Grid Infrastructure Research Articles

An evaluation of 2013 privatization on Benin Electricity Distribution technical and workforce performance

Purpose This study aims to investigate the impact of the 2013 privatization of Nigeria’s energy sector on the technical performance of the Benin Electricity Distribution Company (BEDC) and its workforce. Design/methodology/approach This study used a questionnaire-based approach, and 196 participants were randomly selected. Analytical tools included standard deviation, Spearman rank correlation and regression analysis. Findings Before privatization, the energy sector, managed by the power holding company of Nigeria, suffered from inefficiencies in fault detection, response and billing. However, privatization improved resource utilization, replaced outdated transformers and increased operational efficiency. However, in spite of these improvements, BEDC faces challenges, including unstable voltage generation and inadequate staff welfare. This study also highlighted a lack of experience among the trained workforce in emerging electricity technologies such as the smart grid. Research limitations/implications This study’s focus on BEDC may limit its generalizability to other energy companies. It does not delve into energy sector privatization’s broader economic and policy implications. Practical implications The positive outcomes of privatization, such as improved resource utilization and infrastructure investment, emphasize the potential benefits of private ownership and management. However, voltage generation stability and staff welfare challenges call for targeted interventions. Recommendations include investing in voltage generation enhancement, smart grid infrastructure and implementing measures to enhance employee well-being through benefit plans. Social implications Energy sector enhancements hold positive social implications, uplifting living standards and bolstering electricity access for households and businesses. Originality/value This study contributes unique insights into privatization’s effects on BEDC, offering perspectives on preprivatization challenges and advancements. Practical recommendations aid BEDC and policymakers in boosting electricity distribution firms’ performance within the privatization context.

Energy Transition in Urban Water Infrastructures towards Sustainable Cities

The world’s water infrastructures suffer from inefficiencies, such as high energy consumption and water losses due to inadequate management practices and feeble pressure regulation, leading to frequent water and energy losses. This strains vital water and energy resources, especially in the face of the worsening challenges of climate change and population growth. A novel method is presented that integrates micro-hydropower plants, with pumps as turbines (PATs), in the water network in the city of Funchal. Sensitivity analyses evaluated the microgrid’s response to variations in the cost of energy components, showing favorable outcomes with positive net present value (NPV). PV solar and micro-wind turbines installed exclusively at the selected PRV sites within the Funchal hydro grid generate a combined 153 and 55 MWh/year, respectively, supplementing the 406 MWh/year generated by PATs. It should be noted that PATs consistently have the lowest cost of electricity (LCOE), confirming their economic viability and efficiency across different scenarios, even after accounting for reductions in alternative energy sources and grid infrastructure costs.

Hierarchical-Based Dynamic Scenario-Adaptive Risk Assessment for Power Data Lifecycle

In an era marked by rapid advancements in information technology, the task of risk assessment for data security within the complex infrastructure of the power grid has become increasingly vital. This paper introduces a novel methodology for dynamic, scenario-adaptive risk assessment, specifically designed to address the entire lifecycle of power data. Integrating hierarchical analysis with fuzzy comprehensive evaluation, our approach provides a flexible and robust framework for assessing and managing risks in various scenarios. This method enables the generation of adaptive weight matrices and precise risk level determinations, ensuring a detailed and responsive analysis of data security at each lifecycle stage. In our study, we applied predictive analytics and anomaly detection to conduct a thorough examination of diverse data scenarios within the power grid, aiming to proactively identify and mitigate potential security threats. The results of this research demonstrate a significant enhancement in the effectiveness of risk detection and management, leading to improved data protection and operational efficiency. This study contributes a scalable, adaptable model for data security risk assessment, meeting the challenges of big data and complex information systems in the power sector.

Intelligent Electric Grid Maintenance via an Adaptive Predictive Maintenance Optimization Algorithm

This study presents a novel framework for the predictive maintenance of electric grid infrastructure, leveraging an Adaptive Predictive Maintenance Optimization (APMO) algorithm. Our comprehensive system architecture integrates IoT sensors and drone surveillance, harnessing grid computing for data processing and machine learning for analytics. The APMO algorithm, underpinned by reinforcement learning, dynamically refines maintenance schedules, enhancing operational efficiency. Results from simulated sensor data exhibit a negligible correlation, indicating a multi-faceted approach to predictive analytics. Specifically, the Health Index distribution showed a wide range of 0.7 to 1.0, while the electrical current maintained a stable mean at 10 A. Further, the APMO algorithm demonstrated a promising improvement in maintenance efficiency from 40% to 75% over a year. This research introduces a scalable, robust system for grid management, paving the way for smarter infrastructure maintenance.

