Residential Electricity Prices Research Articles

Does news propaganda really affect residents’ electricity rebound effect: New evidence of non-price information

The average electricity consumption per capita in China still has significant room for growth. Under the current rigid residential electricity pricing, it is very important to study how factors other than price can promote electricity conservation among residents. This paper uses micro-survey data of 2018 and 2020 to analyze energy efficiency and the rebound effect through a stochastic energy demand frontier model, as well as to examine the role of news propaganda in this context. The research conclusions are as follows: (1) News propaganda promoting electricity saving can weaken the rebound effect of electricity use, which can lead to better electricity savings. (2) The energy efficiency of residential electricity has improved significantly compared to previous years, and the rebound effect has decreased. (3) Electricity prices have a negative impact on electricity consumption, while income, household size, home area, and extreme temperatures have a positive impact. Based on these conclusions, we propose policy recommendations to address the current inflexibility in residential electricity pricing and to improve the efficiency of electricity use among residents.

Has retail competition reduced residential electricity prices in Texas?

We estimate the impact of introducing retail competition on retail electricity prices paid by residential consumers in Texas's two largest cities, Dallas and Houston. Using the synthetic control method to obtain the counterfactual prices, we find that retail competition raised average prices by $0.0112/kWh ($11.2/MWh) in the transition period from 2001 to 2006 and by $0.0134/kWh ($13.4/MWh) during the period of unfettered competition from 2007 to 2020. However, when the US wholesale natural gas prices are relatively low, actual retail electricity prices in areas opened to retail competition are close to the counterfactual prices that would have prevailed had retail competition not been introduced, as measured by the counterfactual prices estimated using the synthetic control method.

Uncertainty aware optimal battery sizing for cloud energy storage in community microgrid

Cloud energy storage systems (CES) are a new paradigm for the application of consumer-side energy storage in residential community microgrids. By transforming traditional consumers into self-sustaining and utility consumers, CES facilitates interaction between consumers and utilities as well as between consumers. Residential CES development is complicated by the flexible capacity of CES batteries and the uncertainty of supply. This paper recommends the CES system for residential prosumers. A machine learning-based uncertainty quantization and an artificial ecosystem optimization (AEO) method are used to determine optimal battery capacity considering PV, load, and price uncertainty. Compare this algorithm to the other four optimization algorithms to find out how well it works. The feasibility and profitability of deploying CES with residential PV are assessed. This problem minimizes the user’s cost and maximizes the profit of the CES operator. The sensitivity analysis of CES is analyzed for various storage capacity penetrations. Further, optimal battery capacity determines the use of an AEO algorithm. Based on a PJM dataset of residential PV, load, and electricity price, simulation results demonstrate that the suggested framework integrated with a distributed PV system is more economical to DES.

Residential rooftop solar demand in the U.S. and the impact of net energy metering and electricity prices

We use a panel dataset of residential rooftop solar adoption for 27 states from 2008 to 2018 to estimate demand for rooftop solar and the impact of net energy metering compensation. We find demand is highly price elastic and that income is elastic as well. We find a large cross-price effect with respect to residential electricity price indicating that rooftop solar is a strong substitute for utility-provided electricity driven, in part, by poor residential rate design that primarily recovers fixed system costs through volumetric charges. We find that net energy metering has a large positive impact on the demand for residential rooftop solar, resulting in at least a doubling of demand and in uneconomic customer bypass of utility-supplied electricity, as net energy metering is not a market-based compensation rate. Our analysis lends support to ongoing state policy efforts to reform net energy metering and to improve electricity rate design.

