Potential for reducing electricity demand for lighting in households: An exploratory socio-technical study

Illuminance data were collected from 18 UK dwellings during 1-week periods in spring 2007, to establish when luminaires were used and to calculate electricity consumption for lighting. Householders were also interviewed about lighting use and choices. The potential for reducing lighting electricity consumption by replacing incandescent bulbs with compact fluorescent lamps (CFLs) is assessed. Mean weekly electricity consumption for lighting was 3.756 kW h and mean proportion of total electricity consumption used for lighting was 6.55%. It is notable, however, that participants generally expressed high levels of environmental awareness and that electricity consumption figures for less environmentally-aware households may differ. On average, households could have reduced lighting electricity consumption by 50.9% if all incandescent bulbs were replaced with CFLs. Even householders making extensive use of efficient lighting technologies expressed concerns about these technologies’ performance, but seemed willing to tolerate perceived shortcomings for environmental reasons. However, the study raises questions about whether people without strong environmental motivations can be convinced that efficient lighting technologies will meet their needs. It also raises questions about the effectiveness of policies phasing out general lighting service incandescent bulbs, as there is a risk that householders may switch to tungsten halogen bulbs rather than low-energy options.     

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.     

Joint supply — demand optimization in electricity supply

In most energy sub-sectors today a basic ignorance and lack of understanding of consumer response has meant that electricity supply systems have been conceived, planned, operated and priced mainly according to the basic criterion of engineering economics, ie cost-minimization. The few benefits taken into account have been included merely as negative costs. Recently this approach has been queried by energy policy makers. The cost-minimization process could be markedly strengthened by including further data, meanwhile information is methodically being gathered on consumer response to changes in electricity price and its should soon be possible to change over to the basic criterion of economic efficiency and welfare economics. A start can already be made, with joint supply-demand optimization in respect of the planning and operation of most interconnected electricity supply systems.     

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.     

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.     

Future demand for electricity in Nigeria

Availability and reliability of electricity supplies have always been vexed issue in Nigeria. With an estimated population of 130 million people in AD 2005, Nigeria is the most populous country in Africa and belongs to the group of countries with the lowest electricity consumption per capita in the continent. Nigeria is also ranked among the poorest countries in the world. This paper examines the likely trend in the demand for electricity over the next 25 years under the assumptions that (i) there is a rapid economic development such that Nigeria transforms from low- to middle-income economy during this period, (ii) Nigeria meets the millennium development goals (MDG) in AD 2015, and (iii) the country achieves the status of an industrializing nation. For these to happen, this paper projects that electric-power generation will have to rise from the current capacity of 6500 MW to over 160 GW in AD 2030. This level of supply will be significant enough to increase the per capita electricity consumption to about 5000 kWh per capita by the year 2030. Even then, this just compares with the AD 2003 per capital consumption of some industrializing countries. Analysis of the level of investment required to meet the projected power demand indicates that annual investment cost will rise from US$3.8 billion in AD 2006 to a peak of US$21 billion in AD 2028. The total investment stream over the 25 year period comes to US$262 billion or roughly US$10 billion per annum.     

A bilevel model for electricity retailers' participation in a demand response market environment

Demand response programmes are seen as one of the contributing solutions to the challenges posed to power systems by the large-scale integration of renewable power sources, mostly due to their intermittent and stochastic nature. Among demand response programmes, real-time pricing schemes for small consumers are believed to have significant potential for peak-shaving and load-shifting, thus relieving the power system while reducing costs and risk for energy retailers. This paper proposes a game theoretical model accounting for the Stackelberg relationship between retailers (leaders) and consumers (followers) in a dynamic price environment. Both players in the game solve an economic optimisation problem subject to stochasticity in prices, weather-related variables and must-serve load. The model allows the determination of the dynamic price-signal delivering maximum retailer profit, and the optimal load pattern for consumers under this pricing. The bilevel programme is reformulated as a single-level MILP, which can be solved using commercial off-the-shelf optimisation software. In an illustrative example, we simulate and compare the dynamic pricing scheme with fixed and time-of-use pricing. We find that the dynamic pricing scheme is the most effective in achieving load-shifting, thus reducing retailer costs for energy procurement and regulation in the wholesale market. Additionally, the redistribution of the saved costs between retailers and consumers is investigated, showing that real-time pricing is less convenient than fixed and time-of-use price for consumers. This implies that careful design of the retail market is needed. Finally, we carry out a sensitivity analysis to analyse the effect of different levels of consumer flexibility.     

Analysis of demand response and wind integration in Germany's electricity market

Demand response represents an additional option for reserve capacity as first market experiences have demonstrated. An analysis for Germany shows capacities up to 3 GW and costs starting at 30 euro/MWh in the industrial sector, 8 GW in the commercial sector and more than 20 GW in the residential sector including night storage heating. Simulations of the German power system showed that using these potentials together with improved wind power predictions can limit the additional balancing costs in Germany to below 2 euro/MWh feed-in by wind turbines with 48 GW wind power in 2020.     