Prediction based charging management for full electric vehicles in a mobility-on demand-concept using local renewable energy

Unavoidably, the aim of ecologically sustainable driving leads to the use of full electric vehicles (FEV) and a charging process based on renewable energy. Ecologically sustainable charging, however, faces a diverse range of challenges. First of all the additional integration of renewable energy sources into the distribution and regional grids requires an expansion of grid infrastructure. In addition the FEV represents a high-power load inside the electrical grid; this may cause overloads and deviations from the preset voltage band. The management system in [1] was developed to address the problem of the high-power load as well as to maximize the usage of already available renewable energy. This paper details a charging management system which is based on [1] and incorporates the prediction of available renewable energy inside the electric grid. The management system also considers the special challenges presented by a mobility-on-demand-concept, above all the permanent availability of mobility. First results show that the developed prediction based management system is capable to raise the percentage of renewable charging energy in comparison to a dump charging as well as to the charging management presented in [1].

Enabling mass integration of electric vehicles through distributed energy resource management systems

Global initiatives are actively progressing to integrate large numbers of electric vehicles (EVs) as part of efforts to electrify and decarbonize the transportation sector. This approach plays a crucial role in addressing global greenhouse emissions and pollution problems. However, mass integration of EVs imposes unprecedented challenges to electrical power systems, especially to their distribution grids. These challenges range from planning issues, where the traditional panning procedures are not adequate for integration of EVs and their charging stations, to operational challenges, where a huge number of dispersed EVs can cause peak load increase, voltage violations, congestions, feeder overloads, etc. Hence, despite the significant importance of maximizing the deployment of EVs, their stochastic integration can give rise to critical challenges in electric power systems, thereby impeding their subsequent expansion. Therefore, in order to properly integrate mass amounts of EVs, without causing technical violations in the electrical power system, intelligent software tools are required, to aid distribution system operators and engineers in the transition to a new era of electrified transportation. In this paper, we argue that novel software solutions called Distributed Energy Resource Management Systems (DERMSs) are a key solution for enabling a safe integration of mass amounts of EVs into emerging distribution grids. Specifically, we introduce a novel Hybrid DERMS framework, as an integration of multiple hierarchical levels of DER management solutions, and propose this solution as a suitable instrument for distribution system operators and grid engineers to safely integrate and manage high volumes of EVs within the grid infrastructure. The hypothesis is validated through a real-life use case for integration and management of high amounts of EVs into an existing distribution grid, using an industrial DERMS solution. Specifically, an in-depth analysis of adding two large charging stations, boasting capacities of 5 MW each, has been performed through all stages of integration, from the planning stage, up to using their flexible output to manage technical constraint violations in real time. The influence of these EV charging stations on both the feeder where they are planned to be integrated, as well as on the entire supply substation, have been analyzed in detail. The resultant findings, especially on successfully managing overloads, peak load increases, as well as voltage violations through intelligent use of aggregated EVs through DERMS, have proven to be notably encouraging, further substantiating the paper’s hypothesis.

Experimental validation of a low‐voltage regulator model—A case study of a Maltese low‐voltage feeder

AbstractSolar photovoltaics in Malta have increased substantially over the years, both at large scale and residential scale. The present installed capacity is already having a negative impact on Malta's’ grid infrastructure, causing problems due to the reverse power flows (resulting from oversupply) and related issues such as voltage rise. This paper considers the integration of a low voltage regulator in a low voltage feeder in a semi‐rural area in Malta. A thyristor‐controlled transformer‐based low voltage regulator was modelled and simulated in MATLAB/Simulink using the PLECS blockset. Experimental results were also obtained via measurements of the voltage profiles at 70% of the actual low voltage feeder that showed several instances of over and under voltage events. The outputs obtained from the simulation model strongly agreed with those obtained from the field testing thereby validating the simulation models that were developed in this study. Results show that the voltage on the feeder was successfully regulated to around the nominal voltage of 240 V. The regulator failed to correct the voltage to within ±10% in one occurrence where the load demand and hence the voltage drop was too high.

