Electricity Distribution Infrastructure Research Articles

Impact of Fraxinus snag fall on electric distribution and infrastructure stability: An empirical analysis

With increasing climate variability and the movement of exotic pests and diseases, the rate of forest mortality has become an issue of global concern. Emerald Ash Borer (EAB), as one such pest, is causing the mass mortality of ash trees, Fraxinus spp., thus leading to an ongoing surge in the number of snags across North America. Snags are dead-standing trees that pose an extant threat to nearby infrastructure and buildings. In this article, we evaluate the impacts which snags pose to electrical distribution infrastructure. New Jersey, a state with a high degree of urbanization, has an extensive electric grid located in forested areas. New Jersey is currently in the process of upgrading the electric distribution network, which will increase the height of electric distribution lines to increase resiliency and potential capacity. This article demonstrates a yearly risk assessment methodology for Fraxinus snags using an integrated GIS, differential equation, and applied regression model framework under two distribution network parameterizations. The framework is applied to three northern New Jersey counties (Warren, Sussex, and Morris), which are managed by one utility, New Jersey Central Power and Light, and which are home to most of New Jersey’s Fraxinus trees.

Optimal design and analysis of a grid‐connected hybrid renewable energy system using HOMER Pro: A case study of Tumbatu Island, Zanzibar

AbstractThis study addresses the pressing issue of quality electricity access in remote regions, with a specific focus on Tumbatu Island in Tanzania. Most studies on quality improvement concentrate on low‐voltage distribution lines and leave high‐voltage (HV) transmission lines behind. It is essential to address quality issues in HV lines due to their critical role in electricity transmission and distribution infrastructure. The objective of the study is to improve the voltage profile on the island's HV transmission line by identifying the optimal hybrid energy system comprising solar PV, wind turbine, and battery technologies. The study begins by presenting the total power demand and consumption on Tumbatu Island, which are important factors in designing an efficient energy system. The findings reveal a power demand of 7173 kW and a consumption of 28540 kWh/day, with an average scaled value of 1507.9 and 6000 kWh/day. To achieve the desired voltage profile improvement, the research incorporates HOMER Pro software for simulating and analyzing various hybrid system configurations. Essential input parameters, such as costs, resources, technology components, and load, are considered in the simulation process. The software generates a ranked list of options based on the system's net present cost (NPC). The simulation results demonstrate that the integration of solar PV, wind turbine, and HV lines provides a substantial improvement in the voltage profile, increasing it from 29.6 to 31.23 kV during peak demand periods. This solution proves to be the most economically viable, with the lowest NPC of $4,003,851 and a relatively short payback period of 3.79 years. In addition, a sensitivity analysis is performed to identify the most influential parameter in the system's performance. Wind speed is found to have the greatest impact, emphasizing its significance in the design and operation of the hybrid energy system. The implementation of this optimal hybrid renewable energy system on Tumbatu Island will not only improve the voltage profile and meet the island's energy needs but also shall contribute to global efforts in reducing pollution and the cost of electricity to the Tumbatu Island population. Moreover, it addresses the current and future demands for clean energy, underlining its importance in achieving sustainable and accessible electricity in sub‐Saharan Africa and beyond.

