Smart Grid Applications Research Articles

Detecting anomalous electricity consumption with transformer and synthesized anomalies

Non-technical losses are consistently a troubling issue for power suppliers. With the application and popularization of smart grid and advanced measurement systems, it has become possible to use data-driven methods to detect anomalous electricity consumption to reduce non-technical losses. A range of machine learning models have been utilized for detecting anomalous electricity consumption and have achieved promising results. However, with the evolution of techniques like electricity theft, coupled with the exponential increase in electricity consumption data, new challenges are constantly being posed for anomalous electricity consumption detection. We propose a Transformer-based method for detecting anomalous electricity consumption. The Transformer is composed of multi-head attention, layer normalization, point-wise feed-forward network, etc., which can effectively handle electricity consumption time-series data. Meanwhile, to alleviate the problem of imbalanced training data between anomalous and normal electricity consumption, we propose a method for synthesizing anomalies. The experimental results demonstrate that our proposed Transformer-based method outperforms the state-of-the-art methods in detecting anomalous electricity consumption, achieving a precision of 93.9%, a recall of 96.3%, an F1-score of 0.951, and an accuracy of 95.6% on a dataset released by the State Grid Corporation of China.

The Use of Artificial Intelligent Applications In Electrical Power Distribution and Automation Control System

Artificial intelligence has provided great potential and space for the optimization of electrical engineering and it will bring about great improvement not only in economic aspect but also in safety and actual operation control. Since the advent of artificial intelligence it has been widely applied in all fields of life and achieved notable application effect its emergence has even pointed out the direction for the development of many fields and the field of electrical automation control is no exception. In reality, the application of artificial intelligence technology has greatly promoted the progress of the concrete practice of electrical automation and should be paid full attention to by related enterprises and staff. This paper aims to summarize the emergence and development of artificial intelligence and its components and finally summarize the advantages and functions of artificial intelligence in electrical automation control. Advances in machine learning and artificial intelligence (AI) techniques bring new opportunities to numerous intractable tasks for operation and control in modern electric distribution systems. Revolving around the characteristics of distribution system operation and integration of distributed energy resources this paper also illuminates prospects and challenges typified by the privacy explain ability and interpretability of such AI applications in smart grids. KEYWORDS: - Distribution systems, smart buildings, neural networks, Artificial Intelligence, Electrical Engineering, power systems.

Hartley Transform Signal Compression and Fast Power Quality Measurements for Smart Grid Application

Hartley Transform Signal Compression and Fast Power Quality Measurements for Smart Grid Application

Smart Grid Simulation with LEACH (Low-Energy Adaptive Clustering Hierarchy) Clustering

Power industry applications for smart grid are quite promising. In recent years, network life extension has attracted a lot of study attention. By examining the business needs of the Smart grid, this study enhances the wireless sensor network LEACH algorithm. To ensure the rationality of the cluster head selection, the remaining energy and node density are first taken into consideration. The cluster radius of various sizes is then constructed while clustering using a non-uniform clustering process. To achieve the goal of balancing the energy load, data is transmitted to the base station using the multi-hop mode, and the distance between the cluster head nodes and the remaining energy are utilised as the selection criteria for the next-hop relay node. In order to provide the computational intelligence required to create the Smart Grid, this paper discusses the future of the electric energy system, covering every aspect from power generation to substations, distribution, and customer feedback loops.

Comprehensive evaluation of lossless compression algorithms in a real use case for smart grid applications

The so-called “energy digitalization” is pervading the power and energy industry by providing many state-of-the-art digital technologies to collect, store, and analyze the very heterogeneous information managed in modern power systems. Requirements in terms of sampling frequency, temporal and user aggregation, measured quantities, measurement aggregations, involved players, and applications can be very different. Compression strategies, removing redundancy and over-specification in collected samples, can play a relevant role in efficiently move and store such heterogeneous data. In this work, a reference dataset from a real-world use case has been collected for comparing the performance of 62 lossless compression algorithms derived from the LZ77/LZSS/LZMA strategies. Compression of the six-day long acquisition, including active power measurements of a prosumer’s system equipped with a Photovoltaic (PV) and a Battery Energy Storage System, highlights the different obtainable performance when the aggregation interval is changed from 15 min to 24 h. In particular, once the top performing algorithms have been selected, it has been possible to stress the impact of night–day cycle, mainly due to the different sparsity of PV-related data. The obtained results demonstrate that, globally, the compression ratio increases by increasing the aggregation interval, by reaching values close to 9.7. In particular, when offline operation is tolerated, optimal compression schemes can be easily applied, leading to consistent improvement of the compression ratio (up to 24%, depending on the actual algorithm and aggregation interval), which can be very significant when large number of data sources is considered.

