Smart Grid Architecture Research Articles

Responsive FLEXibility: A smart local energy system

The transition towards a more decarbonised, resilient and distributed energy system requires local initiatives, such as Smart Local Energy Systems (SLES), which lead communities to gain self-sufficiency and become electricity islands. Although many SLES projects have been recently deployed, only a few of them have managed to be successful, mostly due to an initial knowledge gap in the SLES planning and deployment phases. This paper leverages the knowledge from the UK’s largest SLES demonstrator in the Orkney Islands, named the Responsive FLEXibility (ReFLEX) project, to propose a framework that will help communities to successfully implement a SLES. First, this paper describes how the multi-services electrical SLES implemented in Orkney reduces the impact of the energy transition on the electrical infrastructure. We identify and discuss the main enablers and barriers to a successful SLES, based on a review of SLES projects in the UK. Second, to help future communities to implement SLES, we extend the Smart Grid Architecture Model (SGAM) into a comprehensive multi-vector Smart Local Energy Architecture Model (SLEAM) that includes all main energy services, namely power, heat and transport. This extended architecture model describes the main components and interaction layers that need to be addressed in a comprehensive SLES. Next, to inform successful deployment of SLES, an extensive list of key performance indicators for SLES is proposed and implemented for the ReFLEX project. Finally, we discuss lessons learnt from the ReFLEX project and we list required future technologies that enable communities, energy policy makers and regulatory bodies to best prepare for the energy transition.

Scalability and replicability analysis of smart grid projects: Insights from the H2020 CROSSBOW project

The scalability and replicability analysis (SRA) is usually performed for Smart Grid Architecture Model (SGAM) projects to assess the applicability of proposed solutions in various regions, conditions, densities, and sizes. In this framework, the replication and scaling-up roadmap of the Horizon 2020 (H2020) CROSSBOW project encompasses a variety of interesting aspects. This study evaluates CROSSBOW results and their impact under various technical, regulatory, market, and social factors. The relevant methodology follows the literature and is properly adjusted to investigate the level at which the CROSSBOW concepts can be escalated and transferred to different scenarios and regions in an efficient and cost-effective manner. Limitations and challenges regarding scalability and replicability are also illustrated. The results depict the total impact of specific factors on scalability and replicability while useful conclusions are drawn. Lessons learnt from the CROSSBOW are very insightful for the scalability and replicability of other upcoming projects.

IMPLEMENTATION OF BASIC INTERNATIONAL SMART GRID STANDARDS IN UKRAINE: CURRENT STATE

In this paper, the main components of the basic model of the Smart Grid architecture according to the standard IEC TR 63097:2017 "Smart Grid Roadmap" are considered. The list of basic and particularly important international standards, which have a significant impact on the application of Smart Grid, has been identified and analyzed. A group of standards describing the basic principles of Smart Grid systems application is selected. These groups are unifying and allow to eliminate contradictions between other existing standards, to ensure their consistency. The development of a strategy and the creation of a road map for the adoption of modern European and international standards in Ukraine in the field of Smart Grid substantiated. A list of relevant directions and functional systems necessary for the implementation of the components of the Smart Grid concept is given. These directions require a detailed analysis to establish priority levels during the implementation of relevant modern European and international standards in Ukraine, taking into account the specifics of the Ukrainian electricity market.

SGAM-Based Analysis for the Capacity Optimization of Smart Grids Utilizing e-Mobility: The Use Case of Booking a Charge Session

The description of the functionality of a smart grid’s architectural concept, analyzing different Smart Grid (SG) scenarios without disrupting the smooth operation of the individual processes, is a major challenge. The field of smart energy grids has been increasing in complexity since there are many stakeholder entities with diverse roles. Electric Vehicles (EVs) can transform the stress on the energy grid into an opportunity to act as a flexible asset. Smart charging through an external control system can have benefits for the energy sector, both in grid management and environmental terms. A suitable model for analyzing and visualizing smart grid use cases in a technology-neutral manner is required. This paper presents a flexible architecture for the potential implementation of electromobility as a distributed storage asset for the grid’s capacity optimization by applying the Use Case and Smart Grid Architecture Model (SGAM) methodologies. The use case scenario of booking a charge session through a mobile application, as part of the TwinERGY Horizon 2020 project, is deployed to structure the SGAM framework layers and investigate the applicability of the SGAM framework in the integration of electromobility as a distributed storage asset into electricity grids with the objective of enhanced flexibility and decarbonization.

