Residential Customers Research Articles

Service center in electricity distribution sector with variable peak electricity time: A two-stage queuing model approach with nested simulation

Customer service is essential for any business, significantly impacting customer satisfaction, brand reputation, and reducing associated costs. In the electricity industry, this importance is heightened due to the critical nature of electricity in daily life and growing usage rate of new technologies. Effective customer service in electric distribution companies involves handling a wide range of inquiries, from billing issues to technical support, requiring both remote and on-site assistance. The variability in customer calls, influenced by factors such as peak electricity hours, and different service and customer types, presents unique challenges in designing efficient service centers. This research develops a tailored model for the service center operations of a power distribution company, focusing on optimal staffing levels within budget constraints. A two-stage queuing model is proposed to address the needs of residential and non-residential customers. The study employs nested simulation techniques to account for variability in customer arrival times and peak hours. Key performance indicators (KPIs) are used to evaluate the effectiveness of staffing policies, considering both quantitative and qualitative costs. The study integrates stochastic optimization and ranking and selection methods to identify the best staffing configurations. The findings offer actionable insights for improving customer service efficiency and operational effectiveness in electricity customer service centers.

Resilience‐oriented load restoration method for power‐gas‐water systems considering public safety impact

AbstractFocusing solely on enhancing the resilience of power systems at a systemic level would lead to a significant underestimation of the actual impact of extreme disasters. Equally vital is the assurance of livelihood security amidst such extreme conditions, which is crucial for the development of a truly resilient power system. Hence, this paper attempts to incorporate quantifiable metrics assessing public safety impacts into resilience enhancement works, thereby guiding the precise allocation of funds. Considering that the residents' intuitive feelings are the most direct reflection of the severity of the disaster, this paper employs the modified prospect theory to formulate functions representing residents' psychological risk perception and risk‐taking willingness to tolerate risks during disruptions in power, gas, and water supplies. Meanwhile, in order to accurately calculate the energy loss duration for each residential customer, a resilience enhancement method for post‐disaster collaborative dispatch of electricity‐gas‐water systems is proposed. With the objective of minimizing the public safety and economic impact of disasters, the optimal multi‐source collaborative emergency restoration strategy is developed. The significant necessity and efficiency of the proposed strategy are verified with exhaustive case studies. Numerical results evince the resilience enhancement by considering the livelihood security in the post‐disaster restoration stage.

Heterogeneous effects of government energy assistance programs: Covid-19 lockdowns in the republic of Georgia

During the Covid-19 pandemic, the governments of many countries adopted measures to support the population during the lockdowns and periods of reduced economic activity. In the Republic of Georgia, in April 2020 the government announced that it would pay the electricity bills of residential customers in April and May 2020, effectively making electricity free, as long as usage would not exceed 200 kWh/month. Consumers using more than 200 kWh would pay the regular tariff on the entire consumption (not just the kWh above 200). In August 2020, the government announced that the policy would be in force again in November and December 2020, and January and February 2021. We examine meter readings from a 10 % sample of the country's customers base outside of the Tbilisi city limits to see if the response to the policy is consistent with the predictions from economic theory. We find that, as suggested by economic theory, in the months when the policy is in place low-volume consumers increase their electricity usage and high-volume consumers decrease it in an effort to make the 200 kWh mark. However, we do not find any evidence of “bunching” at 200 kWh or of a “hole” thereafter. The average household increased usage by some 5 % above and beyond their normal. This figure however masks considerable heterogeneity in the effects of the policy across urban, rural, and “high mountain” status areas.

Does Vertical Integration Always Reduce an Electricity Retailer’s Fixed Price Offer?

We use a profit-maximizing model of forward pricing, which revealed that an electricity retailer’s fixed price offer (FPO) contains a large forward premium that encourages vertical integration of a generation company and a retailer. However, vertical integration does not always reduce FPOs, particularly when residential customers are segmented by consumption size and price sensitivity. Hence, a proposed merger of a big generation company and a big retailer requires regulatory scrutiny.

