Power Grid Infrastructure Research Articles

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.

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.

Intelligent agents for advanced power system protection schemes

This study explores the incorporation of intelligent agents to improve power system safety, using several computational models including machine learning, rule-based systems, neural networks, and fuzzy logic. The research assesses the effectiveness and efficiency of these agents in promptly identifying, categorizing, and responding to faults in the power system architecture using empirical analysis. The results demonstrate the higher performance of agents based on neural networks, with an average improvement in fault prediction accuracy of 38% compared to systems based on rules. Furthermore, the evaluation of power system devices demonstrates a direct relationship between greater voltage ratings and increased expenses for both installation and maintenance, underscoring their crucial importance within the system. An examination of fault severity reveals that greater severity failures have a direct and significant influence on system downtime. These problems lead to longer interruptions, which emphasizes the need of implementing effective fault management systems. Intelligent agents' actions have different costs and reaction times. Actions based on neural networks have lower average costs and shorter response times, demonstrating their cost-effectiveness and efficiency in addressing faults. The study of percentage change highlights the importance of using various kinds of intelligent agents and higher-rated devices. This research offers insights into performance differences and the consequences for optimizing protection measures. This research provides a thorough understanding of how intelligent agents may enhance power system protection. It also offers guidance for future improvements in creating power grid infrastructures that are robust, dependable, and adaptable.

Dynamic Response of Transmission Tower-Line Systems Due to Ground Vibration Caused by High-Speed Trains

With the continuous expansion of the scale of power grid and transportation infrastructure construction, the number of crossovers between transmission lines and high-speed railways continues to increase. At present, there is a lack of systematic research on the dynamic characteristics of transmission tower-line structures crossing high-speed railways under vehicle-induced ground vibration. This article focuses on the phenomenon of accidents such as line drops when crossing areas in recent years and establishes a high-speed train track foundation soil finite element model in ABAQUS that considers track irregularity. The three-dimensional vibration characteristics and attenuation law of train ground vibration are analyzed. Acceleration data for key points are also extracted. A separate finite element model of the transmission tower-line system is established in ANSYS, where acceleration is applied as an excitation to the transmission tower-line system, and the coupling effect between the tower and the line is considered to analyze its dynamic response. Subsequently, modal analysis is conducted on the tower-line system, providing the vibration modes and natural frequencies of the transmission tower-line structure. The effects of factors such as train speed, soil quality, and distance from the tower to the track on the dynamic response of the transmission tower-line system under vehicle-induced ground vibration are studied. The results show that the speed range (300 km/h–400 km/h) and track distance range (4.5 m–30 m) with the greatest impacts are obtained. The research results can provide a reference for the reasonable design of transmission tower-line systems in high-speed railway sections.

Site suitability assessment for the development of wind power plant in Wolaita area, Southern Ethiopia: an AHP-GIS model

The primary driver of economic growth is energy, predominantly derived from fossil fuels, the demand for which has experienced a significant increase since the advent of the Industrial Revolution. The emissions of hazardous gases resulting from the utilization of these fuels have been well acknowledged, therefore exerting a notable impact on the environment. In the context of Ethiopia, it is observed that despite the presence of ample renewable resources, the accessibility to power continues to be constrained. In order to effectively tackle this issue, it is imperative to redirect attention towards the utilization of renewable sources, such as wind energy, as a means of enhancing the existing power grid infrastructure. The present study used geospatial tools to evaluate the appropriateness of the Wolayita region for the establishment of a wind power facility. The process of site selection is guided by multiple factors, and a multi-criteria approach is facilitated through the utilization of Geographic Information System (GIS). The evaluation of seven characteristics was conducted utilizing the Analytical Hierarchy Process (AHP) methodology, which involved pairwise comparisons and weighted scoring. The process of suitability mapping involves the classification of locations into four distinct categories, which range from the most suitable to the least suitable. The findings demonstrate that the area of 0.628% (28.00 km2) is deemed the most suitable, while 54.61% (2433.96 km2) is considered somewhat acceptable. Additionally, 0.85% (37.85 km2) is identified as the least suitable, leaving a remaining 43.91% (1060.00 km2) that is deemed unsuitable. The central, northwestern, and southern regions are identified as optimal geographic areas. The results of this study facilitate the process of investing in renewable energy, thereby assisting Ethiopian authorities and organizations in promoting sustainable development. This report serves as a crucial reference point for the wind energy industry.