Energy generation forecasting: elevating performance with machine and deep learning

Distribution System Operators (DSOs) and Aggregators benefit from novel Energy Generation Forecasting (EGF) approaches. Improved forecasting accuracy may make it easier to deal with energy imbalances between production and consumption. It also aids operations such as Demand Response (DR) management in Smart Grid architecture. This work aims to develop and test a new solution for EGF. It combines various methodologies running EGF tests on historical data from buildings. The experimentation yields different data resolutions (15 min, one hour, one day, etc.) while reporting accuracy errors. The optimal forecasting technique should be relevant to a variety of forecasting applications in a trial-and-error manner, while utilizing different forecasting strategies, ensemble approaches, and algorithms. The final forecasting evaluation incorporates performance metrics such as coefficient of determination ({R^{2}}), Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), presenting a comparative analysis of results.

Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review

The smart grid concept is introduced to accelerate the operational efficiency and enhance the reliability and sustainability of power supply by operating in self-control mode to find and resolve the problems developed in time. In smart grid, the use of digital technology facilitates the grid with an enhanced data transportation facility using smart sensors known as smart meters. Using these smart meters, various operational functionalities of smart grid can be enhanced, such as generation scheduling, real-time pricing, load management, power quality enhancement, security analysis and enhancement of the system, fault prediction, frequency and voltage monitoring, load forecasting, etc. From the bulk data generated in a smart grid architecture, precise load can be predicted before time to support the energy market. This supports the grid operation to maintain the balance between demand and generation, thus preventing system imbalance and power outages. This study presents a detailed review on load forecasting category, calculation of performance indicators, the data analyzing process for load forecasting, load forecasting using conventional meter information, and the technology used to conduct the task and its challenges. Next, the importance of smart meter-based load forecasting is discussed along with the available approaches. Additionally, the merits of load forecasting conducted using a smart meter over a conventional meter are articulated in this paper.

A Comprehensive Review on Smart Grid Ecosystem

A smart grid is an intervention technology for the massive energy demand of the world today. It combines cyber-physical technologies, information communication technology, and electrical power networks from the generating company stations to the end-users while ensuring bidirectional communication among the actors. The smart grid is a complex growing technology that is yet to reach its maturity state. This paper seeks to examine the literature on the state of the art of smart grid technology both from the industry perspective and from academia. To this end, a literature review with a qualitative deductive approach built on the National Institute of Standards and Technology (NIST) guideline and the simplified International Telecommunication Union Telecommunication Standardization Sector (ITU-T) five-domain model were used as a guide to this research. Furthermore, the paper reviewed Smart Grid data centre topologies and identified prospects in spine-leaf architecture as a promising architecture that can be adapted in a smart grid ecosystem data centre design. The literature was searched from the databases: IEEE Xplore Digital Library, Springer Link Digital Library, and Google Scholar, IET Digital Library, Frontiers Library, ACM Digital Library repositories resulting in 151 papers after several exclusions. The work reviewed relevant literatures published from 2002 to 2021 and grouped the reviewed papers according to the key domains of the NIST/ITU-T model. Based on the evaluated literature, the need for more built-in predictive learning curves in smart grid systems and robust Smart grid architecture with enhanced data centre design for Smart grid systems is observed and recommended Keywords: Smart Grid, Smart Metre, Data Centre Computing, Distributed Energy, Protocols

Optimal assessment of smart grid based photovoltaic cell operational parameters using simulated annealing

With the ever-increasing load demand throughout the globe, natural renewable resources integrated into the existing network architecture for sustainable energy production are gaining considerable significance. Photovoltaic (PV) generation systems is one such technique to deal with the worldwide challenge for achieving green energy and low carbon footprint while simultaneously providing emission free electrical power from solar radiations. In this paper, we consider smart grid architecture connecting the end-users and the utility power plant with solar energy sources through an effective power optimization system. Multiple performance criteria associated with solar cell operation are evaluated and analyzed using the simulated annealing algorithm. These objectives considered for optimization include the cell saturation current, photo-generated current, material band gap, cell temperature, annualized life cycle cost, fill factor and cell efficiency. The formulated optimization conditions are specified in terms of two independent variables of cell ambient temperature and cell illumination. Moreover, the adaption of distinct values of short circuit current coefficients on the light originated current is measured. Through extensive simulation experiments, two disparate annealing procedures of fast annealing and Boltzmann annealing are applied coupled with three categories of temperature update schemes, viz. exponential, logarithmic and linear.