Incorporating life cycle external cost in optimization of the electricity generation mix

The present work aims to examine the strategic decision of future electricity generation mix considering, together with all other factors, the effect of the external cost associated with the available power generation technology options, not only during their operation but also during their whole life cycle. The analysis has been performed by integrating the Life Cycle Assessment concept into a linear programming model for the yearly decisions on which option should be used to minimize the electricity generation cost. The model has been applied for the case of Greece for the years 2012–2050 and has led to several interesting results. Firstly, most of the new generating capacity should be renewable (mostly biomass and wind), while natural gas is usually the only conventional fuel technology chosen. If externalities are considered, wind energy increases its share and hydro-power replaces significant amounts of biomass-generated energy. Furthermore, a sensitivity analysis has been performed. One of the most important findings is that natural gas increases its contribution when externalities are increased. Summing-up, external cost has been found to be a significant percentage of the total electricity generation cost for some energy sources, therefore significantly changing the ranking order of cost-competitiveness for the energy sources examined.     

An economic welfare analysis of demand response in the PJM electricity market

We analyze the economic properties of the economic demand-response (DR) program in the PJM electricity market in the United States using DR market data. PJM's program provided subsidies to customers who reduced load in response to price signals. The program incorporated a “trigger point”, at a locational marginal price of $75/MWh, at or beyond which payments for load reduction included a subsidy payment. Particularly during peak hours, such a program saves money for the system, but the subsidies involved introduce distortions into the market. We simulate demand-side bidding into the PJM market, and compare the social welfare gains with the subsidies paid to price-responsive load using load and price data for year 2006. The largest economic effect is wealth transfers from generators to non price-responsive loads. Based on the incentive payment structure that was in effect through the end of 2007, we estimate that the social welfare gains exceed the distortions introduced by the subsidies. Lowering the trigger point increases the transfer from generators to consumers, but may result in the subsidy outweighing the social welfare gains due to load curtailment. We estimate that the socially optimal range for the incentive trigger point would be $66–77/MWh.     

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.     

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.     

The optimization of demand response programs in smart grids

The potential to schedule portion of the electricity demand in smart energy systems is clear as a significant opportunity to enhance the efficiency of the grids. Demand response is one of the new developments in the field of electricity which is meant to engage consumers in improving the energy consumption pattern. We used Teaching & Learning based Optimization (TLBO) and Shuffled Frog Leaping (SFL) algorithms to propose an optimization model for consumption scheduling in smart grid when payment costs of different periods are reduced. This study conducted on four types residential consumers obtained in the summer for some residential houses located in the centre of Tehran city in Iran: first with time of use pricing, second with real-time pricing, third one with critical peak pricing, and the last consumer had no tariff for pricing. The results demonstrate that the adoption of demand response programs can reduce total payment costs and determine a more efficient use of optimization techniques.     

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.     

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.     

Identification of reservation capacity in critical peak pricing electricity demand response program for sustainable manufacturing systems

Electricity demand response is considered an effective approach to balance the electricity demand and supply with existing infrastructure of generation, transmission, and distribution. A majority of existing literature on the electricity demand response has mainly centered on the commercial and residential building sectors while the application for the industrial sector is largely neglected. This paper presents a methodology for the application of a typical demand response program, Critical Peak Pricing (CPP) program, for the manufacturing enterprises. The configuration of the reservation capacity (in kilowatt, kW) in the CPP program, which plays a critical role in the cost of the final bill charge, will be identified by optimal production scheduling for the typical manufacturing systems with multiple machines and buffers. Mixed Integer Nonlinear Programming formulation is used to establish the mathematical model with the objective to minimize the electricity bill cost as well as the potential penalty cost due to the non‐fulfillment of the target production. An approximate technique is introduced to find a near optimal solution, and a numerical case study is used to illustrate the effectiveness of the proposed method. Copyright © 2013 John Wiley & Sons, Ltd.     

Effect of optimal spinning reserve requirement on system pollution emission considering reserve supplying demand response in the electricity market

Pollution emission reduction is becoming an inevitable global goal. Incorporating pollution reduction goals into power system operation affects several different aspects, such as unit scheduling and system reliability. At the same time, changes in the energy scheduling change the required optimal reserve amount. Optimal spinning reserve scheduling also affects the energy market scheduling. Optimal reserve allocation changes the energy scheduling, which affect the amount of pollution emission. Therefore, incorporating pollution emission reduction and optimal spinning reserve scheduling cannot be studied separately. Analysis of the system effects of pollution reduction should be performed considering the ancillary service market, specificity the optimal spinning reserve scheduling. This problem is addressed in this paper by incorporating optimal spinning reserve scheduling in a combined environment economic dispatch (CEED) in one objective function. The framework of this paper enables the study of the effect of optimal reserve scheduling and emission reduction as well as an analysis of the system effects of pollution reduction. With the increased AMI and smart grid realization, the reserve supplying demand response (RSDR) is becoming an important player in the reserve market, and thus, these resources are also taken into account. In this paper, the objective function is social cost minimization, including the costs associated with energy provision, reserve procurement, expected interruptions and environmental pollution. A MIP-based optimization method is developed, which reduces the computational burden considerably while maintaining the ability to reach to the optimal solution. The IEEE RTS 1996 is used as a test case for numerical simulations, and the results are presented. The numerical results show that optimal reserve scheduling and RSDR utilization resources have a considerable impact on environmental–economic cost characteristics.     

城市道路照明节电措施探讨

提出了提高负载功率因数、调整供电变压器配置、适度降低线路电压、推广符合“绿色照明”标准的高效光源等主要节电措施,旨在节约道路照明电能。     

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.