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This study investigates the application of Deep Convolutional Neural Networks (DCNNs) in power system signal processing. The research addresses the growing challenges in modern power systems, including increased complexity and data volume. We comprehensively analyze DCNN-based methods for electric load forecasting, fault diagnosis, and power quality assessment. Through extensive experiments and case studies, we demonstrate that DCNNs consistently outperform traditional approaches in accuracy, real-time performance, and robustness. The study explores various DCNN architectures and proposes improvements tailored to power system characteristics. Results show significant enhancements in prediction accuracy and processing speed across different tasks. While challenges such as model interpretability remain, the findings highlight the potential of DCNNs to revolutionize power system signal processing. This research contributes to advancing intelligent power system management and provides a foundation for future developments in smart grid technologies.

Smart Grid (SG) has promoted a new round of technological changes in the electric power field. As various smart devices are connected to the network, real-time collection of power data makes it possible to accurately grasp the operating status of the power grid. When managing huge data in smart grids, we face problems such as the inability of third-party cloud storage to meet data privacy requirements, the high cost of data sharing between different systems, and the lack of effective sharing incentive mechanisms. In view of the problem that third-party cloud storage solutions cannot meet the problem of data security storage and privacy protection, we studied a smart grid power data storage solution based on blockchain. Use hash calculation on the original power data to obtain a fixed-length hash summary, and then upload the hash summary to the blockchain for storage. The original power data and summary information are stored on IPFS off-chain. We designed smart contracts to implement user identity authentication and query log records to reflect the power data circulation process. In particular, we design a Byzantine Fault Tolerance protocol (Aggregate-signature Byzantine Fault Tolerance, ABFT) based on aggregate signatures. This protocol reduces communication complexity by adjusting the node broadcast method, and is more suitable for use in scenarios with many nodes. A large number of experimental results show that the solution we proposed can resist common attack types, and has a greater improvement in data storage overhead, average delay and throughput compared with similar solutions.

The incorporation of Information and Communication Technologies (ICT) into traditional power grids has transformed them into smart grids, revolutionizing energy management systems. At the core of this transformation are Intelligent Electronic Devices (IEDs), which provide essential data for key Energy Management System (EMS) applications, such as state estimation and optimal power flow. IEDs are critical for ensuring the stability and security of smart grid operations, but they are also vulnerable to various anomalies, including infrastructure faults, equipment malfunctions, energy theft, and cyberattacks. Detecting these anomalies is vital to maintaining the reliability of smart grid systems and preventing potential threats to national security. This study introduces a statistical data-driven framework designed to detect and explain anomalies in IED-based smart grid systems. The framework includes a preprocessing module for ensuring high-quality input data and an anomaly detection module that prioritizes interpretability and explainability. Using methods like the Gaussian Mixture Model (GMM), Kalman Filter (KF), and ExtraTree Classifier, the framework achieves 99% accuracy in anomaly detection and 88% accuracy in classifying events as either natural occurrences or cyberattacks.

Smart Grid Cyber Physical Systems (SG-CPS) have a substantial impact on power grid infrastructure upgrading. Nonetheless, due to the sophisticated nature of the infrastructure and the critical demand for resilient intrusion prevention systems, the task of protecting its security against data integrity attacks is a significant challenge. The simulation and assessment of security performance in SG-CPS present substantial hurdles in real-world power grid systems, owing mostly to experimental constraints. This necessitates the development of novel ways to improve distribution chain security. This research introduces a novel approach, employing a Deep Adversarial Probabilistic Neural Network (DAPNN)-based Intrusion prevention system in a cloud environment. Combining Bayesian Probabilistic Neural Networks (BEPNNs) with adversarial training and rule-based decision-making enhances precision and resilience. The major goal of this research is to detect and counteract false data injection (FDI) attacks that have the potential to compromise the integrity of power grid data. This paper proposes a novel methodology for intrusion detection in SG-CPS that combines BEPNNs with adversarial training. The addition of rule-based decision-making improves the system’s precision and resilience. The IEEE 24-bus system provides the foundation for providing data points relevant to normal operating conditions, contingency scenarios, and intentional attacks. The training procedure includes the use of a BEPNN for feature extraction, as well as the use of adversarial training approaches. The intrusion detection system has decision-making logic based on rules. The cloud infrastructure solution used in the study is Microsoft Azure. The results show that the DAPNN-based Intrusion Prevention System is effective in detecting and mitigating FDI attacks in SG-CPS. The system outperforms in terms of accuracy, precision, recall and F-measure, hence improving the security of the power grid infrastructure.