Multi-layer defense algorithm against deep reinforcement learning-based intruders in smart grids

The Internet of Energy envisions the next generation of smart grids as a highly interconnected network, including advanced metering infrastructures, distributed energy resources, and bidirectional communication systems. The open architecture of IoE-based smart grid results in manifold security concerns, especially the risk of False Data Injection Attacks. The attack may target the technical aspects of a system since fabricating the network's data misleads power scheduling and routing strategies and interrupts the healthy operation of the power system. Additionally, monetary motivation for the intruder sometimes is the main motivation. The conventional cyber defense strategies are unable to detect well-developed False Data Injection Attacks, particularly once the intruder takes advantage of a Deep Reinforcement Learning-based attack development framework that analyzes the dynamic nature of the smart grids. This paper primarily outlines various possible passive attacks using statistical methods. Then, a reinforcement learning-based intruder as an active attack generator is developed, initialized by modeled passive attacks. The attack generator algorithm can simulate the network environment and subsequently creates unclassified attacks. After creating a dynamic attacker, a multilayer defense framework is developed using Snapshot Ensemble Deep Neural Network and an adoptable Deep Auto Encoder network to detect known and unknown threats. Performance evaluations and a real-world simulation prove that the proposed framework can successfully detect both passive and active, where the accuracy and false positive detection rate of the developed framework are 98.82% and 97.42%, respectively.

Cost Optimal Hybrid Communication Model for Smart Distribution Grid

There have been significant changes and dynamic growth in power grids since the inception of the smart grid. To ensure a self-sustainable grid, the power grid should facilitate the real-time sharing of the dynamic attributes of the power grid between the spatially distributed power grid elements. This demands a flawless design of the overlay communication network for a smart grid. However, instrumenting the entire grid with intelligent communication devices would be prohibitively expensive. A cost-optimal model is necessary to select the actual cardinality of intelligent communication devices required for the data transmission by ensuring its Quality of Service (QoS) metrics. This research work presents a smart distribution grid architecture with a network of microgrids and details the need for hybrid communication for intra-microgrid communication. A cost-optimal model is proposed to offer an optimal combination of technologies for intelligent devices in the hybrid communication overlay network. The formulation considers QoS metrics such as data packet latency, bandwidth requirement, link reliability, packet drops, and communication range of the technology to derive a cost-optimal solution. Based on this model, a Recursive Algorithm for Cost Optimal Combination of Communication Technology (RACOCCT) for a power grid topology is presented in this work. The simulation study performed on selected power grid topology based on the Standard IEEE 33 bus network reveals the cost-optimal combination of hybrid wireless communication technologies for the varying probability of link unreliability.

Interoperability Testing of a Smart Home Automation System under Explicit Demand Response Schemes

Interoperability becomes a key issue for smart grid systems, as the interaction between diverse components needs to lead to a normal system operation. In this paper, we test interoperability issues with respect to home automation. In particular, interaction of a home energy management system (HEMS) is examined with an external actor for home/building remote control. We show the importance and the feasibility of remotely controlling domestic loads from outside the house premises, which can be crucial for energy saving operations, such as demand response. The Smart Grid Architecture Model (SGAM) is used, where the different actors are depicted. The interoperability testing methodology for smart grids, developed by our unit, is followed in order to design the necessary tests and execute them. For the experimental part, we develop an HEMS in our lab along with a Home Automation End Device (HAED), used to transform two normal plugs, and consequently, normal loads into smart ones, thus creating a system for home automation and control. The described configuration is only one possible configuration out of the available ones existing in the market for home automation. LabVIEW programming is used in order to realize the actual explicit demand response program through remote load control and scheduling. The results show that explicit demand response can be achieved by an external actor with success and interoperability is preserved.

