Household Electricity Bills Research Articles

Time-varying rates prompt different responses as a function of home energy efficiency

Spiking global energy prices make it all the more important to understand the distributional effects of commonly used energy transition policies such as financially-based demand management. We examine how building energy efficiency impacts the effect of time-of-use rates; peak prices could differently affect curtailment behaviour and bills in homes that require more continual energy use for comfort. Using a sample of 3,145 households in the Australian Capital Territory with panel data for up to five years, we use two-way fixed effects to estimate the effect of switching to a time-of-use (TOU) rate on household electricity use and bills, allowing for heterogeneous effects as a function of building Energy Efficiency Rating (EER). Analyses indicate that low-EER households dependent on electricity for heating curtail use when on TOU rates, while high-EER households do not. This does not appear to financially disadvantage low-EER households, but may impact comfort. Our findings suggest that the household responses to TOU rates can differ as a function of building energy efficiency for those households that are reliant on electricity for heating. This has implications for TOU rate design and demand management policies, particularly in the context of policies for electrification.

Centralized smart energy monitoring system for legacy home appliances

The increasing global population and reliance on electrical devices for daily life resulted in sharply rising energy consumption. Also, this leads to higher household electricity bills. As a result, there is a growing demand for energy monitoring systems that can accurately estimate energy usage to help save power, especially for older home appliances that are difficult or expensive to update with monitoring sensors. However, current energy monitoring systems have some drawbacks, such as the inability to detect different types of appliances and the deployment complexity. Moreover, such systems are too costly to use in older power infrastructures. To address this issue, we proposed a centralized smart energy monitoring system designed for legacy home appliances, aiming to address the limitations of current energy monitoring systems by avoiding costly infrastructure upgrades to calculate the power consumption of legacy home appliances. The proposed system employs a two-layered architecture comprising hardware (Emontx device, Analog-to-Digital Converters (ADC), and Current Transformer (CT) sensors) and a software layer that includes Artificial Intelligence (AI) predictors using a pre-defined set of rules and K Nearest Neighbours (KNN) algorithms. We conducted three experiments on real home appliances to evaluate the proposed work. The accuracy of the proposed system showed positive results after several modifications and hard tuning of several parameters in devices, specifically for Jordanian power plants.

Method for evaluating fairness of electricity tariffs with regard to income level of residential buildings

Modern advancements in energy technology, such as smart meters and renewable power generation, have contributed to the increasing penetration of time-variable electricity pricing plans. Under such plans, consumers experience a higher financial burden for consuming electricity when overall demand is high. Lower-income households may be disproportionately burdened by the transition to time-variable pricing because they tend to have less efficient homes and appliances, which may necessitate greater overall electricity consumption, especially during peak times. The goal of this work is to create a broadly applicable framework for evaluating the fairness of utility pricing plans. The proposed slope analysis method examines the distribution of a fairness metric across income levels in order to determine the level of fairness exhibited by a pricing plan. This work utilizes three fairness metrics, which are based on total household electricity bill and income, as a proof-of-concept for the slope analysis method and assesses their viability for fairness research. The proposed method also utilizes household energy models to represent various income levels for any location with sufficient data. The framework is evaluated using simulated households across 5 income levels and 3 climate zones in the United States. Fairness metrics are applied to the utility bills calculated under Real-Time Pricing and existing tariffs offered at each location. The proposed fairness evaluation method provides a quantitative measure of fairness and is broadly applicable across location and pricing plans. The metric based on the change in percentage of income spent on utilities considers the relative financial burden on households, which results in the slope analysis method outputting conclusive, accurate fairness determinations more often than the other examined metrics. The results demonstrate disparity in energy affordability, and the proposed slope analysis and model simulation methods provide a readily transferable testbed to evaluate energy policy equity.

Optimization of peak-valley pricing policy based on a residential electricity demand model

In order to deal with the rapid growth in residential electricity consumption, residential peak-valley pricing (PVP) policies have been implemented in 12 provinces in China. However, being inappropriate, the residential PVP policies have delivered no significant results. The challenge to China's PVP policy research lies in obtaining high-resolution electricity consumption data and modeling residents' behavior. By simulating household electricity load profiles, an electricity price policy response model and a residential PVP policy optimization model, are constructed and applied in this paper to simulate the effect of the current residential PVP policy and optimize the policy. The results show that the peak-shaving capability (represented by the reduction in peak load) of the PVP policy in 11 provinces is less than 3%, while the PVP policy in Gansu Province has increased household electricity bills by about 5.7%. In addition, the optimized PVP can reduce household electricity bills by 3% and reduce peak electricity consumption by about 9%. The 12 provinces should adopt the 3-phase division method and optimize the electricity price in the peak and valley (i.e. off-peak) periods respectively. This paper promotes the research on China's residential PVP policy and provides an effective reference for the design of the PVP policy.