DP[formula omitted]-NILM: A distributed and privacy-preserving framework for non-intrusive load monitoring

Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household level into appliance-level consumption, can help analyze the electricity consumption behaviors of users and enable practical smart energy and smart grid applications. Recent studies have proposed many novel non-intrusive load monitoring frameworks based on federated deep learning. However, there is a lack of comprehensive research exploring the utility optimization schemes and the privacy-preserving schemes in different federated learning-based NILM application scenarios. In this study, a distributed and privacy-preserving non-intrusive load monitoring (DP2-NILM) framework was developed to make the first attempt to conduct federated learning-based NILM focusing on both utility optimization and privacy-preserving. Specifically, two alternative federated learning strategies are examined in the utility optimization schemes, i.e., the FedAvg and the FedProx. Moreover, different levels of privacy guarantees, i.e., the local differential privacy federated learning and the global differential privacy federated learning are provided in the DP2-NILM. Extensive comparison experiments are conducted on three real-world datasets to evaluate the proposed framework.

Experimental End-To-End Delay Analysis of LTE Cat-M With High-Rate Synchrophasor Communications

Micro-Phasor Measurement Units (μ-PMUs) are devices that permit monitoring voltage and current in the distribution grid with high accuracy, thus enabling a wide range of smart grid applications, such as state estimation, protection and control. These devices need to transmit the synchronous measurements of voltage and current, also known as synchrophasors, to the power utility control center at a high rate. The use of wireless networks, such as LTE, to transmit synchrophasor data is becoming increasingly popular. However, synchrophasors are included in small frames and it would be more efficient to use low-power cellular solutions, such as LTE cat-M. In this work, we present experimental research on the deployment of a μ-PMU with the ability to connect over a commercial LTE cat-M network. The deployed μ-PMU is built with off-the-shelf hardware, such as Arduino microcontrollers, and is used to transmit data—compliant with the IEEE C37.118.2 standard—at a variable rate from 1 frame/s to 80 frames/s. A detailed network performance analysis is carried out to show the suitability of LTE cat-M to support μ-PMU communications. Experimental results on performance indicators, such as delay and jitter, are reported. The effect of the LTE cat-M access mechanism on the time distribution of frame arrivals is also thoroughly analyzed.

Predictive Fraud Detection: An Intelligent Method for Internet of Smart Grid Things Systems

Today, grid resilience as a feature has become non-negotiable, significantly when power interruptions can impact the economy. The widespread popularity of Intelligent Electronic Devices (IED) operating as smart meters enables an immense amount of fine-grained electricity consumption data to be collected. However, risk can still exist in the Smart Grid (SG), as valuable data are exchanged among SG systems; theft or alteration of this data could violate consumer privacy. The Internet of Things for Smart Grid (IoSGT) is a promising ecosystem of different technologies that coordinate with each other to pave the way for new SG applications and services. As a use case of IoSGT for future SG applications and services, fraud detection, ıNon-technical losses (NTL), emerges as an important application for Smart Grid (SG) scenarios. A substantial amount of electrical energy is lost throughout the distribution system, and these losses are divided into two types: technical and non-technical. Non-technical losses (NTL) are any electrical energy consumed and not invoiced. They may occur due to illegal connections, issues with energy meters such as delay in the installation or reading errors, contaminated, defective, or non-adapted measuring equipment, very low valid consumption estimates, faulty connections, and disregarded customers. Non-technical losses are the primary cause of revenue loss in the SG. According to a recent study, electrical utilities lose $89.3 Billion per year due to non-technical losses. This article proposes ensemble predictor-based time series classifiers for NTL detection. The proposed predictor ministers the user’s energy consumption as a data input for classification, from splitting the data to executing the classifier.It encompasses the temporal aspects of energy consumption data during preprocessing, training, testing, and validation stages. The suggested predictor is Time Series (TS) oriented, from data splitting to the classifier’s performance. Overall, our best results have been recorded in the fraud detection-based time series classifiers (TSC) model scoring an improvement in the empirical performance metrics by 10% or more over the other developed models.

