Artificial Intelligence Techniques in Smart Grid: A Survey

Intelligent Monitoring Technology of Partial Discharge Based on an Integrated Sensing-Memory-Computation System

Discharge phenomena, from electrostatic discharges in integrated circuits to lightning in the atmosphere, are pervasive and can lead to economic consequences in the electronic industry. Among these, partial discharge (PD) is a critical factor causing insulation damage in power equipment, significantly impacting power networks. Moreover, due to the trend of digitization, the number of pieces of power equipment has significantly increased. Real-time online monitoring of equipment can alleviate the impact of unavoidable PD events, and its necessity is significantly enhanced. In response, co-designing advanced materials, non-volatile memory (NVM), and software (SW) for discharge monitoring offers a compelling solution. This is achieved through an integrated sensing-memory-computation system. This review thoroughly examines different technologies used for monitoring PD, focusing on how sensing, memory, and computation play a role. It also looks at the possible difficulties and future expectations when SW and hardware technologies are combined in these systems. The solution to the three challenges of energy consumption, dedicated architecture, and circuit aging in integrated systems will greatly improve the energy efficiency, processing speed, and service life of intelligent monitoring systems. Eventually, this provides a feasible strategy for designing systems that can efficiently reduce the negative impacts of PD through smart monitoring and analysis.

Interpretable adaptive fault detection method for smart grid based on belief rule base

An effective fault detection strategy has always been the focus of smart grid system research. Fast and accurate fault detection is the basis for complex systems to maintain reliability and security. However, traditional fault detection methods often ignore the interpretability of the model while pursuing high detection accuracy. Complex models can usually provide higher detection accuracy, but often lack transparency and interpretability, making it difficult for operators to understand and trust the detection results of the model. Therefore, a new fault detection strategy based on an adaptive interpretable belief rule base (AI-BRB) is proposed. This method considers the adaptive updating of the search domain of the model accuracy to achieve the balance and optimization between the two conflicting objectives of the model interpretability and the detection accuracy. The fault detection model based on AI-BRB considers the interpretability of modeling, inference and optimization processes. In the optimization process, interpretability constraints are added to maintain the interpretability of the optimized model. In addition, in order to avoid falling into the local optimal solution in the optimization process, the search domain is updated adaptively according to the accuracy of the model, which improves the interpretability and robustness of the fault detection model. Finally, an example is given to prove that the proposed method can improve the accuracy of fault detection and the interpretability of the model compared with the existing methods.

Improving micro-grid management: A review of integration of supercapacitor across different operating modes

The field of electrical energy is moving through many rapid changes and continuous development, especially with the huge boom in the field of artificial intelligence (AI) and the presence of renewable energy systems (RESs). Simultaneously with this development, it was necessary to have energy storage systems such as super capacitors and batteries. This necessity increased with sudden changes in climate or loads that affect the output of generation systems such as solar and wind energy systems. The main goal of this study is to focus on supercapacitors as a future solution with high reliability. It studies all the various factors and operational modes such as the principle of operation, installation, types and modeling of supercapacitors. The study also focused on the employment of supercapacitors in diverse applications, especially in microgrids with their various systems including control and energy management strategies for these systems and investigating the integration between AI, supercapacitors and microgrids and the outcome of this integration. Besides, this research intends to a proposal for a hybrid energy storage systems (HESS) suitable for different loads, including solar cells as main source with supercapacitors and batteries. In addition, it proposes the energy management system (EMS) as well as appropriate control system, to solve the problems of utilities and microgrids and meet the power demand of loads for different operating conditions.

Data-driven and privacy-preserving risk assessment method based on federated learning for smart grids

Timely and precise security risk evaluation is essential for optimal operational planning, threat detection, and the reliable operation of smart grid. The smart grid can integrate extensive high-dimensional operational data. However, conventional risk assessment techniques often struggle with managing such data volumes. Moreover, many methods use centralized evaluation, potentially neglecting privacy issues. Additionally, Power grid operators are often reluctant to share sensitive risk-related data due to privacy concerns. Here we introduce a data-driven and privacy-preserving risk assessment method that safeguards Power grid operators' data privacy by integrating deep learning and secure encryption in a federated learning framework. The method involves: (1) developing a two-tier risk indicator system and an expanded dataset; (2) using a deep convolutional neural network -based model to analyze the relationship between system variables and risk levels; and (3) creating a secure, federated risk assessment protocol with homomorphic encryption to protect model parameters during training. Experiments on IEEE 14-bus and IEEE 118-bus systems show that our approach ensures high assessment accuracy and data privacy.

