Intelligent Smart Grid Research Articles

Blockchain and Machine Learning for Future Smart Grids: A Review

Developments such as the increasing electrical energy demand, growth of renewable energy sources, cyber–physical security threats, increased penetration of electric vehicles (EVs), and unpredictable behavior of prosumers and EV users pose a range of challenges to the electric power system. To address these challenges, a decentralized system using blockchain technology and machine learning techniques for secure communication, distributed energy management and decentralized energy trading between prosumers is required. Blockchain enables secure distributed trust platforms, addresses optimization and reliability challenges, and allows P2P distributed energy exchange as well as flexibility services between customers. On the other hand, machine learning techniques enable intelligent smart grid operations by using prediction models and big data analysis. Motivated from these facts, in this review, we examine the potential of combining blockchain technology and machine learning techniques in the development of smart grid and investigate the benefits achieved by using both techniques for the future smart grid scenario. Further, we discuss research challenges and future research directions of applying blockchain and machine learning techniques for smart grids both individually as well as combining them together. The identified areas that require significant research are demand management in power grids, improving the security of grids with better consensus mechanisms, electric vehicle charging systems, scheduling of the entire grid system, designing secure microgrids, and the interconnection of different blockchain networks.

Development of an Intelligent Smart Hub Renewable Generation System (ISHRGS) for both Urban and Rural Centres in Offa, Kwara State

Intelligent smart grids combine the generation, storage and consumption of energy systems. The opportunities to generate renewable energy from wind, solar and hydropower are generally higher in rural areas than in cities. The future development of the energy system is based on the planning and management of the distribution grid according to the Smart Hub (SH) component. This study comprehensively reviews the accessibility to the generation of electricity and the accessibility of renewable generation systems in urban and rural centres in Offa. It also examines the potential of an intelligent smart hub renewable generation system (ISHRGS) resources in Offa, Kwara State, that harnesses continuous energy supply and sustainability. Therefore, the historical background of the energy distribution in Offa, which led to this approach, is briefly presented with a comprehensive schematic diagram. Using a typical hypothetical example, modelling different components of SH and using them together to optimize ISHRGS using a genetic algorithm is presented. However, the study compares ISHRGS to different rural development approaches based on the feature of high adaptability to meet the temporal needs efficiently, and the parts reflect the intelligent character of the smart hub concerning electricity demand in the region.

AUTOMATED MONITORING OF TIME SYNCHRONISATION DEVICES AND DIGITAL PROCESSING OF VECTOR MEASUREMENTS OF DYNAMIC CHARACTERISTICS OF SMART GRID POWER SYSTEMS

Currently, customers of energy companies use devices to generate electricity (solar panels, windmills, etc.) and return energy to the grid to save costs or generate income. Therefore, it is urgent to solve the problem of creating an automated system for diagnosing the quality of electricity for consumers who use renewable sources of electricity. The purpose of this study was to improve the efficiency of management in intelligent SMART Grid power systems. To fulfil the said purpose, it was proposed to use an automated system for monitoring synchro signals of PMU microprocessor equipment and software tools for digital processing and statistical analysis of the results of synchronised vector measurements of dynamic characteristics of power grids. Software tools for digital processing and statistical analysis of the obtained vector measurement data were developed. Linear regression, Random Forest Regression, and Ridge regression methods were employed to analyse and predict electricity quality indicators. The results of a comparative analysis of the obtained results of the predictive model were presented. To conduct experimental studies, a laboratory sample of a quality control device for generating synchro signals was developed and manufactured, which directly affects the reliability of digital data from distributed synchronised vector measurements in transient modes (in real time). The multichannel nature of the device increases the performance and reliability of the automated monitoring system. The paper presents the results of experimental studies of the device as a part of the developed laboratory stand, which is connected to the current local IP network of the National University of Life and Environmental Sciences of Ukraine. The practical significance of the conducted experimental studies lies in the establishment of the technical possibility of simultaneous monitoring of time synchronisation signals generated by two GPS receivers. In this case, it is obvious to increase the performance of the developed and manufactured device for monitoring the quality of clock signal generation, compared to a single-channel one. This material can be useful for engineers who design electrical systems connected to renewable energy sources.

