Smart Grid 3.0

The power grid has undergone significant transformations over the past century, expanding its role beyond providing reliable electricity to consumers. Today, it has evolved into a sophisticated and intelligent network, encompassing various applications that rely on advanced technologies. This article explores the concept of Smart Grid 3.0, the next phase of evolution in power grid systems, which has been made possible by recent advancements in computational power, storage capabilities, and high-speed communication. One key aspect of Smart Grid 3.0 is proactive intelligence, which enhances the grid's efficiency and reliability. This chapter highlights the importance of proactive intelligence and discusses how emerging technologies such as artificial intelligence (AI), the Internet of Things (IoT), Blockchain, Big Data, 5G, edge computing, cloud computing, etc., can equip the power grid with proactive intelligence. These technologies enable the grid to gather and analyze real-time data, make informed decisions, and take preemptive actions to optimize performance. It will delve into the various technologies, their applications in a smart grid context, and the relevant protocols employed. By presenting a thorough analysis, this chapter aims to serve as valuable reference material for future research on smart grid systems and their integration with cutting-edge technologies, leading to a more efficient, resilient, and sustainable energy infrastructure.

Today, more than ever, reducing energy consumption, air pollution, and other causes and effects associated with global warming are topics of interest to researchers. Traditionally, when making transactions with trust between parties, the help of banks or through an intermediary institutional framework is used. However, blockchain technology can change this system by imposing a decentralized architecture without an intermediary between parties when making a transaction. The applicability of blockchain technology in the present case is applied in the energy sector. The architectural principles, events and benefits that can be obtained in this direction are analyzed, such as the adoption of renewable resources, the influence produced due to climate changes for the technical balancing of the network, the creation of neighborhood microgrids (tenants’ associations) in order to lower energy prices by using photovoltaic panels (PV) and micro-wind turbines networks at the community level, and the elimination of intermediaries between producers and consumers. It can be said that this sector can be addressed in applications aimed at smart meters, the smart home and electric vehicle charging. In the energy system’s architecture, a prosumers hybrid appears between the producer and the consumer. In this way, through the additional quantity achieved through renewable systems, an increase in the generated energy and the corresponding decrease in the purchase price are obtained. This chapter proposes to create an association of green energy producers to manage the entire production and respective energy consumption and surplus. If the energy produced is not consumed or sold, the association will find storage solutions to make consumption available within the imposed limits, and the purchase price is low. Through a mobile application, the entire process will be followed by each association member and globally by its board of directors.

The future active distribution networks (ADNs) must ensure “smart” features like flexibility, accessibility, reliability, and high power quality for all consumers. Increased adoption of small-scale distributed energy sources (SSDES) helps decarbonise ADNs. In the present context, society must ensure the comprehensibility of the benefits stemming from smart electricity across the entirety of the population while concurrently ensuring that the provisioning process is achieved in an environmentally sustainable and efficient manner. Energy poverty is a lack of access to clean and affordable energy, resulting in soaring energy costs. The crowdsourcing concept, introduced by Surowiecki (The wisdom of crowds, Anchor, San Diego, CA, 2005), can be used to mitigate energy scarcity. It can be a useful tool for allowing the crowd to do community service within a specific geographic region. According to Romania’s Energy Regulation National Agency’s Order No. 228, launched on December 28, 2018, the prosumers can sell the energy-produced SSDES on the free market. More automated trading strategies aim to improve the benefits of peers who trade electricity in local community markets. The main aim is to quantify the distortion effects and introduce a stringent and comprehensive methodology integrating the distribution network operator (DNO), prosumers, and consumers. This chapter compare the ADNs cost saved by the households when prosumers move to increase their revenue, and the DNOs act to improve the benefits derived from an optimal network operation.

Modern power technology represents the epitome of engineering achievement, comprising a remarkable interconnected network of elements spanning vast regions. Transmission lines are vital in facilitating power transfer across extensive distances worldwide. The continuous operation and reliability of these transmission lines heavily rely on their effective monitoring and fault mitigation capabilities. Various factors, including natural disasters and other causes, can give rise to faults in transmission lines, which impede the seamless delivery of power. Timely identification and resolution of such faults are paramount to avoid service disruptions and mitigate the risk of cascading blackouts. Phasor Measurement Units (PMUs) have emerged as indispensable devices for monitoring and analyzing transmission lines, offering a dynamic perspective of their behavior due to their high reporting rate. PMUs enable operators to monitor power flow at different locations within the grid, thereby aiding in maintaining system stability and optimizing grid efficiency. These capabilities are essential in realizing the objectives of the smart grid 3.0 paradigm. Swift restoration of transmission line functionality necessitates the rapid detection, classification, and clearance of faults. Digital signal processing algorithms and machine learning techniques have emerged as critical tools in achieving these objectives efficiently. The advent of numerous machine learning algorithms, coupled with their real-time implementation capabilities, has empowered their robust deployment for fault detection and classification in physical transmission lines. This chapter presents the real-time implementation of the machine learning algorithms on a physical laboratory 200 km transmission line. Additionally, it compares the effectiveness of machine learning methods like K-Nearest Neighbour, Support Vector Machine, and Logistic Regression.

