Residential demand for electricity

Short- and long-run responses by households to changes in the price of electricity are estimated using data which permit measurement of the marginal price of electricity, the infra-marginal demand charge, and estimates of household appliance stocks. The price elasticities of high- and low-level users of electricity are compared. The theoretical bias in price elasticity estimates resulting from neglect of the infra-marginal demand charge is shown to be empirically insignificant.     

Variable elasticity models for electricity demand

It is demonstrated analytically that the demand models as specified by Betancourt do not yield ‘unitless’ measures of elasticity. A simple transformation is suggested and the results are compared with other variable price elasticity demand models.     

Television peaks in electricity demand

This paper discusses the effect that breaks in television programmes have upon the demand for electricity. The results of a study which correlates the pattern of household electricity consumption with the timings of commercial and end-of-programme breaks are summarized. A theory of audience release is developed and various policy implications are explored.     

Indications for a changing electricity demand pattern: The temperature dependence of electricity demand in the Netherlands

This study assesses the electricity demand pattern in the relatively temperate climate of the Netherlands (latitude 52°30′N). Daily electricity demand and average temperature during the period from 1970 until 2007 are investigated for possible trends in the temperature dependence of electricity demand. We hypothesize that the increased use of cooling applications has shifted the temperature dependence of electricity demand upwards in summer months. Our results show significant increases in temperature dependence of electricity demand in May, June, September, October and during the summer holidays. During the period studied, temperature dependence in these months has shifted from negative to positive, meaning that a higher temperature now leads to an increased electricity demand in these months, rather than a decreased demand as observed historically. Although electricity demand in countries with moderate summer temperatures such as the Netherlands generally peaks in winter months and shows a minimum in summer months, this trend may signal the development of an additional peak in summer, especially given the expected climatic change. As power generating capacity may be negatively influenced by higher temperatures due to decreasing process cooling possibilities, an increasing electricity demand at higher temperatures may have important consequences for power generation capacity planning and maintenance scheduling.     

The demand for residential electricity in South Africa

This paper examines the residential demand for electricity in South Africa as a function of real gross domestic product per capita, and the price of electricity during the period 1978–2005. We make use of the bounds testing approach to cointegration within an autoregressive distributed framework, suggested by Pesaran et al. [2001. Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics 16(3) 289–326]. Following the literature, we use a linear double-logarithmic form using income and price as independent variables in the empirical analysis. In the long run, we find that income is the main determinant of electricity demand, while electricity price is insignificant.     

The agricultural demand for electricity in the united states

This study looks at how farmers adjust their consumption of electricity in response to changes in the price of energy. A demand model is specified and estimated. The conclusions suggest that the price of electricity is a factor impacting the quantity of electricity demanded by farmers for irrigation and for other (non-irrigation) uses, but there is no indication that other types of energy are substitutes for electricity. Additionally, the number of acres irrigated is an important factor driving the demand for electricity for irrigation and the number of acres planted is a factor determining the demand for electricity for other uses. Finally, the estimated models of electricity for irrigation and electricity for other uses are structurally stable over the period 1971–92.     

Industrial electricity demand in New York State

We will discuss electricity demand in manufacturing industries in upstate New York. Empirical results are presented based on data obtained at the electric utility service area level for the years 1969–1981. The equations are based on a partial adjustment model including relevant input prices and industrial output by SIC group. The estimated coefficients have the expected signs and are for the most part, statistically significant. There is considerable variation in price and output elasticities among SIC groups and between service areas, which suggests that energy policy and industrial policy at the state level should be tailored to specific industries and perhaps to specific subregions within the state. Forecasts of industrial electricity demand are based on assumptions of prices and industrial output growth made by the utility companies themselves. A comparison of these forecasts with those made by the utility companies indicates that electricity demand forecasts are sensitive to the inclusion of alternative fuel prices and to the level of sectoral disaggregation.     

A seasonal hybrid procedure for electricity demand forecasting in China

Electricity is a special energy which is hard to store, so the electricity demand forecasting in China remains an important problem. This paper aims at developing an improved hybrid model for electricity demand in China, which takes the advantages of moving average procedure, combined method, hybrid model and adaptive particle swarm optimization algorithm, known as MA-C-WH. It is designed for making trend and seasonal adjustments which simultaneously presents the electricity demand forecasts. Four actual electricity demand time series in China power grids are selected to illustrate the proposed MA-C-WH model, and one existing seasonal autoregressive integrated moving average model (SARIMA) is selected to compare with the proposed model using the same data series. The results of popular forecasting precision indexes show that our proposed model is an effective forecasting technique for seasonal time series with nonlinear trend.     

The theory of maximum kW demand charges for electricity

The kilowatt (kW) demand charge characterizes most electricity price schedules for industrial customers, yet it has not been analysed thoroughly (compared with time-of-use rates). This article shows how such charges complicate interpretations of past estimates of the industrial demand for electricity. Using a utility maximization (cost minimization) model, we derive consistent demand curves, which are shown potentially to be subject to discontinuities.     

