Overview: This article explores the integration of smart grids, renewables, and communication technologies in the energy sector. It highlights the importance of energy storage systems, home energy management, and electric vehicles. The incorporation of a "smart grid" into today's electrical infrastructure is essential. Notable studies in the field of smart grids that relate to the Energy Internet can be broken down into the various subfields that will be covered below.Home Energy ManagementWith the aid of home energy management systems, the consumer can monitor the energy usage of each appliance in their home and make changes as necessary. The Energy Internet can be managed and operated by household energy cells through a home energy management system. Traditional energy infrastructure typically sends customers monthly bills detailing their energy consumption. The Energy Internet's home energy management systems offer a wealth of data, including consumption data, electricity generated locally via rooftop solar PV, current market rates, and storage capacity, all in real-time. Smart home energy management systems are built on a foundation of connected appliances, controls, networks, and displays. Home energy management systems provide feedback on energy use and other smart features. Consumers can make choices about their energy usage via in-home displays. For instance, Smarter Homes is a company that installs home energy management technologies to control solar rooftop PV, storage devices, and home appliances through the use of the internet of things and consumer electronic devices like iPads and Amazon Alexas. Energy management systems for the home make it easier to connect energy storage to the home's electrical network. An effective home energy management system is necessary for the envisioned energy internet to enable extensive energy trade.The Concept of Vehicle-to-Grid (V2G)Rechargeable batteries and an electric motor provide the power for plug-in electric vehicles. An energy port installed in a home or public space supplies power to a rechargeable battery. If electric vehicles are managed in a distributed fashion along with other electrical loads, they can play an important role in the demand-side management of the smart grid. When compared to stationary energy storage devices, electric vehicles have the distinct advantage of portability, as they can be driven from one location to another. Therefore, vehicle-to-grid and grid-to-vehicle initiatives can't be carried out without the widespread adoption of electric vehicles. Range anxiety is the key factor in determining how many people will sign up for vehicle-to-grid programs. Thus, even in developed nations, the rate of adoption of electric vehicles is low. But from the perspective of the power grid, vehicle-to-grid provides a variety of useful ancillary services, such as peak load management and voltage and frequency regulation. Even privately owned electric vehicles parked in a parking lot can contribute significantly to grid power during periods of inactivity with minimal disruption to the owner. Despite these advantages, people still have doubts about vehicle-to-grid. A lack of knowledge about vehicle-to-grid technical aspects is cited as the cause of this doubt. Policy-wise, many nations lack a well-developed plan for vehicle-to-grid. On the technological side, researchers are focusing on planning the distribution infrastructure to incorporate vehicle-to-grid and planning the vehicle-to-grid infrastructure to optimally operate the distribution network.Renewable Energy Integration into Grid and Distributed GenerationWith the help of a smart grid, renewable energy sources can be easily incorporated into power transmission and distribution systems. Due to the high cost of extending the power grid to rural areas, the electrification process in many countries is on hold. Research into completely independent island energy systems has been going on for a long time. The decentralized storage systems can guarantee a safer energy supply than large centralized systems. Such a system can use V2G technology to take advantage of renewable energy's full potential while also regulating peak demand. Surprisingly, the incorporation of renewable energy can resolve the challenging energy-water nexus that island nations face. For these countries, going from a state of "full input of energy and water" (FIEW) to "zero input of energy and water" (ZIEW) means they can stop relying on the mainland for their energy and water needs. The decarbonization of centrally managed energy systems and the installation of distributed energy systems with renewable energy as their main source are accelerating the transformation of the energy landscape. Based on the basic principle of incorporating distributed energy sources, controllable loads, and storage devices, the concept of a micro-grid has emerged. However, due to the fluctuation and interruption issues of renewable energy systems, managing distributed energy sources in the microgrid is a challenging task. Multi-agent-based approaches are able to handle such complexities. Distributed generation has many benefits, including efficiency gains, reduced carbon emissions, and the delaying of costly transmission line upgrades and expansions. The numerous economic, technological, and environmental advantages of distributed generation have led to its widespread acceptance as the future power paradigm. Additionally, unlike large traditional grids, distributed energy systems that are connected to small-scale generators can respond more quickly and effectively to changes in load curves. So, one of the primary goals of ongoing smart grid research and development activities is to better integrate distributed generation resources into the grid.Energy Storage SystemsFaster adoption of renewable energy sources and smart grids relies heavily on electric power storage facilities. Because of their high price and low