Power system differentiation planning based on an improved marine predator algorithm

Abstract Effective power system differentiation planning is crucial for enhancing the resilience of power grid infrastructure and bolstering the ability of power systems to manage blackouts. At the heart of power system differentiation planning lies core backbone grid planning. This study involves modeling core backbone grid planning as a multi-objective 0–1 planning problem, which enables the formulation of a multi-objective function that incorporates various factors such as operational and maintenance costs, the significance of nodes and transmission lines, as well as compliance with connectivity and security operation constraints. Moreover, the basic marine predator algorithm was upgraded into a multi-objective optimization algorithm for core backbone grid planning by implementing file management and enhancing the top predator selection mechanism, which managed to fulfill the multi-objective function optimization standards. The results show that in the IEEE 39-node system, the algorithm successfully forms a core backbone grid comprising 22 nodes and 19 transmission lines, achieving economic feasibility with a node-to-line ratio of 1.158. Similarly, for the IEEE 300-node system, the algorithm constructs a larger core backbone grid consisting of 81 nodes and 80 transmission lines, maintaining economic efficiency with a node-to-line ratio of 1.0125. This expanded grid covers a significant number of critical nodes and transmission lines, ensuring optimal network connectivity. Furthermore, the algorithm’s load satisfaction analysis showcases its ability to effectively balance active and reactive power demands, with maximum outputs meeting the respective load demands.

Automatic monitoring and early warning method for power grid infrastructure investment progress using building information model and blockchain

AbstractIn order to improve the precise control level of power grid infrastructure investment, this paper proposes a monitoring and early warning method of power grid infrastructure investment progress based on building information model and blockchain. By capturing the project construction progress images, the features of the power grid infrastructure are extracted automatically. Combined with the technical characteristics of distributed, tamper‐proof, and traceable blockchain, statistical indicators are generated automatically, monitoring and early warning of the investment progress execution deviation are triggered by the rules running on the smart contracts. The case study results show that the mean absolute error of the target image recognition method based on the image features is 4.32%, and the prediction accuracy of the incoming line is better than that of the engineering civil and substation. The early warning model of investment statistics based on smart contracts can automatically monitor the investment progress and generate early warnings, which provides a basis for the dynamic adjustment of the investment plan, and effectively improves the refined management level of power grid infrastructure investment projects.

A Graph-Based Neural Approach to Linear Sum Assignment Problems.

Linear assignment problems are well-known combinatorial optimization problems involving domains such as logistics, robotics and telecommunications. In general, obtaining an optimal solution to such problems is computationally infeasible even in small settings, so heuristic algorithms are often used to find near-optimal solutions. In order to attain the right assignment permutation, this study investigates a general-purpose learning strategy that uses a bipartite graph to describe the problem structure and a message passing Graph Neural Network (GNN) model to learn the correct mapping. Comparing the proposed structure with two existing DNN solutions, simulation results show that the proposed approach significantly improves classification accuracy, proving to be very efficient in terms of processing time and memory requirements, due to its inherent parameter sharing capability. Among the many practical uses that require solving allocation problems in everyday scenarios, we decided to apply the proposed approach to address the scheduling of electric smart meters access within an electricity distribution smart grid infrastructure, since near-real-time energy monitoring is a key element of the green transition that has become increasingly important in recent times. The results obtained show that the proposed graph-based solver, although sub-optimal, exhibits the highest scalability, compared with other state-of-the-art heuristic approaches. To foster the reproducibility of the results, we made the code available at https://github.com/aircarlo/GNN_LSAP.

Knowledge reasoning in power grid infrastructure projects based on deep multi-view graph convolutional network

With the rapid development of power grid infrastructure, especially the increasing number of ultra-high voltage (UHV) projects, knowledge extracted from historical engineering data is collected and can be potentially used to assist in the review of power transmission and transformation projects. However, conventional knowledge modeling and knowledge reasoning methods cannot meet the current needs of power grid construction. In this paper, considering the more supernumerary and distinctive information brought by multi-view data which could be beneficial for feature representation and knowledge reasoning from the constructed knowledge base, a multi-view graph convolutional network (GCN) based on knowledge graph is proposed to make classification for power grid infrastructure projects. Specifically, several views are constructed based on attribute information of a knowledge graph. In addition, a Haar convolution-based pooling mechanism is employed to capture the structural features represented by a chain of subgraphs. And then an aggregator that combines both attribute and structural information is used to classify UHV projects. Results from both UHV and NCI-1 datasets indicate that our proposed method is more has higher accuracy and generalization ability.

