Electric Utility Research Articles

The potential of utilizing vertical borehole heat exchangers in residential buildings for the various climate zones of Saudi Arabia

Most of the literature concerning geothermal energy in Saudi Arabia has focused on power generation applications. This paper investigates the potential of utilizing ground-source heat pump (GSHP) with vertical borehole heat exchangers in residential buildings in the various climate zones and subsurface geologies of Saudi Arabia. Thermal loads of a typical residential building in Saudi Arabia were generated using eQuest software, then used in a model of GSHP system in TRNSYS. The performance and economics of the GSHP system was evaluated and compared with a typical air-source heat pump (ASHP) system for each location. The total borehole lengths in all zones were determined using the cooling load (the dominant load). The potential for employing GSHP systems was found to be not uniform across Saudi Arabia. The required length of the GHE ranged between 12 and 148 m/kW of cooling. The annual energy saving when employing GSHP instead of ASHP systems varied between 3 % to 19 %, and the building's electricity peak demand could be reduced between 5 % to 43 %. Although GSHPs reduced electrical and maintenance costs, their high drilling cost makes them economically unattractive under the present electric utility charge in Saudi Arabia.

Many-objective optimization for overall performance of an electric sport utility vehicle under multiple temperature conditions based on natural gradient boosting model

In this study, significant efforts have been devoted to optimizing energy consumption and driving range of an electric sport utility vehicle (ESUV). Vehicle energy flow tests were conducted at different temperatures, and integrated simulation models were built and validated. Subsequently, Natural Gradient Boosting (NGBoost) model was constructed based on extensive datasets obtained by parallel simulation framework, and many-objective optimizations of ESUV design parameters were performed. The results show that energy consumption per 100 km under high and low temperatures increases by 19.3 % and 34.9 % compared to room temperature, while effective work of half-axis decreases by 12.9 % and 28 %, suggesting extreme environmental temperature significantly affects ESUV performance. Mean square errors (MSEs) of NGBoost model predictions are less than 0.12, indicating its reliable predictive capability for unknown data. Many-Objective Random Walk Grey Wolf Optimizer (MORW-GWO) algorithm achieves optimal or suboptimal results on 10 test problems, fully demonstrating its efficient convergence and excellent robustness. Battery recovered energy, effective work of half-axis and electricity consumption per 100 km of final optimization scheme are increased by 20.2 %, 9.0 % and 13.0 %, with the largest overall percentage improvement of 17.3 %. These findings can provide data support and directional guidance for optimization designs and performance improvements of ESUVs.

Minimizing the reliability trade-off in wildfires risk mitigation

One of the major concerns of electric power utilities in recent years is mitigating wildfire risks. While different mitigations have been proposed and some are implemented, customer experience is less focused in some methods, and not even considered in some extreme methods. This paper focuses on minimizing such tradeoff: reliability vs. wildfire risk reduction. This paper first proposes a methodology to quantify the wildfire risk raised by electric distribution systems, utilizing the vegetation factor at proper locations as well as the probability of fault. An optimization framework is then developed in this work that considers system reliability improvement in decision-making while reducing the risk of wildfires. The means to achieve such optimization are reclosers and fuses: optimally locate them to minimize the wildfire risk while the negative impact on reliability is minimized. Both fuse-blowing and saving schemes are investigated in this work. Moreover, the temporary and permanent types of faults are taken into account by formulating their impact on the operation of reclosers and fuses in both fuse-blowing and saving schemes. The optimization framework utilizes the exchange market algorithm (EMA) to find optimal results. The effectiveness of the proposed approach is verified by utilizing a modified IEEE 123 node system.