efficiency, traditional energy storage systems were not particularly useful, relevant, or functional. It is crucial to take advantage of renewable energy generation and storage in order to set up a fully functional and optimized dynamic grid. The development of these industries requires the formulation of a crucial set of financial and regulatory policies. Devices that store and release energy can meet peak power demands without using additional, costly forms of generation. In addition, storage devices can play a crucial role in enabling cost-effective, efficient, and environmentally friendly operation of the distribution network by offsetting the demand and supply mismatch.Communication TechnologiesThe term "advanced metering infrastructure" (AMI) refers to the combination of "smart" meters, "communication networks," "meter data management systems," "software platforms," and "user interfaces". Through AMI, the utility and the end-user are able to have a two-way interaction about the end-user's energy consumption as well as the utility's price signals and load-control signals. The evolution of the smart grid’s communication technology is shown in Fig. 1.Fig. 1: Smart Grid Evolution Source: IEEE AccessThe data is sent to a centralized server, where it is stored and processed. Therefore, there must be a means of communication established that allows for the free flow of data. The information exchange channel is two-way communication. The utility's capacity for asset maintenance, energy demand management, and energy planning can all be managed through two-way communication. It is anticipated that AMI will become "smarter" in the future. It is predicted that in the near future, consumers will opt for Artificial Intelligent Meters (AIMs) that can regulate their power usage independently, irrespective of external signals. AIM also reduces the amount of human involvement in particular decision-making processes. With computational power and channel bandwidth being limited factors, it is difficult to provide a lightweight communication architecture for the transmission of big data that can quickly respond to network congestion and management requirements. As a result, many different algorithms for transmitting large amounts of data are currently under development.Summarizing the Key PointsSmart grid research aims to integrate distributed generation resources into the grid for improved efficiency and functionality. Energy storage systems are crucial for the adoption of renewable energy sources and the optimization of the dynamic grid. Electric vehicles have the advantage of portability and can contribute to the grid through vehicle-to-grid initiatives. Range anxiety and lack of knowledge hinder the widespread adoption of electric vehicles and vehicle-to-grid programs. Communication technologies play a vital role in enabling the flow of data and information exchange in the energy sector. Advanced metering infrastructure (AMI) enables two-way communication between utilities and end-users for efficient energy management. Artificially Intelligent Meters (AIMs) are predicted to become smarter, reducing human involvement in decision-making processes.ReferenceJoseph, Akhil, and Patil Balachandra. “Smart Grid to Energy Internet: A Systematic Review of Transitioning Electricity Systems.” IEEE Access 8 (2020): 215787–805. https://doi.org/10.1109/access.2020.3041031.

Overview: This article discusses cybersecurity's importance for electric vehicles and their charging infrastructure, highlighting vulnerabilities and protective measures against cyberattacks.   Electric vehicles (EVs) have developed into one of the key technologies to help society meet challenging clean energy and decarbonization goals over the past ten years. The electric vehicle market has expanded by 60% annually on average. In the near future, this growth is anticipated to continue with even higher adoption rates. Electric Vehicle Advantages Many nations have implemented policies to promote the use of clean-fuel vehicles. The main obstacle to the adoption of electric vehicles is frequently identified as range anxiety. Recent advancements in battery and charging technology are reducing this range anxiety. For instance, the 100 kWh battery in the Tesla Model S is enough for a trip of up to 402 miles.    Similarly, electric vehicle charging stations and the infrastructure that supports them have grown significantly in size and number. At the end of the year, there were 7.3 million electric vehicle charging stations implemented worldwide, an increase of 60% from the previous year. Additionally, the electric vehicle charging stations now have a higher charging capacity and can provide faster charging services. These electric vehicle charging stations with a rated charging power of up to 350 kW have been recently developed. An electric vehicle can be charged using these chargers in under 15 minutes.   Smart electric vehicle charging features like remote control through smartphone apps are not only making electric vehicle charging faster but also more approachable and, therefore, more available to broader customer audiences.   Cyberattacks in Electric Vehicles Although significant and well-publicized cyberattacks have not yet targeted smart electric vehicle charging stations, threats and reasonable ways of attack have been reported. According to Kaspersky Lab, ChargePoint Home's smartphone electric vehicle charging app has security flaws. Through the charging device's WiFi connection, this flaw would allow a remote attacker to break into the charger and interfere with electric vehicle charging.    Cyberattacks might also target electric vehicle charging station web applications, such as those from Circontrol, an electric vehicle charging station vendor with over 80,000 electric vehicle charging stations in 60 nations. This flaw would make use of the poor login information for electric vehicle charging. These well-known vulnerabilities highlight the cyber risks associated with electric vehicles and electric vehicle charging stations. Protective Measures Against these Cyberattacks Because of these attacks and the social costs they produce, efforts are being made to standardize cyber-physical interfaces for both residential and commercial electric vehicle charging.    