Intelligent Control of DC Microgrid Involving Multiple Renewables for Fast Charging Control of Electric Vehicles

Electric vehicles (EVs) are now considered to be a cutting-edge form of transportation. However, the electrical grid infrastructure is not sufficiently developed to meet the rising charging demand for electric vehicles. Therefore, it is impossible to rely entirely on grid-generated electricity. This article discusses a control approach for extremely rapid charging of EV batteries driven by a hybrid DC microgrid, consisting of isolated, Photovoltaic (PV), Wind Turbine, Fuel Cell and Energy Storage Systems. An adaptive sliding mode controller is designed to provide the appropriate power for charging and discharging EVs and energy storage units (ESUs) under a variety of power generation and demand scenarios. The proposed adaptive sliding mode controller operates in a decentralized manner to maintain power distribution amongst the microgrid DC-link EVs and the ESUs. In order to validate the effectiveness of the sliding mode controller in battery charging applications, a comprehensive comparison between fuzzy logic and adaptive sliding mode control is conducted. The proposed controller operates exceptionally well in the presence of unpredictable operating point variations such as rapid changes in EV charging load, fluctuations in solar irradiation, unexpected wind conditions, and so on. Finally, the proposed controller is verified by both simulation and experimental studies.

Automatic Electric Vehicle Charging Station

Our Automatic Electric Vehicle Charging station is a autonomous power supply unit which works without the need for human attendants. The system is designed to facilitate the existing power grid infrastructure. The system employs the Raspberry Pi as our primary controller board and hosts the User Interface on the LCD Display mounted on the Charger Housing. The Pi Board interfaces with our custom Pilot PCB to communicate with the Electric Vehicle Battery Management Systems to negotiate the charging speed, capacity of the car battery and perform safety checks. The user can choose AC level 1, 2 or DC level 3 charging. The system uses a 30A relay circuit to isolate the battery from the power supply until all the necessary checks and payment processes are completed. RazorPay is our real time Payment gateway for payment transactions. Once the user has completed the formalities the charging process will begin. We also have a charging monitoring system developed that will help users track the battery charge percentage and charging time remaining on the LCD display. The station has emergency Kill Switches and Emergency Stop buttons mounted on the housing to address safety concerns that arise on the advert of damaged high power output utilities, when engaged the entire system will kill all the power inputs from the grid so that any fault can be safely repaired without risk of electrocution. For our implementation and testing we have built a 18650 NMC chemistry 3S5P Battery pack with a 3S BMS. The unit utilizes level 3 AC charging SAE (Society of Automotive Engineering) standards to ensure the safety and quality of the service.

Micro Phasor Measurement Units: a Review from the Prosumer Point of View

In recent years, power grid infrastructure has moved from a centralized power generation model to a paradigm in which generation capacity is distributed across an increasing number of small power plants that rely on renewable energy sources. Systems for emerging distribution systems need a very new protection paradigm, architecture and philosophy making use of new protection devices and sensors such as digital relays, phasor measurement units (PMUs), smart reclosers and line sensors. This article examines the application of PMUs in smart distribution grids and from the point of view of prosumers.

Reliability-aware multi-objective approach for predictive asset management: A Danish distribution grid case study

The transition towards sustainable practices and a reliable electricity grid accommodates the rising electrification of the heating and transportation sectors. Aging, environmental factors, and operational conditions of electrical grid infrastructure contribute to a higher likelihood of faults. This leads to a reduced level of reliability, emphasizing the importance of renewing electrical grid infrastructure, particularly underground cables. Optimally replacing cables is essential, taking into account various factors like reducing the fault probability, minimizing the cost of power outages, and enhancing reliability within the budgetary constraint. This paper introduces an innovative methodology to predictive asset management for replacing underground cables using multi-objective optimization approach. Three objective functions are formulated: number of replaced cables, cost of power outages, and interruption-related index, which is determined through metrics like SAIFI, SAIDI, and ASIDI. These objectives are modeled as mixed-integer programming creating a multi-objective optimization problem, which is addressed using the epsilon-constraint approach. The optimization model identifies the cables that should be replaced within the budget constraint, aiming to optimize the objectives. The effectiveness of this approach is assessed using a real Danish distribution grid. The findings indicate that, compared to methods based on the cable age, fault vulnerability, and risk assessment, the proposed method demonstrates superior performance in terms of reliability metrics and power outage cost.

The ALICE Grid Workflow for LHC Run 3

The ALICE Grid Workflow for LHC Run 3

Optimization study of computational methods in coordinated integrated carrying capacity assessment of multi-voltage hierarchies

Abstract In this paper, we take a two-level distribution grid as the infrastructure of a multilevel distribution grid and study the comprehensive carrying capacity assessment model in depth. The minimum value of the maximum access capacity under each scenario is used to assess the maximum carrying capacity of the distribution network, and the maximum access volume of the multilevel distribution network is taken as the objective function, and the particle swarm algorithm is improved to establish the maximum carrying capacity assessment model. Using the alternating direction multiplier method, a two-level distribution grid carrying capacity distributed optimization model is established, and the multilevel distribution grid access volume problem is transformed into the maximum access volume subproblem at all levels of the distribution grid. Comparing the whale algorithm and particle swarm algorithm, the model in this paper converges at 7465 kW access capacity and about 12 iterations, respectively, and the weak degree is below 0.4 at different distribution network states, which is a good performance in terms of optimization accuracy and weak degree. We conduct simulation experiments with the modified IEEE33 nodes and find that using this paper’s model to participate in optimal scheduling reduces the total operating cost of the distribution network system by more than 6.24% compared to not using it. Finally, we use the model in this paper to evaluate the comprehensive carrying capacity of the distribution network in Ningxia, China.