Changes in hourly electricity consumption profiles and price elasticities in Denmark 2019–2022

Changes in consumption profiles affect the need for investments in production and grid capacities. Extending a model with categorical variables for hours of the day, months and types of days to include short-term electricity prices, and applying Danish data for the hourly electricity consumption by aggregated categories of customers, consumption profiles for each of the years from 2019 to 2022 are estimated and it is tested if the profiles change significantly. Further, the short-term price elasticity of hourly electricity demand is estimated. During the first COVID-19 lockdown, residential electricity consumption increased and non-residential consumption decreased and the residential consumption profile on weekdays resembles the weekend profile. However, long-term effects are quite limited. For 2022, the residential consumption profile is significantly different from the profile in 2019. The profile has become flatter, increasing consumption during the night and reducing consumption during the daytime. An increasing number of electric vehicles and individual heat pumps contribute to this change. The short-term price elasticity for total electricity consumption increased in 2022. Especially, the price elasticity for residential customers increased and became significant in the second half of 2022 where electricity prices were high and volatile and customers were aware of prices.

Enhancing water efficiency programming in the City of Calgary

ABSTRACT This article presents the steps undertaken in a two-phase study to enhance the City of Calgary's (the City) water efficiency programming for indoor and outdoor industrial, commercial, and institutional (ICI) customers and outdoor residential customers. Study objectives included evaluating programs in other urban jurisdictions for suitability to Calgary, and developing short-, medium-, and long-term recommendations for water efficiency programming for the City. Phase I of the study included a literature review of Calgary's water consumption trends and explored over 150 water efficiency programs implemented across jurisdictions in North America, identifying a subset of 33 programs for further evaluation. Phase II evaluated program options through an integrated assessment, including gap, Strengths, Weaknesses, Opportunities, and Threats, and cost-benefit analysis. An implementation strategy was developed for seven water efficiency programs, grouped into complementary bundles of indoor and outdoor ICI and residential landscape transformation programs. The study also identified areas for further research, and key supporting elements or ‘success factors’ for water efficiency programming in the City. This article adds value to the discussion on approaches to select suitable indoor and outdoor ICI and outdoor residential water efficiency programs, and aids in informing the City's current and future strategic water planning and programs.

Expediting battery investment returns for residential customers utilising spot price-aware local energy exchanges

This paper presents a local energy exchange mechanism to benefit residential customers and retailers under variable wholesale market spot prices. To do so, a peer-to-peer (P2P) trading-enabled local energy market (LEM) is proposed to facilitate frequent energy transactions among participants. The proposed LEM mechanism considers market operational constraints and maximises electricity cost reductions. Further, the proposed LEM optimises the customers' investment return for battery energy storage systems (BESSs) and minimises retailers’ financial losses during higher spot prices in the wholesale market. Finally, a case study is demonstrated using real-world Australian data in which P2P trading is performed between interested buyers and sellers. Two scenarios with high and low wholesale market spot prices are analysed comprehensively considering five metrics such as solar-to-spot (STS); solar-to-load (STL); forward buy contract (FBC)-to-load (FBC-L); spot-to-load (Short); and FBC-to-spot (Long). The simulation results reveal that the customers reap substantial benefits from the proposed LEM in contrast with business-as-usual (BAU), allowing them to expedite BESS investment returns. Also, the proposed LEM helps a retailer decrease the effect of higher spot prices by reducing its unexpected financial losses while improving self-consumption and self-sufficiency in the local energy network.

Comparative analysis of the reliability assessment of commercial and residential feeders in the power distribution utility of Nigeria

Reliability studies serve as valuable tools for assessing and optimizing system performance. Utilities with higher reliability indices are more likely to achieve break-even points due to significantly reduced downtime. This paper explores a comparative assessment of two 11 kV feeders supplying electricity to residential and commercial customers, addressing concerns about distribution system reliability in Nigeria and its impact on the country's GDP. The study involves a comprehensive reliability analysis, utilizing a flowchart to outline procedural steps and employing the ETAP Software program for data analysis collected over a one-month period from a power utility company. The data encompass operational parameters such as day-hourly consumption, outage records, and network equipment data. Results indicate higher reliability indices in the commercial feeder compared to the residential feeder, with the Customer Average Interruption Duration Index (CAIDI) being lower in the commercial feeder. The research underscores the significance of reliability assessment in improving operational efficiency, facilitating maintenance planning, and enhancing customer satisfaction.