Hybrid AC/DC architecture in the CE.D.E.R.-CIEMAT microgrid: demonstration of the TIGON project

This article presents the demonstrative development of the Towards Intelligent DC-based hybrid Grids Optimizing the Network performance (TIGON) project at the Centre for the Development of Renewable Energy - Centre for Energy, Environmental and Technological Research (CE.D.E.R.-CIEMAT), as well as the established objectives to be achieved with the implementation of a microgrid with smart grid architecture based on direct current (DC) and integrated into the current energy system. This type of architecture is proposed as a future solution to reduce energy losses caused by DC-alternating current (AC) conversions, increasing the overall performance and profitability of hybrid grids. All this without forgetting to ensure the supply, stability and reliability of the system with the development of all the necessary equipment and protections to make this approach a reality. The microgrid design and process of implementation start from a transformation centre, from which the medium voltage direct current (MVDC) grid will be created by the solid-state transformer (SST). In the MVDC grid, we will find a bank of lead-acid batteries and other essential equipment in the microgrid, a DC/DC converter that will create the low voltage direct current (LVDC) grid. On the LVDC side, several branches have been designed to connect the rest of the systems: generation (mini-wind and photovoltaic), storage (lithium ferro-phosphate [LFP] batteries) and loads (AC and DC loads). Each of the equipment will have a connection to the DC grid through converters made exclusively for this equipment and connexion to the AC grid, which will allow us to obtain all the necessary data to carry out the required studies to achieve the established objectives of the project.

Power grid surveillance: Topology change detection system using power line communications

This paper proposes an efficient channel impulse response (CIR)-based technique to detect topology changes in the power grid. The features of the proposed approach include the following aspects: (i) it is a software-only solution, not requiring any intervention on the current smart grid architecture; (ii) topology changes can be detected via a simple distributed algorithm that requires only local communications; and, (iii) both memory and computational footprints of the proposed solution are minimal. The above-mentioned features make this contribution notably appealing for the resource-constrained smart grid domain. Furthermore, the paper provides a detailed discussion of the introduced technique, accompanied by an implementation reflecting a realistic use case, and presents an extensive simulation campaign to show the quality and viability of the proposed approach. A unique feature of our solution is that it performs well even when the communication channel is affected by a high noise level. For instance, with noise levels below 100 dBμV (the typical PLC noise power range), the proposed solution showed a ≈100% detection rate.To the best of our knowledge, our solution is the first one proposing a fully CIR-based, distributed, deterministic algorithm for intrusion detection and localization. We believe that the performance and advantages of the proposed technique pave the way for further smart grid applications and solutions.

A Survey on IoT-Enabled Smart Grids: Emerging, Applications, Challenges, and Outlook

Swift population growth and rising demand for energy in the 21st century have resulted in considerable efforts to make the electrical grid more intelligent and responsive to accommodate consumers’ needs better while enhancing the reliability and efficiency of modern power systems. Internet of Things (IoT) has appeared as one of the enabling technologies for smart energy grids by delivering abundant cutting-edge solutions in various domains, including critical infrastructures. As IoT-enabled devices continue to flourish, one of the major challenges is security issues, since IoT devices are connected through the Internet, thus making the smart grids vulnerable to a diverse range of cyberattacks. Given the possible cascading consequences of shutting down a power system, a cyberattack on a smart grid would have disastrous implications for the stability of all grid-connected infrastructures. Most of the gadgets in our homes, workplaces, hospitals, and on trains require electricity to run. Therefore, the entire grid is subject to cyberattacks when a single device is hacked. Such attacks on power supplies may bring entire cities to a standstill, resulting in massive economic losses. As a result, security is an important element to address before the large-scale deployment of IoT-based devices in energy systems. In this report, first, we review the architecture and infrastructure of IoT-enabled smart grids; then, we focus on major challenges and security issues regarding their implementation. Lastly, as the main outcome of this study, we highlight the advanced solutions and technologies that can help IoT-enabled smart grids be more resilient and secure in overcoming existing cyber and physical attacks. In this regard, in the future, the broad implementation of cutting-edge secure and data transmission systems based on blockchain techniques is necessary to safeguard the entire electrical grid against cyber-physical adversaries.