Study of smart grid cyber-security, examining architectures, communication networks, cyber-attacks, countermeasure techniques, and challenges

Smart Grid (SG) technology utilizes advanced network communication and monitoring technologies to manage and regulate electricity generation and transport. However, this increased reliance on technology and connectivity also introduces new vulnerabilities, making SG communication networks susceptible to large-scale attacks. While previous surveys have mainly provided high-level overviews of SG architecture, our analysis goes further by presenting a comprehensive architectural diagram encompassing key SG components and communication links. This holistic view enhances understanding of potential cyber threats and enables systematic cyber risk assessment for SGs. Additionally, we propose a taxonomy of various cyberattack types based on their targets and methods, offering detailed insights into vulnerabilities. Unlike other reviews focused narrowly on protection and detection, our proposed categorization covers all five functions of the National Institute of Standards and Technology cybersecurity framework. This delivers a broad perspective to help organizations implement balanced and robust security. Consequently, we have identified critical research gaps, especially regarding response and recovery mechanisms. This underscores the need for further investigation to bolster SG cybersecurity. These research needs, among others, are highlighted as open issues in our concluding section.

Security Baseline for Substation Automation Systems

The use of information technology and the automation of control systems in the energy sector enables a more efficient transmission and distribution of electricity. However, in addition to the many benefits that the deployment of intelligent and largely autonomous systems brings, it also carries risks associated with information and cyber security breaches. Technology systems form a specific and critical communication infrastructure, in which powerful control elements integrating IoT principles and IED devices are present. It also contains intelligent access control systems such as RTU, IDE, HMI, and SCADA systems that provide communication with the data and control center on the outer perimeter. Therefore, the key question is how to comprehensively protect these specialized systems and how to approach security implementation projects in this area. To establish rules, procedures, and techniques to ensure the cyber security of smart grid control systems in the energy sector, it is necessary to understand the security threats and bring appropriate measures to ensure the security of energy distribution. Given the use of a wide range of information and industrial technologies, it is difficult to protect energy distribution systems using standard constraints to protect common IT technologies and business processes. Therefore, as part of a comprehensive approach to cyber security, specifics such as legislative framework, technological constraints, international standards, specialized protocols or company processes, and many others need to be considered. Therefore, the key question is how to comprehensively protect these specialized systems and how to approach security implementation projects in this area. In this article, a basic security concept for control systems of power stations, which are part of the power transmission and distribution system, is presented based on the Smart Grid domain model with emphasis on substation intelligence, according to the Purdue model. The main contribution of the paper is the comprehensive design of mitigation measures divided into mandatory and recommended implementation based on the standards defined within the MITRE ATT&CK matrix specified, concerning the specifications of intelligent distribution substations. The proposed and industry-tested solution is mapped to meet the international security standards ISO 27001 and national legislation reflecting the requirements of NIS2. This ensures that the security requirements will be met when implementing the proposed Security Baseline.

Smart grid energy scheduling based on improved dynamic programming algorithm and LSTM

The optimal scheduling of energy in a smart grid is crucial to the energy consumption of the entire grid. In fact, for larger grids, intelligent scheduling may result in substantial energy savings. Herein, we introduce an enhanced dynamic programming algorithm (DPA) that utilizes two state variables to derive the optimal power supply schedule. The algorithm accounts for the dynamic states of both batteries and supercapacitors in the power supply system to augment the performance of the dynamic programming model. Additionally, this study incorporates a long short-term memory (LSTM) deep learning model, which integrates various environmental factors such as temperature, humidity, wind, and precipitation to predict grid power consumption. This serves as a mid-point pre-processing step for smart grid energy consumption scheduling. Our simulation experiments confirm that the proposed method significantly reduces energy consumption, surpassing similar grid energy consumption scheduling algorithms. This is critical for the establishment of smart grids and the reduction of energy consumption and emissions.