Option value of electricity demand response

As electricity markets deregulate and energy tariffs increasingly expose customers to commodity price volatility, it is difficult for energy consumers to assess the economic value of investments in technologies that manage electricity demand in response to changing energy prices. The key uncertainties in evaluating the economics of demand–response technologies are the level and volatility of future wholesale energy prices. In this paper, we demonstrate that financial engineering methodologies originally developed for pricing equity and commodity derivatives (e.g., futures, swaps, options) can be used to estimate the value of demand-response technologies. We adapt models used to value energy options and assets to value three common demand–response strategies: load curtailment, load shifting or displacement, and short-term fuel substitution—specifically, distributed generation. These option models represent an improvement to traditional discounted cash flow methods for assessing the relative merits of demand-side technology investments in restructured electricity markets.     

Regional analysis of electricity demand growth

This paper presents the regional electricity demand-forecasting model developed at the Oak Ridge National Laboratory. The model forecasts electricity demand and price by sector and by state. Econometric models are estimated for each of the nine census regions separately, using pooled time-series and cross-sectional (state) data. Thus, the estimated demand elasticities used in the forecasting model vary from region to region.The paper also presents and analyzes the most recent forecasts for nine selected states. The results show that electricity demand will continue to grow in all sectors and in all states. However, the rates of growth will be considerably lower than those observed in the 1950s and 1960s. Furthermore, the forecasted rates of growth in electricity demand vary considerably from state to state. The reasons for these variations being the different patterns of projected future growth in population, income, and industrial activities, as well as the different degrees of demand responsiveness to price changes among states. These regional variations of electricity demand growth suggest that forecasts at the national level should not be used as the basis for making energy policies at the regional or state level.     

Automated demand response system in lighting for optimization of electricity cost

Daylighting is an important factor in improving visual comfort and energy efficiency. Lighting control using daylighting can reduce energy consumption in buildings. This thesis proposes an automatic demand response system for lighting based on wireless sensor networks (WSN) in order to reduce the peak electricity demand according to the stage of electricity rate with real-time pricing (RTP), time of use pricing (TOUP), and critical peak pricing (CPP). The proposed system automatically controls the slat angle of the venetian blind with a cut-off angle according to the altitude of the sun, automatically executing light dimming according to measured current luminance to remove an unpleasant glare caused by daylighting. The target illuminance of area at this time is set at a minimum illuminance required for the work execution in the office during the time zone where the electricity load is high to save the lighting energy cost, while a maximum illuminance is set during the time zone where the electricity load is low according to the real-time electricity pricing stages obtained through the advanced metering infrastructure (AMI) in order to improve the work efficiency of the occupants. In this study, two testbeds having the same environments, as well as a control system targeting a fixed illuminance per price system, were established. The illuminance energy consumption and cost were then measured and the effect of the proposed illuminance system was evaluated.     

Determinants of electricity demand for newly electrified low-income African households

Access to clean, affordable and appropriate energy is an important enabler of development. Energy allows households to meet their most basic subsistence needs; it is a central feature of all the millennium development goals (MDGs) and, while a lack of access to energy may not be a cause of poverty, addressing the energy needs of the impoverished lets them access services which in turn address the causes of poverty.     

Exergy-based control of electricity demand and microgeneration

As the penetration of weather-dependent embedded generation grows, the variations in apparent demand experienced by distribution network operators will become more extreme. A method for the autonomous control of domestic electricity demand and microgeneration is proposed using the established, but underdeveloped, radio teleswitch technology. The disadvantages of cost or carbon-emission based optimisation using this method are discussed and an alternative approach based on minimisation of exergy loss is described. Results obtained from a computer model demonstrate its potential contribution to the balancing of electricity supply and demand.     

Modelling and forecasting Turkish residential electricity demand

This research investigates the relationship between Turkish residential electricity consumption, household total final consumption expenditure and residential electricity prices by applying the structural time series model to annual data over the period from 1960 to 2008. Household total final consumption expenditure, real energy prices and an underlying energy demand trend are found to be important drivers of Turkish residential electricity demand with the estimated short run and the long run total final consumption expenditure elasticities being 0.38 and 1.57, respectively, and the estimated short run and long run price elasticities being −0.09 and −0.38, respectively. Moreover, the estimated underlying energy demand trend, (which, as far as is known, has not been investigated before for the Turkish residential sector) should be of some benefit to Turkish decision makers in terms of energy planning. It provides information about the impact of past policies, the influence of technical progress, the impacts of changes in consumer behaviour and the effects of changes in economic structure. Furthermore, based on the estimated equation, and different forecast assumptions, it is predicted that Turkish residential electricity demand will be somewhere between 48 and 80 TWh by 2020 compared to 40 TWh in 2008.     