From trust to transition: Residential customer acceptance of circular solar business models

While the contribution of circular business models to achieving sustainable development goals is increasingly acknowledged, most studies tend to focus on conceptual, technological, or supply-side considerations. Customer acceptance, however, is also a necessary condition for a transition to a circular economy. In this study, we employ household survey data to investigate the impact of trust on customer acceptance of circular business models in solar photovoltaics (PV). Results highlight the importance of high trust-levels in governments, suppliers, and technology to gain customer acceptance, as well as the importance of time-consistent policies to support interest in circular solar solutions. Furthermore, results suggest that vulnerable groups may miss beneficial opportunities of circular business models because of lower levels of institutional trust. This is also reflected in an analysis of barriers and value propositions, where low institutional trust levels appear to limit rational cost-benefit considerations. The paper formulates policy and management recommendations and identifies research gaps related to the redistributive impact of the circular economy. Moreover, it coins the concept of a circular risk premium that may vary along socio-economic groups and calls for further study of the impact of vocal opinions on social interactions that may spur or impede the transition to a circular economy.

IoT-based smart net energy meter with advanced billing feature for residential buildings including solar PV system

Electricity consumption is rising across all industries. Residential electricity use dominates the sector. Solar photovoltaic (PV) systems on residential roofs are increasing quickly, notably in Dhaka, Bangladesh. PV power generation is high at peak sun irradiance. Due to light loads, residential structures use less electricity. PV system surplus electricity may be transmitted to the national grid. Residential customers may sell power to the government, lowering their electricity expense. Traditional energy meters make it difficult to calculate PV system consumption by load and grid injection. This is possible with net metering. Thus, this study presents an internet of things (IoT)-based smart net energy meter for home users to provide surplus solar PV power and consume grid electricity when needed. With the government's new power tariff rate, the net bill will be calculated automatically. A dedicated mobile application is used to monitor all the activities. The billing statement will be generated automatically, and the payment of that bill will be payable using a redirect link with the same mobile application. The suggested smart net energy meter will inform SMS/mobile app users of gas, smoke, and tempering. The suggested meter's performance and efficacy were evaluated using software simulation and hardware analysis.

Detecting False Data Injection Attacks Using Machine Learning-Based Approaches for Smart Grid Networks

Current electricity sectors will be unable to keep up with commercial and residential customers’ increasing demand for data-enabled power systems. Therefore, next-generation power systems must be developed. It is possible for the smart grid, an advanced power system of the future, to make decisions, estimate loads, and execute other data-related jobs. Customers can adjust their needs in smart grid systems by monitoring bill information. Due to their reliance on data networks, smart grids are vulnerable to cyberattacks that could compromise billing data and cause power outages and other problems. A false data injection attack (FDIA) is a significant attack that targets the corruption of state estimation vectors. The primary goal of this paper is to show the impact of an FDIA attack on a power dataset and to use machine learning algorithms to detect the attack; to achieve this, the Python software is used. In the experiment, we used the power dataset from the IoT server of a 10 KV solar PV system (to mimic a smart grid system) in a controlled laboratory environment to test the effect of FDIA and detect this anomaly using a machine learning approach. Different machine learning models were used to detect the attack and find the most suitable approach to achieve this goal. This paper compares machine learning algorithms (such as random forest, isolation forest, logistic regression, decision tree, autoencoder, and feed-forward neural network) in terms of their effectiveness in detecting false data injection attacks (FDIAs). The highest F1 score of 0.99 was achieved by the decision tree algorithm, which was closely followed by the logistic regression method, which had an F1 score of 0.98. These algorithms also demonstrated high precision, recall, and model accuracy, demonstrating their efficacy in detecting FDIAs. The research presented in this paper indicates that combining logistic regression and decision tree in an ensemble leads to significant performance enhancements. The resulting model achieves an impressive accuracy of 0.99, a precision of 1, and an F1 score of 1.

Characterizing residential sector load curves from smart meter datasets

Understanding how and when residential electricity is used throughout the day is integral to the successful implementation of potential residential demand management strategies. Our analysis characterizes the daily hourly load profiles of approximately 160,000 residential electricity customers across the Southern California Edison (SCE) service area during the period spanning 2015 to 2016 and 2018 to 2019 across weekends, weekdays, seasons, and climate zones. We find that total daily electricity usage was highest in the hottest months of the year compared to milder months, particularly for households located in the hottest climate zones. The most energy-consumptive hours occurred during the mid-afternoon during the hottest months, in contrast to early evening high consumption in cooler months. We find that customers with average daily consumption at or above the 80th percentile cumulatively consume over 40% of electricity during the hottest months of the year residential load, while the bottom half of customers cumulatively consume <25% of the total residential load. The disparities in electricity usage across SCE households are higher in the mid-day, especially in milder months across all regions, and in mild climate zones compared to hotter climate zones since loads are not as dependent on high HVAC loads.