Evaluation of electrical load demand forecasting using various machine learning algorithms

The energy sector heavily relies on a diverse array of machine learning algorithms for power load prediction, which plays a pivotal role in shaping policies for power generation and distribution. The precision of power load prediction depends on numerous factors that reflect nonlinear traits within the data. Notably, machine learning algorithms and artificial neural networks have emerged as indispensable components in contemporary power load forecasting. This study focuses specifically on machine learning algorithms, encompassing support vector machines (SVMs), long short-term memory (LSTM), ensemble classifiers, recurrent neural networks, and deep learning methods. The research meticulously examines short-term power load prediction by leveraging Chandigarh UT electricity utility data spanning the last 5 years. The assessment of prediction accuracy utilizes metrics such as normalized mean square error (NMSE), root mean squared error (RMSE), mean absolute error (MAE), and mutual information (MI). The prediction results demonstrate superior performance in LSTM compared to other algorithms, with the prediction error being the lowest in LSTM and 13.51% higher in SVMs. These findings provide valuable insights into the strengths and limitations of different machine learning algorithms. Validation experiments for the proposed method are conducted using MATLAB R2018 software.

Simulation based comparative analysis of electric field stress on insulated cross-arm

Insulated cross-arms for overhead transmission lines have become increasingly common in the past decade. The 3D finite element analysis (FEA) of the electric field distribution in insulated cross-arms is particularly intriguing due to the differences from conventional glass or ceramic insulators. The complex geometry of composite cross-arms, which feature four separate insulator strings and varying shed profiles, poses a challenge in modelling them using 3D FEA packages. The findings indicate that insulated cross-arms can operate within suitable parameters for traditional composite insulators. This paper presents and analyses the electric field distribution around an insulated cross-arm, which aims to replace existing high-voltage insulators and the steel cross-arms of transmission towers. The design of grading devices plays a crucial role in ensuring the safety of these structures. The use of grading ring devices at both ends of the insulator has proven effective in relieving stress on the insulator string under normal and abnormal conditions, enabling the insulated cross-arm to function effectively with conventional composite insulators. Significant reductions in electric field strength were observed, amounting to approximately 35.93 % inside the core, 31.14 % on the core surface, 35.64 % through the shed, and 16.67 % above the shed for compression members. For tension members, reductions from the low-voltage end to both ends were around 33.82 % inside the core, 38.55 % on the core surface, and 17.48 % through the shed. However, an 8.43 % increase was observed above the shed, indicating a deviation from the decreasing trend observed in other areas. These research findings provide valuable insights for designing and managing high-voltage systems, ensuring effective insulation and enhanced safety. It benefits electrical utilities and transmission line manufacturers by improving reliability and safety for overhead transmission lines.

Switch to pollution control bonds, else carbon risk will switch us: Evidence from the U.S. electric utility firms

This study explores the implications and effectiveness of pollution control bonds (PCBs) in reducing carbon risk, focusing on a sample of 242 U S. electric utility firms from 2012 to 2022. The research investigates the association of PCB issuances with (i) absolute (unscaled) carbon emissions levels and their three scopes; and (ii) carbon emissions intensity and its three scopes. Using quantile regressions covering the 5th, 25th, 50th, 75th, and 90th quantiles, along with the Propensity Score Matching (PSM) methodology (where the treatment group includes firms using PCBs and the control group comprises firms that do not opt for PCBs), the findings show a significant reduction in carbon emissions due to PCB issuance. Substantial differences were observed between the treatment group and the control group across various quantiles of carbon emissions, particularly for companies with medium to high carbon footprints, both in terms of overall CO2 emissions and scope 2 CO2 emissions. Moreover, disparities between the two groups were notable across all quantiles of scope 1 CO2 emissions. Additionally, among the companies using PCBs, those with lower risk profiles exhibited a smaller carbon footprint, measured by scope 3 CO2 emissions, in comparison to their counterparts. Furthermore, the study highlights a more pronounced impact of PCBs issuance during the second phase of the Kyoto Protocol and the commitment period of the Paris Agreement. The results remain robust when alternative measures of carbon risk are applied. These findings carry significant implications for municipal and financing authorities, as well as investors within the U.S. electric utility industry. This research contributes novel insights to the field of electric utility management.

A new Paecilomyces from wooden utility poles in South Africa.

