Utility Customers Research Articles

Information Freshness in Service Queues With Strategic Customers

We examine a scenario in which a service provider updates its queue length information to customers at a specific frequency. Customers decide whether to join the queue based on the most recent update. Our objective is to determine the optimal updating frequency that benefits both the service provider and customers. Using a two-dimensional continuous-time Markov process, we model the actual and announced queue length processes. By proving the identical distributions of these two processes under a Poisson updating scheme, we derive closed-form solutions for customers’ utility functions. Our findings demonstrate that customers adopt a generalized mixed-threshold strategy at equilibrium, and their certainty about whether to join the queue or balk does not always increase with fresher information. Furthermore, we reveal that due to customers’ different sensitivities to information freshness, system performance metrics such as throughput and total customer utility exhibit non-monotonic behavior in response to the updating frequency. Consequently, providing fresher information does not necessarily lead to improved system performance. To address this, we propose algorithms to determine the optimal updating frequency for each system performance metric and identify the conditions under which different updating frequencies are optimal. We demonstrate that any positive updating frequency can achieve customer utility no worse than the no-information system. Additionally, in systems with high customer arrival rates, updating with a positive updating frequency can improve throughput. Furthermore, we prove conditions under which a positive and finite updating frequency can yield higher throughput and total customer utility compared to real-time information systems.

Performance analysis of hybrid metaheuristic assisted collateral FO controller for HRES system integrated UPQC

Due to the fluctuating characteristics of the environment, it is advisable to connect a minimum of two renewable energy sources (RES) to the grid to mitigate unreliable power supply. The integration of Hybrid Renewable Energy Storage (HRES) with nonlinear loads introduces harmonics and other power quality issues, which are significant concerns for both utility providers and customers. Power quality challenges, such as real power fluctuations, voltage interruptions, and reactive power imbalances, can be mitigated through the use of a Unified Power Quality Conditioner (UPQC). For optimal performance, the series and shunt compensators within the UPQC rely on precise PWM signals to control the converters' outputs. These signals are correctly tuned by controllers using updated algorithms. The paper develops an optimized Hybrid Metaheuristic assisted Collateral Controller for the improvement of the 3-phase HRES system-based Distribution Grid incorporated with UPQC. It consists of a Fractional order Proportional Integral derivative (FOPID) controller combined with a proportional integral (PI) controller. As a consequence, the DC link voltage regulation and controller optimization are achieved with the optimal tuning of gain parameters by using a hybrid Metaheuristic Algorithm named Amplified Slime Mould with WildeBeest Herd Optimization (ASM-WHO) Algorithm. The optimization of the weights (W1 and W2) and parameters of the Collateral Fractional Order Controller is achieved using an innovative hybrid metaheuristic method that combines the Wildebeest Herd Optimization (WHO) and Slime Mould Algorithm (SMA). The proposed system is implemented in MATLAB Simulink to analyze the compensation efficiency during voltage sag/swell. The proposed controller showcases robust performance, emphasizing its potential for enhancing power quality and distribution stability in Three-Phase Hybrid Renewable Energy Storage systems integrated with UPQC, outperforming or matching the performance metrics of conventional methods such as PI, FOPID controller, FOPID-PI with various optimization algorithms (CSO,SOA, WHO, SMA, AQO, HBA). The settling minimum and maximum of 170.5668 and 189.4736 for the proposed FOPID-PI controller with ASM – WHO is notably superior to that of the controller without optimization, the proposed collateral controller method (using SOA or CSO), WHO optimization, and the PI controller, improving by 1.7 %, 13.78 %, 1.338 %, and 13.66 %, respectively. The ASM-WHO algorithm shows robust performance and improved convergence, as validated by benchmark function tests, and surpasses competing algorithms.

Incentive Impact on Data Quality, Sample Composition, and Respondents’ Topic Interest

This article investigates the impact of offering a small incentive for a web survey of utility customers in the Mid-Atlantic United States. We explore background demographics and characteristics, response patterns, and data quality. Offering a small incentive was found to increase response rates. We did not find convincing evidence that offering an incentive affects data quality. However, we did find some evidence that the lack (or presence) of an incentive may impact sample composition and failing to provide an incentive may lead to more responses from people who are more interested in the survey topic.