What Are the Benefits of Government Assistance with Household Energy Bills? Evidence from Ukraine

On February 24, 2022, Russian Federation troops invaded Ukraine. Ukraine was previously at war with Russia in 2014, and in April 2015, the government abruptly raised the natural gas tariffs to residential customers. It also scaled up its energy assistance program, the HUS. We examine the welfare effects of the HUS. Using Ukraine’s Household Budget Survey, we find that after the tariff hike, the average household that did not receive the HUS spends 11% of its income on electricity, gas, and fuels, meeting the definition of "fuel poor." The average share among households that do receive the subsidy is [Formula: see text]. The HUS cuts the rate of fuel poverty in half and brings considerable consumer surplus gains (6% of income), at a price tag of 1-2.5% of GDP. Meaningful savings would be achieved with only a moderate loss of consumer surplus if the HUS was cut in half. Social tariffs or replacing the HUS with a one-time energy efficiency subsidy would be sustainable and entail modest or no welfare losses.

Effects of time-of-use pricing for residential customers and wholesale market consequences in South Korea

Using a large-scale pilot test on time-of-use (TOU) pricing for residential customers, this study analyzes the effects of TOU pricing on the load patterns of residential customers and on producer surplus in South Korea. We estimate the difference in the electricity demand functions of residential customers across peak, intermediate, and off-peak periods before and after TOU pricing. In addition, we assess the supply curve in the wholesale electricity market and calculate the changes in electricity purchase costs by integrating the supply curve over the range of consumption changes across the periods. This study simulates the effects of TOU pricing on producer surplus that would be obtained if all residential customers in South Korea switch from increasing block pricing to TOU pricing. Our results show that TOU pricing can be an effective measure for load transfer and simultaneously increase producer surplus. We find that although the revenue of electric utility generally decreases, the cost savings are greater and thus producer surplus increases. The results can be a valuable reference for the South Korean government regarding the nationwide expansion of TOU for residential electricity in the future.

Regulating Roof-Top Solar Generation: Defending Sustainable Projects from Utility Proposals

Residential owned solar panels mounted on roof-tops generated 1.4% of the U.S. electricity supply in 2022. Utilities compensate residential customers using a net metering system by which each kilowatt-hour generated by the customer reduces the customer’s bill by the amount the utility charges for one kilowatt. Despite the small impact of roof-top solar on the total market, in 2022, thirty-two states conducted ninety-four proceedings investigating whether to change the net metering system. In these proceedings, utilities advocated changing the rates to discourage residential investment in solar panels. Utilities argue that net metering provides a subsidy to solar customers because the rate the utility pays is greater than the avoided cost of energy. That excess payment allows solar customers to avoid paying for the distribution network. The cost of the distribution network is shifted to non-solar customers. This article will evaluate these claims. It will show that if the social cost of carbon were in the calculation, it would generate a Kapp cost shift from solar customers to non-solar customers. The size of the Kapp cost shift is greater than the cost of distribution networks, reversing any private cost shift that utilities have estimated.

P2P credit auction vs. net metering: Benefit analysis for prosumers under incremental block rate electricity tariff

In order to increase the economic benefits of residential photovoltaic (PV), many countries have considered diverse compensation policies for surplus energy, such as fixed feed-in tariff, time-varying feed-in tariff, and net metering, where the surplus energy is offset against consumption. More recently, there has been a growing interest in schemes that allow end-users to trade surplus energy via peer-to-peer (P2P) electricity trading, rather than relying on government incentives. This study conducts a quantitative analysis of on how the introduction of P2P trading affects the benefits of each stakeholder, offering a guide for policy adoption. Firstly, this study devises a P2P trading mechanism based on credit auctions tailored for residential, while considering the application of the increasing block rate tariff often applied to residential customers in the real world, and assesses its benefits in comparison to the current net metering scheme. Multiple case studies were undertaken, encompassing various scenarios, including the level of purchase price of utility for uncleared auction offers, the proportion of prosumers participating in the trading network, and the level of capacity of PV. This study shows that the P2P trading does not always yield positive benefits for prosumers compared to the prior policy. Nevertheless, this study identifies specific conditions under which the P2P trading can stimulate residential PV installations.