Electricity cross-subsidies in China: Social equity, reverse Ramsey pricing, and welfare analysis

The so-called “cross-subsidies” in China’s electricity market have existed for decades, i.e., residential electricity prices are much lower than industrial electricity prices. The claimed rationale of social equity for such a pricing scheme (i.e., to ensure low-income households with basic living standards) was left unsubstantiated. Many argued that China’s government should adopt Ramsey pricing in the electricity industry to achieve efficiency, increasing the residential electricity price beyond the industrial electricity price based on their relative price elasticities. This paper first proposes a theoretical model to explain the reverse Ramsey pricing in the Chinese power sector for social equity reasons. We then calibrate the model with observed industrial data. We find that the reverse Ramsey pricing could be optimal if the residential sector were given a higher weight than the industrial sector; the greater the weight of the residential sector, the lower the residential electricity prices. Based on the calibrated model, we use the actual electricity prices to find the most likely weighting scheme used by the regulator. Using these most likely weights, we further conduct social welfare analyses under different scenarios, providing helpful guidance for policymakers in future electricity market reforms.

Revisiting the Effect of Urbanization on Residential Electricity Consumption

Under the background of electrification, it is significant to explore the changes in residential electricity consumption for energy conservation and emission reduction. Based on the staggered difference-in-difference (DID) approach, this paper investigates urbanization's effect on residential electricity consumption using the longitudinal dataset from 2010-2018 China Family Panel Studies (CFPS). It is found that China's urbanization has increased the average electricity consumption by 12.8% with electricity substitution as the main channel. After urbanization, the scale effect of population and area levels does not exist. The authors also find that this positive effect is affected by electricity price (i.e., the higher the price level, the smaller the positive impact). However, as the electric charge is insignificant relative to the income, the moderating effect of price remains small. This indicates that the positive effect of urbanization will not decrease soon in the future unless the residential electricity price system is reformed.

Designing the pricing mechanism of residents’ self-selection sales electricity based on household size

The current tiered electricity pricing (TEP) system for residents in China reflects neither the cost of electricity supply nor the real demand of residents of different household sizes; therefore, we design a classified self-selection sales electricity pricing (CSSSEP) mechanism for different household sizes in this paper. First, based on the log-log model and single threshold model, residential electricity demand models of different household sizes are constructed. Then, the combination of factors other than electricity price is regarded as the residential electricity consumption type, and on this basis, a residential electricity price information screening model is proposed. Finally, the model is empirically compared and analysed. The results indicate that (1) the optimal power supply and electricity charges increase with changes in the type of residents. In addition, (2) the CSSSEP mechanism is better than the self-selection sales electricity pricing (SSSEP) mechanism and the TEP system; i.e., the CSSSEP mechanism can not only save energy and increase the income of power suppliers but also reduce residents' electricity consumption. Furthermore, (3) the average electricity price under the TEP system is relatively low, and the power regulation department should greatly increase it to better realize the reasonable allocation of resources. Finally, (4) the price cap has a positive impact on total social welfare, total profit and average surplus, but the increased total social welfare is mainly manifested in the profit of the power supplier.

The role of renewable energy sources in residential electricity prices: A club convergence analysis across selected European countries

ABSTRACT This research analyses the convergence of residential electricity prices in 22 European countries between 1995 and 2019. The findings reveal the existence of two sub-convergent clubs of residential electricity prices. Half of the countries studied belong to the club with higher residential electricity prices, while the other half belong to the lower-price club. Household income increases the probability of belonging to the club of lower residential electricity prices. An increase in heating degree days decreases the probability of belonging to the club of lower residential electricity prices. A similar effect is observed for the price of non-renewable energy sources used in electricity production. Further, although a country’s likelihood of belonging to the club of lower residential electricity prices diminishes with a rise in the share of renewable electricity production, the converse pattern is obtained at the residential level, such that residential renewable electricity share has a beneficial effect. Our findings suggest that households have benefitted in paying lower prices for renewable sourced electricity. Therefore, this research indicates that ongoing clean energy initiatives and the availability of affordable substitutes for conventionally generated electricity are being realized to a certain extent, improving electricity markets for the countries we analysed.