During a survey of fungi on electricity utility poles in South Africa, a diverse range of fungi were discovered. Paecilomyces was frequently isolated, with five species identified using ITS and β-tubulin (BenA) sequences. These were P. brunneolus, P. dactylethromorphus, P. lecythidis, P. paravariotii and a potential new species. The genomes of 30 of these strains were subsequently sequenced and used in a phylogenomic analysis with 45 previously published genomes of the genus. Phylogenetic analyses were also conducted using ITS, BenA, calmodulin (CaM), RNA polymerase II second largest subunit (RPB2), RNA polymerase II largest subunit (RPB1), the genes coding for the theta subunit of the TCP-1 chaperonin complex (Cct8), and for a putative ribosome biogenesis protein (Tsr1). Both phylogenomic and phylogenetic analyses supported the 15 Paecilomyces species currently accepted and confirmed the novelty of the new species, which we describe as P. lignorum. The latter is the sister species of P. brunneolus and belongs to a clade also containing P. variotii and P. paravariotii. Morphologically, the new species produces longer ellipsoidal conidia and grows more restricted on malt extract agar at 30 °C compared to its closest relatives. Citation: Visagie CM, Cruywagen EM, Duong TA (2024). A new Paecilomyces from wooden utility poles in South Africa. Fungal Systematics and Evolution 13: 163-181. doi: 10.3114/fuse.2024.13.10.

Developing a Practical Framework for Applying the Work from Home Concept to Technical Jobs in Electricity Utilities Using the Unified Theory of Acceptance and Use of Technology

Developing a Practical Framework for Applying the Work from Home Concept to Technical Jobs in Electricity Utilities Using the Unified Theory of Acceptance and Use of Technology

Mechanical, Fire, and Electrical Insulation Properties of Polyurethane Fly Ash Composites.

This paper demonstrates that ash composites, comprising fly ash and polyurethane, can be used to develop value-added products that exhibit an effective decrease in the leaching of coal ash inorganics to less than one-third of the Environmental Protection Agency (EPA)'s maximum contaminant level (MCL) when soaked in a water circulation system for 14 months. Furthermore, the composite blocks remain safe even with ruptured surfaces. The concept of encapsulating fly ash within ash composites by using a polar polymer to bind the fine inorganic particles, mimicking how nature does it in the original unburned coal, ensures the safety of the composite. The ash composites can be formulated to have designed mechanical, fire, and electrical properties by controlling the formulation and the density. The properties of typical density composites were produced, measured, and compared with commercial materials. This paper also demonstrates that ash composite technology can be extended to coal ash stored in ponds. Finally, a typical electric utility box cover was designed, fabricated, and test validated. The box cover has less than one-half the weight of the original box cover for the same design limits. Finally, the benefits of this ash-composite technology for product manufacturers, society, and ash producers are summarized.

Enhancing grid resiliency in distributed energy systems through a comprehensive review and comparative analysis of islanding detection methods

Reduction of fossil fuel usage, clean energy supply, and dependability are all major benefits of integrating distributed energy resources (DER) with electrical utility grid (UG). Nevertheless, there are difficulties with this integration, most notably accidental islanding that puts worker and equipment safety at risk. Islanding detection methods (IDMs) play a critical role in resolving this problem. All IDMs are thoroughly evaluated in this work, which divides them into two categories: local approaches that rely on distributed generation (DG) side monitoring and remote approaches that make use of communication infrastructure. The study offers a comparative evaluation to help choose the most efficient and applicable IDM, supporting well-informed decision-making for the safe and dependable operation of distributed energy systems within electrical distribution networks. IDMs are evaluated based on NDZ outcomes, detection duration, power quality impact, multi-DG operation, suitability, X/R ratio reliance, and efficient functioning.

