Cooperative fuzzy controllers for autonomous voltage regulation in Smart Grids

The large-scale deployment of the Smart Grid paradigm will support the evolution of conventional electrical power systems toward active, flexible and self-healing web energy networks composed of distributed and cooperative energy resources. In this field, the application of hierarchical control paradigms for addressing power generation dispatch has some disadvantages that could hinder their application in Smart Grids where the constant growth of grid complexity and the need for massive pervasion of Distribution Generation Systems (DGS) require more scalable, more flexible control and regulation paradigms. To try to overcome these challenges, this paper intends to give a first contribution toward the definition of a bio-inspired computing paradigm aimed at solving the economic dispatch problem in a fully decentralized/non hierarchical scenario.

A survey on applications of Alternating Direction Method of Multipliers in smart power grids

Smart grid technologies and applications

Communication network requirements for major smart grid applications in HAN, NAN and WAN

Today, continuous technical and emerging advances between power communication systems and smart grids and applying swarm intelligence have increased for data sharing and analytics in our life. On the other side, Internet of things (IoT) has important key role to establish constructive interactions between smart devices and smart grid and power communication applications. For enhancing data transformation and improvements of multi-objective Quality of Service (QoS) factors, Swarm Optimization Techniques (SOT) are applied simultaneously in a cooperative smart environment to solve NP-hard problems. This paper provides a comprehensive analysis to address a new technical taxonomy and categorization of existing SOT-based smart grid applications in power communication systems in the IoT. Also, existing service and resource management case studies on smart grids and power communication systems are briefly analyzed and discussed. Existing evaluation factors on smart grid applications using SOT are represented. Possible advantages and weaknesses of each category are discussed with respect to new challenges and open research directions.

A seamless, sustainable and reliable communication infrastructure is crucial to realise smart grid operations and functionalities. The communications infrastructure in smart grid is not only required to be pervasive, scalable, and secure but also expected to be resilient and guarantee a required Quality of Service (QoS) to an end node. Narrow band power line communications (NBPLC) is believed to be a natural and cost effective choice for smart grid communication because it uses the existing infrastructures of the power grid that is much more extensive and pervasive than any other wired or wireless alternatives. This also eliminates the need for additional wire installation and external subscription for data transfer. However, because most smart grid applications have critical timing requirements, the frequency and time varying characteristics of the power line channel together with its background noise, limited band width and harmonic interference results in unacceptable packet loss during service transmission. Communication solution with Reliable, timely delivery which supports a given QoS for smart grid applications with critical timing requirements will although challenging is highly desirable. This paper investigates the resilience and performance of time critical smart grid application data over power line channels; specifically DLC (Distribution Line Carrier). It was shown that employing RoHC compression scheme on smart grid application IP packets can maximise bandwidth usage and enhance low delay and latency on both uplinks and downlink in a DLC channel.

The Smart Grid (SG) is one of the most ultimate topics within the energy communities. It refers to the modernized electric grid, including innovative Information and Communication Technologies (ICTs) to support enhanced power management, protection, security, reliability, and improved costumer choice. To that, SG systems depends on reliable and scalable networking solution to allow efficient communications amongst applications, as power monitoring, protection, demand response and advanced metering, and Intelligent Electronic Devices (IEDs) that provide SG system measurements. However, the deployment of SG applications is complex and very challenging, given the missing of standards and convergent technologies to enable the jointly operation of the underlying heterogeneous systems. SG applications must implement different interfaces to intercommunicate with IEDs in their platforms, process-bus communication protocol, network technologies, and other, limiting the system scalability and time response. We propose the Session Aware Multicast based Architecture (SAMbA) to decouple the intercommunication amongst SG applications and multiple heterogeneous IEDs. SAMbA also leverages multihoming control to keep best connected sessions, and adopts multicast-based channels to achieve optimized communications, while hiding network aspects, as naming and addressing, of IEDs. Simulation results from scenarios under Advanced Metering Infrastructure (AMI) application traffic show that the SAMbA architecture outperforms the typical communication scheme in terms of network performance aspects.

The inclusion of renewable energy in the conventional grid system and the digitalization of the various aspects of the power system have precipitated the transformation of the traditional grid system to a smart grid. Key to the implementation of the smart grid is various communication technologies. The emerging dominance of communication technologies in power systems applications is pivotal to modernizing the conventional grid system. This research presented an overview of the application of communication technologies in the digitalization of the power systems network. It reviews smart grid communication technologies, their features, relevance, and various roles being played toward delivering an effective electrical service to stakeholders. Hence, this study focused on wireless and wired communication technologies applicable to smart grid applications. Based on the review, each communication technology has its strengths and weaknesses; hence in selecting the most appropriate technology for application in a smart grid, a compromise must be made. Therefore, for choosing the most preferred communication technology in any smart grid application, it is important to deploy the efficacy of multi-criteria methods. To serve as a case study, a framework for selecting a communication technology for advanced metering infrastructure is proposed and applied. Based on the spectrum, data rate, and coverage range, the study revealed that WiMAX communication technology is the most preferred, followed by Zigbee; the least preferred communication technology for smart grid communication is Power Line Communication (PLC).