Vehicle-to-grid workplace discharging economics as a function of driving distance and type of electric vehicle

Vehicle-to-grid (V2G) is a promising way to reduce the peak valley difference of the power load. The economic and environmental benefits of V2G workplace charging as a function of driving distance and type of electric vehicle (EV) as well as V2G electricity price in Shanghai are explored. The characteristics of EVs and the grid in Shanghai are also described at length. The results show that private EVs have much idle time and can participate in peak shaving and valley filling service for the grid by V2G while electric buses are not suitable for daytime V2G services because they run for a long time during the day. The plug-in hybrid electric vehicle (PHEV) energy scheduling strategy prioritizes driving demand rather than V2G service ensures low carbon dioxide emissions. The peak-shaving compensation electricity price (PSCEP) should be set as 4–9 times of the residential valley electricity price (RVEP) after weighing V2G benefits and carbon dioxide emissions. If PSCEP is greater than 9 times RVEP, it will be profitable for PHEV to increase additional gasoline consumption for V2G services, however, which will result in a larger carbon footprint. In the power structure, the renewable energy supply which contributes 40% during the valley period at night and 29% during the peak time during the day reduces the carbon footprint of EVs in Shanghai. The maximum peak–valley load difference can be reduced by 14% after V2G services, suggesting that a large part of peak shaving generation units can be replaced by V2G in Shanghai. • V2G energy dispatch strategies are proposed based on the driving behavior of EVs. • Recommended that the V2G feed-in price be 4–9 times of the valley price in Shanghai. • High proportion of renewable energy reduces the carbon footprints of EVs and V2G. • The maximum peak–valley load difference can be reduced by 14% after V2G services.

Do gentailers charge higher residential electricity prices in the Texas retail electricity market?

In areas of Texas where energy consumers can choose among retail providers of electricity, we find that the prices offered by companies which are involved in both power generation and retail sales (“gentailers”) tend to be higher than the prices offered by Retail Electric Providers (REPs) which are independent of any generation company. Yet, within the group of gentailers, we find significant differences in their pricing levels and patterns. These findings are based on graphical and regression analysis of pricing plans advertised on the Power to Choose website. We offer hypotheses that might explain these pricing behaviors. As retail competition in electricity markets is considered or refined in other regions, these findings from Texas may prove instructive.

The Impact of Renewable Energy on Residential Electricity Prices in the U.S

The Impact of Renewable Energy on Residential Electricity Prices in the U.S

The incidence of VAT reforms in electricity markets: Evidence from Belgium

In April 2014, the Belgian government reduced the VAT rate on the electricity price from 21% to 6% to support low-income families. In September 2015, the tax cut was repealed, and the VAT rate was reinstated to 21% in the context of a change of government. This paper investigates the impact of such temporary and (plausibly) exogenous VAT reform on the Belgian electricity market. We study the pass-through of the VAT reform to electricity prices and the effect of this (exogenous) price change on electricity demand. We estimate the VAT pass-through on residential electricity prices by a difference-in-differences method, using business electricity prices (not subject to VAT) as a control group. Our findings reveal that both the tax cut and the tax hike were entirely shifted to the electricity price (100% pass-through). To assess the impact of the VAT change on demand, we perform a counterfactual demand analysis of the electricity flowing monthly over the grid at the network operator level. Exploiting VAT and non-VAT related price variations, our results show a price elasticity of residential demand for electricity between -0.09 and -0.17. Interestingly, we also find that demand reacted quickly and symmetrically to the VAT cut and the subsequent VAT hike.

Hybrid photovoltaic-thermoelectric system: Economic feasibility analysis in the Atacama Desert, Chile