Optimized Decision-Making for Multi-Market Green Power Transactions of Electricity Retailers under Demand-Side Response: The Chinese Market Case Study

With the energy structure transition and the development of the green power market, the role of electricity retailers in multi-market green power trading has become more and more important. Particularly in China, where aggressive green energy policies and rapid market transformations provide a distinct context for such studies, the challenges are pronounced. Under demand-side response, electricity retailers face the uncertainty of users’ electricity consumption and incentives, which complicates decision-making processes. The purpose of this paper is to explore the optimization decision-making problem of multi-market green power trading for electricity retailers under demand-side response, with a special focus on the Chinese market due to its leadership in implementing substantial green energy initiatives and its potential to set precedents for global practices. We first construct a two-party benefit optimization model, which comprehensively considers the profit objectives for electricity retailers and utility maximization for users. Then, the model is solved by the Lagrange multiplier method and distributed subgradient algorithm to obtain the optimal solution. Finally, the effectiveness of the incentive optimization strategy under the multi-market to promote green power consumption and improve the profit of electricity retailers is verified by arithmetic simulation. The results of this study show that the incentive optimization strategy under multi-market, particularly within the Chinese context, is expected to provide a reference for electricity retailers to develop more flexible and effective trading strategies in the green power market and to contribute to the process of promoting green power consumption globally.

Attention Focus and New Opportunities: The Moderating Role of Managerial Attention to Alternative Issues

The strategy literature highlights the critical role that managerial attention plays in shaping firms’ responses to industry change. However, attention to change alone is not sufficient to precipitate action. The context in which this attention is paid is also critical in determining the translation of attention to action as managers generally pay attention to multiple issues within their strategic agendas. We argue that attention to a competing issue and the breadth of managerial strategic attention impair the relationship between attention to a focal issue and related organizational action as these attentional constraints may lead to hesitancy or confusion at an individual level and reduced coordination at an organizational level. Realizing that at an organizational level, firms vary tremendously with respect to their sizes, resources, and by implication their overall attentional capacities, we argue that the negative moderation impacts of attention to a competing issue and the breadth of strategic attention on the attention to a focal issue–subsequent action relationship are larger for smaller firms. Studying the U.S. electric utility industry and the emerging new distributed electricity generation business model, we find broad support for our arguments and also important nuance. By melding theory, important details of our empirical setting, and intriguing empirical results, we develop greater insights into when and how the other strategic issues to which managers pay attention can shape the translation of managerial attention to a new business model into subsequent organizational action. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.15861 .

Deep Anomaly Detection Framework Utilizing Federated Learning for Electricity Theft Zero-Day Cyberattacks.

Smart power grids suffer from electricity theft cyber-attacks, where malicious consumers compromise their smart meters (SMs) to downscale the reported electricity consumption readings. This problem costs electric utility companies worldwide considerable financial burdens and threatens power grid stability. Therefore, several machine learning (ML)-based solutions have been proposed to detect electricity theft; however, they have limitations. First, most existing works employ supervised learning that requires the availability of labeled datasets of benign and malicious electricity usage samples. Unfortunately, this approach is not practical due to the scarcity of real malicious electricity usage samples. Moreover, training a supervised detector on specific cyberattack scenarios results in a robust detector against those attacks, but it might fail to detect new attack scenarios. Second, although a few works investigated anomaly detectors for electricity theft, none of the existing works addressed consumers' privacy. To address these limitations, in this paper, we propose a comprehensive federated learning (FL)-based deep anomaly detection framework tailored for practical, reliable, and privacy-preserving energy theft detection. In our proposed framework, consumers train local deep autoencoder-based detectors on their private electricity usage data and only share their trained detectors' parameters with an EUC aggregation server to iteratively build a global anomaly detector. Our extensive experimental results not only demonstrate the superior performance of our anomaly detector compared to the supervised detectors but also the capability of our proposed FL-based anomaly detector to accurately detect zero-day attacks of electricity theft while preserving consumers' privacy.

What does capital consume? Racial capitalism and the social reproduction of surplus people

This intervention considers uneven development and social reproduction within racial capitalism. Social reproduction refers to the range of practices that form the conditions of possibility for the life of capital, as well as life and death within racial capitalism. This spans a range of institutions and networks within households, communities, states and across national borders as well as the labour practices, relations and organization that reproduce racial capitalism. Here, we examine the extraction of time, taking up theorizations across carceral geographies, postcolonial theory and Caribbean studies to demonstrate how coercive relations of social reproduction contribute to uneven development. In particular, we look at the role of the state in racial capital’s capture of reproductive activities across our work on electric utilities in Atlanta, Georgia and extralegal land tenure on Jamaica’s north coast. In bringing these distinct sites into conversation, we re-affirm the need to study uneven development by understanding how the circulation and accumulation of capital is imbricated with the production of hierarchies of all kinds of difference. We show how a conjunctural countertopography can reveal how state practices advance accumulation under conditions of widespread surplus lives, as capital wagers on captive life and premature death.