Going, Going, Gone: Optimising the bidding strategy for an energy storage aggregator and its value in supporting community energy storage

Energy storage aggregation can bring many advantages to electrical power systems; these include improving flexibility, lowering system costs and reducing the need for carbon-intensive peaking plants. In this work linear and quadratic optimisation models are applied to examine the value derived by an aggregator, using energy storage for arbitrage in Great Britain’s day-ahead and balancing energy markets. The optimum arbitrage strategy is determined when the aggregator is considered a price-taker and a price-maker with access of up to 500 MW battery storage. In the day-ahead market the choice of price-taker or price-maker strategy made negligible difference, however, in the balancing market the price-maker profits were 10.9% higher for 500 MW storage when prices were perfectly forecasted. For imperfect forecasting, the choice of strategy made a negligible difference. A sensitivity analysis challenged the modelling assumptions. System parameters that had the greatest impact on results were the battery efficiency (more efficient, higher profits) and its duration (shorter duration, higher profits). The model was then applied to investigate the potential value that can be obtained by an aggregator with partial access to a community-owned energy storage. Results showed that household electricity bills could be improved by this arrangement. In particular, households that use their batteries less during the winter months, could improve their profits by up to 13.2% if aggregator access is allowed.

Do high electricity bills undermine public support for renewables? Evidence from the European Union

The clean energy transition has long been framed in terms of its technological and economic feasibility. An increasingly salient constraint lies in its political feasibility. The transition requires vigorous public support to be completed. Yet increased consumer costs associated with the deployment of renewable electricity could make voters - and, by extension, governments - less supportive of it. As a result, overly aggressive government support for renewables could lead to its own downfall. To examine this threat, I document two stylized facts. First, the expansion of renewable electricity capacity has been followed by an increase in household electricity bills, and this has mostly happened because of energy-specific taxes. An increase of renewable electricity capacity by one within-country standard deviation raises a typical household's bill by €5.7 per MWh (95% CI: [3, 8.3]), most of which comes from an increase in non-VAT taxes (+€3.8/MWh [2.6; 5.1]). Second, these taxes have hurt popular support for aggressive renewable energy policy. An increase of non-VAT taxes by one standard deviation increases the share of people who find renewable energy too ambitious by 0.7 percentage points (95% CI: [0.1; 1.3]). Climbing costs could therefore undermine further political support toward renewable electricity deployment and threaten its contribution to greenhouse gas reductions.

Company participation in supporting national energy security through collective installation of rooftop solar panel of employees’ houses as an effort to accelerate increased utilization of solar energy

To support the achievement of government targets in the New and Renewable Energy Program, and to play an active role in responding to global issues related to energy, environment, global warming, and energy conservation issues, large and medium-sized companies can encourage them to use renewable energy. energy through a campaign for all employees to take advantage of the solar panel system in every home, which can be integrated with the electricity grid of the State Electricity Company (PLN). The direct benefit that employees get from using a solar panel system is that it can reduce household electricity bills. The campaign was continued by realizing a collective rooftop solar panel installation program for employee rooftops with an instalment system that was not burdensome for 24 to 36 months, which was deducted from wages through the payroll system, of course taking into account the Company’s ability to fund the purchase and installation of rooftop solar panels. With this program, assuming a total capacity of 200 kWp in each company (around 100 employee houses), the maximum cost impact for the company is around Rp. 3.6 billion. If this program can be implemented simultaneously by all large and medium-sized companies in Indonesia, the potential for achieving solar energy use in 7 months could reach 6.72 GWp. Meanwhile, the Indonesian government is targeting the use of solar energy for the national energy mix in 2025 at 6.5 GW. With the participation of companies and supporting them, it is one way to achieve the target of using solar energy in Indonesia, which is 6.5 GW by 2025.

Tension in Mexico's energy transition: Are urban residential consumers in Aguascalientes willing to pay for renewable energy and green jobs?