Integrating a Blockchain-Based Governance Framework for Responsible AI

This research paper reviews the potential of smart contracts for responsible AI with a focus on frameworks, hardware, energy efficiency, and cyberattacks. Smart contracts are digital agreements that are executed by a blockchain, and they have the potential to revolutionize the way we conduct business by increasing transparency and trust. When it comes to responsible AI systems, smart contracts can play a crucial role in ensuring that the terms and conditions of the contract are fair and transparent as well as that any automated decision-making is explainable and auditable. Furthermore, the energy consumption of blockchain networks has been a matter of concern; this article explores the energy efficiency element of smart contracts. Energy efficiency in smart contracts may be enhanced by the use of techniques such as off-chain processing and sharding. The study emphasises the need for careful auditing and testing of smart contract code in order to protect against cyberattacks along with the use of secure libraries and frameworks to lessen the likelihood of smart contract vulnerabilities.

Control and Optimisation of Power Grids Using Smart Meter Data: A Review

This paper provides a comprehensive review of the applications of smart meters in the control and optimisation of power grids to support a smooth energy transition towards the renewable energy future. The smart grids become more complicated due to the presence of small-scale low inertia generators and the implementation of electric vehicles (EVs), which are mainly based on intermittent and variable renewable energy resources. Optimal and reliable operation of this environment using conventional model-based approaches is very difficult. Advancements in measurement and communication technologies have brought the opportunity of collecting temporal or real-time data from prosumers through Advanced Metering Infrastructure (AMI). Smart metering brings the potential of applying data-driven algorithms for different power system operations and planning services, such as infrastructure sizing and upgrade and generation forecasting. It can also be used for demand-side management, especially in the presence of new technologies such as EVs, 5G/6G networks and cloud computing. These algorithms face privacy-preserving and cybersecurity challenges that need to be well addressed. This article surveys the state-of-the-art of each of these topics, reviewing applications, challenges and opportunities of using smart meters to address them. It also stipulates the challenges that smart grids present to smart meters and the benefits that smart meters can bring to smart grids. Furthermore, the paper is concluded with some expected future directions and potential research questions for smart meters, smart grids and their interplay.

A Comprehensive Analysis of Smart Grid Stability Prediction along with Explainable Artificial Intelligence

As the backbone of modern society and industry, the need for a more efficient and sustainable electrical grid is crucial for proper energy management. Governments have recognized this need and have included energy management as a key component of their plans. Decentralized Smart Grid Control (DSGC) is a new approach that aims to improve demand response without the need for major infrastructure upgrades. This is achieved by linking the price of electricity to the frequency of the grid. While DSGC solutions offer benefits, they also involve several simplifying assumptions. In this proposed study, an enhanced analysis will be conducted to investigate how data analytics can be used to remove these simplifications and provide a more detailed understanding of the system. The proposed data-mining strategy will use detailed feature engineering and explainable artificial intelligence-based models using a public dataset. The dataset will be analyzed using both classification and regression techniques. The results of the study will differ from previous literature in the ways in which the problem is handled and the performance of the proposed models. The findings of the study are expected to provide valuable insights for energy management-based organizations, as it will maintain a high level of symmetry between smart grid stability and demand-side management. The proposed model will have the potential to enhance the overall performance and efficiency of the energy management system.

Intelligent based hybrid renewable energy resources forecasting and real time power demand management system for resilient energy systems

The rapid growth of hybrid renewable Distributed Energy Resources (DERs) generation possess various challenges with inaccurate forecast models in stochastic power systems. The prime objective of this research is to maximum utilization of scheduled power from hybrid renewable based DERs to maintain the load-demand profile with reduce distributed grid burden. The proposed mixed input-based cascaded artificial neural network ( C A N N M F ) is realized for the prediction of a short-term based hourly solar irradiance and wind speed. The testing approach is performed through a historical hourly dataset of the proposed site. Further, the normalized data sets are divided into hourly-based samples for validating the load demand power with respect to the variation in metrological data. In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) model is simulated for short-term power demand prediction. This adaptive methodology is an effective approach for load-demand management which is based on cross-entropy. It also confirmed that during testing, the forecasting mean error and cross-entropy are less than 5% under a specific time slap of an individual day. The regression analysis is performed through the time series fitting simulation tool at different time horizons. The performance evaluation of the designed model is compared with the multi-layer perceptron model. Simulation results display the proposed mixed input-based cascaded system has enhanced accuracy and optimal performance than the multi-output correlated perceptron model.