Investigating the influence of clustering techniques and parameters on a hybrid PSO-driven ANFIS model for electricity prediction

The availability of reliable electrical power, which is essential for a comfortable lifestyle worldwide, requires realistic power usage projections for electric utilities and policymakers, leading to the adoption of machine learning-based modelling tools due to the limitations of traditional power usage projection approaches. However, successful modeling of power usage in neuro-fuzzy models depends on the optimal selection of hyper-parameters. Consequently, this research looked at the major impact clustering methods and hyper-parameter modifications on a particle swarm optimization (PSO)-based adaptive neuro-fuzzy inference system (ANFIS) model. The study examined two distinct clustering methods and other key hyperparameters such as the number of clusters and cluster radius, resulting in a total of 10 sub-models. The performance of the developed models was assessed using four widely recognized performance indicators: root mean square error, mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of variation of the root mean square error (CVRMSE). Additionally, the robustness of the optimal sub-model was evaluated by comparing it with other hybrid models based on three different PSO variants. The results revealed that the combination of the ANFIS approach and PSO, specifically with two clusters, yielded the most accurate forecasting scheme with the optimal values for MAPE (7.7778%), MAE (712.6094), CVRMSE (9.5464), and RMSE (909.4998).

Experiment investigation for interconnected effects of driving cycle and ambient temperature on bidirectional energy flows in an electric sport utility vehicle

The constrained driving range has emerged as a pivotal quandary impeding the advancement of electric sport utility vehicles (ESUVs). In this study, energy flow tests were executed on an ESUV to investigate interconnected effects of driving cycle and ambient temperature on bidirectional energy flows throughout the entire driving range. The results reveal that air conditioner singularly imposes a substantial surge in energy consumption at high-temperature settings. Notably, energy utilization efficiency (ηuti) reaches its zenith under Fixed speed cycle at room temperature (FSR_R) and energy recovery efficiency (ηrec) attains its peak under World Light Vehicle Test Cycle at room temperature (WLTC_R). Nevertheless, ηuti and ηrec attain their acme at room temperature and reach their nadir under low-temperature conditions, regardless of the specific driving cycle employed. Battery average temperature exhibits an ascending trajectory at room temperature and displays cyclic fluctuations at high temperature, while first increasing rapidly and then decreasing gradually in low-temperature ambient. Battery output energy per cycle initially decreases and eventually stabilizes with increasing the cycle count, and the battery recovery energy shows an initial upward trend followed by a stabilization phase. These findings provide empirical substantiation and prescriptive insights for mitigating energy consumption and enhancing driving range of ESUVs.

How to Refine Electric Utility Compensation for Increasing DER Market Participation

As the energy sector embraces the core principles of sustainability, North America's electricity markets are experiencing a paradigm shift. The proliferation and acceptance of distributed energy resources (DERs) have fundamentally altered the dynamics of electricity generation and distribution, and how we leverage energy storage and demand response.1 This is especially true for all utility types and DER owners/representatives, on the distribution system. While many consumers with DERs have successfully navigated the wholesale and retail energy markets, many challenges still exist with traditional utility models, sparking a reevaluation of compensation mechanisms for energy providers on the distribution system.