Recognizing the nexus between grid infrastructure, renewable energy, net interregional transmission and carbon emissions: Evidence from China

AbstractOver the past few decades, China has undergone an unprecedented structural transformation, stupendously transforming its energy and environmental systems and decarbonizing pathways. Nevertheless, around the share of clean and renewable energy generation with the goal of low‐carbon, it is still ambiguous how the dynamic relationship between the upgrading of grid infrastructure, interregional power trading and the investment and economic foundation that support their collaborative promotion forms. Thus, under the framework of panel vector autoregressive models, we study the bridge that links the key dominant factors in the pattern of electricity supply and demand such as grid infrastructure, power generation share of clean and renewable energy, net power input and carbon emissions. The results show that the governance framework dominated by power grid infrastructure upgrading, interregional power input dependence and carbon constraint mechanism to promote clean‐oriented energy mix is still flawed. In addition to improve the utilization rate of power grid infrastructure, relevant policy makers should devote more efforts to software conditions including interregional and interprovincial power trading and grid‐connecting mechanisms of clean and renewable energy.

A REVIEW ON INTEGRATION OF ELECTRIC VEHICLES AND OPTIMAL POWER MANAGEMENT STRATEGY FOR SUSTAINABLE DEVELOPMENT

The integration of electric vehicles (EVs) into our transportation system is a pivotal step towards sustainable development. EVs are gaining traction as a cleaner and more energy-efficient alternative to traditional internal combustion engine vehicles. Their adoption holds the promise of reducing greenhouse gas emissions and decreasing our reliance on fossil fuels, contributing significantly to environmental sustainability .One of the critical aspects of effective EV integration is the development and implementation of optimal power management strategies. These strategies play a pivotal role in ensuring that the charging and discharging of EV batteries are not only efficient but also seamlessly integrated with the existing power grid infrastructure. An optimal power management system takes into consideration factors such as load balancing, peak demand management, and the integration of renewable energy sources, making it a key component of a sustainable and resilient energy ecosystem. By efficiently scheduling when and how EVs charge or discharge, these strategies can reduce the strain on the electrical grid during peak periods, lower electricity costs, and minimize the carbon footprint associated with charging. Moreover, they can contribute to grid stability and reliability, making the entire energy system more sustainable. This research explores the synergistic potential of EVs and smart grid technologies, aiming to create a harmonious relationship between transportation and energy systems. Key Words: Electric Vehicles, Sustainable Development, Optimal Power Management, greenhouse gas emissions, fossil fuels, clean environment, smart grid technology.

Power distribution in developing countries — Planning for effectiveness and equity

Power grid expansion planning is a challenging problem that typically considers the facility location, sizing, and transmission line upgrade aspects, with the objective of minimizing the upgrade and operational costs. The consideration of fairness and equity between the populations being served by the power grid has not been addressed previously in the literature. These issues are of special interest regarding the power grid infrastructure in developing countries, where certain populations might be “last in line” to be connected to the grid. In this paper, we develop a power grid expansion optimization model that considers both effectiveness and equity, given a budget constraint on upgrade expenditure. Effectiveness is measured by the deprivation costs of all populations served by the power grid, while equity is measured by their Gini mean absolute difference. Node upgrade rules are applied, and the upgrade plan is provided over a given planning horizon.We optimally solved our model for small instances and performed sensitivity analysis on its parameters. We then developed an LNS (Large Neighborhood Search) heuristic for solving large instances and using publicly available data. Additional instances are generated based on the Myanmar power grid. Our analysis shows that the LNS can provide good solutions relative to a greedy approach.The approach taken in this paper can be applied to a wide range of infrastructure planning problems in which both effectiveness and equity should be considered.

Deep learning-based meta-learner strategy for electricity theft detection

Electricity theft damages power grid infrastructure and is also responsible for huge revenue losses for electric utilities. Integrating smart meters in traditional power grids enables real-time monitoring and collection of consumers’ electricity consumption (EC) data. Based on the collected data, it is possible to identify the normal and malicious behavior of consumers by analyzing the data using machine learning (ML) and deep learning methods. This paper proposes a deep learning-based meta-learner model to distinguish between normal and malicious patterns in EC data. The proposed model consists of two stages. In Fold-0, the ML classifiers extract diverse knowledge and learns based on EC data. In Fold-1, a multilayer perceptron is used as a meta-learner, which takes the prediction results of Fold-0 classifiers as input, automatically learns non-linear relationships among them, and extracts hidden complicated features to classify normal and malicious behaviors. Therefore, the proposed model controls the overfitting problem and achieves high accuracy. Moreover, extensive experiments are conducted to compare its performance with boosting, bagging, standalone conventional ML classifiers, and baseline models published in top-tier outlets. The proposed model is evaluated using a real EC dataset, which is provided by the Energy Informatics Group in Pakistan. The model achieves 0.910 ROC-AUC and 0.988 PR-AUC values on the test dataset, which are higher than those of the compared models.