The future distribution networks must ensure smart features like flexibility, accessibility, reliability, and high-power quality for all consumers. If the first three features can be satisfied by the Distribution Network Operators (DNOs) until a certain level depending on the investments, the power quality in each node and consumption point is not always easy, and there is no way for non-compliant electricity to be withdrawn from the supply chain or rejected by the consumer. Monitoring the power quality indicators and the analysis of their compliance within the established limits in each electric distribution substation (EDS) and consumption point (currently, all consumers, especially those which use the modern technologies, can be considered as disruptors) is of particular interest to both in terms of limiting damage to consumers, but, also, to ensure the appropriate economic indicators for the DNOs. The authors analyzed the disturbances from files containing the current and voltage measurements (CVMs), downloaded from the power analyzers installed in the electric distribution substations, to find the “hot” areas where there are problems with power quality. The analysis has been performed with Data Mining techniques, particularly clustering, including the extraction of the features associated with the performance indicators corresponding to the voltage quality and continuity of electricity supply. The obtained results highlighted the effectiveness of the new approaches to identify the areas with power quality issues and can help the Decision-Maker in the planning and operation process of electric distribution networks.

Energy management systems (EMS) in smart grid (SG) are complex and dynamic systems that require intelligent decision-making to optimize energy usage and reduce costs. Integrating renewable energy sources, energy storage systems, and SG technologies has significantly increased data volume and complexity in EMS. Machine learning (ML) and deep learning (DL) techniques are increasingly being applied to the EMS to address these challenges and to make them more efficient and reliable. ML algorithms can be used to analyze large amounts of data from smart meters, IoT devices, and other sources to identify patterns and trends. This can help predict energy demand and supply and identify areas of inefficiency that can be improved. On the other hand, DL techniques can be used to model complex relationships between variables and make accurate predictions. For example, neural networks can be used to predict energy consumption based on historical data and weather patterns. Overall, ML and DL techniques can help optimize energy usage, reduce costs, and improve the efficiency of EMSs in smart grids. This chapter systematically reviews various ML and DL algorithms covering their critical applications, advantages, disadvantages, research gaps, and solutions. The several ML and DL strategies employed to meet various restrictions and achieve various EMS objectives are also compared and critically analyzed in this review study. It also discusses potential research directions and recommendations for implementing ML and DL techniques in EMS for SG 3.0.

With the advancement in renewable energy technology, the concepts of smart grids and microgrids are becoming more popular. A smart grid utilizes bi-directional digital communication techniques to identify and respond to the network’s dynamic changes. A smart grid can also incorporate several microgrids in a large area. A microgrid combines distributed energy resources (DER) such as solar, wind, and diesel generators, energy storage devices, and loads. A merger of different microgrids at the distribution level gives a concept of a multi-microgrid. During islanded mode, the multi-microgrid experiences heavy fluctuations in voltage and frequency due to dependency on DER, which drives the multi-microgrid towards instability. An efficient load frequency control (LFC) strategy is required to enhance the multi-microgrid’s dynamic performance. LFC is used for regulating the output frequency of the microgrid within a specified limit after a disturbance. An effective LFC technique reduces frequency fluctuations and improves the microgrid’s dynamic performance within acceptable limits. Evolutionary algorithms are one of the efficient LFC methodologies during island and grid-connected modes. In this chapter, four popular evolutionary algorithms, such as Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), Teaching Learning-based Optimization (TLBO) and Grey Wolf Optimization (GWO), are used for optimizing the parameters of PID controller for LFC of microgrid during different operating scenarios. In the multi-microgrid test system, non-linear DERs such as photo-voltaic and wind energy generators and diesel and battery storage systems are incorporated. It is observed that LFC using evolutionary algorithms, effectively reduces the frequency fluctuations that are observed during dynamic conditions in the microgrid. The results of all four evolutionary algorithms are also compared for designing a suitable load frequency controller.