Cyber Threats to Smart Grids: Review, Taxonomy, Potential Solutions, and Future Directions

Smart Grids (SGs) are governed by advanced computing, control technologies, and networking infrastructure. However, compromised cybersecurity of the smart grid not only affects the security of existing energy systems but also directly impacts national security. The increasing number of cyberattacks against the smart grid urgently necessitates more robust security protection technologies to maintain the security of the grid system and its operations. The purpose of this review paper is to provide a thorough understanding of the incumbent cyberattacks’ influence on the entire smart grid ecosystem. In this paper, we review the various threats in the smart grid, which have two core domains: the intrinsic vulnerability of the system and the external cyberattacks. Similarly, we analyze the vulnerabilities of all components of the smart grid (hardware, software, and data communication), data management, services and applications, running environment, and evolving and complex smart grids. A structured smart grid architecture and global smart grid cyberattacks with their impact from 2010 to July 2022 are presented. Then, we investigated the the thematic taxonomy of cyberattacks on smart grids to highlight the attack strategies, consequences, and related studies analyzed. In addition, potential cybersecurity solutions to smart grids are explained in the context of the implementation of blockchain and Artificial Intelligence (AI) techniques. Finally, technical future directions based on the analysis are provided against cyberattacks on SGs.

Investigation and analysis of effective approaches, opportunities, bottlenecks and future potential capabilities for digitalization of energy systems and sustainable development goals

Taking into consideration of substantial role of energy system and sustainable development goals (SDGs) in modern society, it is critical to analyse current situation and forthcoming renewable energy development strategies under the impact of COVID-19. For this purpose, this paper provides significant new insights to assess effective approaches, opportunities, challenges and future potential capabilities for the development of energy systems and SDGs under on-going pandemic and in case of a future global crisis. The digital energy systems with Industry 4.0 (I4.0), which provide noteworthy solutions such as enhancing energy efficiency policy, providing clean, secure and efficient energy and achieving SDG targets, has been discussed and evaluated. Integration of the smart grid (SG) architecture with blockchain-Internet of Things (IoT)-based technologies is also offered. Alongside the various discussions, short-term, mid-term and long-term plans have been suggested in determining the well-defined renewable energy development and SDGs targets, struggling with climate change, transition to a more sustainable energy future and reaching global net-zero emissions. To achieve SDGs and provide more strong and sustainable energy systems under the continuing pandemic and in case of potential risk of forthcoming global crisis, this paper reveals significant perceptions that inform politicians and legislators in performing successful policy decisions.

Online management and assessment of power quality issues through smart metering in smart grid architecture

With the growing Smart Grid concept, it has become very important to formulate and device the ways of intelligent control and supervision of electrical power distribution systems. As the distribution part is a major area among generation, transmission, and distribution, so without complete automation of the distribution system, a complete smart grid concept cannot be achieved. Therefore, a smart distribution system concept has been developed in this paper, where a vital role plays in the operation of smart meters having communication capabilities. This paper proposed two-level control architecture. MATLAB/SIMULINK model has been developed. The model studied consists of two low voltage (LV) distribution networks falling under one substation, thus two local controllers and one main controller have been modeled. To simulate the functionalities of controllers, different case scenarios such as overcurrent protection, power theft, no shutdown of essential loads (like hospitals, VIPs), available power loss, and phase unbalancing for 60 houses together in two areas have been studied and were compared with the real-time system through OPAL-RT. Performance of Dynamic Voltage Restorer (DVR) in case of voltage sag/swell and harmonics have been shown for enhancing power quality in distribution systems.