Cybersecurity of photovoltaic systems: challenges, threats, and mitigation strategies: a short survey

Photovoltaic (PV) systems, as critical components of the power grid, have become increasingly reliant on standard Information Technology (IT) computing and network infrastructure for their operation and maintenance. However, this dependency exposes PV systems to heightened vulnerabilities and the risk of cyber-attacks. In recent times, the number of reported cyber-attacks targeting PV systems has increased significantly. This paper provides an overview of the cybersecurity challenges faced by PV systems, emphasizing their susceptibility to anomalies and cyber threats. It highlights the urgency of implementing robust cybersecurity measures to protect the integrity and reliability of PV installations. By understanding and addressing these challenges, stakeholders can ensure the resilience and secure integration of PV systems within the power grid infrastructure.

Parallel turbochargers for small-scale power generation

For countries with insufficient power supply and failing power grid infrastructure, it is usually also expensive to import microturbines. Local manufacturing or local assembling in these countries is therefore an attractive solution. Off-the-shelf turbochargers used in the motor vehicle industry are readily available at relatively low costs because of large-scale manufacturing in this sector. A turbocharger can be developed into a microturbine for power generation; however, coupling a turbocharger shaft directly to a high-speed generator or gearbox can be challenging and complex due to the high speeds and high temperatures involved, as well as the limited space available on the shaft for such a conversion. The novelty of this work was to investigate the effect of mounting two off-the-shelf turbochargers in parallel, where the second turbocharger’s compressor wheel is replaced with a generator. This allows for a higher pressure ratio over the power turbine. A parallel configuration may also have an advantage over a series configuration in terms of cost since the power turbine can be smaller in size. Two different parallel configurations were modelled, namely, a low-temperature turbine (LTT) and a high-temperature turbine (HTT), where the only difference was the position of the power turbine in parallel with the main shaft. Both configurations were modelled at steady state up to compressor pressure ratios of 2, with and without a recuperator, and with and without pressure losses. It was found that the recuperated LTT configuration outperformed the recuperated HTT configuration in terms of efficiency when pressure losses were introduced. Results showed that the optimum LTT combination generated up to 4.06 kW of power at 87 kPa ambient pressure, with a thermal efficiency of 13.26 %, when coupled with a 90 % effective recuperator. For the same case, the HTT configuration produced a maximum efficiency of 8.66 % with a power output of 5 kW.

Opinions on power grid infrastructure investments for renewable energy accommodation in China

Opinions on power grid infrastructure investments for renewable energy accommodation in China

ВЛИЯНИЕ ИНВЕРТОРНОГО РЕЖИМА ЗАРЯДНЫХ СТАНЦИЙ ЭЛЕКТРОМОБИЛЕЙ НА КАЧЕСТВО ЭЛЕКТРОСНАБЖЕНИЯ ПОТРЕБИТЕЛЯ В ЭНЕРГОСИСТЕМЕ

Recently, issues of increasing the environmental friendliness of technological productions, increasing the exergetic component and energy saving issues have become particularly relevant. Therefore, when organizing and designing electrical supply systems, a more correct solution is the use of an inverter-battery backup power supply system. Such a system will ensure reliable and uninterrupted operation of consumers in the building. The paper considers the solution of the problem of designing the inverter mode of the power supply system for connecting the charging station of electric vehicles to the substation of the city of Novosibirsk. The connected charger of an electric vehicle, being a non-linear load, leads not only to an increase in the load on the power grid infrastructure, but can also be a source of power supply. They can also cause a deterioration in the quality of electricity due to the generation of harmonic distortion. Therefore, special attention is paid to the influence of the inverter mode of electric vehicle charging stations on the quality of power supply to the consumer in the power system. A simulation simulation of the charging station was performed using the MATLAB Simulink software package

Development Potential Assessment for Wind and Photovoltaic Power Energy Resources in the Main Desert–Gobi–Wilderness Areas of China

The large-scale centralized development of wind and PV power resources is the key to China’s dual carbon targets and clean energy transition. The vast desert–Gobi–wilderness areas in northern and western China will be the best choice for renewable energy development under multiple considerations of resources endowment, land use constraints, technical conditions, and economic level. It is urgent to carry out a quantitative wind and PV resource assessment study in desert–Gobi–wilderness areas. This paper proposed a multi-dimensional assessment method considering the influence of the power grid and transportation infrastructure distributions, which includes three research levels, namely, the technical installed capacity, the development potential, and the development cost. Nine main desert–Gobi–wilderness areas were assessed. The wind and PV technical installed capacities were 0.6 TW and 10.7 TW, and the total development potentials were over 0.12 TW and 1.2 TW, with the full load hours of 2513 and 1759 and the average development costs of 0.28 CNY/kWh and 0.20 CNY/kWh. Finally, this paper proposed the meteorological–electrical division distribution. A case study in the Kubuqi and Qaidam Deserts was carried out on wind–wind and wind–PV collaborative development across different meteorological–electrical divisions, which can reduce by 58% the long-term energy storage capacity and decrease the total system LCOE from 0.488 CNY/kWh to 0.445 CNY/kWh.