Reachability-Based False Data Injection Attacks and Defence Mechanisms for Cyberpower System

With the push for higher efficiency and reliability, an increasing number of intelligent electronic devices (IEDs) and associated information and communication technology (ICT) are integrated into the Internet of Things (IoT)-enabled smart grid. These advanced technologies and IEDs also bring potential vulnerabilities to the intelligent cyber–physical smart grid. State estimation, as a primary step of system monitoring and situational awareness, is a potential target for attackers. A number of other smart grid applications, such as voltage stability assessment and contingency screening, utilize state estimation results as input data. False data injection (FDI) is a specific way to attack state estimation by manipulating input data. Existing research mainly focuses on the mathematical analysis of FDI attacks; however, in these methods, discussions of reachability requirements to compromise measurements considering cyberinfrastructure are limited. Reachability is defined as a measure that estimates the number of hosts to compromise for the possible FDI. Most of the existing FDI attack methods require the simultaneous manipulation on multiple measurement devices in different substations, in order to bypass the bad data detection, which may be impractical. In this paper, a new type of reachability-based FDI attack considering the cybernetwork with a practical attack is proposed and validated on two IEEE test systems. The corresponding defence mechanisms are (a) decentralized state estimation (DSE), (b) DSE with additional backup computational nodes, (c) communication network rerouting, and (d) intrusion detection system, and they were developed and presented with validation for two IEEE test systems with superior performance for an IoT-enabled intelligent smart grid system.

Cybersecurity for the Smart Grid

The articles in this special section focuses on cybersecurity for the smart grid. Smart grid security measures have proven to be inadequate to deter costly malicious cyberattacks. The six articles in this theme issue focus on challenges in developing stronger cybersecurity measures for the smart grid. The security and well-being of societies and economies are tied to the reliable operation of power systems. Due to the advancements of information and communication technologies, the traditional electric grid is evolving toward an intelligent smart grid. A smart grid is essentially a cyberphysical system (CPS) that can be called an energy CPS, integrating computing, communication, and control capabilities with the physical world of the traditional grid. Despite the reliability and efficiency benefits, the inadequate level of security measures in the smart grid is leading to a greater threat landscape.

A fog‐based collaborative intrusion detection framework for smart grid

SummaryWith the rapid development of information and communication technologies (ICTs), the conventional electrical grid is evolving towards an intelligent smart grid. Due to the complexity, how to protect the security of smart grid environments still remains a practical challenge. Currently, collaborative intrusion detection systems (CIDSs) are one important solution to help identify various security threats, through allowing various IDS nodes to exchange data and information. However, with the increasing adoption of ICT in smart grid, cloud computing is often deployed in order to reduce the storage burden locally. However, due to the distance between grid and cloud, it is critical for smart grid to ensure the timely response to any accidents. In this work, we review existing collaborative detection mechanisms and introduce a fog‐based CIDS framework to enhance the detection efficiency. The results show that our approach can improved the detection efficiency by around 21% to 45% based on the concrete attacking scenarios.

Demand-Side Management of Smart Distribution Grids Incorporating Renewable Energy Sources

The integration of renewable energy resources (RES) (such as wind and photovoltaic (PV)) on large or small scales, in addition to small generation units, and individual producers, has led to a large variation in energy production, adding uncertainty to power systems (PS) due to the inherent stochasticity of natural resources. The implementation of demand-side management (DSM) in distribution grids (DGs), enabled by intelligent electrical devices and advanced communication infrastructures, ensures safer and more economical operation, giving more flexibility to the intelligent smart grid (SG), and consequently reducing pollutant emissions. Consumers play an active and key role in modern SG as small producers, using RES or through participation in demand response (DR) programs. In this work, the proposed DSM model follows a two-stage stochastic approach to deal with uncertainties associated with RES (wind and PV) together with demand response aggregators (DRA). Three types of DR strategies offered to consumers are compared. Nine test cases are modeled, simulated, and compared in order to analyze the effects of the different DR strategies. The purpose of this work is to minimize DG operating costs from the Distribution System Operator (DSO) point-of-view, through the analysis of different levels of DRA presence, DR strategies, and price variations.

Load Monitoring Solutions for the Smart Buildings – An Overview

Abstract The development of intelligent smart grids requires new electrical monitoring solutions in order to optimize power consumption. The implementation of intelligent energy-monitors leads to new technical developments to optimize consumption using intrusive and non-intrusive techniques. This paper makes a review of the main methods used for load monitoring systems, indicating their advantages and disadvantages. The emergence of high-harmonic consumer electronics in electrical networks such as LED lighting sources implies the choice of the appropriate method and equipment for optimizing energy consumption based on energy signatures.

