Grid Architecture Research Articles

Implementation of ANFIS-based UPQC for Power Quality and Thermal Management Enhancement in the Distribution System

Conventional distribution systems generally operate with voltage and current waveforms being sinusoidal, though they slightly deviate from the ideal sinusoidal waveform, which is measured as distortion. The increase in power demand has led to the local generation and storage of power (Micro Grid systems). For its adoption, we need to implement smart grid architecture. Power electronics is a technologically sound way to limit different kinds of power quality (PQ) disturbances. It can also be used to control power flow, boost energy transmission capacity, enhance dynamic behavior and voltage stability, and ensure better power quality at distribution within established bounds. Unified Power Quality Conditioners (UPQC), one of the well-known Flexible AC Transmission System (FACTS) devices, are typically used to address problems in distribution systems such as voltage surges, flickers, neutral current reduction, and PQ. Additionally, thermal management is crucial for maintaining system stability and preventing overheating. While dealing with sensitive loads, a UPQC injects harmonics into the system, which can compromise system stability. This article describes an artificial neural network with harmonics elimination techniques for a modified UPQC connected with a Smart Grid. The performance of the proposed system is evaluated using MATLAB/Simulink software.

Offshore Market Design in Integrated Energy systems: A Case Study on the North Sea Region towards 2050

Offshore grids, with multiple interacting transmission and generation units connecting to the shores of several countries, are expected to have an important role in the cost-effective energy transition. Such massive new infrastructure expanding into a new physical space will require new offshore energy market designs. Decisions on these designs today will influence the overall value potential of offshore grids in the future. This paper investigates different possible market configurations and their impacts on operational costs and required congestion management, as well as prices and emissions. We use advanced integrated energy system optimisation, applied to a study case on the North Sea region towards 2050. Our analysis confirms the well-known concept of nodal pricing as the most preferable market configuration. Nodal pricing minimises costs (0.2–1.6 b€/year lower) and CO2 emissions (0.6–5.6 Mton/year lower) with respect to alternative market designs investigated. The performance of the different market designs is highly influenced by the overall architecture of the offshore grid, and the rest of the energy system. E.g., flexibility options help reducing the spread between the designs. But the results are robust: nodal pricing in offshore grids emerges as the preferable market configuration for a cost-effective energy transition to carbon neutrality.

Integration of distributed PV into smart grids: A comprehensive analysis for Germany

Modern smart grids typically combine physical and communication networks for efficient information exchange and innovative applications. Aligned with digitalization and advancements in smart grids, the integration of photovoltaic (PV) systems comprises a variety of regulatory and technological aspects. However, no previous study has conducted an extensive and systematic analysis of the PV-grid integration framework, particularly for one country. To fill this gap, this paper uses Germany as an example to present a comprehensive, state-of-the-art analysis of integrating distributed PV systems into smart grids, focusing on the regulation and technical implementation of the German Smart Meter Infrastructure and PV control interfaces. Starting from a standardization perspective, this analysis utilizes the Smart Grid Architecture Model to identify crucial roles, components and processes specifically in Germany. Furthermore, it outlines the current implementation of PV integration into distribution networks at a national level. The results of this study show the overall complexity of PV integration in the smart grid context, confirm the feasibility of the German integration approach, and highlight the necessity of deploying standardized information models and communication technologies. These key findings can help market participants with different roles to identify potential technical bottlenecks or other critical points in the regulation and technical implementation. For instance, the proposed in-depth analysis framework provides an orientation for characterizing the PV integration or, more generally, the grid integration scenario of renewables in other countries.