Optimal parameters estimation of lithium-ion battery in smart grid applications based on gazelle optimization algorithm

The principal contribution of this article focusing on obtaining a precise model of the lithium-ion battery (LiB). This in fact affects the simulation analyses and dynamics of such batteries in several applications including electric vehicles, microgrids, distribution systems, and smart grids. The main challenge here is the heavy nonlinearity of the optimization problem. The proposed gazelle optimization algorithm (GOA) is utilized in minimizing the fitness function, which depends on the integral squared error approach. The error is considered between the identified and practical battery voltage. The validity of the proposed approach is checked considering various operating conditions such as the loading effect, fading impact, and other dynamic responses. The effectiveness of introduced approach is validated by comparing the simulation results with practical results on a real Panasonic LiB of 2.6 Ahr capacity. These results are performed by MATLAB software. Furthermore, GOA-based LiB model is compared with various heuristic and conventional algorithms-based models. With the proposed GOA-based LiB model, an efficient and accurate battery model can be built.

An enhanced MSU‐TSCH scheduling algorithms for industrial wireless sensor networks

SummaryThe reliability and latency requirements of wireless sensor network‐based smart grid communications are met in large part by MAC protocols. Developed by IEEE, time slotted channel hopping (TSCH), a method that is effective, dependable, and predictable. However, the TSCH standard does not enable mobility and does not offer any solution for scheduling, especially in IEEE 802.15.4 TSCH, the most recent generation of extremely dependable and low‐power MAC protocols. Recently, MSU‐TSCH a time‐frequency communication schedule was proposed to provide mobility to TSCH. Despite its distinctive qualities, MSU‐TSCH has the drawback of computing the TSCH schedule at each node independently of its traffic load, which can significantly increase the communication delay. Due to this limitation, MSU‐TSCH is not suitable for some delay‐sensitive smart grid applications. In this article, we provide an improved MSU‐TSCH‐based TSCH protocol, called Mobility‐TSCH, that dynamically adjusts time slot assignments based on traffic volume and latency requirement. Moreover, the protocol Mobility‐TSCH adapts to topology changes and supports mobility. Additionally, it optimizes time slot allocation by prioritizing nodes closest to the sink. The performance and evaluation analysis of Mobility‐TSCH compared to the original MSU‐TSCH, reveal that the communication delay is greatly reduced, decreases the average end‐to‐end latency, the high packet delivery ratio (PDR), Increase the performance of both metrics, parent connectivity ratio (PCR) and convergence time (CT), while maintaining a minimal overhead signaling.

Quantum–classical co-simulation for smart grids: a proof-of-concept study on feasibility and obstacles

With the rising complexity of our electricity infrastructure, smart grid simulations increasingly rely on co-simulation, which involves jointly executing independent subsystem simulations. However, in large-scale simulation scenarios, such as those involving costly power-flow analysis, co-simulation may experience computational-performance issues. Quantum computing offers a potential solution through quantum–classical co-simulation, in which one or more simulators of an otherwise classical co-simulation are executed on quantum hardware. However, there is no practical realization of this concept that establishes its feasibility. To address this gap, we integrate a quantum power flow simulator with a smart grid co-simulation and conduct an exploratory simulation study using a fictitious case-study scenario. The experiments demonstrate the feasibility of quantum–classical co-simulation; at the same time, they highlight four obstacles to the concept’s realization in practice: (1) To use quantum computing for co-simulation, session-based scheduling is required. (2) Distributed simulation limits possible applications and requires proximity of computing resources. (3) For the efficient extraction of classical information from the quantum states, we need carefully designed operators. (4) Current hardware limitations—such as noise susceptibility and the lack of quantum random access memory—limit practical near-term uses of quantum power flow; therefore, attention should be turned to alternative applications that are more promising in the near term. These findings pave the way for future research on quantum–classical co-simulation and its potential applications in smart grids.

Emerging 6G/B6G Wireless Communication for the Power Infrastructure in Smart Cities: Innovations, Challenges, and Future Perspectives

A well-functioning smart grid is an essential part of an efficient and uninterrupted power supply for the key enablers of smart cities. To effectively manage the operations of a smart grid, there is an essential requirement for a seamless wireless communication system that provides high data rates, reliability, flexibility, massive connectivity, low latency, security, and adaptability to changing needs. A contemporary review of the utilization of emerging 6G wireless communication for the major applications of smart grids, especially in terms of massive connectivity and monitoring, secured communication for operation and resource management, and time-critical operations, are presented in this paper. This article starts with the key enablers of the smart city, along with the necessity of the smart grid for the key enablers of it. The fundamentals of the smart city, smart grid, and 6G wireless communication are also introduced in this paper. Moreover, the motivations to integrate 6G wireless communication with the smart grid system are expressed in this article as well. The relevant literature overview, along with the novelty of this paper, is depicted to bridge the gap of the current research works. We describe the novel technologies of 6G wireless communication to effectively perform the considered smart grid applications. Novel technologies of 6G wireless communication have significantly improved the key performance indicators compared to the prior generation of the wireless communication system. A significant part of this article is the contemporary survey of the considered major applications of a smart grid that is served by 6G. In addition, the anticipated challenges and interesting future research pathways are also discussed explicitly in this article. This article serves as a valuable resource for understanding the potential of 6G wireless communication in advancing smart grid applications and addressing emerging challenges.