Desert areas are the favorable geographical locations for desired solar resource and temperature variations for improving the performance of hybrid photovoltaic-thermoelectric generator (HPV-TEG) systems. Therefore, economic feasibility analysis of HPV-TEG system is carried out under real environment and market conditions for the Atacama Desert, Chile. The thermal, electrical and economic models of HPV-TEG system are developed and analyzed in MATLAB. Five different possible scenarios are considered for economic feasibility based on energy losses, system costs, nominal efficiencies of TEG and photovoltaic module and their contribution in the economic feasibility of HPV-TEG system is identified and payback period for all scenarios is determined at minimum and maximum PV temperatures for Atacama Desert including residential and industrial electricity prices. The results showed that with existing market costs and TEG efficiency, HPV-TEG system could not be economically competitive with photovoltaic system for environmental conditions of the Atacama Desert. However, the calculated levelized cost of energy (LCOE) of the HPV-TEG system is 0.071 USD/kWh which is relatively close to current LCOEs for PV systems in Chilean energy market. Further, LCOE analysis economically quantifies the advantages of HPV-TEG system over PV system and opens the possibility for HPV-TEG systems to be competitive in desert locations.

Measurement of the direct rebound effect of residential electricity consumption: An empirical study based on the China family panel studies

The rebound effect is a potential threat to energy efficiency related policies to save energy. Based on data from China family panel studies over the period 2010–2018, this paper measures the energy efficiency and direct rebound effect of residential electricity consumption by using the stochastic energy demand frontier model. The result shows that the average value of efficiency is 0.491, and the average rebound effect is 48%. It means that there is still room for improvement in China’s household electricity consumption efficiency. But the expected electricity- saving after improving efficiency can only achieve 52%. Besides, we find that electricity price and household size have a negative impact on the rebound effect, while the impact of income is inverted U-shaped. Finally, the rebound effect varies between before and after the implementation of the increasing block pricing policy, considering rural and urban residents and different regions. These findings provide some policy implications for the residential electricity pricing system under the background of energy conservation and climate change.

Effects of fuel cost and driving behaviors on operational costs of gasoline and electric vehicles in the US

Internal combustion engine vehicles have long dominated personal vehicle sales in the United States. As electric vehicles gain market share, understanding fuel costs of gasoline versus electric vehicles (EVs) is increasingly relevant for potential buyers and for researchers. In particular, understanding the influence of fuel price volatility on fuel costs is of interest. Although it is well known that fueling an EV is cheaper than fueling a gasoline vehicle, costs are less commonly expressed in explicit spatially and temporally resolved terms. This note presents an Excel model that includes state-specific gasoline and residential electricity price data for US states between 2015–2020, including both average overall electricity prices and lowest-cost time-of-use electricity rates (measured over a consecutive 8-hour potential residential charging period) for each state. This model, which we call the Fuel Cost Calculator, also allows users to evaluate the impact of several major external and driver behavior factors on fuel costs for gasoline versus electric vehicles. These combinations of these factors result in unique scenarios which make price differences for the fuel costs more variable. With the Fuel Cost Calculator, the user can create specific scenarios reflecting potential real-world conditions to create cost estimates for gasoline and residential EV fueling over user-determined time periods and distances. Fuel costs for gasoline vehicles are generally higher than for electric vehicles in the US, consistent with prior studies. We also explicitly show that fuel cost budgets are much more volatile for gasoline versus electric vehicles charged at a residence, given the much less stable price of oil versus electricity. This volatility is sufficiently large so as to be relevant for vehicle owner budgets. The FCC also shows carbon dioxide (CO2) emissions associated with vehicle use, resolved at the state level.

Elasticities of residential electricity demand in China under increasing-block pricing constraint: New estimation using household survey data

China has implemented the residential increasing-block electricity pricing (IBEP) policy since the second half of 2012, which is considered the most effective economic instrument in improving residential energy efficiency. Price and income elasticity are two fundamental parameters to guide both scholars and policy-makers in assessing whether and to what extend Chinese households respond to the policy. However, it presents the challenge of simultaneous determination of marginal price and electricity consumption; further, it is less examined from an empirical perspective due to the absence of micro-level data. To fill this gap, this study estimates price and income elasticity by establishing two instrumental variables, based on a unique dataset from the Chinese Residential Energy Consumption Survey 2014. Results show that the residential demand for electricity is price inelastic and that electricity is an essential commodity for households in the short run. It also shows great urban-rural disparity and regional heterogeneity of household electricity consumption behavior regarding short-run income elasticity. The estimated parameters of short-run price and income elasticities provide a valuable reference for policy-making regarding both a nationwide uniform and a differential regional perspective.