Hybrid Metaheuristics with Deep Learning Assisted Intrusion Detection on Cyber-Physical Smart Grid Environment

Smart grids (SGs) offer can ensure that users with a continuous power supply, decreased line losses, improved renewable output and storing, user participation in current electricity, and demand-side responsiveness. The development of cyberphysical SG (CPSG) systems has transformed the standard power grid by allowing bi-directional energy flow among utilities and users. But, because of increased data change among consumers, it is presented a major problem to the firewall systems for the transmission networks at either cyber or physical planes. Intrusion Detection Systems (IDSs) can role an essential play in maintaining SGs systems against cyber threats by generating a second wall of defense, complementing conventional preventive security procedures (for instance, authorization, encryption, and authentication). Therefore, this article concentrates on the design and development of Hybrid Metaheuristics with Deep Learning Assisted Intrusion Detection in a Cyber-Physical Smart Grid (HMDL-IDCPSG) infrastructure. The major objective of the HMDL-IDCPSG system provides the effectual recognition of the intrusions using feature selection and classification processes in the CPSG infrastructure. In the presented HMDL-IDCPSG method, a binary dragonfly algorithm with the hybrid directed differential operator (BDA‐DDO) algorithm could be implemented for the feature selection (FS) method. Besides, attention-based bi-directional long short-term memory (ABiLSTM) algorithm could be carried out for the recognition and classification of the intrusions. At last, the sparrow search algorithm (SSA) can be exploited for highest chosen the hyperparameter values of the ABiLSTM algorithm which supports in achieving a better solution. For demonstrating the greater outcome of the HMDL-IDCPSG technique, a comprehensive simulation value can be executed. The obtained results reported the supremacy of the HMDL-IDCPSG methodology with other existing approaches

Prediction of EV Charging Behavior using BOA-based Deep Residual Attention Network

In smart city applications, electric vehicles (EVs) are rapidly gaining popularity due to their ability to help cut down on carbon emissions. Numerous environmental conditions, including terrain, traffic, driving style, temperature, and so on, affect the amount of energy an EV needs to operate. However, the burden on power grid infrastructure from widespread EV deployment is one of the biggest obstacles. Smart scheduling algorithms can be used to handle the rising public charging demand. Scheduling algorithms can be improved using data-driven tools and procedures to study EV charging behaviour. Predictions of behaviour, including temperature, departure time, and energy requirements, have been the focus of research on past charging data. Weather, traffic, and surrounding events are all factors that have been mostly ignored but which could improve representations and predictions. The DRA-Net, or Deep Residual Attention Network, was developed by the researchers and is used to recognize EV charging patterns. To minimize data loss, the Res-Attention component utilized tighter connections and smaller convolutional kernels (3 x 3). In addition, an Artificial Butterfly Optimisation Algorithm (BOA) model is used to fine-tune the DRA-Net's hyper-parameters. We highlight the significance of traffic and weather info for charging behaviour predictions, and the study's experimental forecasts show a considerable improvement over prior work on the same dataset. The future of electric vehicle (EV) research has been mapped out thanks to in-depth study, and as a result, EVs will soon significantly impact the auto industry.

Study on China’s Position on Its Strategic Location, Planning, and Future Outline

This comprehensive analysis highlights the potential of China, the world’s largest energy producer. The research paper provides direction to China in the energy sector. Although currently relying on coal for power generation, in the long term, the country will become the largest exporter of electricity. The transition to sustainable energy production is critical, considering global commitments to reduce greenhouse gas emissions under the Paris Agreement. China’s ability to shift to clean energy is strengthened by its vast labor force, financial strength, and the Chinese government’s commitment to national rejuvenation. By establishing an “Electro-Yuan” system for trading electricity, China could challenge the global dominance of the US dollar. The One Belt, One Road (OBOR) initiative further facilitates a clean electricity trade network with strategic partners worldwide. However, a successful transition hinges on shifting from coal to renewable energy sources, enhancing existing grid infrastructure for cross-border transmission, and effectively implementing the Electro-Yuan system.