Power grid monitoring based on Machine Learning and Deep Learning techniques

<p><strong>Background:</strong> In this work, some application examples of machine learning and deep learning techniques in the monitoring of electricity distribution services and infrastructures are proposed. Three different fields of application are considered to highlight the use of techniques based on neural networks: detection and classification of power quality disturbances, monitoring of underground cables in medium voltage lines and diagnosis of joints in high voltage overhead power lines. <strong>Methods:</strong> In the field of power grid monitoring, this work proposes a classification method based on a complex valued neural network to assess working conditions of junction regions in high-voltage overhead lines and insulating materials in medium voltage underground networks. The purpose of this method is to prevent the rupture of joint structures and the abnormal degradation of underground cables via frequency response measurements. This approach allows the direct processing of complex measurements and to reduce the computational effort compared to other methods available in the literature. <strong>Results:</strong> The results obtained in the monitoring of underground cables and joints of high voltage lines guarantee an overall classification rate higher than 90%. In the field of power quality, several deep learning and machine learning methods are proposed to detect the most common voltage disturbances. <strong>Conclusions:</strong> In this paper, an innovative use of widespread algorithms such as convolutional neural networks is proposed with excellent results. Furthermore, the use of a complex-valued neural network in electrical infrastructure monitoring is presented, introducing a minimally invasive classification method that could be instrumental in the transition from corrective to predictive maintenance in the near future.</p>

Towards zero energy infrastructure buildings: optimal design of envelope and cooling system

Optimal design of building envelopes/HVAC systems and free cooling strategies are today necessary to reduce energy, economic and environmental impact of telecommunication, electricity distribution, or electric transportations infrastructures. To this aim, designers and operators require advanced techniques and tools.Target of this paper is to present the development of a simulation model for assessing and optimizing cooling performance of new/existing infrastructures to be designed/refurbished from the energy point of view. The model is implemented in a computer tool to assess the related potential benefits of different energy saving technologies/strategies and optimize different objective functions.By the presented approach new design and operating criteria are developed by varying all the occurring boundary conditions (weather, temperature limitations, electricity tariffs, etc.).To show the capability and suitability of the proposed approach, a case study concerning the equipment cooling of a railway substation is developed. The analysis is conducted for different Italian climates, obtaining remarkable energy savings. Specifically, by optimizing free cooling operation and thermal insulation the cooling energy consumption can be decreased up to 80% and 10%, respectively. By an energy refurbishment of all the Italian railway substations an annual electricity cost reduction due to equipment conditioning of about 0.5 M€ can be achieved, 47% lower.

Predicting future GB heat pump electricity demand

To meet net zero carbon targets by 2050s, countries with cold winters, such as Great Britain (GB), will turn to low carbon domestic heating systems such as electric heat pumps. In this paper, a statistical model is developed using measured performance data from over 550 heat pumps installed in GB homes in 2012. The model is used to predict the additional load on the GB electricity generation and distribution infrastructure for various current and future (2050) climates, dwelling energy efficiencies and heat pump deployment scenarios. For a cold year in the 2020s, a 100% uptake of heat pumps in the existing GB dwelling stock gave a peak electricity demand for the heat pumps of 78 GW and an annual electricity demand of 189 TWh. When added to the other, existing electricity demand, this represents an increase in the GB peak electricity demand in excess of 100% and an annual electricity demand increase of around 60%. Making reasonable assumptions about heat pumps’ future efficiency and use, and the future climate and dwelling stock fabric efficiency, and assuming 80% heat pump uptake and no load shifting, the peak heat pump electricity demand for a cold year in the 2050s halved to 37 GW. By shifting demand for water and space heating the 2020s cold year peak demand reduced by 15%. The work provides a more robust estimate of future GB heat pump electricity demands than any previously available. The predicted future ramp rates, peak and annual total electricity demands, are lower than predicted by previous analysts.

Household access to the public electricity grid in Cameroon: Analysis of connection determinants

This article empirically examines the determinants of household access to the public electricity grid in Cameroon, using data from a survey conducted by NIS (2014) on a sample of 46560 households. The key findings reveal that social class, housing tenure, education level, area of dwelling, and distance to the electric pole all have a substantial (p 0.001) influence on public grid connection. According to multinomial logistic regression estimates, the main significant predictors of individual connection to the public grid are: residing in a villa (43.27%), higher education (34.91%), and home ownership (34.27%). Furthermore, the main significant predictors of connection without a personal subscription or through intermediaries are: residing in a concession area (42.44%), living in the western region (35.13%), and poverty (25.60%). Finally, the male gender (15.55%) and the home's distance from the electric pole of more than 8 m (9.24%) are the primary predictors of direct connections without a meter. Our findings suggest that increasing the household's access to its own housing and increasing educational attainment could raise formal demand for individual grid subscriptions. Increased investment in electricity distribution infrastructure, particularly in outlying areas, would also benefit public authorities.