A Comprehensive Review of Modern Methods for Load Prediction in the Smart Grid

: Load forecasting plays a crucial role in mitigating risks for utilities by predicting future usage of commodity markets transmission or supplied by the utility. To achieve this, various techniques such as price elastic demand, climate and consumer response, load analysis, and sustainable energy generation predictive modelling are used. As both supply and demand fluctuate, and weather and power prices can rise significantly during peak periods, accurate load forecasting becomes critical for utilities. By providing brief demand forecasts, load forecasting can assist in estimating load flows and making decisions that prevent overloading. Therefore, load forecasting is crucial in helping electric utilities make informed decisions related to power, load switching, voltage regulation, switching, and infrastructure development. Forecasting is a methodology used by electricity companies to forecast the amount of electricity or power production needed to maintain constant supply as well as load demand balance. It is required for the electrical industry to function properly. The smart grid is a new system that enables electricity providers and customers to communicate in real-time. The precise energy consumption sequence of the consumers is required to enhance the demand schedule. This is where predicting the future comes into play. Forecasting future power system load (electricity consumption) is a critical task in providing intelligence to the power grid. Accurate forecasting allows utility companies to allocate resources and assume system control in order to balance the same demand and availability for electricity. In this article, a study on load forecasting algorithms based on deep learning, machine learning, hybrid methods, bio-inspired techniques, and other techniques is carried out. Many other algorithms based on load forecasting are discussed in this study. Different methods of load forecasting were compared using three performance indices: RMSE (Root Mean Square Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and Accuracy. Machine learning-based techniques showed a reduction of 9.17% in MAPE, 0.0429% in RMSE, and 5.23% in MSE, and achieved 90% accuracy. Deep learning-based techniques resulted in a 9.61% decrease in MAPE and achieved 91% accuracy. Bioinspired techniques provided a reduction of 9.66% in MAPE, 0.026% in RMSE, and 5.24% in MSE, and achieved 95% accuracy. These findings concluded that optimization techniques are more encouraging in predicting load demand and, as a result, can represent a reliable decision-making tool.

Electric Grid of Japan before the Great Depression: A Comparison with Germany

Japan is one of the most successfully electrified countries in the world, but it does not have a unified national electric power grid: the country is divided into two zones with different standards for alternating current (50 and 60 Hz). To explore the root of Japan's split grid, this essay adopts technology historian Thomas Hughes’ socio-technical system frameworks and compares the history of national grid formation in Germany and Japan in the 1920s. In both countries, foresighted engineers and entrepreneurs noticed the necessity of a unified national grid—something we see from the case study of Matsunaga Yasuzaemon and Arthur Koepchen. However, government policy in Japan regarding frequency unification was inconsistent, whereas the German provincial governments set up clear visions for grid construction. Moreover, Japanese ideology of the day preferred market competition, whereas in Germany electric utilities coordinated with each other. For Germany, the 1920s heralded the beginning of a European grid, whereas Japan's bifurcated frequency standard hindered the formation of a unified national grid. And Japan's split grid can even result in the electric power system's fragility in the face of natural disasters such as the Great East Japan Earthquake of March 2011.

The Impact of Privatization of Nigeria’s Electricity Industry on the Quality of Electricity Service Delivery in Lokoja Metropolis

This study empirically examined the impact of the privatization of the electricity industry on electricity service delivery in the Lokoja metropolis by Abuja Electricity Distribution Company (AEDC). Data for the study were elicited from copies of structured questionnaire administered to 400 (four hundred) residential electricity consumers in the Lokoja metropolis via the cluster sampling technique. Data were analyzed using simple percentages, frequency distribution tables and the mean (x) score respectively. The rational choice theory which provides insight into how the vested interests of Nigeria’s ruling class compromised the privatization process of the Nation’s electricity industry constituted the theoretical foundation of the study. Findings revealed, among other things, that the privatization of the electricity industry in Nigeria has not translated to significant improvement in power supply in the Lokoja metropolis and that electricity billing is not a fair reflection of the electricity consumption rate in the metropolis. To this end, the study concluded that the privatization of Nigeria’s electricity industry has not impacted positively on electricity service delivery in the Lokoja metropolis. Consequently, the study recommended, amongst others, that the Nigerian government should put in place measures to attract globally reputable electricity utility companies to invest in the Nation’s electricity industry and prepaid metering as against estimated billing should be the sole criterion for determining electricity consumers' tariff in the country.