In the new generation of power grids, the smart grid (SG) integrates sophisticated characteristics, including situation awareness, two-way communication, and distributed energy supplies. Integrated SG uses various operational metrics, including devices with sensors, meters, and renewable power sources. There are several challenges when securely disposing and storing electricity data acquired from an SG. It is vulnerable to cyberattacks due to its digitization and integration of an increasing number of links. Issues with latency, security, privacy, and excessive bandwidth consumption will arise when this enormous amount of data is transmitted directly to the cloud. Edge computing (EC) solves this problem by moving data processing to the network’s periphery, close to the embedded devices. With improved data processing speeds, a more responsive and resilient grid may be achieved, instantly responding to energy demand and supply changes. EC reduces the volume of sensitive data sent to central servers, reducing potential security breaches. Data may be better protected from intrusions by being analyzed locally and only pertinent information transferred to the cloud. Thus, a blockchain is an intriguing SG paradigm solution with many benefits. The SG’s decentralization and improved cybersecurity have prompted a lot of work into using blockchain technology; since it is well-known that data saved in the blockchain is immutable, it is crucial to find foolproof ways to verify data are accurate and comply with high-quality standards before storing it in the blockchain. A practical solution for storing precise power data that enables the safe execution of adaptable transactions is a Cloud-Edge Fusion Blockchain model for the smart grid (CEFBM-SG). Consequently, the SG’s dependability, resilience, and scalability will be improved as the number of distributed energy sources (DERs) connected to it increases. Utilizing the idea of computing at the edge to enhance responsiveness and dependability. Executed security analyses and performance evaluations demonstrate CEFBM-SG’s exceptional security and efficiency.

Modern information and communication technologies are being incorporated into traditional power grid systems to create the smart grid of the real world. The newly provided information flow and intrinsic creation, transport, storage, and the use of electricity are all facilitated by the energy transfer, especially as the complete deployment of the Internet of Things in the power grid, also known as the power Internet of Things (PIoT). These new 5G technologies and the value generated by novel services and market processes can all be used to maximize the value of scarce resources like energy. This paper develops a framework for a cyber-physical power system based on IoT (CPPS-IoT). Automobiles, aircraft, defense, factory equipment, wellness equipment, industrial control, connected cars, and other sectors and industries are all benefiting from the fast growth of CPS technology. A smart electric grid is created when dispersed sources of energy and electrical infrastructure are linked together to provide global exchange of information, sensible decision, including true flexible control using the CPPS. Cyber-physical systems have great benefits because they combine IoT with physical processes and mediate how humans interact with the natural environment. Cyber-physical systems use sensor networks and embedded computers to keep tabs on and manipulate the physical world around them. They include built-in feedback loops that enable the environment to trigger their communication, control, or processing. The proposed method makes systems safer and more efficient, decreasing the cost of developing and running these systems. The accuracy is 89%, and the proposed method’s error rate is 48%.

The home network is one of the emerging areas from the last century. However, the growth of the home network market is stationary at present. This paper describes the limitations of the home network system and the requirements for overcoming the current limitations. Also described is a new home network service system known as COWS and its easy installation and scalable operation. COWS consists of power consumption monitor and control devices along with a service server that is a complementary combination of Open Service Gateway initiative (OSGi) and web services. A home network system has a dynamic, heterogeneous, distributed, and scalable topology. Service Oriented Architecture (SOA) has been proposed as a solution that satisfies the requirement of a home network, and OSGi and web services are two successful SOA-based frameworks. An included service server has a flexible architecture that consists of a core and extendable service packages. A power consumption monitor and control function provides useful context information for activity-based context-aware services and optimizes the power consumption. The system can be installed easily into existing and new houses to solve the current barrier of the popularization of home network services.