Mitigation of limitation imposed on hosting capacity in low voltage networks by their distribution transformer loading and degradation considerations

AbstractA distribution transformer's thermal operating conditions can impose a limitation on the Hosting Capacity (HC) of an electrical distribution feeder for PV interconnections in the feeder's low‐voltage network. This is undesirable as it curtails PV interconnection of both residential and commercial customers in the secondary networks at a time when there are record numbers of interconnection requests by utilities' customers. The authors analyse the limitations on HC due to transformer loading and degradation considerations. Then, the paper proposes a battery energy storage system (BESS) dispatch strategy that will mitigate the limitation on distribution feeder HC by distribution transformers. Three scenarios of HC were simulated for a test network—HC evaluation without restrictions by the distribution transformer (scenario 1), HC evaluation with restrictions by the distribution transformer (scenario 2), and HC evaluation without restriction by the distribution transformer, and with the implementation of the proposed BESS mitigation strategy (scenario 3). Simulation results show that transformer lifetime is depleted to about 6% of expected lifetime for unrestricted HC in scenario 1. Curtailing the HC by 32% in scenario 2 improves the lifetime to 149% of expected lifetime. Implementing the proposed BESS in scenario 3 improves the transformer lifetime to 127% and increases the HC by 62% above the curtailed value in scenario 2, and by 10% above the original HC in scenario 1. The BESS strategy implementation produced cost savings of 49% and 27% of the transformer cost in scenarios 2 and 3, respectively, due to deferred transformer replacement. Conversely, there is a 1600% replacement cost incurred in scenario 1, which underscores the need for a mitigation strategy. The proposed BESS strategy does not only improve the HC of a distribution feeder but also increases a distribution transformer's lifetime leading to replacement cost savings.

Disaggregation Model: A Novel Methodology to Estimate Customers’ Profiles in a Low-Voltage Distribution Grid Equipped with Smart Meters

In the context of increasingly necessary energy transition, the precise modeling of profiles for low-voltage (LV) network consumers is crucial to enhance hosting capacity. Typically, load curves for these consumers are estimated through measurement campaigns conducted by Distribution System Operators (DSOs) for a representative subset of customers or through the aggregation of load curves from household appliances within a residence. With the instrumentation of smart meters becoming more common, a new approach to modeling profiles for residential customers is proposed to make the most of the measurements from these meters. The disaggregation model estimates the load profile of customers on a low-voltage network by disaggregating the load curve measured at the secondary substation level. By utilizing only the maximum power measured by Linky smart meters, along with the load curve of the secondary substation, this model can estimate the daily profile of customers. For 48 secondary substations in our dataset, the model obtained an average symmetric mean average percentage error (SMAPE) error of 4.91% in reconstructing the load curve of the secondary substation from the curves disaggregated by the model. This methodology can allow for an estimation of the daily consumption behaviors of the low-voltage customers. In this way, we can safely envision solutions that enhance the grid hosting capacity.

Water productivity is in the eye of the beholder: benchmarking the multiple values produced by water use in the Phoenix metropolitan area

Abstract. Water productivity (or efficiency) data inform water policy, zoning, and planning, along with water allocation decisions under water scarcity pressure. This paper demonstrates that different water productivity metrics lead to different conclusions about who is using water more effectively. In addition to supporting the population's drinking and sanitation needs, water generates many other public and private social, environmental, and economic values. For the group of municipalities comprising the Phoenix metropolitan area, we compare several water productivity metrics by calculating the water value intensity (WVI) of potable water delivered by the municipality to its residential and non-residential customers. Core cities with more industrial water uses are less productive by the conventional efficiency measure of water used per capita, but core cities generate more tax revenues, business revenues, and payroll per unit of water delivered, achieving a higher water productivity by these measures. We argue that policymakers should consider a more diverse set of socio-economic water productivity measures to ensure that a broader set of values are represented in water allocation policies.

Improving Power Quality Using D-STATCOM with Capacitor as Energy Storage System

Power electronics are used more frequently to raise awareness of power quality which must be considered when designing modern power systems for effective operation and continuous power supply to various load centers. Otherwise, it will be hazardous for residential and commercial customers, reducing the production of their work due to faults and equipment damage. Electronic technology known as Flexible AC Transmission System (FACTS) may improve power stability to make the electric power system efficient. Oscillation damping in the Power system is still a big concern for operating a safe and dependable power system. The static synchronous compressor (STATCOM) is connected to assist power networks with poor power quality and voltage regulation. Although there are several uses, voltage stability is the most frequently used. An average model of the D-STATCOM control approach is presented in this work to improve the transmission of power quality flow and voltage stability in electric power systems. An increase in system voltage stability, reactive power flow control for different loads, and dynamic response of STATCOM are the primary goals of this research work.