A review on condition assessment technologies for power distribution network infrastructure

Reliable performance of the electricity distribution networks is critical to avoid outages and disruptions which may lead to substantial economic losses. Some of the important components of the electricity distribution networks include overhead cables, utility poles, cross-arms, substations, and transformers. These components are subjected to deterioration with aging and pose risks of failure creating safety concerns, specially under extreme weather conditions. Efficient maintenance and the use of accurate condition assessment techniques to monitor the integrity of the aforementioned components are key factors to be considered in managing the electricity distribution assets. This article presents a review of different condition assessment techniques employed for monitoring the electricity distribution infrastructure. The conventional inspection techniques and advancements in non-destructive testing methods are discussed outlining the relative advantages and disadvantages. Other than the normal failures of distribution infrastructure due to component ageing, unexpected and less frequent extreme weather events can have significant impacts on the resilience of the electrical power infrastructure. Thus, the effects of extreme weather conditions on the reliability of the electricity distribution networks and the failure rates of assets are reviewed in this study. Further, the maintenance and decision-making approaches implemented in the power distribution networks are also discussed.

Strategic Integration Of Electric Vehicles: An Australian Analysis

This paper presents the challenges and opportunities of strategic electric vehicle (EV) grid integration with a particular focus on its status and outlook in Australia. EVs are a critical part of many countries net zero commitments because electrification is a key piece in the puzzle to decarbonise the transport sector. Approaches are needed to ensure EVs and their charging practices can be incorporated within the electricity grid cost-effectively, while increasing system reliability and stability, supporting transport decarbonisation, and ensuring other societal benefits. Australia is a large country with a relatively small population which poses challenges for both EV charging and electricity distribution infrastructure provision. However, other market characteristics (such as high solar PV adoption among households) and the rapidly decarbonising grid make Australia an useful case study. Especially for how challenges over range, grid integration, and lack of domestic EV production is leading to new innovations that could inform steps towards more rapid EV adoption in other jurisdictions. This paper will discuss the challenges and opportunities of electric vehicle (EV) uptake in Australia based on a national consultation and workshop series involving stakeholders from the energy and transport sectors.

The Impact Analysis of Electricity Prices on the Energy Intensity of the Kazakhstani Economy and Sustainable Development

The independent Kazakhstan is highly inefficient electricity production, transmission and distribution infrastructure, while electricity prices both for industry and residential consumers is significantly lower that world average level due to abundance of oil, gas and coal resources. As a result of resource usage inefficiency, the country loss of 3-5% of national growth domestic product and competitiveness on international economic arena. The aim of this study is to determine the possibilities of ensuring economic growth in Kazakhstan by improving the energy efficiency of the economy. The paper proposes to identify the relationship between economic growth and energy efficiency in Kazakhstan by applying the correlation-regression and comparative analysis of the dynamics of the national gross product and its specific energy intensity, which will determine and substantiate the priority direction of increasing energy efficiency and sustainable development in Kazakhstan. The result of correlation-regression analysis reveals that an increase in the price of electricity supply for industry by 2.5 KZT tenge per 1 kW leads to an improvement in the energy intensity of GDP by 0.012 kg per 47,000 KZT (or 100 USD), and an increase in the price of electricity supply for the population by 2.5 KZT per 1 kW leads to an improvement in energy intensity GDP by 0.005 kg per 47,000 KZT. In discussion, the article argues that country is mostly focuses on development of renewable energy sector, while considering the situation that Kazakhstan is geographically large country with high energy losses during the transmission and distribution electricity system, the application of the Smart Grid technologies seems as a prerequisite for improvement of energy efficiency and economic growth in Kazakhstan.