Dynamic Trust Management System for Internet of Things

Internet of Things (IoT), which is one of the key enabling technologies for many critical applications, such as intelligent transportation, smart grid, smart building and mobile healthcare. However, security has become a key challenge for the wide deployment of IoT, because of the environmental influences. Moreover, the IoT devices may be compromised by attackers to intentionally generate fake data, to deviate the communication, and so on. To address the security issue in IoT, Dynamic Trust Management System (DTMS) for Internet of Things (IoT) is proposed. The DTMS for IoT involves recommendation system for trust model selection based on the context, generating and evaluating trust for heterogeneous system, and forwarding the data via trustworthy devices by considering interoperability and dynamic environment. This DTMS supports to take appropriate decision by avoiding false data and improve network utilization in the heterogeneous network.

A Cloud Associated Smart Grid Admin Dashboard

Intelligent smart grid system undertakes electricity demand in a sustainable, reliable, economical and environmentally friendly manner. As smart grid involves, it has the liability of meeting the changing consumer needs on the day-to-day basis. Modern energy consumers like to vivaciously regulate their consumption patterns more competently and intelligently than current provided ways. To fulfill the consumers’ needs, smart meters and sensors make the grid infrastructure more efficient and resilient in energy data collection and management even with the ever-changing renewable power generation. Though cloud acts as an outlet for the energy consumers to retrieve energy data from the grid, the information systems available are technically constrained and not user-friendly. Hence, a simple technology enabled utility-consumer interactive information system in the form of a dashboard is presented to cater the electric consumer needs.

Transmission Line Monitoring Network Design in Smart Grid

In this paper, we provide a cost optimized algorithm to design a real-time data transmission network architecture and realize real-time monitoring of transmission line in smart grid. How to deliver this real-time data to the control center efficiently is a critical challenge to build an intelligent smart grid. We formulate a three-layer network structure that can provide cost effective data transmission by the meantime, the bandwidth, delay, and connectivity constraints can be well met. Furthermore, the optimal placement of the LTE transceivers is studied aiming at maximizing the bandwidth and minimizing the delay in information delivery. The results we present in the paper individually analyze the effect of variation in each of the constraint on the cost of the network while other constraints are assumed to take up average values. Our analysis shows that three-layer wireless network used in smart grids can solve the problem of real-time data transmission and reduce the overall cost of transmission line monitoring network by the algorithm proposed in this paper.

Distributed active demand response system for peak power reduction through load shifting

Abstract The large variability in power consumption in electrical power systems (EPS) influences not only growth balance losses and technical losses, but also in some cases reduces energy security. Delayed restoration of power generation, combined with unpredictable weather events leading to the loss of generating power can lead to a situation in which to save the stability of the power system there must be introduced in the system a load power limit or even disconnection of end-user in a given area, which will significantly reduce the comfort of use of energy. This situation can be prevented through either the building of new intervention power units or the aggregated use of new energy technologies, such as distributed network resources (DER), which are part of an intelligent Smart Grid network. Such resources bring together virtual power plants (VPP) and demand side management (DSM). The article presents an alternative decentralized active demand response (DADR) system, that by acting on selected groups of loads reduces peak loads with minimized loss of comfort of energy in use for the end-user. The system operates without any communication. The effectiveness of the proposed solution has been confirmed, outlined in test results obtained by the authors from a developed analytical model, which also contains stochastic algorithms to decrease the negative impact of such DSM systems on the power system (power overshoot and oscillation).

Sensor Network Infrastructure for AMI in Smart Grid

Utility companies incur financial loss due to transmission losses as well as non technical losses like power theft and billing irregularities. Smart meters with the required functionalities along with an intelligent grid form an Automated Meter Reading (AMR) infrastructure which can improve the distribution efficiency. AMR infrastructure can be expanded by allowing for bi-directional communication between the grid components to form an Advanced Metering Infrastructure (AMI). This helps for the smooth interaction between grid components to load and off-load the distributed energy sources while supporting an efficient Demand Side Management of power. Communication technologies play a vital role in the implementation of smart grid and Wireless Sensor Networks are gaining attraction in this field. Routing protocols with high reliability, low latency and that can co-exist with the established protocols are the need of the hour. Routing Protocol for Low Power Lossy Networks (RPL) fit into this space well. An attempt to build an automated infrastructure using RPL for collecting the metering information, which at a later stage can be easily upgraded to an AMI setup by simply adding the supporting end devices for a much more intelligent smart grid, is carried out in this paper

Improving Battery Lifetime of Gridable Vehicles and System Reliability in the Smart Grid