Investigating the factors affecting the intention to adopt smart electricity meters in Indian households

Smart electric meters play a pivotal role in making energy systems decarbonized and automating the energy system. Smart electric meters denote huge business opportunities for both public and private companies. Utility players can manage the electricity demand more efficiently whereas customers can monitor and control the electricity bill through the adoption of smart electric meters. The study examines the factors affecting the adoption intention of smart electric meters in Indian households. This study draws a roadmap that how utility providers and customers can improve the smart electric meters adoption. The study has five independent variables (performance expectancy, effort expectancy, social influence, environmentalism, and hedonic motivation) and one dependent variable (adoption intention). The sample size for the study is four hundred and sixty-two respondents from Delhi and the National Capital Region (NCR). The data was analysed using structural equation modelling (SEM). The results of this study have confirmed that performance expectancy, environmentalism, and social influence have a significant impact on the intention of adopting smart electric meters. Therefore, utility providers can improve their strategies to attract more customers to adopt smart electric meters by focusing more on the performance of smart electric meters and by making them environmentally friendly. This research offers meaningful insights to both customers and utility providers to make energy systems decarbonized and control energy consumption.

A Scientific Discussion of Post-Materialism Values and Environmental Behavior

Inglehart developed a theory examining the changes of values in post-industrial societies from materialist towards post-materialist views. Inglehart contended that the rise of post-materialism helps explain the rise of environmentalism since the 1960s. Through a 2022 survey targeting individual utility customers, we employed Inglehart’s post-materialist index examining the impact of environmental behavior and other attributes. This study considers demographic profiling for material and post-materialist views in light of recent social movements and inflationary pressures. We observed that materialists, predominately Asians and Whites, tend to be older, are homeowners, are either married or widowed, and have lived in their homes the longest. Post-materialists were also found to be young, partnered, politically liberal, and least likely to be Asian. While investigated, environmental behavior was not explained by any of the demographic characteristics. We suggest research assessing any value shifts pertaining to attitudes and behaviors towards environmental behavior. Future research should obtain a more representative minority-group sample.

The marginal generation and emissions impacts of purchased hydropower: Evidence from the Colorado River Storage Project

As the electric grid continues to modernize, utilities and regulators are continuing to emphasize low-emission, low-cost resources. While wind, solar, and storage are capacity additive targets, marketed hydropower continues to be an attractive source of reliable power for utilities. These utilities rely on hydropower as a cheaper and cleaner alternative to traditional fossil fuel generation. However, hydropower production is subject to continuously evolving ecological and regulatory constraints. Understanding the economic benefits of hydropower purchases to utilities and their customers is useful in the discourse surrounding management of hydroelectric dams. To date, there has been no work that directly examines the fossil fuel generation, emissions, and regional air quality impacts associated with hydropower purchases. In this study, we address this paucity of research by conducting a case study of a non-profit power wholesaler engaged in long-term hydropower purchase agreements in the western United States. We collect hourly generation, stack-level emissions (CO2, SO2, and NOX), and regional ground-level ozone data across a five-year period to assess how purchases of marketed hydropower effect these outcomes. We quantify the average hourly offsets of purchased hydropower, solar, and wind generation. We find that most of the purchased hydropower is passed through to other utility customers, but that overall, significant reductions in fossil fuel generation (particularly coal), emissions, and regional ozone levels occur. We conclude by approximating conservative, lower-bound health incidence benefits associated with marketed hydropower purchases. Our results are useful to policymakers, utilities, and other stakeholders for better understanding the potential benefits of marketed hydropower.

Is it wise for manufacturers to implement preventive maintenance in the sharing business model?

In the sharing business model, manufacturers typically increase customer usage through product design but contend with escalating failure costs stemming from heightened customer usage and moral hazard concerns. The preventive maintenance (PM) typically serves as a means for manufacturers to tackle the growing failure cost problem. However, manufacturers face the challenge of how to make PM strategies in intricate operational environments. We formulate a theoretical model to investigate when a manufacturer should implement PM, taking into account customer usage and product design. Our results suggest that when the fixed cost of PM is low and the customer moral hazard is not sufficiently high, or when both the fixed cost of PM and the customer moral hazard are at moderate levels, it is wise for the manufacturer to implement PM. We also find that the PM can lead to higher consumer surplus by increasing customer utility, achieving a win-win situation. In addition, our findings reveal a positive correlation between the PM effort and product quality, with higher PM effort leading to higher product quality and thus increasing customer usage. In extension models, we further explore the issues of maximising social welfare, non-zero maintenance time and endogenous maintenance frequency