Electric vehicles and electric vehicle charging stations are vulnerable to attacks that could harm equipment due to non-standard cyber-physical interfaces. For a number of electric vehicle charging architectures, the European Network on Cybersecurity suggested security standards. These standards provide security for both electric vehicle charging stations and their possessions. It also secures communications between charging station operators and power grid operators.    The standard specifies access control, future security compatibility of charging stations, monitoring and controlling system security, and message encryption for secure communication. Additionally, due to flaws in these interfaces, it is possible to weaponize electric vehicles and use them to launch extensive demand-side cyberattacks against the power grid.    Demand-side cyberattacks on electricity grids involve manipulating appliances like electric vehicles, distributed energy resources, and heating, ventilation, and air conditioning (HVAC) loads. These appliances are internet-connected and have high power. Although such attacks on power grids have not occurred in the past, there is growing awareness among electric vehicle owners that they could be carried out using vulnerabilities that already exist. Power grid operators won't be able to handle them.  Smart Grids Cybersecurity A cyber-physical overview of the smart electric power grid is shown in Fig. 1. Resources that are IoT-enabled are still being used in all four power sectors, including generation, transmission, distribution, and customer service. However, by utilizing IoT-enabled devices, it also introduces new cyber threats to the power grid. An overview of these threats as they relate to smart grid cybersecurity can be found below. Fig. 1: A cyber-physical overview of the smart electric power grid Source: IEEE Access Stride Threat Model The STRIDE threat model, originally created by Microsoft to assess software threats, can be used to categorize cyberattacks. It is a categorical risk assessment model for spoofing, tampering, repudiation, integrity, denial-of-service (DoS), and elevation of privilege threats to a given cyber-physical system. Smart Grid Threats SCADA Threats SCADA is a centralized monitoring and control system that is frequently used in real-world power grids. It has four main parts: a central master terminal unit, a human-machine interface (HMI), field units like power line communications (PLC), remote terminal units (RTU), and communication channels. Despite having industry-standard defenses, the SCADA network is still susceptible to insider attacks.   SCADA Field Unit Threats Intelligent electronic devices (IEDs), PLCs, RTUs, and phasor measurement units (PMUs) are examples of SCADA field units. Relays, sensors, and breakers are examples of microprocessor-based IEDs. IEDs are monitored by RTUs, which send measurements to PLCs, SCADAs, or both. PLCs and SCADAs, in turn, use RTUs to communicate control signals to IEDs. This control capability of PLCs enables some control actions to be performed decentralized without involving SCADA.   The field units communicate with one another by using protocols. Additionally, these protocols are open to online threats. The system can become unstable if erroneous data is introduced into it.   Advanced Metering Infrastructure (AMI) Threats To allow interaction between the utility, consumers, and distributed energy resources (DERs), power grids are increasingly deploying AMI, such as smart meters. Residential consumers or prosumers may have IoT-enabled devices like smartphones linked to the exact same network as their smart meter, while commercial DER operators plan to protect their smart meter-connected network with a VPN. Smart meters and their communication channels are vulnerable to all types of cyberattacks because of this attack surface.   Demand Response Threats Demand response resources make use of AMIs and Smart meters, making them equally susceptible to threats. The data input and output of these devices can be manipulated causing problems to the grid operators.   Threats from Devices With IoT Intruders can get into high-wattage devices and appliances with IoT interfaces by taking advantage of weak passwords on local networks and the fact that they can connect to remote devices like smartphones and smart TVs, which are vulnerable to supply chain threats.    Electric Vehicle Charging Threats Cyberattacks on charging electric vehicles and the power grid pose greater social and economic risks. The charging methods can be wired charging or wireless charging, but the risks possessed are the same.   Summarizing the Key Points Cybersecurity is crucial for the growing electric vehicle industry and its charging infrastructure to prevent potential cyberattacks.Standardizing cyber-physical interfaces and implementing security measures are essential to protect electric vehicles and charging stations.Vulnerabilities in electric vehicle charging systems can be exploited, posing risks to equipment and potentially impacting power grids.The European Network on Cybersecurity has suggested security standards to safeguard communication between charging station operators and power grid operators.The rapid growth of electric vehicles and charging infrastructure necessitates a proactive approach to address cybersecurity challenges and ensure a secure and sustainable future. References [1] Acharya Samrat, Yury Dvorkin, Hrvoje Pandzic, and Ramesh Karri. “Cybersecurity of Smart Electric Vehicle Charging: A Power Grid Perspective.” IEEE Access 8 (2020): 214434–53. https://doi.org/10.1109/access.2020.3041074.