Closing the void in energy planning modeling: Integrating local realities

Closing the void in energy planning modeling: Integrating local realities

Resilient distribution system leveraging distributed generation and microgrids: a review

With the aging of electricity transmission and distribution infrastructures and increasing intensity of extreme weather events, the aggravated vulnerability of electric distribution systems to extreme weather events has motivated the study of resilient distribution systems. This study presents a review of the state-of-the-art research on distribution grid resilience. First, the definition and quantifying metrics of resilience in the electrical distribution system are summarised. Second, the long-term and short-term measures to enhance the distribution system resilience are discussed. In particular, the recent studies on distributed generation and microgrid-assisted resilience enhancements are reviewed. Finally, recommendations for future research are presented.

Assessment of peak power demand reduction available via modulation of building ventilation systems.

Peak power demand strains electrical grids and increases cost of electricity generation, transmission and distribution infrastructure. Many studies have examined ways of reducing peak power demand, including modification of room air temperature setpoints or reduction of lighting levels. However, very few or no studies have examined the peak power reduction resource offered by temporary curtailment of building ventilation systems. For this reason, we conducted a simulation campaign in which we examined the resource offered by temporary ventilation curtailment in commercial buildings of different use types across the United States and in residences in the state of California, with the essential constraint that any changes resulted in air quality acceptable to occupants through additional ventilation to compensate for the curtailment. To do this, we employed previously validated building models implemented in the airflow and contaminant transport tool CONTAM and building thermal and systems modeling tool EnergyPlus, in some cases co-simulated. Results show savings are highly dependent on building type and climate but range from 0–2 W/ft2 and up to 40% of total peak building power demand. Depending on building type, this power shed can be conducted for 1.5–8 h before acute exposure or odor concerns are expected, assuming a safety factor of 2. This reduction is of the same magnitude as that offered by thermal control strategies such as setpoint increase, or from lighting reduction strategies.

Climate change impacts and costs to U.S. electricity transmission and distribution infrastructure

This study presents a screening-level analysis of the impacts of climate change on electricity transmission and distribution infrastructure of the U.S. In particular, the model identifies changes in performance and longevity of physical infrastructure such as power poles and transformers, and quantifies these impacts in economic terms. This analysis was evaluated for the contiguous U.S, using five general circulation models (GCMs) under two greenhouse gas emission scenarios, to analyze changes in damage and cost from the baseline period to the end of the century with three different adaptation strategies. Total infrastructure costs were found to rise considerably, with annual climate change expenditures increasing by as much as 25%. The results demonstrate that climate impacts will likely be substantial, though this analysis only captures a portion of the total potential impacts. A proactive adaptation strategy resulted in the expected costs of climate change being reduced by as much as 50% by 2090, compared to a scenario without adaptation. Impacts vary across the contiguous U.S. with the highest impacts in parts of the Southeast and Northwest. Improvements and extensions to this analysis would help better inform climate resiliency policies and utility-level planning for the future.

Climate change scenarios for Paraguayan power demand 2017–2050

Although Paraguay has a surplus of electricity generation capacity, an underdeveloped electricity transmission and distribution infrastructure has constrained economic growth. The trajectory of future electricity demand is therefore important for planning purposes. We create electricity demand scenarios for Paraguay between 2017 and 2050 for two climatic scenarios, the Representative Concentration Pathways (RCPs) 4.5 (medium atmospheric CO2 concentration) and 8.5 (high atmospheric CO2 concentration), in combination with three socio-economic scenarios, the Shared Socio-economic Pathways SSP1, SSP3 and SSP5. Using historical climatic and socio-economic data from 1985 to 2010, we estimate an autoregressive distributed lag model for Paraguayan power demand with an in-sample symmetric mean absolute percentage error (sMAPE) of 2.3% and an out-of-sample (2011–2016) ex-post sMAPE of 4.6%. We re-estimate the parameters on the full dataset 1985–2016 and produce electricity demand projections until 2050 for the selected scenarios. The scenarios show an increase in power demand until the period 2045, after which two of the six scenarios show a decline and the remainder continue increasing at a slower rate. The SSP1- and SSP5-based scenarios reach an annual demand of 65–80 TWh/year around 2045–2050, and the scenarios based on SSP3 reach an annual demand of 100–115 TWh/year around 2050. In addition to aid in energy planning, these scenarios may provide input to negotiations with neighbouring countries regarding Paraguay’s surplus generation capacity.