An optimized estimation techniques for enhancing the efficiency of power demand in smart grid application

An optimized estimation techniques for enhancing the efficiency of power demand in smart grid application

Detecting and classifying man-in-the-middle attacks in the private area network of smart grids

The sustainable development of smart grids requires the massive deployment of renewable energy, in a highly distributed manner, introducing new challenges for the system operation. Therefore, the integration of information and communication technologies in sites with Distributed Energy Resources (DERs) is needed to monitor and control the DERs operation. In this scheme, a local controller is installed at each DER site to interact with the centralized applications at the grid level and the power equipment at the site level. This local controller uses client–server protocols (e.g., Modbus TCP/IP and IEC 61850 Manufacturing Message Specification) to communicate with different power equipment in the Private Area Network (PAN) of the site. Such protocols often lack information confidentiality and integrity mechanisms. As a result, the smart grids become vulnerable to cyber-attacks. To safeguard smart grid applications, this paper proposes a Hybrid Network Intrusion Detection System approach (HNIDS), where machine learning-based anomaly and signature-based are combined. The proposed methodology detects and classifies Man-In-The-Middle (MITM) attacks in eavesdropping mode in PANs, without violating customer privacy. The ability to detect unknown MITM attack techniques, identify affected packets, and determine the victim device(s) are the major advantages of this approach. An experimental testbed has been used to collect real-life data and validate the effectiveness of the proposed approach in smart grid applications. The proposed HNIDS is evaluated using a simulation as well as real-life laboratory experiments, demonstrating very high accuracy in detection rate, from 97.6% to 100%, with an average of the weighted F1-score over 98%.

A Detection and Response Architecture for Stealthy Attacks on Cyber-Physical Systems

There has been an increased reliance on interconnected Cyber-Physical Systems (CPS) applications. This reliance has caused tremendous growth in high assurance challenges. Due to the functional interdependence between the internal systems of CPS applications, the utilities' ability to reliably provide services could be disrupted if security threats are not addressed. To address this challenge, we propose a multi-level, multi-agent detection and response architecture built on the formalisms of Hidden Markov Models (HMM) and Markov Decision Processes (MDP). We have evaluated the performance of the proposed architecture on one of the critical smart grid applications, Advanced Metering Infrastructure (AMI). This paper utilizes a simulation tool called SecAMI for performance evaluation. A Stealthy attack scenario contains multiple distinct multi-stage attacks deployed concurrently in a network to compromise the system and stop several critical services in a CPS. The results show that the proposed architecture effectively detects and responds to stealthy attack scenarios against Cyber-Physical Systems. In particular, the simulation results show that the proposed system can preserve the availability of more than 93% of the AMI network under stealthy attacks. A future study may evaluate the effectiveness of various stealthy attack strategies and detection and response systems. The high availability of any AMI should be protected against new attack techniques. The proposed system will also determine a distributed IDS's efficient placement for intrusion detection sensors and response nodes within an AMI.

Smart grid technologies and application in the sustainable energy transition: a review

ABSTRACT The smart grid is a product of the advances in computer and communication technology and power electronics that creates a more resilient, reliable and one that supports a two-way flow of electricity and information. The smart grid enables more uptake of the variable renewables like wind, solar and variable loads like the plug-in cars and improves the efficiency of power systems and facilitate several products and services supported by the grid like automatic healing and re-routing of power in case of a fault and demand side management. The deployment and use of smart grids will enhance the realization of shared goals of grid stakeholders, promote energy security, enable economic growth, and help in the mitigation of climate change.