Overview: This article provides an overview of how artificial intelligence methods, including expert systems, fuzzy logic, metaheuristic techniques, and machine learning, can optimize power electronics systems. Artificial intelligence methods refer to a set of techniques and algorithms that enable machines to perform tasks that typically require human intelligence, such as learning, reasoning, perception, and decision-making. Artificial Intelligence MethodsAdvances in artificial intelligence are likely to yield substantial benefits for power electronics. Artificial intelligence methods can be broadly divided into expert systems, fuzzy logic, metaheuristic techniques, and machine learning.  Figure 1. Sankey diagram of artificial intelligence methods and applications in each phase of the life-cycle of power electronic systems. Image used courtesy of IEEE Transactions on Power ElectronicsExpert SystemThe oldest artificial intelligence technique that has been successfully used in industrial applications is the expert system. The expert system is essentially a database that incorporates the expert information into a catalog of Boolean logic that serves as the foundation for simulating the IF-THEN logic rules used by human brains to reason. An intelligent system that simulates the inference process using the database answers the why-and-how questions. The database comprises simulation data, facts, and claims or field expert knowledge. It can be updated continuously.  It is important to note that the utilization data in Figure 1 shows that expert system applications are as low as 0.9%. The expert system lacks universality since it is typically built on system principles and norms, which are closely related to the system of interest. It only applies to well-defined domains with reliable expert rules. Because of the quick growth of computer platforms, advanced artificial intelligence with improved inference and approximation skills, such as fuzzy logic and machine learning, can perform expert system functions. Fuzzy LogicFuzzy logic, which extends Boolean logic into multivalued conditions, is a rule-based approach similar to expert systems. To deal with system uncertainties and noisy measurements, fuzzy logic is the perfect instrument.  Fuzzification is first carried out using fuzzy sets made up of many membership functions with a range of 0-1 rather than using the exact input crisp value directly. The inference step then aggregates the fuzzy input signals using fuzzy rules. The inference result undergoes defuzzification by considering the level of fulfillment that produces a crisp value. To complete the nonlinear mapping between the input and output, the crisp value is modified in a fuzzy space using precisely constructed principles.  Components of Fuzzy LogicIn most applications, the four basic components of a fuzzy logic method are fuzzification, rule inference, knowledge base, and defuzzification. First, fuzzification is applied to the input of linguistic variables with membership functions such as triangular, trapezoidal, Gaussian, bell-shaped, singleton, and other custom-made shapes. Second, the inference module combines the signals following IF-THEN fuzzy rules drawn from expert experience and stored in the knowledge base. Third, defuzzification of the output signal is carried out. Metaheuristic MethodsOnce the optimization objective for a given application has been stated, the best solution can be found using either a deterministic programming method (such as linear or quadratic programming) or a nondeterministic programming method, such as the metaheuristic method. In deterministic programming techniques, their complexity in calculating the gradient and Hessian matrices makes them difficult to use in most optimization problems in power electronics.  For various optimization tasks, metaheuristic approaches act as a general end-to-end tool that requires less specialized knowledge and is effective and scalable. The development of metaheuristic algorithms frequently draws inspiration from biological evolution, as seen in the genetic algorithm (GA) that uses the natural selection process and the ant colony optimization (ACO) algorithm that mimics ants in looking for an effective food path. Trial-and-error is a method that promotes the search for the ideal response.  Metaheuristic Techniques and MethodsThe metaheuristic techniques fall into two categories: trajectory-based techniques (tabu search method, simulated annealing method, etc.) and population-based techniques (GA, particle swarm optimization (PSO), ACO, differential evolution, immunity algorithm (IA), etc.).  Trajectory-Based TechniquesFor the trajectory-based techniques, there is only one candidate solution included in each exploration step, and it develops into another solution by a set of rules. The standard and effectiveness of the rule largely determine the approach's effectiveness. As a result, for nonconvex optimization tasks, the ultimate solution is frequently a local rather than a global solution, and the convergence speed of trajectory-based approaches is typically slow.  Population-Based TechniqueThe population-based methods generate a large number of candidate solutions at random. To enhance the quality of the population in the current generation, these candidate solutions are either varied (e.g., crossover in the GA) or incorporated and replaced with fresh candidate solutions at each iterative exploration. As a result, the population's suitability is gradually increasing to get closer to the ideal solution. They are more effective than trajectory-based approaches regarding convergence speed and global searching ability and are particularly helpful for multiple optimization tasks.  However, population-based approaches have a heavier computing requirement. For online application scenarios where effectiveness and speed are crucial, this difficulty must be considered. A list of power electronics-related metaheuristic techniques, together with their benefits and drawbacks, is presented in Table I. In terms of several crucial characteristics, such as implementation ease, global convergence, convergence speed, and parallelism, these metaheuristic algorithms are qualitatively compared. Most optimization issues in power electronics are resolved using population-based approaches due to their significant advantages. Table 1 shows various population-based techniques with enhanced versions for power electronics optimization problems. They are created and enhanced using various biological influences.  