Limited battery lifetime is one of the most critical issues for wireless sensor networks (WSNs)-based smart grid (SG) applications. Recently, ambient energy harvesting (AEH) has been considered to significantly improve the network lifetime of the WSNs-based SG applications. However, extracting a significant amount of energy from the ambient energy resource due to time varying links quality affected by power grid environments is the main issue for WSNs-based applications in SG. In this paper, we propose a novel multi-source energy harvesting mechanisms for WSNs-based SG applications. The propose hybrid ambient energy harvesting framework through the designed circuitry successfully harvests massive power density by capturing the radial electric field (EF) and ambient radio frequency WiFi 2.4GHz band signals present in the vicinity of 500kV power grid station. The design energy harvesting schemes have been implemented on the recently developed routing protocol for SG applications. The experiments using EstiNet9.0, demonstrate that the designed framework is efficient in terms of energy harvesting capabilities to enable a long-lasting lifetime of the WSNs-based smart grid applications.

Current energy management faces its most significant power optimization struggle because rising demand meets with environmental pressures and requires effective resource utilization. The research develops an advanced implementation of bitterling fish optimization (BFO) combined with the genetic algorithm (GA) to perform power consumption optimization in intricate systems. The proposed framework connects BFO swarm intelligence to GA evolutionary strategies through machine learning adaptation, which simultaneously minimizes power losses and improves system efficiency throughout multiple applications. The hybrid approach produces quicker convergence through improved optimization performance and delivers flexibility toward dynamic energy requirements. The model requires consideration of three vital elements: load distribution management and renewable integration, and power loss reduction, along with interactive visual controls to monitor power distribution and optimization progress, and system output. Additionally, cloud and edge computing technologies enable large-scale data processing and ensure low-latency optimization for real-time applications. The framework also supports adaptive learning mechanisms, allowing it to continuously refine optimization strategies based on real-time energy data and consumption patterns. The proposed power optimization framework has wide-ranging applications in smart grids, industrial automation, and renewable energy systems, offering a scalable, high-accuracy solution for modern energy management. The integration of predictive analytics and real-time monitoring empowers decision-makers with proactive energy-saving strategies, ultimately contributing to a sustainable, efficient, and intelligent power infrastructure. This research provides a novel, data-driven approach to power optimization, addressing critical challenges in energy management while enhancing reliability and operational efficiency in future energy infrastructures.

This paper introduces Smart Grid and associated technical, environmental and socio-economic, and other non-tangible benefits to society, and articulates the need for the concept and the fact that it is a dynamic interactive, real-time infrastructure that responds to the challenges of designing and building the power system of the future, rather than being simply a marketing term. To illustrate the diversity of terminology, the paper compares an Electric Power Research Institute (EPRI) definition with that suggested by a study group of the International Electrotechnical Commission (IEC). Next, a paper sponsored by the Canadian Electricity Association (CEA) that cites three example definitions to highlight the diversity of views of Smart Grid is briefly reviewed. Early misconceptions and characterizations of Smart Grid are discussed as a prelude to addressing challenging issues that motivate developing and implementing related innovative technologies, products and services. The paper then discusses the potential promise of the Smart Grid, which is embedded in its often-cited attributes of efficiency, accommodating, quality focus, enabling and self-healing to name some. The paper then addresses some of the often-cited impediments to accepting Smart Grid which are based on concerns and issues confronting its forward progress, adoption and acceptance. Distribution Automation (DA) and embedded intelligence are discussed emphasising self-healing, optimizing operation and facilitating recreation and recovery from abnormal events. Functional and integration requirements of Distributed Energy Resources (DER,) are detailed. Smart Consumption Infrastructure elements of Distribution Management Systems (DMS,) Automated Metering Infrastructure (AMI,) Smart Homes (SH), and Smart Appliances (SA,) are discussed. Following the introductory section, this paper summarizes contributions included in the double issue 42(3–4) of the Electric Power Components and Systems Journal. To begin, papers are offered discussing smart grid activities in China, India, and the development of a Smart Grid roadmap for the US State of Kentucky. The approaches of each of these cases reflect the diversity of policy initiatives in these jurisdictions. Two state of the art reviews are given next. The first considers distribution network active management and future development trends in technologies and methods, where centralized and decentralized management frameworks and applying agent-based coordination are discussed. The second offers a review of smart home technologies and the goals of an energy management system (SHEMS). This section is concluded by a letter providing an overview of recent and expected advances nanotechnology applications in Smart Grid. Following the state of the art review section, ten papers offering new and innovative research approaches and results are included. These papers cover Smart Grid topics such as real-time energy control approach for smart home energy management systems, optimal operation of energy- efficient buildings with Combined Heat and Power (CHP) systems, energy management and control of Electric Vehicle (EV) charging stations, voltage-frequency control of a voltage source inverter (VSI) in a smart islanded microgrid, smart generation scheduling for wind-thermal-pumped storage systems, optimized power system restoration, robust data transmission upon compressive sensing, data fusion for wide-area oscillation monitoring, satellite based GPS synchronized monitoring systems, stability in Smart Grid with emerging renewable energy techno- logies.