Development of an Automatic Phase Selector for Nigerian Power Utility Customers

Power utility customers in a developing country like Nigeria have constituted a habit of changing the electricity supply line from an unavailable or unstable phase to the most available or stable phase. The category of customers involved in this character are those on single phase power supply. However, this act is being carried out manually at the meter point using the cut-out fuses. This attitude results in phase unbalances, overheating electrical equipment including feeder pillars, transformer coils, network faults, and overall system instability. Thus, this paper presents the development of an Automatic Phase Selector for Nigerian Power Utility Customers. The device automatically selects an available phase from the three-phase power supply lines. The research comprises designing an automatic phase selector circuit, simulation of the designed circuit, programming code development in C- Language for the microcontroller, construction of the designed circuit, and carrying out tests on completed work done to ascertain the effectiveness of the developed system. The system operation involved a three-phase supply from the closest distribution network of the power utility company which is connected to a three-in-one gang switch while the switching ON and OFF of their static switches represent phase-off in an ideal situation. The operational results of this system are presented in the form of the truth table which indicates that the affected customer would not have a power supply only when the 3-phases are under voltage or overvoltage or unavailable. This implies that one of the three phases that meet the three criteria would be switched ON. A pure sine wave was used as input into the Optocoupler and the output waveform of the rectified pulsating signal is separately displayed. This output waveform is very clean and noiseless. Finally, the system when practically tested with an unbalanced three-phase supply, worked perfectly enhancing the flexibility of operating an Automatic Phase selector and hence avoiding manual switching of the phase selector which has been attributed to changing of cut-out fuses and associated stress as well as having a user-friendly phase selector.

Impact of higher income on environmental interest

ABSTRACT This study examines the relationship between income levels and pro-environmental behavior using a utility customer survey. Amid urgent environmental challenges like pollution and climate change, it investigates how household income influences such behavior. Respondents were divided into high-income (90% or above) and lower income (below 90%) groups. While results were inconclusive, indicating no significant inclination toward pro-environmental behavior in higher-income groups, commonalities in perceived behaviors were observed. Notable differences included larger household sizes, greater satisfaction with natural gas utilities, and longer residency among higher-income respondents. However, factors like age, political preferences, and utility satisfaction showed no significant disparity. Although the high-income group was slightly more inclined toward energy-saving measures, both groups prioritized energy conservation similarly. Income-related discrepancies in self-awareness regarding home efficiency compared to neighbors were negligible. This study augments existing research but suggests the necessity for further research across different jurisdictions and timeframes to measure income’s impact.

A smart electricity consumption management framework using internet of things (IoT) to optimize electricity demand

The emerging technologies in the field of the electricity consumption are focusing the strengthening of the infrastructure of the electricity trading utilities. The infrastructure includes the smart devices which can generate real-time parameters for electricity supply monitoring in an efficient manner. This paper proposes an efficient artificial intelligence-based framework for optimal electricity demand load shifting in electricity consumption to minimize power outages in instant grid load fall and reduce the peak-to-average ratio. The framework is analyzing the acquired electricity consumption data for forecasting and load shedding at real-time which can significantly reduce customers' power outage. The proposed framework for optimal electricity demand load shifting is based on the emerging IT/OT (Information Technology/Operational Technology) technologies. We first analyzed the historical consumption of the electricity segment and then developed this framework. The analysis predicts the electricity consumption, can be utilized by the utility end as well as the consumer end by accruing economic benefits through shifting the electricity demand/load away from the very high-priced periods. The model is using various sensors and actuators in IoT Device for collecting the various real-time parameters for the electrical appliances and the machine learning algorithms to optimize the load over the electricity distribution assets. The proposed methodology improves the effectiveness of load optimization across electricity distribution assets by integrating a variety of sensors and actuators in Internet of Things devices to gather real-time metrics from electrical appliances. In addition to helping to forecast patterns in power consumption, the framework's previous consumption data analysis allows utility providers and customers to save money by strategically shifting electricity demand and load away from expensive times.

Reference Dependence in Queue Design and Pricing Strategies

This research investigates the effect of reference dependence on waiting times in service systems which formerly used a first-in-first-out (FIFO) service but have introduced a priority line with a fee. Our model combines reference-dependent gain-loss utility with standard customer utility, and we posit that customers are pleased with shorter-than-expected waiting times, whereas longer-than-expected times lead to dissatisfaction and an increased likelihood of balking. The study explores two scenarios: a captive customer system (CCS) and a noncaptive customer system (NCCS), with a focus on optimal pricing and segmentation strategies for revenue and social welfare maximization. The results reveal that, in a CCS, the service provider should implement observed and unobserved customer segmentation to optimize revenue and social welfare, respectively. In an NCCS, the impact of customer segmentation on revenue maximization depends on the value of regular customers, their loss reference-dependent preferences, and the system’s offered load. Alternatively, if the service provider seeks to maximize social welfare, the provider’s use of customer segmentation relies solely on the system’s offered load and customers’ reference-dependent preferences. Our findings also indicate that reference dependence can have varying impacts under different conditions, suggesting the effectiveness of tailored service and pricing strategies. Notably, a CCS generates more revenue than does an NCCS because of its captive nature, and, surprisingly, increasing the service rate can decrease revenue while improving social welfare. These insights have significant implications for service management strategies for a CCS and an NCCS. Funding: J. Liu was supported by the National Natural Science Foundation of China (General Program) [Grant 72071112]. J. Chen was supported by the National Natural Science Foundation of China (Major Program) [Grant 71490723]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/serv.2023.0033 .