Prioritizing protection communication in a 5G slice: Evaluating HTB traffic shaping and UL bitrate adaptation for enhanced reliability

Abstract5G network slicing is promising in prioritizing time‐critical protection communication in wireless networks of smart grids. However, network slicing offered by telecommunication providers encompasses all smart grid applications, lacking granularity. Smart grid automation standards provide recommendations on prioritization of protection communication to improve reliability but only for wired connections. Therefore, this paper investigates hierarchical token bucket (HTB) traffic shaping and uplink (UL) bitrate adaptation of a live video stream for prioritizing protection communication within a 5G slice. An experimental setup combines controller‐hardware‐in‐the‐loop (CHIL) with a quality of service (QoS) measurement system for validation. The system under test consists of commercial 5G networks, intelligent electronic devices (IEDs), and merging units for validation with three smart grid applications: fault location, line differential, and intertrip protection. HTB traffic shaping improves protected faults by 47.57% in congested and 1.16% in non‐congested scenarios. UL bitrate of the video stream adaptation by 2 Mbps increases protected faults by 3.69%. HTB traffic shaping even improves prioritization with a wired connection without introducing additional delays. HTB traffic shaping must be deployed in routers to maintain good QoS for critical applications. Collaboration between utilities and telecommunication providers is essential to effectively deploy 5G network slicing on smart grids.

Learning based short term wind speed forecasting models for smart grid applications: An extensive review and case study

This paper provides an extensive review of learning-based short-term forecasting models for smart grid applications. In addition to this, the paper also explores forecasting models including physical, statistical, hybrid, and uncertainty analysis models for wind speed forecasting. The learning-based models are classified into three broad categories, namely classical machine learning, advanced machine learning, and probabilistic learning. In this work, 41 different models are employed to forecast the wind speed. Dataset for this case study is collected from the site of Jodhpur, India. Dataset have 8759 sample with five features i.e., wind speed, pressure, humidity, temperature, and dew point. This forecast also includes the seasonal effects. Model accuracy has been tested considering single and multiple features in the input data. A comparative analysis of the performance of these 41 learning-based models is conducted based on coefficient of regression and error indices. It is observed that the performance of these models varies with the variability in the season. On the basis of the evaluation of these models, future recommendations are also framed out. These recommendations target of energy storage planning, energy market and policymakers, and reliability and reserve sizing direction. These recommendations can be utilized by authorities for effective planning and coordination of power.

An Experimental Field Comparison of Wi-Fi HaLow and LoRa for the Smart Grid.

IEEE 802.11ah, or Wi-Fi HaLow, is a long-range Internet of Things (IoT) communication technology with promising performance claims. Being IP-based makes it an attractive prospect when interfacing with existing IP networks. Through real-world performance experiments, this study evaluates the network performance of Wi-Fi HaLow in terms of throughput, latency, and reliability against IEEE 802.11n (Wi-Fi n) and a competing IoT technology LoRa. These experiments are enabled through three proposed network evaluation architectures that facilitate remote control of the devices in a secure manner. The performance of Wi-Fi HaLow is then assessed against the network requirements of various smart grid applications. Wi-Fi HaLow offers promising performance when compared to rival technology LoRa. This study is the first to evaluate Wi-Fi HaLow in an authentic experimental way, providing performance data and insights that are not possible through simulation and modelling alone. This work provides the basis for further evaluation and implementation of this emerging technology.

Blockchain-Based Applications for Smart Grids: An Umbrella Review

This article presents an umbrella review of blockchain-based smart grid applications. By umbrella review, we mean that our review is based on systematic reviews of this topic. We aim to synthesize the findings from these systematic reviews and gain deeper insights into this discipline. After studying the systematic reviews, we find it imperative to provide a concise and authoritative description of blockchain technology because many technical inaccuracies permeate many of these papers. This umbrella review is guided by five research questions. The first research question concerns the types of blockchain-based smart grid applications. Existing systematic reviews rarely used a systematic method to classify these applications. To address this issue, we propose a taxonomy of these applications, first by differentiating them based on whether the application is focusing on functional or non-functional aspects of smart grid operations, and then by the specific functions or perspectives that the application aims to implement or enhance. The second research question concerns the roles that blockchain technology plays in smart grid applications. We synthesize the findings by identifying the most prominent benefits that blockchain technology could bring to these applications. We also take the opportunity to point out several common technical mistakes that pervade the blockchain literature, such as equating all forms of blockchains to data immutability. The third research question concerns the guidelines for deciding whether a blockchain-based solution would be useful to address the needs of smart grids. We synthesize the findings by proposing benefit-based guidelines. The fourth research question concerns the maturity levels of blockchain-based smart grid applications. We differentiate between academic-led and industry-led projects. We propose a five-level scale to evaluate the maturity levels. The ranking of the industry-led projects is performed through our own investigation. Our investigation shows that more than half of the industry-led projects mentioned in the systematic reviews are no longer active. Furthermore, although there are numerous news reports and a large number of academic papers published on blockchain-based smart grid applications, very few have been successfully embraced by the industry. The fifth research question concerns the open research issues in the development of blockchain-based smart grid applications. We synthesize the findings and provide our own analysis.