The intermittent nature of renewable energy sources (RESs) and unpredictable load demands are two major challenges in providing uninterrupted power supply from a smart grid. One way to address these challenges is to use storage devices that can store surplus energy from RESs and discharge the energy back to the grid when needed. Researchers have recently introduced the idea to use electric vehicles with vehicle-to-grid capability, which are called “gridable vehicles” (GVs), as storage devices in the smart grid. Using GVs as loads is well accepted, but as sources, they disrupt system reliability if insufficient GVs are available for discharging when needed. An availability planning model is thus required to address this issue. GV owners' concern over battery lifetime reductions is another issue that impedes the required participation rates for GVs in vehicle-to-grid discharge programs. In this paper, we present an intelligent smart grid system model, which mitigates real-time unavailability of GV sources via an availability planning model. We also propose a GV selection model that prevents GV batteries from premature expiry due to their vehicle-to-grid operations. Simulation results confirm that our proposed models maintain better overall reliability and increase average battery lifetime by up to 3.5 years compared with existing vehicle-to-grid models.

Intelligent Research about Automate Substation Primary Equipment

With the acceleration of smart grid construction, intelligent become the development direction of the automatic substation, primary equipment is an important part of intelligent smart grid, running for safe and reliable operation of the substation automation is significant. This article briefly describes a device in the form of intelligence, once analyzed the working principle of intelligent devices and describes the intelligent transformers, circuit breakers and other primary equipment implementation.

Design and Implementation of Metering Abnormal and Online Diagnosis System of New Generation Intelligent Substation

In order to strengthen the accuracy and stability of the measurement information, and to enhance the safe and stable operation of the new generation intelligent substation and smart grid, it studied the metering abnormal diagnosis technology, put forward the design scheme of the metering abnormal and online diagnosis system of the new generation intelligent substation, realized real-time processing of the metering abnormal and online diagnosis analysis of the abnormal reason. The analysis model for experiment was set up. The results and tests show that the metering abnormal and online diagnosis system of the new generation intelligent substation can make the real-time analysis and review, and real-time monitor the working condition of the metering system for the whole substation. The design and implementation of the system provides a good platform for realizing the reliability, stability, versatility and interconnection of the metering system of the intelligent substation. It is suitable to the construction of the new generation intelligent substation.

Smart grid data analytics for digital protective relay event recordings

Information systems and intelligent smart grid data analytics will have a critical role in managing the massive amount of data becoming available in power system substations. Digital protective relays are multi-functional intelligent electronic devices based on microprocessors, which are being installed in substations throughout the power grid. New digital relays are replacing old-fashioned electro-mechanical or solid-state relays, and besides their protective function, they are coming equipped with monitoring capabilities. These monitoring capabilities are creating potential for better observability of power systems, redundancy in measurements, and improved decision-making process when operating the system. This article discusses the implementation requirements for a fully automated data analytics solution that provides data integration, fault analysis, and visualization based on the event data recorded by digital protective relays.

Study on communication technologies for the optimal operation of smart grids

ABSTRACTThe traditional electrical grid must evolve to an intelligent smart grid (SG) to achieve efficiency, reliability, environmental and power quality improvements. Communication techniques and protocols will be crucial to manage the SG. This paper studies the communication requirements for SGs and describes the most suitable communication protocols, wired and wireless, with special attention to the latest proposals in this field. The security issues derived from data transmission through electrical grids are also analysed with a deep description of the current best practices employed to assure the SG protection face to intrusions. Copyright © 2013 John Wiley & Sons, Ltd.

A Methodology for Emergent Telecommunication Technology Experiments Based on Virtual Environment and AI Techniques

Advanced telecommunications technologies innovations evolve quickly and are often considered as a main component of critical and emerging applications such as Intelligent Transportation Systems (ITS) and Smart Grid (energy systems). The need of rapid deployment of these technologies requires their testing to assess their performance and if necessary seek appropriate configurations that optimize the QoS (Quality Of Service) of associated applications. Existing simulation tools for testing these technologies remain limited when it comes to appraise their innovations involving additional knowledge. We present in this paper a methodology to design and develop a tool for training and experimentation of technologies innovations such as telecommunication. Our approach is focused on the integration of virtual reality concepts based on object oriented modeling and 3 D techniques for realistic scenarios experiments. We also consider artificial intelligence techniques such as inference mechanisms to enable the integration of new knowledge of the technologies to be tested and their management.

Intelligent Distributed System of Power Generating Unit's Control Coordination

Abstract This paper addresses the concept of developing an intelligent Smart Grids control system. It suggests using the ideology of multi-agent systems and intelligent algorithms for predicting the power plant dynamics in the tasks of optimizing facility operation.