Emergency aware congestion control for smart grid neighborhood area networks

The evolution of traditional electricity distribution infrastructures towards Smart Grid networks has generated the need to carry out new research. There are many fields that have attracted the attention of researchers, among which is the improvement of the performance of the so-called Neighborhood Area Networks (NAN). In this sense, and given the critical nature of some of the data transmitted by these networks, maintaining an adequate quality of service (QoS) is absolutely necessary. In emergency situations, this need becomes even more evident. This article presents a congestion control mechanism, whose parameters are modified according to the network state of emergency. The mechanism also applies a multi-channel allocation technique, together with a differentiation in the QoS offered to the different data flows according to their relevance. These proposals have been evaluated in the context of a wireless mesh networks (WMN) made up by a set of smart meter devices, where various smart grids (SG) applications are sending their data traffics. Each SG application must meet its unique quality of service (QoS) requirements, such as reliability and delay. To evaluate the proposals, some NAN scenarios have been built by using the ns-3 simulator and its 802.11s basic model, which was modified to implement the proposed techniques. Compared with the basic Hybrid Wireless Mesh Protocol (HWMP), Emergency Aware Congestion Control proposal (EA-HWMP), shows significant improvements in terms of packet delivery ratio, network throughput and transit time.

Alternating current and direct current-based electrical systems for marine vessels with electric propulsion drives

The initial widespread electrification of marine vessels primarily used alternating current (AC)-based systems as they were prevalent in the electrical distribution infrastructure for land-based systems. At present, there are marine vessels that operate based on a hybrid diesel-electric system, in which the on-board diesel engines generate AC power to a common AC bus, which in turn supplies power to the electrical propulsion drives and other service loads. Recently, there has been an active interest in a transition to direct current(DC)-based electrical distribution system with diesel-electric hybrid propulsion systems for marine vessels due to potential improvements in electrical performance and fuel consumption. This paper evaluates the improvement in the move to DC-based distribution system for marine vessels in terms of electrical performance and fuel efficiency. An overview of a typical AC electrical system currently in use is provided, and modifications of the system to a DC-based system is presented. The power factor, total harmonic distortion, and voltage regulation are discussed. Both symmetrical and asymmetrical generator scheduling are examined and the potential fuel savings for an example diving support vessel is presented. A discussion on the recommendations to shift towards DC-based systems is then provided based on the findings of this paper.

Critical infrastructure vulnerability—A method for identifying the infrastructure service failure interdependencies

Modern society has become increasingly dependent on the resilience of critical infrastructures (CI), such as electricity distribution, telecommunications, and IT infrastructure. These systems can be interdependent in a variety of ways. The dependencies may lead to a situation where a failure in one system can cause failures in other systems. The comprehension of failure interdependencies is important for the continuous functioning of CI, especially from the viewpoints of preparedness and the response. The sector-specific interdependencies are usually well known but there is a lack of comprehension of the cross-sector interdependencies. This paper presents a qualitative method for identifying and describing the CI service failure interdependencies. The proposed method is a scenario-based approach in which CI stakeholders create a common understanding of the causes and effects of a described threat scenario. The method can be used to identify and describe the potential sector-specific and cross-sector failure interdependencies. Through the method, we have identified and described the interdependencies between the failures in CIs together with domain experts involved in the Finnish regional preparedness committees and experts working in high-level positions in CIs in Finland. The results of the case study can be utilized in the process of developing the preparedness and response activities in Finland. The method presented here can be applied in the process of identifying and describing the failure interdependencies in other countries and contexts.