The world is witnessing a sudden shift in the paradigm of technology moving from centralized to decentralized approach. Centralized approach leads to single point of failure if any fault occurs and hence a whole system comes to rest. Hence, a decentralized approach like Multi-Agent System is trending now-a-days. A MAS is a collection of a number software entities (agents) working together in pursuit of specified tasks. This paper presents a comprehensive review on various aspects of multi-agent system. The paper explains the basic concepts of MAS with various ways it has been defined in literature. A comparison has been made on the standards to be followed for applying MAS. Classification of MAS architecture has been investigated and compared. Application of MAS in various areas of optimization technique, software platform and real-time simulation are listed in the paper. The paper draws attentions toward benefits and limitations of using MAS based on the survey done. Finally, after visualizing the wide scope of research in the field of MAS, an attempt has been made to identify future research avenues.

The world is witnessing a transformation from the conventional electrical grid into the smart grid. The smart grid can provide an effective solution to alarming problems associated with a conventional grid with increased reliability, efficiency, and sustainability. Integration of distributed energy resources (DERs) comprising of renewable energy sources (RESs) is a vital component of the smart grid. DERs not only can provide a viable solution for environmental concerns arising due to conventional fossil fuel-based plants, but can also contribute towards the system reliability. However, the integration of DERs is associated with several challenges.  Thus, the successful deployment of DERs in smart grid framework calls for a comprehensive analysis. This paper presents an exhaustive review of various challenges associated with increased penetration of DERs. An organized classification of various technical challenges along with their mitigation measures has been critically reviewed. Smart inverters equipped with advanced control structure are emerging as a potential solution to address these challenges effectively. Hence, a review of smart inverter along with its functional capabilities has also been discussed in this paper.

Electric vehicles play a key role in the transition to an environmental-friendly transportation system and can meanwhile enhance the power system’s evolution to the smart grid. With the adoption of dynamic pricing and usage scheduling enabled by the smart grid equipment, a variety of smart charging strategies have been designed to make the most of flexibility contained in their considerable electricity demand, whereas less effort is devoted to users’ willingness to participate. In this paper, we model a noncooperative pricing game between two types of charging stations. One offers conventional fast charging and the other uses the electric vehicles’ onboard batteries to provide regulation service to the grid. With drivers’ risk attitudes and bounded rationality taken into consideration, we design a prospect theory-based decision model to calculate the proportion of users that would go for the regulation-providing charging option. The decision model of the customer base is a critical determinant of profitability and it enables two competitors to strategically set their prices that optimally balance between gaining in market share and growing in profit per client. We prove the existence of a pure strategy Nash equilibrium for the game proposed and compute the equilibrium prices in different circumstances with respect to market settings and user segments. A comprehensive analysis of the results gives insights into the key factors at play and provides the grid operators with indications of how to increase the penetration of electric vehicles in the ancillary service market.

Distributed Constraint Optimization Problem (DCOP) is a powerful paradigm to model multi-agent systems through enabling multiple agents to coordinate with each other to solve a problem. These agents are often assumed to be cooperative, that is, they communicate with other agents in order to optimize a global objective. However, the communication times between all pairs of agents are assumed to be identical in the evaluation of most DCOP algorithms. This assumption is impractical in almost all real-world applications. In this paper, we study the impact of empirically evaluating a DCOP algorithm under the assumption that communication times between pairs of agents can vary. In addition, we evaluate a DCOP algorithm using ns-2, a discrete-event simulator that is widely used in the computer networking community, to simulate the communication times, as opposed to the standard DCOP simulators that are used to evaluate DCOP algorithms in the AI community. Furthermore, we propose heuristics that exploit the non-uniform communication times to speed up DCOP algorithms that operate on pseudo-trees. Our empirical results demonstrate that the proposed heuristics improve the runtime of those algorithms up to 20%. These heuristics are evaluated on different benchmarks such as scale-free graphs, random graphs, and an instance of the smart grid, Customer-Driven Microgrid (CDMG) application.

