence to enhance decision-making techniques. DT includes the process of simulation, amalgamation, observation, analysis, and conservation. The DT is simply the exact reproduction of the physical structures. DT is used in the identification and evaluation of problems through real-time analysis. It is important to have prior analysis and evaluation of the object before existing in the real world. These digital twins help in the manufacturing and implementation of the production line system. DT includes the production line with the station division and the hours needed for the operating conditions for the assembly process. The systems are integrated to reduce the overall cost parameter. The physical simulation model is employed to obtain higher performance with reduced cost. An artificial neural network with a genetic algorithm is used for the optimization process to achieve a production line system using digital twins.

An efficient hybrid technique for energy management system with renewable energy system and energy storage system in smart grid

This manuscript proposes a hybrid technique for an energy management system (EMS) with a renewable energy system and energy storage system using the smart micro grid. This work's novelty liesin the integration of Flying Foxes Optimization (FFO) algorithm and Deep Attention Dilated Residual Convolutional Neural Network (DADRCNN). Unlike existing methods, the FFO-DADRCNN method effectively leverages both optimization and predictive modelling capabilities to enhance grid stability, efficiency, and load prediction accuracy. The main goals of the proposed approach are to increase grid stability and efficiency, decrease micro grid system costs, and increase load prediction accuracy. The micro grid system incorporates PV, an energy storage system, wind turbine and load demand. To regulate the micro grid system and estimate load utilizing the DADRCNN most effectively, the proposed FFO technique is used. The proposed method's performance is evaluated using the MATLAB platform and compared with existing approaches, including Giza Pyramids Construction, Heap-based optimizer, and Nomadic People Optimizer. The proposed technique shows better outcomes when compared to other methods. Compared to other methods currently available, our proposed approach boasts an impressive 98 % efficiency rate and comes at a low operating cost of $1840.

Preface

We are glad to introduce you that the 2024 International Conference on Smart Grids and Power Systems (ICSGPS) was successfully held on January 12–14, 2024 in Zhangjiajie, China. The vision of the ICSGPS 2024 is to provide a premier interdisciplinary platform for researchers, practitioners, and educators to come together and discuss the latest innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of Smart Grids and Power Systems. At ICSGPS 2024, participants have the opportunity to present their research findings and engage in meaningful discussions with experts and scholars from universities and research institutions worldwide. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches and Q&A discussion. There were 3 keynote speeches, 4 invited talks both from local and international presenters. The time spent delivering the talks by each keynote speaker was 45 minutes, the oral presentation speaker was 15 minutes following by a Question Answer (Q & A) Session 5 minutes for each. Thanks for wonderful speeches from our keynote speakers Professor Loi Lei Lai, Professor Fushuan Wen and Professor Zhaoyang Dong. ICSGPS 2024 has attracted participants from over 40 universities, their support is greatly appreciated. We would also like to thank all the members of the Technical Program Committee and the Organizing Committee for providing competent, efficient, and friendly help that made this conference success. We also appreciate for all the reviewers for their hard work in providing constructive critical comments, improvements and corrections to the authors. A particularly thank for Professor Wenxuan Yao from Hunan University and Professor Chengxi Liu from Wuhan University for their valuable advice as guest editors to ensure the quality of the submissions. Moreover, thanks for the excellent publishing service from Journal of physics: Conference Series. Finally, many thanks for all the participants and we wish you enjoyed and were inspired by the conference. We are convinced that this gathering of minds will foster collaboration and knowledge sharing, enabling participants to gain valuable insights into practical challenges encountered. The Committee of ICSGPS 2024 List of Committee Member and Workshops are available in this pdf.

Empowering Bangladesh: A Study on Synergizing Renewable Energy and Smart Grid Technologies for Sustainable Development

This paper shows a study of the Smart Grid in Bangladesh, emphasizing the incorporation of renewable energy sources. Its aim is to provide an overview of global renewable energy demand and highlight the significance of a smart grid system in fostering sustainability and dropping CO2 emissions.The total renewable energy generation of Bangladesh stood at a meager 1.34% in 2009, representing its low comparative position with other nations. Unfortunately, there has been a little improvement over time, as the total renewable energy generation was 1.51% in 2014 and only slightly increased to 1.57% by 2019. To overcome the addressed issue, it requires massive potential of renewable energy sources to ensure a lucrative, secure, and high-quality supply. This can be achieved through effective monitoring and control mechanisms within the grid system.The integration of cutting-edge technologies such as bi-directional current flow, bi-directional communication systems, automatic working capacity, automatic control systems, and rear systems are required if the upgradation of the current power structure is needed. These advancements will contribute to the enhanced performance, reliability, and security of the renewable energy infrastructure. By embracing a smart grid system and optimizing the integration of renewable energy, Bangladesh can pave the way towards a sustainable future. This will not only boost the country's renewable energy generation but also play a vital role in mitigating CO2 emissions. IUBAT Review—A Multidisciplinary Academic Journal, 7(1): 26-44