Several other recently developed approaches, such as biogeography-based optimization, the crow search algorithm, grey wolf optimization, the firefly optimization algorithm, the bee algorithm, the colonial competitive algorithm, teaching-learning-based optimization, etc., have also been used on a limited scale in addition to the earlier, widely used metaheuristic methods.It is important to note that choosing the optimal strategy is a difficult task that depends on the application. As indicated in Figure 2, the two most common metaheuristic techniques used in power electronics are GA and PSO. They serve as the foundation and models, respectively, for evolutionary algorithms and swarm intelligence algorithms, upon which numerous variants are built. Practitioners can pick the method based on its superiority, as shown in Table I. Figure 2. Usage statistics of population-based metaheuristic methods in the optimization of power electronics Image used courtesy of IEEE Transactions on Power Electronics Machine Learning Machine learning is intended to automatically identify patterns and principles through experience gained from either data collection or interactions through trial and error. It is divided into three categories for use in power electronics: supervised learning, unsupervised learning, and reinforcement learning (RL). Summarizing the Key PointsArtificial intelligence methods have the potential to revolutionize power electronics by improving system efficiency, reliability, and performance.Expert systems can be used to diagnose faults in power electronics systems and provide recommendations for repair or replacement.Fuzzy logic can improve the accuracy of power electronics control systems by accounting for uncertainty and imprecision in sensor data.Metaheuristic techniques, such as genetic algorithms and particle swarm optimization, can be used to optimize power electronics systems by searching for the best combination of design parameters.Machine learning techniques, including supervised learning, unsupervised learning, and reinforcement learning, can automatically identify patterns and principles in power electronics data and improve system performance over time. ReferencesZhao, S., Blaabjerg, F., & Wang, H. (2021, April). An Overview of Artificial Intelligence Applications for Power Electronics. IEEE Transactions on Power Electronics, 36(4), 4633–4658. https://doi.org/10.1109/tpel.2020.3024914

Overview: This article explores the opportunities and challenges of integrating clean technologies and information and communication technologies for efficient and sustainable energy management in smart grids.  Decarbonization has accelerated the fundamental shift in society toward clean technologies. Electrical energy will be a significant factor in the decarbonization process. Electrical energy is one of the most common forms of energy carriers and is seeing growing usage. Increasing electricity demand forces the expansion of the generation and transmission systems, requiring a significant amount of investment.  Power loss and reactive power flow in the transmission systems make the conventional, centralized structure of power systems less efficient. Distributed generations (DGs) have been incorporated into low- and medium-voltage distribution networks in order to increase system availability, efficiency, and cost-effectiveness. Furthermore, renewable-based distributed generation aids in the decarbonization of the electric energy sector.Evolution of Smart GridDistribution systems that have been powered up can function as a microgrid in the absence of the utility grid. A microgrid is an island-based distribution system that uses local distributed generation and energy storage to provide critical loads in island mode. Distribution systems with microgrid capabilities will have some benefits, such as increased productivity, dependability, accessibility, and power quality.  However, information and communication technologies (ICTs) are necessary for the optimal and reliable operation of various distributed generation and energy storage systems in microgrids. To operate modern energy distribution systems as efficiently and dependably as possible, the smart grid concept has been introduced. To operate and plan grid systems with irregular output and variable power sources, ICTs must be available at both the generation and transmission levels. These systems enable power systems to meet customer demands by intelligently monitoring, making decisions, and controlling contemporary power systems. In addition to incorporating DGs into distribution networks, large-scale renewable power plants like photovoltaic (PV) and wind energy systems have been widely installed in power systems, and the power grids are currently moving toward more fully renewable energy systems. Figure 1. Concept of a Distributed Power Generation System Source IEEE Access Along with efficiency, flexibility, and operability benefits, smart grid technologies also present new difficulties for the design and management of modern power systems. Restructuring the power grids to incorporate renewable energy sources, microgrid technologies, ICTs, and power electronics can result in these difficulties. Smart Grid’s Future DirectionsThe idea of smart grids has changed with the development of technology. In recent years, the smart grid's research and development have increased. As a result, the implementation of smart grids has changed from virtual to real-time. However, there are several situations in which action needs to be taken to turn it into a complete real-time network service.Big Data ManagementThe input of real-time data is a key factor in a smart grid. It serves as the backbone of the network's functioning. Power transmission, generation, transformation, and utilization data are being collected for reliable and efficient working. All decisions are made based on the information gathered. The collection and management of such a vast amount of real-time data is a significant problem.  