This paper is a review of uncertainty modeling techniques used in smart grid studies. The literature dealing with uncertainty from various sources in smart grid is analyzed and presented. In a modern power grid, the risk may arise due to different reasons; in-termittent renewable energy sources, uncertain consumer reactions on demand response, driving patterns of electric vehicles, etc. The paper has two objectives. First is to bring out the trends in uncertainty handling techniques used in electrical power system problems, and second to introduce the scope of new risk processing techniques with the perspective of recent smart grid issues.

Peer-to-peer electricity transaction is predicted to play a substantial role in research into future power infrastructures as energy consumption in intelligent microgrids increases. However, the on-demand usage of Energy is a major issue for families to obtain the best cost. This article provides a machine learning predictive power trading framework for supporting distributed power resources in real-time, day-to-day monitoring, and generating schedules. Furthermore, the energy optimization algorithm used in machine learning (EOA-ML) is proposed in this article. The machine learning-based platform suggested two modules: fuel trading and intelligent contracts based on machine learning implemented predictive analytical components. The Blockchain module enables peers to track energy use in real-time, manage electricity trading, model rewards, and irreversible transaction records of electricity trading. A predictive analysis component based on previous power usage data is designed to anticipate short-term energy usage in the Intelligent Contracts. This study utilizes data from the provincial Jeju, Korea’s electricity department on true energy utilization. This study seeks to establish optimal electricity flow and crowdsourced, promoting electricity between consumers and prosumers. Power trading relies on day-to-day, practical environmental control and the planning of decentralized power capitals to satisfy the demands of smart grids. Furthermore, it employs data mining technologies to obtain and study time-series research from the past electricity utilization data. Thus, the time series analytics promotes power controllingto better future efficient planning and managingelectricity supplies. It utilized numerous statistical methods to assess the effectiveness of the suggested prediction model, mean square error in different models of machine learning, recurring neural networks. The efficacy of the proposed system regarding the delay, throughput, and resource using hyperleader caliper is shown. Finally, the suggested approach is successfully applied for power crowdsourcing between prosumer and customer to reach service reliability based on trial findings. The actual and predicted cost analysis has been increased (95%). It minimizes the delay rate to (40.3%) by improving the efficiency rate.

A Smart Hybrid Home (SHH) system studies power concerns for individual clients with an off-grid electricity connection are studied in a Smart Hybrid Home (SHH) system. The creation of autonomous hybrid electricity systems has been regarded as a way to improve energy independence. The primary objective is to ensure efficient management of energy. Detailed modeling is proposed in which the solar energy component is regarded as the significant source to achieve this aim. A multi-agent energy management system (MA-EMS) is presented in this article. It also includes the storing devices for power (Fuel Cell/Capacitors) for safe power delivery. The technical performance of the decentralised solution is in line with the existing central solution, which offers improved financially and operationally for the implementation and operation of the autonomous method. Furthermore, an IT-management system is created and described to guarantee that the system functions correctly utilizing a multi-agent structure. The proposed electricity management system is meant to recognize multi-agent jobs and comprehend probable situations based on energy attributes and requirements. The findings showed that the method suggested meets the aims of an operational database taken from different climatic services using the Matlab/Simulink Application set for the Smart Electricity Management Method. By implementing the proposed technique the power consumption is increased to 410kw, and it improves the efficiency rate to 88.4%.

Unconditional security for smart grids is defined. Cryptanalyses of the watermarked security of smart grids indicate that watermarking cannot guarantee unconditional security unless the communication within the grid system is unconditionally secure. The successful attack against the dynamically watermarked smart grid remains valid even with the presence of internal noise from the grid. An open question arises if unconditionally authenticated secure communications within the grid, together with tamper resistance of the critical elements, are satisfactory conditions to provide unconditional security for the grid operation.