Via a discrete choice experiment (DCE), a sample of urban residents that contribute to their household electricity bill in Aguascalientes, Mexico, has been asked to choose from among four electricity contracts —a status quo alternative, and three alternatives described in terms of type of renewable energy source (RES), % of RES in current electricity mix, new jobs in RE sector, and % increase in self-reported bimonthly electricity bill. Respondents report a positive willingness to pay (WTP) for both RES and new jobs in RE sector, and a higher WTP for solar energy in comparison to biomass energy. These results are timely as there is a tension in Mexico due to the energy strategy pursued by the current federal administration —which in practice has slowed down the energy transition initiated in 2015. This paper's findings suggest that respondents to our DCE would support a just energy transition aiming to boost both RES and the creation of green jobs.

Loyalty taxes in retail electricity markets: not as they seem?

A common view in retail electricity markets is that retailers discriminate based on consumers’ loyalty: loyal consumers pay more and switchers can (and do) select the cheapest offers) when they switch. The premium is colloquially known as a “loyalty tax” or “loyalty premium”. Reflecting this understanding Australia’s governments, regulators and consumer advocates have encouraged consumers to switch electricity retailers. Using a sample of 47,114 household electricity bills we test whether consumers that had switched in the previous 12 months (“switchers”) pay less than consumers who remained with their retailers (“remainers”) in the previous 12 months. We find that the annual bills of switchers are expected to be AU$48 (4%) lower than remainers and that the median switcher could reduce their bills by 21% by selecting the cheapest offer. Classifying retailers into tiers however provides some nuance to the main conclusion: the third tier of retailers (the new entrants with market shares of less than 3%) impose higher loyalty taxes than the other two tiers (incumbents and mid-sized retailers). The middle tier of retailers impose the lowest loyalty tax, and in fact for many consumers they may reward loyalty. These findings suggest that the loyalty tax is (typically) smaller than widely considered, it varies across tiers of retailers and even engaged consumers typically do not select the lowest priced offer. This raises the question of whether switchers are motivated by lower bills as well as other factors or whether the main challenge is search difficulties.

Loyalty Taxes in Retail Electricity Markets: Not as They Seem?

A common view in retail electricity markets is that retailers discriminate based on consumers’ loyalty: loyal consumers pay more. The premium is colloquially known as a “loyalty tax” or “loyalty premium”. Reflecting this understanding Australia’s governments, regulators and consumer advocates have encouraged consumers to switch electricity retailers. Using a sample of 47,114 household electricity bills we test whether consumers that had switched in the previous 12 months (“switchers”) pay less than consumers who remained with their retailers (“remainers”) in the previous 12 months. We find that the annual bills of switchers are expected to be AU$48 (4%) lower than remainers and that the median switcher could reduce their bills by 21% by selecting the cheapest offer. Classifying retailers into tiers however provides some nuance to the main conclusion: the third tier of retailers (the new entrants with market shares of less than 3%) impose higher loyalty taxes than the other two tiers (incumbents and midsized retailers). The middle tier of retailers impose the lowest loyalty tax, in fact for many consumers they may reward loyalty. These findings suggest the loyalty tax is (typically) smaller than widely considered, that it varies across tiers of retailers and that even engaged consumers typically do not select the lowest priced offers. This raises the question of whether switchers are motivated by lower bills as well as other factors or whether the main challenge is difficulties in search.

Driving Simulator for Electric Vehicles Using the Markov Chain Monte Carlo Method and Evaluation of the Demand Response Effect in Residential Houses

It is essential to secure flexible resources in power systems to increase the proportion of variable renewable energy generation systems. One flexible resource is demand response (DR) of the batteries of electric vehicles (EVs). In this study, we propose an electric vehicle driving simulator using the Markov chain Monte Carlo (MCMC) method and an EV demand response evaluation model. The former is a highly versatile EV driving simulator that can produce a random driving pattern based on actual EV fleet data, taking into account various features. The latter is a residential DR evaluation model that minimizes a household electricity bill based on the simulated fleet data and enables realistic DR operation using three processes: forecasting, planning, and operation. The contribution of this paper is to enable evaluation of realistic EV battery value in various housing and EV utilization combinations. We found that the EV battery control provides an economic benefit of US$62 (5% of the night-charging case cost) with only charge control and US$370 (31%) with charge and discharge control, as the average expected value based on our assumption of evaluation. Because 40-kWh EV batteries have a sufficiently large capacity to store surplus power from a rooftop PV, they can be operated by determining the operation schedule based on a fixed-fee structure without forecasting or planning.