To predict the demand for energy at various locations, the algorithms must use all the data gathered from the sensors and associated devices. To produce the best results, the algorithms must be optimized. One of the main study subjects in smart grid technology is IT infrastructure, data gathering, governance, data processing, and, most critically, data security.Investing in Smart Grid InfrastructureTo reduce carbon emissions, a number of countries have started implementing smart grid infrastructure. Many of them are engaged in projects designed to evaluate the feasibility of the network. The construction of the smart grid infrastructure has already started in nations including Australia, South Korea, and Japan. The initial investment, though, is the main concern. The ongoing maintenance of the entire network further raises the overall cost.  Therefore, before making an investment of this size in the infrastructure, a thorough financial report should be made. The price of smart grids in a few emerging nations is shown in Table 1. This will estimate the starting sum that a developing nation must invest in order to create smart grid infrastructure. Additionally, it will provide a general concept of the maintenance costs as well as any other extra expenses necessary to guarantee the network's efficient operation.Business Model RestructuringThe business model has undergone considerable adjustment as a result of the new smart grid's emergence. New technologies have altered consumer perceptions and created a network of distributed power sources. Consequently, business practices are evolving. It is necessary to implement new policies to benefit consumer communications. To integrate the load and the generated power, the utility business model should be put into practice at the distribution level.Modernization of the Energy Production SystemCustomer needs have evolved due to the smart grid's evolution. As a result, there are fluctuations in energy demand. To accommodate the demand response, the system's capacity should be raised. Additionally, the energy-producing systems must change their production policies to integrate into the smart grid network. In the smart grid network, cloud-based data management strategies are applied. The existing system needs to be upgraded and changed in order to establish IoE activities.  Cyber-physical power systems are the smart operation of future power systems, which include distributed generation, microgrids, and demand side management while utilizing information and communication technologies over the physical system. The ICTs are vulnerable to cyberattacks, data loss, and hardware failure. ICT malfunctions will reduce system performance and must be taken into account when planning a power system. Additionally, when operating power systems, cybersecurity must be taken into consideration because malicious intrusions from cyberattacks could result in a loss of power or energy. The network should incorporate security measures against cyberattacks.Summarizing the Key PointsThe paper highlights the importance of information and communication technologies in the optimal and reliable operation of distributed generation and energy storage systems in microgrids.The integration of information and communication technologies with power systems can lead to the development of cyber-physical power systems or smart grids.Smart grids enable power systems to meet customer demands by intelligently monitoring, making decisions, and controlling contemporary power systems.However, the adoption of clean technologies and information and communication technologies presents new challenges for the design and management of modern power systems.Smart grid technologies also present new difficulties for design and management but offer significant benefits such as flexibility, efficiency, operability, reliability, accessibility, and power quality. Reference(s)1.Peyghami, S., Palensky, P., & Blaabjerg, F. (2020). An Overview on the Reliability of Modern Power Electronic Based Power Systems. IEEE Open Journal of Power Electronics, 1, 34–50. https://doi.org/10.1109/ojpel.2020.29739262.Pal, R., Chavhan, S., Gupta, D., Khanna, A., Padmanaban, S., Khan, B., & Rodrigues, J. J. P. C. (2021, August 28). A comprehensive review on IoT‐based infrastructure for smart grid applications. IET Renewable Power Generation, 15(16), 3761–3776. https://doi.org/10.1049/rpg2.122723.Rafique, Z., Khalid, H. M., & Muyeen, S. M. (2020). Communication Systems in Distributed Generation: A Bibliographical Review and Frameworks. IEEE Access, 8, 207226–207239. https://doi.org/10.1109/access.2020.3037196   

Overview: This article overviews communication technologies in smart grid infrastructure, focusing on electric vehicle charging protocols and standards. CatalogSmart Charging SystemCommunication Technologies in Smart Grid InfrastructureSummarizing with Key Points Smart Charging SystemTo develop a power distribution network that is both more effective and more environmentally friendly, the possibility of combining electric vehicles with smart grid technologies plays a significant role. A component of smart grids known as vehicle-to-grid (V2G) enables electric vehicles to not only receive power from the grid but also feed excess energy back into it when they have it available. The convergence of electric vehicles and smart grids has the potential to revolutionize the energy business while simultaneously lowering carbon emissions.Fig. 1 . Overall charging system for battery electric vehicles using wired/wireless charging technologies. Image used courtesy of IEEE Access Communication Technologies in Smart Grid InfrastructureEV charging protocols and standardsFig. 