Optimal coordination of overcurrent relays in microgrids using meta-heuristic algorithms NSGA-II and harmony search

The emergence of distributed generation from renewable energy sources has led to the adoption microgrids as an alternative energy solution. However, implementing microgrids presents challenges, particularly in coordinating relay protection, due to factors like distributed generation sources, bidirectional power flow, variable short-circuit levels, and changes in network behavior. Although overcurrent relays (OCR) are frequently utilized in microgrid protection, a more adaptable strategy is needed as grid architectures transition from radial to non-radial. This paper proposes a new method to optimize the coordination of OCRs in microgrids by adjusting parameters like time multiplier settings (TMS), plug settings (PS), and characteristic curve selection. The study utilizes meta-heuristic techniques such as the harmony search algorithm (HSA) and the non-dominated sorting genetic algorithm-II (NSAGA-II) for optimal coordination. Simulations on a microgrid and bus test system demonstrate the effectiveness of the proposed approach in enhancing protection indicators like sensitivity, speed, selectivity, and reliability in microgrid operations. The results also indicate that the computation time of HSA is less than NSGA-II, but with an increase in DGs capacity, there is a continuous tendency to reduce the relay operation time.

A Lightweight Sequential Convolutional Neural Network for Smart Grid Stability Analysis

A Smart grid stability analysis is essential for ensuring modern power systems' reliable and secure operation. This approach helps identify potential instabilities and disturbances that can lead to blackouts or equipment failures. By analyzing the stability of the grid, operators can take proactive measures to maintain a stable and resilient power infrastructure. Monitoring smart grid data from various sources and analyzing how to control the stability of the grid are challenging tasks. A convolutional neural network (CNN) can effectively capture spatial dependencies and patterns from grid data and can help in enabling accurate prediction and classification of stability-related events in a power system. However, developing a CNN that has fewer learnable parameters and provides high accuracy is challenging. This paper presents a sequential CNN architecture to detect the stability of the Decentral Smart Grid Control (DSGC) system. A mathematical model of the 4-node start architecture of a smart grid was presented. Later, 12 parameter-based grid datasets from the UCI repository were used to validate the proposed network. The proposed CNN accepts sequential data to capture temporal dependencies in the data. The sequential process in a single dimension offers fewer learnable parameters, making the network more compact and computationally efficient. The proposed 11-layered CNN has a total of 12.7K learnable parameters. The detailed analysis of the proposed CNN using ambiguity and the t-SNE score suggested that the model can identify discriminative features for classifying data into stable and unstable classes. A comparison analysis of the quantitative parameters revealed that the model performed well, with 98.82%, 98.55%, 98.88%, and 98.77% accuracy, precision, recall, and F1 score, respectively.

Offshore Market Design in Integrated Energy systems: A Case Study on the North Sea Region towards 2050

Offshore grids, with multiple interacting transmission and generation units connecting to the shores of several countries, are expected to have an important role in the cost-effective energy transition. Such massive new infrastructure expanding into a new physical space will require new offshore energy market designs. Decisions on these designs today will influence the overall value potential of offshore grids in the future. This paper investigates different possible market configurations and their impacts on operational costs and required congestion management, as well as prices and emissions. We use advanced integrated energy system optimisation, applied to a study case on the North Sea region towards 2050. Our analysis confirms the well-known concept of nodal pricing as the most preferable market configuration. Nodal pricing minimises costs (0.2–1.6 b€/year lower) and CO2 emissions (0.6–5.6 Mton/year lower) with respect to alternative market designs investigated. The performance of the different market designs is highly influenced by the overall architecture of the offshore grid, and the rest of the energy system. E.g., flexibility options help reducing the spread between the designs. But the results are robust: nodal pricing in offshore grids emerges as the preferable market configuration for a cost-effective energy transition to carbon neutrality.

Addressing Cross‐Domain Interoperability between Automotive and Smart Grid Architecture Models

AbstractThe rapid advancement and diversification of technical domains, particularly in automotive and smart grid sectors, are pivotal in driving the emerging energy revolution. This evolution is instrumental in governing the future of smart cities, characterized by escalating complexity and diversity within these domains. Such a landscape necessitates seamless collaboration among various domain experts, a task often complicated by the prevalent use of domain‐specific languages and tools tailored to specific engineering needs. This poses a significant challenge towards cross‐domain interoperability.Addressing this challenge, our research introduces a novel approach leveraging abstraction layers inspired by the Software Platform Embedded Systems (SPES) methodology. This approach aims to enhance the compatibility of domain‐specific frameworks, with a focus on the Smart Grid Architecture Model (SGAM) and the Automotive Reference Architecture Model (ARAM). By applying these SPES‐inspired abstraction layers, our work facilitates the reconciliation of varying levels of detail across different domains.The paper culminates in a proof of concept that demonstrates the practical implementation of this approach, showcasing a method to achieve effective cross‐domain interoperability. This implementation not only underscores the feasibility of our proposed solution but also illuminates a pathway for managing the intricate interplay of systems in the rapidly evolving landscape of smart cities.