Review on Interpretable Machine Learning in Smart Grid

In recent years, machine learning, especially deep learning, has developed rapidly and has shown remarkable performance in many tasks of the smart grid field. The representation ability of machine learning algorithms is greatly improved, but with the increase of model complexity, the interpretability of machine learning algorithms is worse. The smart grid is a critical infrastructure area, so machine learning models involving it must be interpretable in order to increase user trust and improve system reliability. Unfortunately, the black-box nature of most machine learning models remains unresolved, and many decisions of intelligent systems still lack explanation. In this paper, we elaborate on the definition, motivations, properties, and classification of interpretability. In addition, we review the relevant literature addressing interpretability for smart grid applications. Finally, we discuss the future research directions of interpretable machine learning in the smart grid.

The Dual Role of Artificial Intelligence in Developing Smart Cities

Defining smart city pillars, and their nature and essence, continues to be debated in the scientific literature. The vast amount of information collected by electronic devices, often regarded merely as a means of rationalizing the use of resources and improving efficiency, could also be considered as a pillar. Information by itself cannot be deciphered or understood without analysis performed by algorithms based on Artificial Intelligence. Such analysis extracts new forms of knowledge in the shape of correlations and patterns used to support the decision-making processes associated with governance and, ultimately, to define new policies. Alongside information, energy plays a crucial role in smart cities as many activities that lead to growth in the economy and employment depend on this pillar. As a result, it is crucial to highlight the link between energy and the algorithms able to plan and forecast the energy consumption of smart cities. The result of this paper consists in the highlighting of how AI and information together can be legitimately considered foundational pillars of smart cities only when their real impact, or value, has been assessed. Furthermore, Artificial Intelligence can be deployed to support smart grids, electric vehicles, and smart buildings by providing techniques and methods to enhance their innovative value and measured efficiency.

Optimal Placement of UDAP in Advanced Metering Infrastructure for Smart Metering of Electrical Energy Based on Graph Theory

This paper presents an algorithm to optimize the deployment of hubs for smart energy metering based on the Internet of Things. A georeferenced scenario is proposed in which each user must connect to a concentrator, either directly or through another user, minimizing the resources required to achieve connectivity. Consequently, to carry out the optimization, the minimum spanning tree between devices is found, in which the maximum connection distance and the capacity of the hubs are limited. Additionally, this work seeks to achieve a scalable algorithm applicable to any georeferenced scenario to be simulated. The main contribution of this work is an IoT-based smart metering architecture that optimizes resources and adapts to a scenario that changes or integrates more users to the energy metering network without losing the connectivity of the initial users. As a result of the application of the algorithm, a scenario route map is generated. The scenario’s parameters include the number of hops in the network, the optimal number of concentrators and their geographical location, the average number of hops, and the total distance of the path, among others. In this project, a georeferenced urban scenario was considered in which residential areas coexist with intelligent buildings. The scenario has growth stages in which the algorithm is applied, and in each one, the optimal route map is generated.

A Comprehensive Survey for Deep-Learning-Based Abnormality Detection in Smart Grids with Multimodal Image Data

In this paper, we provide a comprehensive survey of the recent advances in abnormality detection in smart grids using multimodal image data, which include visible light, infrared, and optical satellite images. The applications in visible light and infrared images, enabling abnormality detection at short range, further include several typical applications in intelligent sensors deployed in smart grids, while optical satellite image data focus on abnormality detection from a large distance. Moreover, the literature in each aspect is organized according to the considered techniques. In addition, several key methodologies and conditions for applying these techniques to abnormality detection are identified to help determine whether to use deep learning and which kind of learning techniques to use. Traditional approaches are also summarized together with their performance comparison with deep-learning-based approaches, based on which the necessity, seen in the surveyed literature, of adopting image-data-based abnormality detection is clarified. Overall, this comprehensive survey categorizes and carefully summarizes insights from representative papers in this field, which will widely benefit practitioners and academic researchers.