1 shows how the system for charging battery electric vehicles with wired and wireless charging works. The smart charging system connects with the entire system and gives the vehicles the best possible charge. A few common protocols are needed to establish proper communication between the entities. Tables 1 and 2 compare and identify some common communication protocols. Table 1: Wired communication technologies in the smart grid Source: IET Renewable Power Generation FamilyStandardData RateCoveragePLCNB-PLC: ISO/IEC 14908–3 (Lon- Works) ISO/IEC 14543–3-5 (KNX), CEA-600.31 (CEBus) BB-PLC: TIA-1113 (Home Plug 1.0), IEEE 1901, ITU-T G.hn (G.9960/ G.9961)NB-PLC: 1–10 Kbps for low data rate, 10–500 Kbps for high data-rate  BB-PLC: 1–10 Mbps (up to 200 Mbps on very short distances)NB-PLC: 150 km or more    BB-PLC: 1.5 kmOptical FibreIEEE 802.3ah ITU-T G.983 (BPON) IEEE 802.3ah (EPON)100 Mbps 155,–622 Mbps 1 Gbpsup to 10 km up to 20–60 km 10–20 kmDSLITU G.992.1 (ADSL) ITU G.992.5 (ADSL2+) ITU G.993.1 (VDSL)1.3–Mbps 3.3–24 Mbps 52–85 MbpsUp to 4 km Up to 7 km Up to 1.2 km Table 2: Wireless communication technologies in the smart grid Source: IET Renewable Power Generation FamilyStandardData RateCoverageWi-FiIEEE 802.11e (QoS enhancements) IEEE 802.11n (ultra-high network throughput)BIEEE 802.11s (mesh networking) IIEEE 802.11p (WAVE: wireless access in vehicular environments) Up to 54 Mbps  Up to 600 Mbps 300 m (outdoors)  Up to 1 kmWiMaxIEEE 802.16 (fixed and mobile broadband wireless access)IEEE 802.16 m (advanced air interface)128 Mbps down and 28 Mbps up 100 Mbps for mobile users, 1 Gbps for fixed usersUp to 10 km 0–5 (optimum), 5–30 (acceptable), 30–100 (reduced) km3G / 4GI3G: UMTS (HSPA, HSPA+)   4G: LTE, LTE-AdvancedHSPA: 14.4 Mbps down and 5.75 Mbps up HSPA+: 84 Mbps down and 22 Mbps upLTE: 326 Mbps down and 86 Mbps up LTE-Advanced: 1 Gbps down and 500 Mbps up0–5 km   LTE-Advanced: 0–5 (optimum), 5–30 (acceptable), 30–100 (reduced) kmSatelliteLEO: Iridium, Global Star  MEO: New ICO  GEO: Inmarsat, BGAN, Swift, MPDS2.4 to 28 Kbps  9.6 up to 128 Kbps  384 up to 450 KbpsDepend on the number of satellites and their beams.Depend on the number of satellites and their beams.Depend on the number of satellites and their beams.Open Charge Point Protocol (OCPP) This application-based protocol implements the communication infrastructure between the charging station and the centrally distributed management system. The application protocol is freely accessible. A vendor-oriented protocol was created by the Open Charge Alliance. Due to the quick access to information that electric vehicle drivers provide, it offers more versatility.  The primary characteristics that this particular system is equipped with include transaction management, security, smart charging, message display, and the generation of warnings in the event of a malfunction. A bidirectional international communication standard is ISO 15118. It is employed as a channel of information exchange between electric vehicles and the infrastructure. Additionally, it is utilized for vehicle-to-grid mode communication.  It needs a standardized platform that can deliver and manage the protocol and its services to implement the protocol. The Driivz platform, an open charge point protocol, is one such platform. It supports the OCPI, OCHP, open intercharge protocol (OICP), and open automated DR protocol (OADR). The Driivz platform also supports ISO 15118 and OCPP 2.0, enabling vehicle-to-grid communication technologies.Open Charge Point Interface (OCPI) This system was implemented to allow charging station operators and the electric mobility service to exchange information about charging points. The following is a list of the open charge point interface's characteristics: The location status and session information are both being updated.Remote command sending.Giving charge information records to give the correct billing amount.Authorizing charging stations through the token exchange.OADRIt is intended for information exchange among the systems to study the DR. To precisely estimate demand at peak periods when it is in operation; it is standardized to send and receive accurate information between distributed energy resources and the control system of the energy management system. It predicts demand accurately at peak times during its operation.Open Smart Charging ProtocolThis protocol enables communication between an energy management system and a charge point management system for a site owner. It can share immediate predictions on the local energy grid's ability to support a charge point operation.OICPHubject was the one who developed it. It is used for standardized communication between charge point operators and e-mobility service provider systems.Global System for Mobile (GSM)It is the most widely used mobile network today. It runs in the range of 900 and 1800 MHz and is based on circuit switching. With a data rate of up to 270 kbps, the modulation method known as Gaussian Minimum Shifting Key is employed. The mobile handset, base station sub-system, networking switching substation, and operation support substation are the four major subcategories of this protocol's architecture. One of the most secure communication system protocols to date is thought to be this one.General Packet Radio ServiceThis is a packet-based data transfer protocol. Compared to the GSM, this network enables IP-based applications to operate at substantially higher data transfer rates. This specific networking protocol is mostly used for smart grid applications in remote regions.Summarizing with Key PointsEffective communication technologies are essential for successfully implementing smart grid infrastructure, particularly in the context of electric vehicle charging protocols and standards.The open charge point protocol is a widely used application-based protocol that enables communication between charging stations and centrally distributed management systems.The open charge point protocol offers versatility and quick information exchange between electric vehicle drivers and infrastructure, with features such as transaction management, security, smart charging, message display, and warning generation.In addition to the open charge point protocol, there are other common communication protocols used in smart charging systems that facilitate proper communication between entities involved in the charging process.Overall, effective communication technologies play a crucial role in ensuring efficient and reliable electric vehicle charging infrastructure within smart grid systems. This blog post is part of a full research article from the IET Renewable Power Generation. The featured image is courtesy of Midjourney.