Joint Costs in Electricity and Natural Gas Distribution Infrastructures: The Role of Urban Factors

This paper analyzes the joint cost structure of electricity and natural gas distribution investments. Assessing the joint costs is critical for urban development and public policy regarding competition at the local level. The paper accounts for the urban and geographic factors at the local level, while the previous literature primarily used company-level data with a few or no site-specific variables in joint cost analyses. An empirical analysis of the multi-utility capital costs suggests that the local urban and geographic conditions affect such costs, with economies of scope present in electricity and natural gas both in terms of total costs and underground investment costs. Hence, the joint service provision makes economic and environmental sense for urban policy makers.

A Novel Method of Protection to Prevent Reverse Power Flow Based on Neuro-Fuzzy Networks for Smart Grid

This paper addresses the energy challenges related to the weak protection of renewable energy from reverse energy flow and expanding access to high-quality energy at the same time. Furthermore, this paper focuses on participation in the global transition to clean and low-carbon energy systems. Moreover, the increased demand for renewable energy seems to likely depend on whether it will be possible to greatly accelerate rates of progress toward increased efficiency, de-carbonization, greater generating diversity and lower pollutant emissions. This paper focuses on the protection of renewable energy technologies because they can be particularly attractive in dispersed areas and therefore, represent an important option for rural areas that lack electrical energy and distribution infrastructure. This paper proposes an improved protection device for a reverse power protection system using a new intelligent decision support system (IDSS). The IDSS is a support system for decision making, which makes extensive use of artificial intelligence (AI) techniques. The new method integrates the powerful specification for neural networks and fuzzy inference systems. The main advantage of this method is that it causes a decrease in the steady state oscillation for the reverse power relay. In addition, the proposed method has the ability to monitor extreme environmental conditions. The generator can be converted into a motor when the steam supply to a turbine is interrupted while the generator is still connected to a grid (or operates in parallel with another generator). As a result, the generator will become a synchronous motor and will actually cause significant mechanical damage. The reverse energy protection device should be included in the generator protection scheme. Smart grids use communication networks with sophisticated algorithms to ensure coordination between protection systems. ZigBee is a newly developed technology that can be used in wireless sensor networks (WSNs) to comply with the IEEE 802.15.4 standard. Low data rates, low power consumption and low cost are key features of ZigBee. The execution of star, tree and mesh topologies as well as support comparison is based on end-to-end delay, throughput, medium access control load in addition to sent and received traffic parameters. The use of star topology obtained a delay of 0.2 s. The simulation results show that this method is superior to the traditional method in terms of speed and steady-state oscillation.

Impact of uncoordinated plug-in electric vehicle charging on residential power demand

Electrification of transport offers opportunities to increase energy security, reduce carbon emissions, and improve local air quality. Plug-in electric vehicles (PEVs) are creating new connections between the transportation and electric sectors, and PEV charging will create opportunities and challenges in a system of growing complexity. Here, I use highly resolved models of residential power demand and PEV use to assess the impact of uncoordinated in-home PEV charging on residential power demand. While the increase in aggregate demand might be minimal even for high levels of PEV adoption, uncoordinated PEV charging could significantly change the shape of the aggregate residential demand, with impacts for electricity infrastructure, even at low adoption levels. Clustering effects in vehicle adoption at the local level might lead to high PEV concentrations even if overall adoption remains low, significantly increasing peak demand and requiring upgrades to the electricity distribution infrastructure. This effect is exacerbated when adopting higher in-home power charging. Electrification of transport offers many benefits for the energy transition but introduces a number of complexities around the electric system. This study undertakes modelling of residential power demand and electric vehicle use to understand the impact of uncoordinated vehicle charging on the electricity load.