With the advancement of the smart grid (SG), it has become suitable for energy consumers to handle and control their consumption. The ingenious practice of the incorporation of renewable energy sources in the SG environment, along with electric vehicles (EVs) and energy storage systems (ESSs) in smart homes (SHs), is a popular approach to minimizing electricity outlays and improving grid stability. Therefore, this chapter presents optimal energy scheduling techniques for EVs and SHs in SG-connected operations to control energy flow in SH that comprises solar photovoltaic generation (SPV), fuel cell (FC), wind turbine (WT), ESS, EVs charging points, SH lightening loads, and protection and control systems. Several energy management and optimization techniques have been presented to minimize overall costs allied with domestic energy consumption and EV charging during different market prices and ESS degradation costs. The finest energy scheduling for both the SH and EVs charge has been studied, and a suitable power distribution structure and cost uncertainty in SG is demonstrated. The EV scheduling is employed via linear programming (LP) based and shaped on the agents-based technique. Autonomous smart agents operate optimally, accomplish their tasks independently, and improve the SG's operational competency. Also, the energy scheduling technique expanded that permits power charging at maximum level to entirely charge the EVs batteries and encourage the vehicle owners to charge the EV batteries at conceivably lower charges and pollutant emissions, while the SG lower and surplus power generation from the available energy sources, robust charging and discharging of EVs and power trading are studied.

Smart grids encompass advanced control functionalities, aiming to accommodate large shares of renewable energy resources in the power system. The latter is critical for reducing the environmental emissions arising from the operation of power systems, which traditionally have been one of the largest emitters. However, the complexity of the smart grid operation, when compared to that of the conventional bulk power system, needs to be addressed through sophisticated control schemes. Moreover, as these control schemes become more and more advanced, their needs regarding communication and computational requirements are also increasing. Particularly advanced computational requirements arise with adopting control approaches based on machine learning, artificial intelligence, deep learning, neural networks etc., while advanced cooperative control schemes, e.g. distributed control, need advanced communication channels. This chapter reviews the advanced control functionalities of the main building blocks of the smart grid, i.e., transmission and distribution system, microgrids, distributed energy resources and smart homes. Particular emphasis is given to the technologies that require advanced computational and communication capabilities.

This chapter examines the load-frequency control (LFC) problem for an islanded fully-renewable microgrid (MG) that meets all users’ power demands with renewable energy sources (RESs). Islanded MGs with substantial RESs penetration face uncertainty. If we want to reap the advantages of RESs, we must equip the MG with a strong and effective control system since these sources, notably wind turbines and photovoltaic systems, are weather-dependent. For LFC, a multistage controller is designed. It removes PID controller flaws and operates quickly and reliably. The proposed controller is a Proportional Derivative (PD) cascaded with One + Proportional Integral (1+PI). The PD controllers operate as filter to speed up controller response, while PI controllers overcome steady-state error. This control approach combines these two controllers in the first and second stages to decrease steady-state error and achieve system stability faster to increase system responsiveness. Demand response programs (DRPs) make up for the MG’s lack of auxiliary services. This chapter discusses how a frequency-based control method for responsive loads in smart MGs may involve in the LFC issue. The DRP presence or absence and response loads involvement amount have been examined. The MG faces uncertainty and nonlinear variables, making LFC controller design challenging. Metaheuristic algorithms are used to find optimum controllers for the LFC issue. The particle swarm optimization with nonlinear time-varying acceleration coefficients (PSO-NTVAC) is used to find the best controller settings. To adjust for frequency variations in a 100% renewable MG, the cascade PD-(1+PI) controller is evaluated under a number of situations, including system modelling uncertainty and nonlinearity, and the existence of the DRP.

The Solid State Transformers (SST), also known as Power Electronic Transformer (PET), combine power electronic converters and medium or high-frequency transformers. The SST provides the same features of the conventional Line Frequency Transformers (LFTs), such as voltage matching and galvanic isolation. Besides, it provides additional features such as improvements in size and efficiency, advanced monitoring and control features, active/reactive power support capabilities, etc. With these advanced control and monitoring functions, SSTs are considered a candidate to enable remarkable improvement in the new grid system and have attracted the attention of many researchers, especially in smart grid and microgrid studies. The SST design process can be explained in two parts, power electronic converter and control design and medium or high-frequency transformer design. The medium or high-frequency transformer is the key component of SST applications. The operating frequency, core material and core form, the number of turns value, resulting magnetizing and leakage inductance, parasitic capacitance values and insulation requirements are important parameters that affect the transformer's efficiency and power density and should be carefully considered during the design phase. This chapter presents the SST concept, components and Finite Element Analysis (FEA) based modern design technique for high and medium frequency transformer design. Terms and definitions are explained in detail, including the power converter topologies, core material and wire specifications. In addition, the Energy Internet Concept and role of the SSTs in the Energy Internet as Energy Routers is discussed. The Multiport Solid-State Transformer (SST), capable of connecting to multiple sources and/or loads, is introduced as a solution for Energy Router applications, showcasing its simplicity, reliability, and high power density.