Smart Grid is a promising power delivery infrastructure integrated with communication and information technologies. By incorporating monitoring, analysis, control and communications facilities, it is possible to optimize the performance of the power system, allowing electricity to be delivered more efficiently. In the transmission and distribution sector, online monitoring of transmission lines and primary equipments is of vital importance, which can improve the reliability of power systems effectively. Optical fiber sensors can provide an alternative to conventional electrical sensors for such applications, with high accuracy, long term stability, streamlined installation, and premium performance under harsh environmental conditions. These optical fiber sensors offer immunity to EMI and extraordinary resistance to mechanical fatigue and therefore they will have great potential in on-line monitoring applications in Smart Grid. In this paper, we present a summary of the on-line monitoring needs of Smart Grid and explore the use of optical fiber sensors in Smart Grid. First, the on-line monitoring needs of Smart Grid is summarized. Second, a review on optical fiber sensor technology is given. Third, the application of optical fiber sensors in Smart Grid is discussed, including transmission line monitoring, primary equipment monitoring and substation perimeter intrusion detection. Finally, future research directions of optical fiber sensors for power systems are discussed. Compared to other traditional electrical sensors, the application of optical fiber sensors in Smart Grid has unique advantages.

Smart grid is an essential part of the world's changing energy landscape. The uptake of smart grid by utilities is however hampered by the perceived complexity and large capital investment required to implement resilient Information Communication Technology (ICT) and networks that can support self-healing smart grids. Furthermore, failure to meet stringent smart grid communication reliability requirements can have disastrous consequences for grid owners and users. Many of the challenges utilities face with their smart grid ICT network development plans can be attributed to a reliance on conventional designs centred on expanding network hardware infrastructure in vast grid networks. What is needed to overcome these challenges is alternative ways of designing smart grid communication networks, using software defined approaches. This paper proposes a framework that supports software defined designs by combining the principles of the Internet of Things (IoT) paradigm and Machine-to-Machine (M2M) communication. Using the frameworks of software defined systems and virtualisation, our proposed architecture aims to provide a more flexible and affordable approach to developing and maintaining grid communication networks while offering a high level of network reliability, required for various smart grid applications. An evaluation of the implementation of this architecture in a design, based on a real city distribution grid, shows that a software defined communication network is easy to implement when using virtual machines running on existing computer infrastructure and that it is capable of supporting the network functions required for improving communication reliability. We conclude that this approach can potentially promote the adoption of smart grid by utilities while offering new innovative ways for smart grid developers to improve their grid designs.

The rapid development of new information and communication technologies (ICTs) and the deployment of advanced Internet of Things (IoT)-based devices has led to the study and implementation of edge computing technologies in smart grid (SG) systems. In addition, substantial work has been expended in the literature to incorporate artificial intelligence (AI) techniques into edge computing, resulting in the promising concept of edge intelligence (EI). Consequently, in this article, we provide an overview of the current state-of-the-art in terms of EI-based SG adoption from a range of angles, including architectures, computation offloading, and cybersecurity concerns. The basic objectives of this article are fourfold. To begin, we discuss EI and SGs separately. Then we highlight contemporary concepts closely related to edge computing, fundamental characteristics, and essential enabling technologies from an EI perspective. Additionally, we discuss how the use of AI has aided in optimizing the performance of edge computing. We have emphasized the important enabling technologies and applications of SGs from the perspective of EI-based SGs. Second, we explore both general edge computing and architectures based on EI from the perspective of SGs. Thirdly, two basic questions about computation offloading are discussed: what is computation offloading and why do we need it? Additionally, we divided the primary articles into two categories based on the number of users included in the model, either a single user or a multiple user instance. Finally, we review the cybersecurity threats with edge computing and the methods used to mitigate them in SGs. Therefore, this survey comes to the conclusion that most of the viable architectures for EI in smart grids often consist of three layers: device, edge, and cloud. In addition, it is crucial that computation offloading techniques must be framed as optimization problems and addressed effectively in order to increase system performance. This article typically intends to serve as a primer for emerging and interested scholars concerned with the study of EI in SGs.

Chapter 4 - Frameworks for Big Data Integration, Warehousing, and Analytics