A new fuzzy logic based approach for optimal household appliance scheduling based on electricity price and load consumption prediction

ABSTRACT Smart Home Energy Management Systems (HEMS) constitute a vital necessity for optimizing electricity usage and saving energy in smart grids. However, these systems rely on dynamic factors that are stochastic and difficult to predict, such as the load consumption and electricity prices. Therefore, constructing an efficient control system for residential buildings requires an accurate prediction process of the associated parameters. This paper proposes an integrated predictive control system that consists of both predictive model and Demand Response (DR) scheme to predict and control the daily electricity usage in the residential sector. First, a Long Short-Term Memory-based (LSTM) optimized predictive model is implemented for predicting both the hourly load consumption and electricity price for a typical smart home. Then, the predicted data are transmitted to a DR fuzzy logic-based controller that can optimally schedule the home appliances usage. In comparison with the state-of-the-art prediction techniques for the residential load consumption and electricity price, the proposed LSTM predictive model outperforms Linear Regression (LR), Decision Tree (DT), Support Vector Regression (SVR), and Ensembled Boosted Trees (EBT). Moreover, the proposed DR-FIS controller has shown good results in terms of reducing the electricity cost by selecting the optimal time schedule.

Price regulation and supplier margins in the Hungarian electricity markets

It is highly debated whether residential electricity markets were mature enough for market liberalization. While most countries are unambiguously committed to the trend of liberalization, the Hungarian government decided to re-regulate the residential electricity prices after the liberalization reforms were completed. This situation provides a unique opportunity to study the price effects of a subsequent regulation process. The present paper examines the interaction between the universal service provider (USP) and free-market trader margins on the Hungarian electricity market. We distinguish three main regulatory periods: the transitional system between 2004 and 2008, the fully liberalized system between 2008 and 2010, and the post-liberalized regime from 2010 to date. A vector autoregression model is applied to assess the relationship between the different margins. The results show that in the transition period, margins were independent, while in the deregulated period, mutual dependence was evidenced between them, and in the third one, only traders' margins affected the margins of the universal service providers. This suggests that the public intervention, which was intended to focus on universal service and residential consumers, surprisingly did not have an indirect horizontal profit reallocation effect between USPs and traders. The findings can support countries that plan to modify the regulatory framework of the electricity market.

To decarbonize industry, we must decarbonize heat

Summary Industry is often termed “hard to decarbonize” because a vast, inhomogeneous array of processes comprise the sector. But developing new, decarbonized process heating technologies represents a single, broadly applicable pathway to eliminating a large portion of sectoral emissions—and approximately one-fifth of global CO2 emissions, overall. We begin this perspective with a brief review of the demand for and cost of industrial heat. Then, we highlight key challenges and R&D needs in developing zero-carbon industrial heating technologies. Technologies in four pathways are discussed: (1) zero-carbon fuels, (2) zero-carbon heat sources, (3) electrification of heat, and (4) better heat management. Finally, we identify cross-cutting challenges to the development and adoption of zero-carbon industrial heat technologies, the solution to any of which would constitute a significant breakthrough on the path to industrial decarbonization.

Forecasting residential electricity demand in the Philippines using an error correction model

This study uses an Error Correction Model (ECM) to forecast residential electricity demand in the Philippines using household final consumption expenditure, residential electricity price, and temperature as explanatory variables. Results show that there is a long-run relationship between household final consumption expenditure and residential electricity demand. Estimates from the ECM are consistent with economic theory, and the model passed standard diagnostic and parameter stability tests. Forecast performance based on within-sample and out-of-sample forecasts of the ECM is also shown to be superior, relative to a benchmark Autoregressive Distributed Lag (ARDL) model. Simulations show that by 2040, residential electricity consumption will range from 42,500 gigawatthours (GWh) based on a weak income growth scenario and 62,000 GWh based on a combined changes scenario.