The objective of the early Intelligent Transportation Systems (ITS) was to improve road and traffic safety and to facilitate the management of the transportation system by providing Vehicle-to-Everything (V2X) communications to the vehicles and the entities surrounding it. With the recent advances in V2X technologies and the increasing popularity of Electric Vehicles (EVs), the EVs are envisaged to become part of the ITS. Moreover, V2X communications enable the integration of the EVs as interconnected entities into the Smart Grid ecosystem. Thus, the Smart Grid can perform enhanced monitoring and control capabilities by collecting data not only from the vehicle but also from the overall road infrastructure. Therefore, this chapter presents the V2X communication standards and protocols enabling the EVs to interact wirelessly with the grid infrastructure and other Road Side Units (RSUs). First, the existing Dedicated Short Range Communications (DSRCs), European Telecommunications Standards Institute (ETSI) ITS, and Long-Term Evolution (LTE) Cellular V2X (C-V2X) wireless protocol stacks and requirements, as well as the protocols used in the Vehicle-to-Grid (V2G) communications are outlined. Next, the emerging V2X wireless communication technologies of IEEE 802.11bd and New Radio (NR) V2X designed to provide high reliability, low latency, and high throughput communications to the new generation of autonomous vehicles and autonomous driving use cases are presented in detail.

With the improvement of fifth generation (5G) and beyond mobile technologies, Internet of Things (IoT) becomes more important in daily life as it provides many facilities for people in their homes and cities. The IoT can be considered a network of physical devices (“things”) used to connect and exchange information with other devices over the Internet. 5G and beyond technologies are expected to provide much more capacity and higher speed, helping the rapid growth of the IoT market. Nowadays, it is estimated that approximately 6–7 billion devices are connected through IoT technology and it is expected to increase to 20–22 billion in the near future. Smart Grid 3.0 is based on smart intelligence, automation, and data-enabled decisions, providing cost-effective electricity efficiency to consumers and reducing peak demand by enabling electrical utilities via smart technologies and improved security. So, IoT is expected to be a major technology for smart home and smart city applications and services that are also a part of the Smart Grid ecosystem. This chapter covers both IoT-based smart homes and smart cities. First, the IoT technology is introduced, and then, the IoT architecture is explained in the context of three layers included in its structure. In addition, new generation mobile technologies, namely 5G and beyond, are discussed for the IoT. After introducing the IoT technology with its architecture and enabling technologies, the chapter’s emphasis is on smart homes and smart cities by explaining protocols and architectures of smart environments with IoT-based services. In the third part of this chapter, the smart homes are presented in detail by mentioning smart home architectures, communication and medium protocols that can be used in smart homes, and several important services based on the IoT. Finally, the smart city concept alongside its architecture is given, and popular smart city services are explored.

DC microgrids are a promising solution for integrating distributed generation into the main grid. These microgrids comprise distributed generation units, energy storage systems, loads, and control units. They can operate in grid-connected and off-grid modes (islanded mode). Compared to the traditional centralized power systems, they offer a more reliable, efficient, and decentralized organization of the power system. Additionally, they provide various other benefits and are considered an environmentally friendly solution. In this chapter, the concept and components of DC microgrids and architecture of microgrids are discussed in detail. In addition, since the control strategies of the DC microgrid has cruical role in the achievent those advantages and system stability, different control strategies used in microgrids are discussed. Furthermore, it highlights the emerging machine learning and communication technologies that make these microgrids even more efficient and reliable. Overall, this chapter provides valuable insights into the advancements in DC microgrids for a decentralized future.

A vast literature now exists on how modern communication and computational technologies (CCTs)—such as artificial intelligence and big data, and their use in smart grids, smart cities, smart health, and energy demand management—can help overcome both the environmental and socio-economic challenges cities (especially large ones) presently face. Smart grids, for example, promise to allow greater percentages of intermittent renewable energy in the grid, particularly the rapidly increasing supplies of wind and solar energy, and to help match electricity production to demand. There are many potential advantages possible with advanced CCT, but they need careful implementation because many potential problems can also occur. This review first examines what is needed to produce ecologically sustainable and more equitable cities. A critical aspect of this is the need for a global Earth Systems Science approach to include the environmental damages caused elsewhere by a given city. It then examines how the new CCT can potentially help achieve these aims. An important conclusion is that, in most cases, advanced technology availability is insufficient; strong policies are also needed. The shortcomings of actually implemented or proposed approaches are also examined. Finally, it discusses what future decades might bring and the implications for the new CCTs.