IOT Based Smart Grid to Remotely Monitor and Control Renewable Energy Sources using Cloud Computing

Abstract: The growing demand for clean and sustainable energy sources necessitates advancements in grid structure. This project explores the potential of an IOT-based smart grid to remotely monitor and control renewable energy sources. In this, we use solar energy as a renewable energy source, and with the help of cloud computing, we monitor and control different types of loads. The paper highlights the key components, functionalities, and benefits of such a system.The increasing demand for sustainable energy solutions fuels the development of smart grid technologies. This project proposes an IOT (Internet of Things)-enabled smart grid architecture for remote monitoring and control of renewable energy sources. The core concept involves integrating sensors with renewable energy sources like solar or wind turbines. These sensors collect real-time data on energy generation, environmental conditions, and system health. This data is then transmitted over a network to a central control system or a cloud platform for further analysis. By implementing remote control capabilities, grid operators can: regulate energy production based on demand; integrate storage solutions for surplus energy; and improve overall grid stability and reliability. The project concludes by outlining the benefits of this approach, including a reduced carbon footprint, cost savings, and a more sustainable energy future.

A Multiscale study of flexible customer’s energy demand under smart grid architecture: A modeling and simulation study

A Multiscale study of flexible customer’s energy demand under smart grid architecture: A modeling and simulation study

Recent advances in smart grid architecture and grid-electric vehicle-charging station interaction

Recent advances in smart grid architecture and grid-electric vehicle-charging station interaction

IoT Security Framework Optimized Evaluation for Smart Grid

Modern systems' needs may be satisfied by smart grid technologies. Since we frequently struggle to effectively manage security, the smart grid's capacity is frequently underutilized. Despite the fact that a variety of solutions have been offered for securing the smart grid, the problem still exists that no single solution can entirely protect the environment. We provide a protection architecture for the IoT-connected smart grid. The proposed framework to secure IoT devices for the smart grid includes three complementary approaches. By conducting a rigorous comparative analysis of our proposed solution alongside four existing models, we contribute to the ongoing discourse on bolstering the security infrastructure of the smart grid IoT environment. Our optimized evaluation provides valuable insights into the strengths, weaknesses, and unique attributes of each model, offering a comprehensive understanding of their respective applicability and efficacy within the intricate realm of sensor-based applications. Two testing configurations were used to evaluate the Threat Mitigation Framework. It demonstrated superior performance in recognizing attacks like XSS across all testing configurations. In each of the two test sets, we also assessed the device management functions, and we found that they accurately recognized and presented IoT for the smart grid controller.

A thorough investigation of the systems supporting E-vehicle charging stations and emerging trends

The percentage of electric vehicles (EVs) in the automobile industry which is increasing; yet, internal combustion engines (ICEs) remain operates a significant number of automobiles. The main barriers to EV use and environmentally friendly travel are their high price, short range, lack of battery charging infrastructure, and electricity pollution from EV chargers these barriers need to be taken down. Energy storage systems (ESS) with a substantial amount of energy are expensive, which contributes to the expensive of EVs. This article offers an analysis of EV technology, focusing on four primary areas of automobile technology. A detailed investigation is conducted into how grid architecture may be integrated with charging facilities and approaches to extend the longevity and productivity of electric vehicles. Since EVs are multienergy systems, reliable power control and management are necessary to maximize the consumption of energy. The assessment of EVs on the electric grid is concluded by an examination of various battery power management and control solutions in this paper. The review article concludes the suggested avenues for further study will be very helpful for researchers and EV users.