Assessment of demand for natural gas from the electricity sector in India

Electricity sector is among the key users of natural gas. The sustained electricity deficit and environment policies have added to an already rising demand for gas. This paper tries to understand gas demand in future from electricity sector. This paper models the future demand for gas in India from the electricity sector under alternative scenarios for the period 2005–2025, using bottom-up ANSWER MARKAL model. The scenarios are differentiated by alternate economic growth projections and policies related to coal reforms, infrastructure choices and local environment. The results across scenarios show that gas competes with coal as a base-load option if price difference is below US $ 4 per MBtu. At higher price difference gas penetrates only the peak power market. Gas demand is lower in the high economic growth scenario, since electricity sector is more flexible in substitution of primary energy. Gas demand reduces also in cases when coal supply curve shifts rightwards such as under coal reforms and coal-by-wire scenarios. Local environmental (SO₂ emissions) control promotes end of pipe solutions flue gas de-sulfurisation (FGD) initially, though in the longer term mitigation happens by fuel substitution (coal by gas) and introduction of clean coal technologies integrated gasification combined cycle (IGCC).     

Flexible demand response programs modeling in competitive electricity markets

In recent years, extensive researches have been conducted on implementation of demand response programs (DRPs), aimed to electricity price reduction, transmission lines congestion resolving, security enhancement and improvement of market liquidity. Basically, DRPs are divided into two main categories namely, incentive-based programs (IBPs) and time-based rate programs (TBRPs). Mathematical modeling of these programs helps regulators and market policy makers to evaluate the impact of price responsive loads on the market and system operational conditions. In this paper, an economic model of price/incentive responsive loads is derived based on the concept of flexible price elasticity of demand and customer benefit function. The mathematical model for flexible price elasticity of demand is presented to calculate each of the demand response (DR) program’s elasticity based on the electricity price before and after implementing DRPs. In the proposed model, a demand ratio parameter has been introduced to determine the appropriate values of incentive and penalty in IBPs according to the level of demand. Furthermore, the importance of determining optimum participation level of customers in different DRPs has been investigated. The proposed model together with the strategy success index (SSI) has been applied to provide an opportunity for major players of the market, i.e. independent system operator (ISO), utilities and customers to select their favorite programs that satisfy their desires. In order to evaluate the performance of the proposed model, numerical studies are conducted on the Iranian interconnected network load profile on the annual peak day of the year 2007.     

The price elasticity of electricity demand in South Australia

In this paper, the price elasticity of electricity demand, representing the sensitivity of customer demand to the price of electricity, has been estimated for South Australia. We first undertake a review of the scholarly literature regarding electricity price elasticity for different regions and systems. Then we perform an empirical evaluation of the historic South Australian price elasticity, focussing on the relationship between price and demand quantiles at each half-hour of the day.     

Grey prediction with rolling mechanism for electricity demand forecasting of Turkey

The need for energy supply, especially for electricity, has been increasing in the last two decades in Turkey. In addition, owing to the uncertain economic structure of the country, electricity consumption has a chaotic and nonlinear trend. Hence, electricity configuration planning and estimation has been the most critical issue of active concern for Turkey. The Turkish Ministry of Energy and Natural Resources (MENR) has officially carried out energy planning studies using the Model of Analysis of the Energy Demand (MAED). In this paper, Grey prediction with rolling mechanism (GPRM) approach is proposed to predict the Turkey's total and industrial electricity consumption. GPRM approach is used because of high prediction accuracy, applicability in the case of limited data situations and requirement of little computational effort. Results show that proposed approach estimates more accurate results than the results of MAED, and have explicit advantages over extant studies. Future projections have also been done for total and industrial sector, respectively.     

Industrial electricity demand for Turkey: A structural time series analysis

This research investigates the relationship between Turkish industrial electricity consumption, industrial value added and electricity prices in order to forecast future Turkish industrial electricity demand. To achieve this, an industrial electricity demand function for Turkey is estimated by applying the structural time series technique to annual data over the period 1960 to 2008. In addition to identifying the size and significance of the price and industrial value added (output) elasticities, this technique also uncovers the electricity Underlying Energy Demand Trend (UEDT) for the Turkish industrial sector and is, as far as is known, the first attempt to do this. The results suggest that output and real electricity prices and a UEDT all have an important role to play in driving Turkish industrial electricity demand. Consequently, they should all be incorporated when modelling Turkish industrial electricity demand and the estimated UEDT should arguably be considered in future energy policy decisions concerning the Turkish electricity industry. The output and price elasticities are estimated to be 0.15 and − 0.16 respectively, with an increasing (but at a decreasing rate) UEDT and based on the estimated equation, and different forecast assumptions, it is predicted that Turkish industrial electricity demand will be somewhere between 97 and 148 TWh by 2020.