Research Status of China's Digital Economy in the Context of Achieving China's Carbon Peak and Carbon Neutrality Goals

This paper studies the positive or negative impacts of the development of digital economy on China under the policy background of carbon peaking and carbon neutrality. First of all, it predicts the future under the rapid development of digital economy based on different literature materials. The increasing demand for calculation, storage and flow data of communication technology as well as the fixed network iteration are influential factors to make an analysis of the overall increase in carbon emissions, and then update measures taken by different enterprises under this policy, such as high-carbon enterprises to develop more clean energy and improve smart grid systems. Low-carbon industries pay more attention to the greening of the entire production line and the digital tracking of products after sale in order to achieve a more effective circular economy. The highlight of this paper lies in the lack of literature on the integration of emission reduction measures for enterprises of different modes based on the increase of carbon emissions brought by the future digital economy, and gives relevant examples.

Wind and photovoltaic systems in sustainable energy mixes: Cost-effective integration approaches

This study introduces an innovative methodology for optimizing the renewable energy sources (RES) mix, specifically wind-based distributed generation (WDG) and photovoltaic distributed generation (PVDG), in smart grids to enhance sustainability and efficiency. Our primary aim is to minimize total system costs, encompassing capital, operational, and maintenance expenses for RES units, alongside grid energy purchases. This methodology incorporates probabilistic models for each generation technology, energy prices, and demand. These models are integrated into a comprehensive multi-state generation-load-price model, addressing uncertainties and enhancing smart grid operations through real-time control. By leveraging the communication infrastructure of smart grids, the approach optimizes investments and boosts RES penetration during the planning phase. Simulation results from a representative distribution system demonstrate the efficacy of this approach in augmenting RES integration within the energy mix and maximizing investment returns. Compared to traditional RES planning, our methodology achieves a significant 2.47-fold increase in RES intake with minimal energy curtailment (8.38 %) and a notable 28.3 % reduction in total costs. These outcomes highlight the potential of our planning approach to significantly enhance the sustainability and economic viability of smart grid systems, proving effective in managing all conceivable system conditions and optimizing the energy mix. This study underscores the benefits of incorporating advanced probabilistic models and real-time control in smart grid planning to meet sustainability goals effectively.

Kernel-based cumulative sum and differential cumulative sum approaches for resilient fault detection in LVDC microgrids

The energy industry is rapidly shifting from conventional to smart grid systems, involving advancements in microgrids and power management systems. However, achieving desired outcomes with the existing grid setup poses significant challenges. To address the above issues, adopting a Low Voltage Direct Current (LVDC) distribution system provides successful operational advantages. The primary objective of this work is to improve the system's security by developing a more resilient fault detection scheme. The presented work has proposed a Kernel-based Cumulative Sum (K-CUSUM) and Kernel-based Differential Cumulative Sum (K-DCUSUM) technique which is designed to identify changes in data under fewer assumptions. Instead of using the classical Maximum Mean Discrepancy (MMD) technique, the K-CUSUM and K-DCUSUM methods use a non-parametric MMD testing framework to evaluate incoming data by comparing it to reference distribution samples. Further, a comprehensive analysis is undertaken on an LVDC test system, considering fault scenarios involving pole-to-pole and pole-to-ground configurations. This research encompassed the determination of fault location, evaluation of fault resistance, examination of noise introduction, and an exploration of the influence of photovoltaic (PV) penetration. Moreover, the proposed approach is tested for a standard LVDC Microgrid. The results are compared with other contemporary techniques and recent literature.

Revolutionizing smart grid-ready management systems: A holistic framework for optimal grid reliability

Existing energy management systems are becoming increasingly insecure and inefficient due to the rapid adoption of smart grid technology. Current research indicates that effectively managing dynamic energy flows, adjusting to changing needs, and protecting against new cyber threats remain significant challenges for the smart grid system. An advanced and comprehensive plan for managing smart grids is therefore required, capable of addressing these delicate and multifaceted problems. The proposed framework addresses these challenges through unifying several key aspects, it includes an advanced data acquisition system that captures real-time data from various grid sources, enabling comprehensive energy monitoring and dynamic flow analysis. By integrating predictive algorithms, the framework provides precise energy demand forecasting, which is essential for adaptive grid management. A significant contribution is the incorporation of an AI-based module for diagnostics and prognostics, which leverages machine learning techniques to shift from reactive to proactive maintenance strategies. The optimal power flow (OPF) optimization module represents a central component of the framework. It employs advanced computational methods to ensure efficient and cost-effective power distribution, particularly in grids incorporating renewable energy sources. Additionally, the architectural framework is strengthened by a robust cybersecurity module designed to safeguard against a wide range of cyber threats, maintaining the integrity of both operational and consumer data. This paper also addresses practical implementation challenges such as compatibility with existing infrastructure, investment costs, and the need for specialized training. This solution represents a new benchmark for smart grid operations, ensuring more sustainable and efficient energy systems.