Encouraging thermostatically controlled loads to provide frequency regulation for smart grid: A comfort-level trading mechanism

This paper presents a comfort-level trading mechanism for thermostatically controlled loads (TCLs), aiming to incentivize customers to provide more frequency regulation service for smart grid. This mechanism firstly divides aggregators composed of TCLs into the leader layer and the follower layer according to comfort adjustment level produced by the fuzzy neural network controller (FNC). The aggregator at leader layer has a low regulation level, while aggregators at follower layer are high. Then we establish a Stackelberg game between leader layer and follower layer, which can optimize the customer utility. Meanwhile, we build a noncooperative game model among aggregators at follower layer, which can maximize the customer utility while supplying regulation capacity for leader layer. Finally, simulation results validate the effectiveness of FNC and the feasibility of trading mechanism. The FNC can improve tracking accuracy to 2.05%, reduce absolute tracking error to 208.4 kW, and decrease average switching times to 9 per hour. The trading mechanism can make the temperature setpoints of aggregators within their comfort range and improve the customer satisfaction.

McDonald's food target recognition and calorie display based on YOLOv5 algorithm

This research paper delves into the development and assessment of a novel food recognition and evaluation system tailored for McDonald's menu items, leveraging the capabilities of the YOLOv5 algorithm. The study demonstrates that the system can successfully identify McDonald's food items from images and seamlessly query calorie and nutritional information from a backend database. The data is then presented to the user, aiding in more informed dietary choices and promoting public health awareness. The system has particular utility for McDonald's customers, facilitating real-time decisions that align with individual health goals and nutritional requirements. Our experimental findings show a high degree of accuracy and efficiency, although the system's scope is currently limited to five key menu items. Future directions for this work include expanding the range of recognizable food categories and implementing user feedback mechanisms to refine recognition accuracy. Moreover, the paper discusses potential optimizations for reducing system response time and further enhancing the practical utility of the technology. This research serves as a significant step towards utilizing computer vision technologies for public health interventions, aiming to combat the rise of obesity and related diseases.

The Impact of Financial Inclusion Dimensions on Improving the Quality of Financial Reports Published by Banks: A Field Study on a Sample of Sudanese Banks

This study aims to investigate the impact of customers’ access to financial services on improving the quality of financial reports. It also explores how customer utilization of financial services and the quality of services provided enhance the quality of published financial statements. A questionnaire was directed at a sample of employees working in Sudanese banks, with a total of 30 respondents. After conducting the field study, the researcher confirmed the hypotheses and obtained several key findings. Notably, a significant obstacle to financial inclusion is the lack of awareness regarding its importance among a large segment of clients. Additionally, the high frequency of bank account usage indicates that many Sudanese society members are under the umbrella of financial inclusion. Financial inclusion is a governmental regulatory tool that controls bank accounts and safeguards clients. Given its impact on financial inclusion, improving the quality of financial reports becomes crucial. The researcher recommends several actions, including raising awareness about financial inclusion and using it to monitor investors within banks. This encourages clients to participate in financial inclusion. Furthermore, banks should emphasize the importance of enhancing financial reporting quality, as it leads to increased investment and engagement with financial institutions. Efforts to establish financial inclusion in the Sudanese banking environment should be intensified, bolstering confidence in the banking sector. Finally, banks should provide specialized services for individuals with limited income and seek help from regional experiences.

Probabilistic Storm and Electric Utility Customer Outage Prediction

Probabilistic Storm and Electric Utility Customer Outage Prediction

Smart Grid Security: A PUF-Based Authentication and Key Agreement Protocol

A smart grid is an electricity network that uses advanced technologies to facilitate the exchange of information and electricity between utility companies and customers. Although most of the technologies involved in such grids have reached maturity, smart meters—as connected devices—introduce new security challenges. To overcome this significant obstacle to grid modernization, safeguarding privacy has emerged as a paramount concern. In this paper, we begin by evaluating the security levels of recently proposed authentication methods for smart meters. Subsequently, we introduce an enhanced protocol named PPSG, designed for smart grids, which incorporates physical unclonable functions (PUF) and an elliptic curve cryptography (ECC) module to address the vulnerabilities identified in previous approaches. Our security analysis, utilizing a real-or-random (RoR) model, demonstrates that PPSG effectively mitigates the weaknesses found in prior methods. To assess the practicality of PPSG, we conduct simulations using an Arduino UNO board, measuring computation, communication, and energy costs. Our results, including a processing time of 153 ms, a communication cost of 1376 bits, and an energy consumption of 13.468 mJ, align with the requirements of resource-constrained devices within smart grids.