An ICT architecture for enabling ancillary services in Distributed Renewable Energy Sources based on the SGAM framework

Smart Grids are electrical grids that require a decentralised way of controlling electric power conditioning and thereby control the production and distribution of energy. Yet, the integration of Distributed Renewable Energy Sources (DRESs) in the Smart Grid introduces new challenges with regards to electrical grid balancing and storing of electrical energy, as well as additional monetary costs. Furthermore, the future smart grid also has to take over the provision of Ancillary Services (ASs). In this paper, a distributed ICT infrastructure to solve such challenges, specifically related to ASs in future Smart Grids, is described. The proposed infrastructure is developed on the basis of the Smart Grid Architecture Model (SGAM) framework, which is defined by the European Commission in Smart Grid Mandate M/490. A testbed that provides a flexible, secure, and low-cost version of this architecture, illustrating the separation of systems and responsibilities, and supporting both emulated DRESs and real hardware has been developed. The resulting system supports the integration of a variety of DRESs with a secure two-way communication channel between the monitoring and controlling components. It assists in the analysis of various inter-operabilities and in the verification of eventual system designs. To validate the system design, the mapping of the proposed architecture to the testbed is presented. Further work will help improve the architecture in two directions; first, by investigating specific-purpose use cases, instantiated using this more generic framework; and second, by investigating the effects a realistic number and variety of connected devices within different grid configurations has on the testbed infrastructure.

Advanced metering infrastructure for low voltage distribution system in smart grid based monitoring applications

Smart grid system is an emerging technology used for solving the issues associated with power and energy. Smart grid system is known to be an automatic network, ensuring power with the transfer of information and electricity between the power plants and household appliances. However, there is a lack in the data flow about the voltage distribution stability, accuracy and applicability. So, the advanced metering infrastructure, personal energy management and Information Technology are overviewed in this model. The features of smart meter enable the advanced metering infrastructure monitoring and investigates low-voltage (LV) network. This is achieved by optimal state, in which the power quality and outage data is transferred by the distribution network from smart meters to the control center. In this model, the smart grid architecture is mapped with optimal states to observe the LV network processing. Recursive Karhunen–Loéve Transform Grid state optimization Model (RK-LTGM) is proposed in this paper for ensuring the usage of new data and narrow down the variations in estimator range. The proposed model is robust against the network loss. Dimension Reduction Estimation Model Probabilistic Loss (DREM-PL), Korhonen Estimation Model (KEM) and Constant Network Loss Rate Estimation Model (CNLREM) are the three state-of-art methods considered for comparing this model. Recursion Least Squares (RLS) and Damped Recursion Least Squares (DRLS) are the index parameters considered in the proposed method. The proposed Recursive Karhunen–Loéve Transform Grid state optimization Model (RK-LTGM) helps in exchanging information in multilevel smart grid, allowing Smart meter to utilize the information to obtain the high quality best technological and economical solution for every consumer. It is found that the proposed RK-LTGM achieves 0.112 % of RMSE, 0.156 % of network loss rate, 56.124 % of success rate under RLS condition, 0.067 % of RMSE, 0.137 % of network loss rate and 88.739 % of success rate under DRLS condition.

Integrating Artificial Intelligence Internet of Things and 5G for Next-Generation Smartgrid: A Survey of Trends Challenges and Prospect

Smartgrid is a paradigm that was introduced into the conventional electricity network to enhance the way generation, transmission, and distribution networks interrelate. It involves the use of Information and Communication Technology (ICT) and other solution in fault and intrusion detection, mere monitoring of energy generation, transmission, and distribution. However, on one hand, the actual and earlier smartgrid, do not integrate more advanced features such as automatic decision making, security, scalability, self-healing and awareness, real-time monitoring, cross-layer compatibility, etc. On the other hand, the emergence of the digitalization of the communication infrastructure to support the economic sector which among them are energy generation and distribution grid with Artificial Intelligence (AI) and large-scale Machine to Machine (M2M) communication. With the future Massive Internet of Things (MIoT) as one of the pillars of 5G/6G network factory, it is the enabler to support the next generation smart grid by providing the needed platform that integrates, in addition to the communication infrastructure, the AI and IoT support, providing a multitenant system. This paper aim at presenting a comprehensive review of next smart grid research trends and technological background, discuss a futuristic next-generation smart grid driven by artificial intelligence (AI) and leverage by IoT and 5G. In addition, it discusses the challenges of next-generation smart-grids as it relate to the integration of AI, IoT and 5G for better smart grid architecture. Also, proffers possible solutions to some of the challenges and standards to support this novel trend. A corresponding future work will dwell on the implementation of the discussed integration of AI, IoT and 5G for next-generation smart grid, using Matlab, NS2/NS3, Open-daylight and Mininet as soft tools and compare with related literature.