In this paper, two intelligent strategies for energy management unit for a home integrated with smart grid are proposed. The strategies are based on classical Boolean and genetic algorithm (GA). The objective is to optimize the cost saving for the end consumer. The price of energy varies by the hour depending on the load on the grid. The two strategies predict when and by how much the storage unit installed in the house should charge and release for 24 h of the day, satisfying the constraint that the load demand of the house at any particular hour should always be met. The strategies were tested by real time data collected by the Department of Energy for a typical house in the Chicago, Illinois region for the year 2013. Both the strategies achieve cost savings; however, it has been found that GA-based strategy results in higher cost saving. The impact of the capacity of the energy storage unit (ESU) on the cost saving has been analyzed for a GA strategy and cost saving obtained when the capacity of ESU is 1.5 times and 2 times the house hold load at any given hour is presented.

Accurate prediction of wind power is of vital importance for demand management. In this paper, we adopt a cluster-based ensemble framework to predict wind power. Natural groups/clusters exist in datasets and learning algorithms benefit from group/cluster wise learning — a philosophy that is not well explored for wind power prediction. The research presented in this paper investigates this philosophy to predict wind power by using an ensemble of regression models on natural clusters within wind data. We have conducted a series of experiments on a large number of locations across Australia and analyzed the existence of clusters within wind data, suitability of linear and nonlinear regression models for the proposed framework, and how well the cluster-based ensemble performs against the situation when no clustering is done. Experimental results demonstrate prediction improvement as high as 17.94% through the usage of the cluster-based ensemble regression algorithm.

The characteristics of the replication is investigated in this paper. By invoking the Potts model versus holographic superconductors for “gauge” versus “string”, the pair forms as a duality in natural manner. It can be shown that resulted characteristics of replication hence deserves to be called as an Autopoietic Smart Grid. Furthermore, we are able to trace the factors contributed to these characteristics; the autopoiesis is contributed via gauge self-energy; the surveillance is due to Maxwell’s demon; the organizational adaptivity is due to the communication capacity of the string and the oscillations of the gauge. Finally, it can also be shown that such a Smart Grid replication exists as long as the string is stable and gauge is synchronized.

In this paper, we revisit the research results of DAD, Daily Artificial Dispatcher, published in 2010 [S. Z. Stefanov, New Mathematics and Natural Computation6 (2010) 275–283], and give it new interpretation bring out the best of its meaning, as well as some more quantitative novel formula. In metaphorical sense, DAD is the analogue to DNA and hence the Smart Grid is analogue to the Creature by invoking the postmodern theory [J.-F. Lyotard, Moralités Postmodernes (Éditions Galilée, 1993) (in French)]. Specifically, the DAD’s binary expressions are generated by an innocent dialogue between DAD and Smart Grid in the form of world-strings. Namely, the living space-time of the DAD’s binary symbols is generated via a discussion between DAD and Smart Grid. Metaphorically speaking, DAD’s world is digitally described as a dramatic game and the Smart Grid as a creating cartoon loaded with an integral holographic complexity. Overall, the so called creatures capable of innocent discussions under near-zero temperature are generated by the epidemic growth of the Smart Grid cartoon. It is further concluded that the number of the Smart Grid creatures is inversely proportional to the half-time of the DAD’s binary “DNA” life cycle. Each Smart Grid can be coded in one of eight colors pool, as an aging in one of the four ways and as being in one of three possible development phases, dynamically. Finally, we take the liberty of calling the DAD’s world, a string prescribed as topology and landscape, to be “Aria”.

The origin of many power system issues are typically based on the electrical distribution systems as they are the tail ends of electric power systems. Whatever is embedded in distribution systems, its impact percolates in the whole power system. Different key technologies are available for distribution system performance improvement such as optimal distributed resources (DRs) allocation and network reconfiguration. The amount of actual benefit of these technologies depends on the modelling of load profile of the distribution system. The system load profile should be modelled by considering ground realities of distribution system such as allocation of dedicated feeders to different types of customers and load profile pattern of different category of customers. In this chapter, the DR allocation problem in radial distribution systems is modelled by considering a more realistic load profile of the system. Also, the consequences of ignoring load diversity among distribution buses on performance of distribution system are presented.