In-store Customer perception and buying behavior towards home furniture- Development of Conceptual Model

Purpose: Intellectually a paper that aims at developing a conceptual model that identifies customer perception and buying behavior of customers while buying home furniture. Home furniture is an individual family choice where all family members are involved in the buying process. The buying process changes when the needs of the family change and the buying behavior makes a difference in the process of buying a product that suits all the family members of a family. Hence the buying behavior of people and their preference of brand shapes the customer preference of in-store buying and a thought process of conceptual model is based on literature review and established theories of customers Design: Data from secondary sources is sourced from various publications, textbooks, and various journal papers engaging various search engines such as ResearchGate SSRN, Base, Semantics, and Inflibnet. Various points and statements, postulates, and a variety of affecting factors to customer perception is written engaging a dedicated group that is a focus group interaction method. Findings: This field of study identified various determining points for customer utility of in-store customer perception such as perceptional benefits, past feelings experience, subjective based norms, and system quality which are proposed to influence the attitude and perception of the customers Originality: This paper highlights the various affecting factors influencing the customer’s preference to use instore purchases of home furniture Paper type: Development of conceptual model based on a review of Literature.

Leveraging water utility customer reporting for resilient operations and management

Water utilities collect information from various sources to gain continuous insights into the state of drinking water systems. Among the available data types, service problems that customers report are critical sources of information—especially during emergencies—which allow utilities to gage the impact of system operations on the resulting level of service. While some utilities increasingly collect and analyze customer reporting data to track key performance indicators (e.g., total complaints, response times), this information remains largely underutilized in research and practice. Further, little is known about spatial variations in the quantity or types of problems reported, which can indicate disparities in level of service and customer engagement. Here, we identify and compare spatial patterns in customer reporting against technical system performance and evaluate how trends change during emergency events (e.g., natural disasters). Our analysis demonstrates tightly coupled sociotechnical interdependencies between end user populations and drinking water systems. For instance, results reveal the prevalence of service problems in certain areas and show that several sociodemographic characteristics were statistically different in areas with higher or lower reporting levels. Finally, we offer recommendations for incorporating customer data into operational decision making and outreach efforts.

Prepaid water meters and water distribution system improvement: A case study of Jenin city, Palestine

A total of 1792 prepaid water meters (PPWMs) were installed along with the activities to improve water distribution in Jenin City, Palestine. The tariff collection rate, previously at 40%, increased to over 90%, thereby resulting in a revenue increase of 1.93 times. PPWMs reduced the need for direct contact between utility staff and customers, resulting in less susceptibility to COVID-19 than conventional meters. The PPWMs fostered water distribution equity by promoting conservation. While these meters boost the revenue of water utilities, increasing user satisfaction by optimizing water distribution is essential, ensuring suitable PPWM installation rates and sustainability. Concerns linger about consistent customer resistance to PPWMs, support for low-income households, and the provision of maintenance services post-warranty.

Modelling public attitude towards drone delivery in Germany

BackgroundLast-mile delivery by drone is expected to be a promising innovation for future urban logistics. However, in addition to adoption of services by customers, leveraging this delivery method will depend essentially on a positive public perception of such services in urban airspace.ObjectiveThis article provides novel and comprehensive insights into factors driving or impeding citizens' attitudes towards drone delivery.MethodologyThe article develops a structural equation model that derives from a sequential exploratory mixed methods design. In the first step, factors affecting attitudes towards drone delivery were identified within the scope of five focus groups and converted into the development of a questionnaire. In the second step, a German population-representative survey was conducted through telephone interviews, which provided reliable data to test the model (n = 819).ResultsExpected risks (particularly stress due to traffic in lower airspace, noise, and visual disturbances), as well as expected benefits (particularly fast and time-flexible delivery), significantly affect attitudes towards drone-based delivery, while the individual level of technological openness (technophilia) does not have a significant association. Moreover, the model reveals that the expected risks of drone deliveries are stronger associated with public attitude than with expected benefits.ConclusionsThe provided framework suggests fashioning policies and drone delivery applications that focus on mitigating social, spatial, and visual risks while achieving maximum utility for customers.