Overview: This article explores how the smart grid, with its IoT-based transactions, can help address energy challenges in the 21st century. Learn about the role of renewable power generation and electrical grid infrastructure in energy conservation.  "Smart Grid" (SG) refers to the upgraded electrical grid that was made possible by advances in communication and sensor technology. Developing smart grid infrastructure is one of the solutions to many problems regarding energy conservation.Challenges in Energy TransactionsThere is an increase in the amount of energy produced by solar and wind sources. Additionally, there are new loads, such as electric vehicles, heat pumps, smart residential cities, commercial and industrial usage, infrastructure, substations, etc. Due to these characteristics, additional technological challenges, notably the unpredictability of solar, wind, and electric vehicle charging stations, represent a significant challenge in the process of distributing energy, which is a critical issue.  Energy demand has been rising rapidly due to the expansion of industries and population density. To prevent an energy crisis in the future, attention is being paid to energy consumption. Due to a lack of dependability, efficiency, security, seamless connectivity, etc., conventional electrical energy and networks would not be able to meet the needs of the industry in the 21st century. As a result, many new technologies (including communication and sensors) have developed to offer the features listed above.Evolution of Internet of EnergyThe Internet of Things (IoT) has evolved due to the expansion of heterogeneous networks and smart devices, enabling all networks and devices to interact with one another and create communication links with one another. The Internet of Things will be very helpful in the smart grid because it manages numerous components and seeks to give users the best possible energy.  The Internet of Things (IoT) is becoming more popular in smart grids under the "Internet of Energy" (IoE). To deliver the best energy and share relevant data among the numerous entities connected to the grid, smart grid technology uses all newly developed communication technologies and creates a completely connected network. A major problem has been the administration of enormous amounts of real-time data and its integration.  In contrast to the Internet of Things, the Internet of Energy is one of the most recent approaches to addressing issues like uninterruptible services, optimal use, etc. This article describes how the smart grid will use the Internet of Things to manage energy effectively. This also discusses how communication technologies integrate various smart grid components, infrastructure entities, substations, electric vehicles, etc. Advantages of Internet of Energy-based Smart GridsThe Internet of Energy enables optimal power distribution to all grid-connected devices and information sharing inside the grid network. Energy management, electric vehicle integration, and network integration will all be crucial in smart grids. Vehicle-to-grid (V2G) and grid-to-vehicle (G2V) technology have established a road to deal with the integration. With this technology, automobiles can communicate data with infrastructure about their state of charge (SOC), battery life, and condition, in addition to receiving the best possible energy supply. Due to the rapidly expanding energy consciousness, a dependable system that can deliver high-quality energy with optimal output and a sustainable backup system is required. This is why the smart grid is so unique because of the way it became linked to the bidirectional network system. The multi-agent system (MAS) will be employed in industries to manage the smart grid without human interaction. The software component known as the multi-agent system is responsible for gathering and delivering necessary data throughout the network. Challenges in SecurityThe effective formation of communication between entities aids in the handling of the massive amount of real-time data using reliable, secure encryption techniques. Only permitted entities should be able to manage network data exchange. Data management and security will become important challenges while dealing with a large volume of data and powering every device connected to the grid. The grid network will be more vulnerable to cyberattacks, which might cause individual components and the network as a whole to malfunction.  It results in the flow of incorrect information between entities and end users. Therefore, it is necessary to give the grid high security. Strong protocols (including encryption and decryption), anti-malware software, and highly secure network management protocols are required for high security.Features of Internet of EnergyThe smart energy infrastructure shown in Fig. 1 is a networked system comprising loads, energy metering units, energy storage devices, and automated and centralized distribution systems. Power and energy distribution across the network is the Internet of Energy’s primary goal, and it also enables information sharing with all linked devices. It deals with the security and management of real-time data. Cloud and edge-based systems are fully necessary for implementing the "Internet of Energy" concept.  Open-source interfaces are necessary for creating customer-specific applications to make the Internet of Energy quick and effective. The cloud-based application system at the power grid substation compares the actual target with the current target demand. It offers services like security management and power delivery to remote locations. The substation-connected assets were tracked, examined, and shared using the Internet of Energy.  Once the data analysis process is complete, the appropriate entity will permit the necessary steps, transforming the power plant and smart grid from a traditional into a virtual system. The advanced distributed energy management system's use of technology improves the effectiveness of power usage. Utilizing appropriate optimization techniques at various levels maximizes output while lowering costs, boosting profitability, improving dependability, and incorporating more renewable resources into the smart grid network.  The administration of smart meters, grid analytics, sub-station devices, low voltage outage management systems, and distributed energy resource management systems are some advanced applications integrated with the Internet of Energy. By integrating real-time data and devices into the digital world, smart grids offer quick and safe transport of information and power. Fig. 1. Internet of Things-based efficient energy transactions at the grid and charging stations. Source: IET Renewable Power Generation Summarizing the Key PointsThe use of Internet of Things-based efficient energy transactions is crucial in addressing the challenges faced by conventional electrical energy and networks in meeting the demands of the industry in the 21st century.The Internet of Things has played a significant role in the evolution of the electrical grid, enabling all networks and devices to interact with one another and create communication links.The Internet of Things-based efficient energy transaction can help prevent an energy crisis in the future by ensuring that energy demand is met efficiently and securely.The Internet of Energy is becoming more popular in smart grids as it seeks to give users the best possible energy by managing numerous components and providing uninterruptible services.The administration of enormous amounts of real-time data and its integration has been a major problem in smart grids, which can be addressed using Internet of Things-based efficient energy transactions. This blog post is part of a full research article from IET Renewable Power Generation. The featured image is courtesy of Midjourney.