Development of an IoT smart energy meter with power quality features for a smart grid architecture

Electricity consumption has been intensifying due to population growth, climate change, urbanization, and the growing use of electronic devices, which are increasingly non-linear loads that cause poor power quality conditions. The trend of the Internet of Things has led to the creation of devices that encourage the efficient and effective utilization of electrical power. This in turn facilitates the development of modern power distribution structures such as smart grids. Consequently, this paper presents in detail the design, construction, and validation of a three-phase IoT smart meter intended to form part of the end-user demand side of a smart grid. The compact embedded system, with a manufacturing cost below $80 USD, features a unique electronic design that enables its installation in any load center and employs a straightforward IoT structure that includes WiFi technology for Internet communication. Also, a deployed web application was developed specifically to display the smart meter measurements. Unlike other smart meters, the proposed meter not only provides the amount of active energy consumption, but total and fundamental RMS current and voltage, active, reactive, and apparent power, reactive energy, power factor, and some power quality parameters such as, line frequency, amplitude of 64 current harmonics, and total harmonic distortion. Additionally, this study shows that the prototype achieves an absolute error of less than 1% in all its measurements. Finally, real-life applications of the developed device are demonstrated in residential environments.

The Egyptian national HPC grid (EN-HPCG): open-source Slurm implementation from cluster to grid approach

Recently, Egypt has recognized the pivotal role of High Performance Computing in advancing science and innovation. Additionally, Egypt realizes the importance of collaboration between different institutions and universities to consolidate their own computational and data resources into a unified platform to serve different disciplines (e.g., scientific, industrial, governmental). Otherwise, additional resources would be needed to be purchased with the associated cost, effort, and time difficulties (e.g., setup, administration, maintenance, etc.). Thus, this paper delves into the architecture and capabilities of the EN-HPCG grid using two different workload management systems: (i) Slurm (Open-Source) and (ii) PBS Pro (Licensed). This paper compares the performance of the grid between Slurm and PBS Pro in specific high-throughput computing (HTC) applications using the NAS Grid parallel benchmark (NGB) to determine which workload manager is more suitable for EN-HPCG. The evaluation includes grid-level performance metrics such as throughput, and the number of tasks completed as a function of time. Also, the presented methodology aims to assist potential partners in their decision-making process to join the EN-HPCG grid, with a focus on the site speed-up metric. Our results showed that, unless an open-source solution without cost and license problems is an obligation (in which case, Slurm is the viable solution), then it is not advisable to integrate a cluster with high-speed hardware with a cluster possessing outdated hardware when using the Slurm scheduler. In contrast, the PBS Pro scheduler takes into account online decision-making in a dynamic environment using a unified grid.

The application of the internet of things technology in the smart grid

The development of electric power and the energy industry is an important guarantee for national economic development. With the explosion of population and the development of science and technology, the power system needs to be more intelligent and networked to facilitate management and optimization. In recent years, the Internet of Things (IoT) technology has continued to develop through the combination of Internet technology and sensor technology, and the fast network transmission speed as well as the miniaturization and refinement of sensing can help the construction of the smart grid. This paper analyzes the application of IoT technology in the smart grid, including different types of smart grid architecture and the practical application of the IoT technology in the whole process of power transmission. Besides, the paper also discusses different application links and network levels. At present, the IoT technology has been applied to the smart grid information collection, transmission, and processing process, participating in information management in the power station, power transmission, substation, and other links. However, due to the complexity of processing the massive data of the power grid and the multiple network connections during the transmission process, ensuring the security of information and networks has become a challenge. While realizing high-speed communication, it is necessary to ensure the security of users as well as the information communication and networks.

Enhancing micro grid sustainability: Genetic-driven reptile search algorithm based carbon emission and cost reduction strategy

Grid architecture is continuously changing in response to modern energy rules that ensure the integration of more renewable sources to lower the carbon footprint. The integration of non-dispatchable renewable energy sources (RESs) using low voltage grids is a possible alternative to a centralized method. Due to a recent trend, microgrids (MGs) are reducing their reliance on the primary grid by incorporating localized sources of electricity including energy storage devices, micro turbines, and fuel cells. Due to the expanding number of spread generating sources with diverse features, power dispatching schedule for distribution micro grids is becoming more challenging. This study offers a dispatch scheduling method from the perspective of an operator. The primary objective of this study is to employ the Genetic-driven Reptile search algorithm (G-RSA) for the dual purpose of minimizing carbon emissions and reducing costs within each microgrid. Additionally, fewer sale and purchase are made from the primary grid. Transmission losses are consequently reduced.