Prosumers in the Electricity System—Household vs. System Optimization of the Operation of Residential Photovoltaic Battery Systems

An increase in distributed small-scale generation and storage in residential prosumer households requires an understanding of how the household-controlled operation of these distributed technologies differ from a system-optimal utilization. This paper aims at investigating how residential photovoltaic (PV)-battery systems are operated, given different assumed incentives, and whether or not a prosumer induced operational pattern differs from what is desirable from a total electricity system point of view. The work combines a household optimization model that minimizes the annual household electricity bill for two price zones in southern Sweden with a dispatch model for the northern European electricity supply system. The results show significant differences in the charging and discharging patterns of residential batteries. A household annual electricity cost minimization gives many hours in which only a fraction of the battery capacity is used for charging and discharging, mainly driven by incentives to maximize self-consumption of PV-generated electricity. In contrast, in a total electricity system operational cost minimization larger fractions of the available battery capacity are utilized within single hours. In the total system optimization case, the batteries are charged and discharged less frequently and the energy turnover in the batteries is only half that of the household optimization case. For all the cases studied, the hourly electricity price provides only a limited incentive for households to operate their batteries in a system-optimal manner.

Distributed Demand Peak Reduction With Non-Cooperative Players and Minimal Communication

Demand response technology offers the exciting potential to reduce peak energy demand, electricity infrastructure expenditure, and household electricity bills. In this paper, a pricing mechanism that relies on non-cooperative heterogeneous loads knowledgeable of future energy consumption—such as electric vehicles—transferring minimal amounts of information to achieve peak demand response in a distributed fashion, whilst maintaining the privacy of the players. The existence of a Nash equilibrium is proven, as well as convergence conditions proving uniqueness of a Nash equilibrium and the stability of an “Iterated Synchronous Best Response Algorithm.” The price of anarchy (PoA) is proven to approach 1 as the number of homogeneous players approaches infinity, indicating there is no advantage to cooperation for a large number of similar players. Finally, simulation results are presented which suggest that the PoA for a system with heterogeneous players is likely to be proportional to the spread of energy consumption constraints.

Energy Management of Electric Vehicle Integrated Home in a Time-of-Day Regime

Electric vehicles (EVs) which are assumed to revolutionize the automobile industry are being adopted widely across the globe at a pace higher than estimated. However, the uncontrolled charging pattern of these loads can raise the peak demand and can potentially damage the transformers that are supplying power to a residential area. In addition, EV’s battery charging in peak hours can increase an average household electricity bill significantly. Thus, this paper proposes a heuristic method which suggests most suitable charging/discharging instances for an EV battery in a time-of-day regime. The key feature of this method is its simplified structure that requires basic input parameters, such as real-time price signal, household demand, and EV’s parameters. In addition, a heuristic method can quickly find the solution such that it can be implemented in simple computing devices. The scheme has been implemented on hardware and has proven its effectiveness. Results show that the algorithm works in two folds in reducing cost and energy dependence on the grid.

A Partially Observable Markov Decision Process Approach to Residential Home Energy Management

Real-time pricing (RTP) is a utility-offered dynamic pricing program to incentivize customers to make changes in their energy usage. A home energy management system (HEMS) automates the energy usage in a smart home in response to utility pricing signals. We present three new HEMS techniques—one myopic approach and two non-myopic partially observable Markov decision process (POMDP) approaches—for minimizing the household electricity bill in such a RTP market. In a simulation study, we compare the performance of the new HEMS methods with a mathematical lower bound and the status quo. We show that the non-myopic POMDP approach can provide a 10%–30% saving over the status quo.

Effective demand response for smart grids: Evidence from a real-world pilot

We show how an electricity customer decision support system (DSS) can be used to design effective demand response programs. Designing an effective demand response (DR) program requires a deep understanding of energy consumer behavior and a precise estimation of the expected outcome. Excessive demand shifting or a high price responsiveness might create new peaks during low-demand periods. We combine insights from a real-world pilot with simulations and investigate how we can design effective DR schemes. We evaluate our pricing recommendations against existing economic approaches in the literature and show that targeted recommendations are more beneficial for customers and for the grid. Furthermore, we conduct robustness tests in which we apply our methods on two independent datasets and observe differences in peak demand and electricity cost reduction, dependent on individual characteristics. In addition, we examine the role of energy policy, as it varies across countries, and we find that the presence of competition in the electricity market creates lower prices and more cost savings for individuals. Finally, we measure the economic value of our DSS and show that our DSS can result in up to 38% savings on household electricity bills. Our results exhibit how the design of effective DR can be achieved and provide insights to energy policymakers with regard to understanding consumers' behavior and setting regulatory constraints.