Research on Risk Evaluation Index System and Model of Power Grid Construction Project

Power grid engineering projects mainly refer to the power grid infrastructure and technical transformation projects, such projects have the characteristics of huge investment amount, complex construction process, long cycle and many risk factors. The planning, construction and operation process are affected and restricted by many factors, and the consequences of risks are relatively serious. Firstly, this paper analyzes the risk theory of power grid construction project from the aspects of related concepts, project implementation process, risk assessment, risk prevention, etc. Secondly, the risk evaluation index system of power grid construction projects is constructed from the aspects of risk identification theory, index selection principle and design process. Thirdly, based on the analysis of evaluation methods, the relative evaluation models are compared, selected and improved, and the risk evaluation model of power grid construction projects is constructed. Finally, it put forward the grid construction project risk management measures, including the development of risk management plan, risk avoidance, risk control, risk transfer, unique risk prevention, risk monitoring and other risk prevention strategies.

Towards Energy Sustainability in University Campuses: A Case Study of Beirut Arab University

Lebanon has been suffering from severe challenges in its electric sector for decades owing to chronic supply shortages and faults in its aging power grid infrastructure. The deplorable situation of the Lebanese electric sector has been made worse by the economic meltdown that started in 2019, which eventually led to total power blackouts across the country. In this paper, we present a case study on the design and implementation of a solar microgrid system for Beirut Arab University, Lebanon. As a first step, simulation software for a microgrid and a distributed generation power system is used to compare different design scenarios. Considering the available installation area and the fact that the greatest demand occurs during the daytime, when both the educational and managerial facilities are running, it is found that a 500-kW photovoltaic system tied to the university’s already present diesel generators is the optimal solution in terms of return on investment. The second step details the actual implementation of the system in the Beirut campus and the evaluation of the system’s performance in terms of diesel cost savings and emissions reduction. We expect that the results of this case study will encourage other institutions and communities to adopt sustainable and renewable energy sources.

The Power Grid/Wildfire Nexus: Using GIS and Satellite Remote Sensing to Identify Vulnerabilities

The effects of wildfire on the power grid are a recurring concern for utility companies who need reliable information about where to prioritize infrastructure hardening. Though there are existing data layers that provide measures of burn probability, these models predominately consider long-term climate variables, which are not helpful when analyzing current season trends. Utility companies need data that are temporally and locally relevant. To determine the primary drivers of burn probability relative to power grid vulnerability, this study assessed potential wildfire drivers that are both readily accessible and regularly updated. Two study areas in Idaho, USA with contrasting burn probabilities were compared. Wildfire drivers were obtained and differentiated between the study areas across the 2018–2021 growing seasons. This study determined that mean wind speed, cumulative growing season precipitation, and the mean Normalized Difference Vegetation Index (NDVI) for an area of interest may be reliable indicators of burn probability on a temporally relevant scale. This assessment demonstrates a method and variables that may be utilized by municipal electric utilities, electric cooperatives, and other power utilities to determine where to harden power grid infrastructure within wildfire-prone areas.

Opinions on power grid infrastructure planning with investment risks

Opinions on power grid infrastructure planning with investment risks

Risk-Based Active Distribution System Planning for Resilience Against Extreme Weather Events

Enhancing the resilience of power distribution systems to extreme weather events is of critical concern. Upgrading the distribution system infrastructure by system hardening and investing in smart grid technologies effectively enhances grid resilience. Existing distribution system planning methods primarily consider the persistent cost of the expected events (such as faults and outages likely to occur) and aim at improving system reliability. The resilience to extreme weather events requires reducing the impacts of the high impact low probability (HILP) events that are characterized by the tail probability of the event impact distribution. Thus, the resilience-oriented system upgrades solutions need to be driven by the risks imposed by extreme weather events on the power grid infrastructure rather than persistent costs. This paper aims to develop a risk-based approach for the long-term resilience planning of active power distribution systems against extreme weather events. The proposed approach explicitly models (1) the impacts of HILP events using a two-stage risk-averse stochastic optimization framework, thus, explicitly incorporating the risks of HILP events in long-term planning decisions, and (2) the advanced distribution grid operations (in the aftermath of the event) such as intentional islanding into infrastructure planning problem. The inclusion of risk in the planning objective provides additional flexibility to the grid planners to analyze the trade-off between risk-neutral and risk-averse planning strategies.