High-resolution Si3N4 spectrometer: architecture & virtual channel synthesis and experimental demonstration.

Up-to-date network telemetry is the key enabler for resource optimization by capacity scaling, fault recovery, and network reconfiguration among other means. Reliable optical performance monitoring in general and, specifically, the monitoring of the spectral profile of WDM signals in fixed- and flex- grid architectures across the entire C-band, remains challenging. This article describes a two-stage spectrometer architecture amenable to integration on a single chip that can measure quantitatively the spectrum across the entire C-band with a resolution of ∼ 1.4 GHz. The first stage consists of a ring resonator with intra-ring phase shifter to provide a tuneable fine filter. The second stage makes use of an AWG subsystem and a novel processing algorithm to synthesize a tuneable coarse filter with a flat passband which isolates individual resonances of a multiplicity of ring resonances. The spectrometer is capable of scanning the entire C-band with high resolution using only one dynamic control. Due to its maturity and low loss, CMOS compatible Si3N4 is chosen for fabrication of the ring resonator and two cyclic AWGs. Complete spectrometer operation is demonstrated experimentally over a selected portion of the C-band. A novel virtual channel synthesis algorithm based on the weighted summation of the AWG output port powers relaxes the conventional AWG design requirement of a flat passband and sharp transition to stopband. The operation of the circuit is invariant to the optical path length between individual components and the algorithm corrects to some extent fabrication process variation impairments of the AWG channel spectra substantially improving robustness.

Edge Offloading in Smart Grid

The management of decentralized energy resources and smart grids needs novel data-driven low-latency applications and services to improve resilience and responsiveness and ensure closer to real-time control. However, the large-scale integration of Internet of Things (IoT) devices has led to the generation of significant amounts of data at the edge of the grid, posing challenges for the traditional cloud-based smart-grid architectures to meet the stringent latency and response time requirements of emerging applications. In this paper, we delve into the energy grid and computational distribution architectures, including edge–fog–cloud models, computational orchestration, and smart-grid frameworks to support the design and offloading of grid applications across the computational continuum. Key factors influencing the offloading process, such as network performance, data and Artificial Intelligence (AI) processes, computational requirements, application-specific factors, and energy efficiency, are analyzed considering the smart-grid operational requirements. We conduct a comprehensive overview of the current research landscape to support decision-making regarding offloading strategies from cloud to fog or edge. The focus is on metaheuristics for identifying near-optimal solutions and reinforcement learning for adaptively optimizing the process. A macro perspective on determining when and what to offload in the smart grid is provided for the next-generation AI applications, offering an overview of the features and trade-offs for selecting between federated learning and edge AI solutions. Finally, the work contributes to a comprehensive understanding of edge offloading in smart grids, providing a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis to support cost–benefit analysis in decision-making regarding offloading strategies.

Advanced Smart Grid Architecture for Sustainable Energy Integration in Modern Urban Environments

Advanced Smart Grid Architecture for Sustainable Energy Integration in Modern Urban Environments

CYBER RESILIENCE USING SMART GRID

With the integration of sophisticated automation, control, and communication capabilities into conventional power systems, the smart grid has developed into a game-changing technological advancement. But greater connectivity also poses serious problems for data security. This article discusses the hazards and vulnerabilities posed by cyberattacks and offers a thorough examination of data security in smart grids. In order to guarantee the availability, integrity, and confidentiality of data within the smart grid architecture, it looks at a number of data security solutions, including intrusion detection, authentication, and encryption. Future developments and cutting-edge technologies that can strengthen data security resilience within the smart grid ecosystem are also covered. Index Terms – Security, Encryption, Smart Grid, Digital signature, Blockchain technology, Quantum cryptography