The Economics Evaluation of Grid-connected Photovoltaic Systems in Residential Houses

Purpose: To evaluate the economic performance of grid-connected photovoltaic system in residential house, household electricity bill policy of Korea Electric Power Corporation (KEPCO) must be applied precisely, and market tendency and uncertainty of system also need to be considered. In this study, to evaluate the economic feasibility of PV system, we measured PV power generation and electricity consumption of six of Green home in Daejeon through web based remote monitoring system. Method: We applied Monte-Carlo simulation based on life cycle cost analysis, to reflect an uncertainty of main factor in economic feasibility evaluation of photovoltaic system. Result: First, with deterministic analysis, the difference of NPV of cumulative financial savings among households varied from –3,310 ~ 24,170 thousand won , portraying notably big range. Also the possibility of getting the same result was 50% when applying uncertainty. Second, the higher electricity consumption is, the more economic feasibility of photovoltaic system increases because KEPCO uses progressive taxation in household electricity bill policy. Third, The contribution to variance of electricity price increases in NPV varied from 98.5% to 99.9%. While the inflation rate and annual degradation contributed very little to none.

The African electricity deficit: Computing the minimum energy poverty line using field research in urban Nigeria

Energy poverty has long been defined in Developing-Rural or Western-Urban contexts. For the parts of the world classified as energy-poor, the Developing-Rural context is used, but the population density of the energy poor is much higher in the urban areas of developing states and this trend is expected to continue over the next 30 years. Moreover, computations of energy use in developing states have been based on electricity use in industrialized states. Drawing on extensive qualitative and quantitative methods, and using field research in Nigeria, this article describes a context-specific way to compute actual energy consumption, with electricity as the proxy. It proposes a minimum energy poverty line of 3068kWh/capyr (350W/cap), which is sufficient to provide for basic needs in an urban household. With the energy poverty line representing an energy-secure household, three scenarios were modeled for name-plate capacity generation based on data collected: low-consumer, high-consumer, and energy-secure households. These scenarios were compared to current generation capacity for select sub-Saharan West African countries. I found that these countries all produced insufficient power to supply all their citizenry, even if the entire populace were comprised of low-energy consumers. I found no relationship between actual household electricity consumption and electricity bills.

Independent regulation of government-owned monopolies: An oxymoron? The case of electricity distribution in Australia

The network component of household electricity bills served by government-owned network service providers in Australia more than doubled between 2007 and 2013. Their regulated assets and profits more than tripled over this period. These outcomes have been delivered under a similar system of price cap regulation that has delivered quite different outcomes in Great Britain, and in Victoria (Australia) to the privately owned network service providers in that state. We conclude that changes in operating conditions, demand, customer numbers or reliability do not explain this. Rather we find that government ownership has undermined the authority and independence of economic regulation. The Australian experience in the regulation of government-owned electricity distributors casts doubt on the assumption that regulation can be entirely independent of government ownership, or would operate in the same way as for an entity in private ownership.

Financial Opportunities by Implementing Renewable Sources and Storage Devices for Households Under ERCOT Demand Response Programs Design

The Electric Reliability Council of Texas (ERCOT) launched a nodal market in December 2010, aiming at benefiting customers who are willing to curtail or shift their loads to help improve system reliability. Demand response (DR) programs may stimulate users to change their electricity consumption patterns based on electricity prices and incentives. Senate Bill 1125 allows residential and commercial class customers to participate in DR programs alongside industrial customers while reliability standards are maintained. By effectively utilizing renewable sources and energy storage devices, household electricity bills might be reduced since the purchase of electricity, particularly when the price is relatively high, can be replaced by either renewable resources or electricity from energy storage devices. This paper presents different scenarios of implementing photovoltaic systems and Li-based batteries under ERCOT's DR program design for a household in order to reap financial benefits. A typical Texas residential load profile and the locational marginal prices information of ERCOT's real-time market are applied to optimize battery capacity and total revenue. Finally, the calculation results illustrate that by participating in the DR programs, residential users are able to obtain financial benefits.