A comparative analysis of time-of-use electricity rate effects: The Arizona experiment

To date, analyses of the data from the Arizona time-of-use (TOU) electricity pricing experiment have failed to find a significant relationship between electricity usage and electricity price. Results indicated that if the TOU rates had an effect on usage, the effect was virtually identical for each TOU rate. In this study, usage in 1976 under the TOU rates is compared to usage in 1975 under the existing (declining block) rate to determine whether or not there was an effect.It is found that peak period usage was reduced under TOU rates by 7–16% after adjusting for differences in weather between the two years. To a lesser extent, customers reduced their usage during the intermediate period and shifted some consumption to the base period. Total consumption appears to have decreased slightly. It is emphasized that these effects of the TOU rates cannot be discovered in demand analyses that do not utilize the baseline year data.     

The importance of marginal cost electricity pricing to the success of greenhouse gas reduction programs

The efficient reduction of GHG emissions requires appropriate retail pricing of off-peak electricity. However, off-peak electricity for residential consumers is priced at 331% above its marginal cost in the United States as a whole (June 2009). Even for the 1% of residences that are on some form of time-of-use (TOU) rate schedule, the off-peak rate is almost three times higher than the marginal cost. A barrier to marginal-cost based TOU rates is that less than 9% of U.S. households have the “smart” meters in place that can measure and record the time of consumption. Policies should be put in place to achieve full deployment. Another important barrier is consumer concern about TOU rate design. Two TOU rate designs (baseline and two-part tariff) are described that utilize marginal-cost based rates, ensure appropriate cost recovery, and minimize bill changes from current rate structures. A final barrier is to get residences on to these rates. Should a marginal-cost based TOU rate design remain an alternative for which residences could “opt-in,” or become the default choice, or become mandatory? Time-invariant rates are a historical anachronism that subsidize very costly peak-period consumption and penalize off-peak usage to our environmental detriment. They should be phased out.     

Optimal real time pricing in an agent-based retail market using a comprehensive demand response model

In this paper, a weighted combination of different demand vs. price functions referred to as Composite Demand Function (CDF) is introduced in order to represent the demand model of consuming sectors which comprise different clusters of customers with divergent load profiles and energy use habitudes. Derived from the mathematical representations of demand, dynamic price elasticities are proposed to demonstrate the customers’ demand sensitivity with respect to the hourly price. Based on the proposed CDF and dynamic elasticities, a comprehensive demand response (CDR) model is developed in this paper for the purpose of representing customer response to time-based and incentive-based demand response (DR) programs. The above model helps a Retail Energy Provider (REP) agent in an agent-based retail environment to offer day-ahead real time prices to its customers. The most beneficial real time prices are determined through an economically optimized manner represented by REP agent’s learning capability based on the principles of Q-learning method incorporating different aspects of the problem such as price caps and customer response to real time pricing as a time-based demand response program represented by the CDR model. Numerical studies are conducted based on New England day-ahead market’s data to investigate the performance of the proposed model.     

Demand side management program evaluation based on industrial and commercial field data

Demand Response is increasingly viewed as an important tool for use by the electric utility industry in meeting the growing demand for electricity. There are two basic categories of demand response options: time varying retail tariffs and incentive Demand Response Programs. Electricity Saudi Company (ESC) is applying the time varying retail tariffs program, which is not suitable according to the studied load curves captured from the industrial and commercial sectors. Different statistical studies on daily load curves for consumers connected to 22 kV lines are classified. The load curve criteria used for classification is based on peak ratio and night ratio. The data considered here is a set of 120 annual load curves corresponding to the electric power consumption (the western area in the King Saudi Arabia (KSA)) of many clients in winter and some months in the summer (peak period). The study is based on real data from several Saudi customer sectors in many geographical areas with larger commercial and industrial customers. The study proved that the suitable Demand Response for the ESC is the incentive program.     

The effect of sociodemographic diversity of residential customers on the financial risk experienced in the retail electricity market

The sociodemographic diversity of residential customers can affect the level of financial risk that an electricity provider experiences in the retail market. To demonstrate the relationship between sociodemographic diversity and financial risk, electricity consumption data drawn from the United Kingdom Power Networks ‘Low Carbon London’ project was analyzed to explore the relationship between sociodemographic diversity and financial risk experienced by electricity retailers. The results show that when increasing the sociodemographic diversity amongst a group of residential customers the effect on financial risk depends on the electricity consumption patterns of individual customers and the relationship of consumption patterns between residential customers. Increasing sociodemographic diversity amongst residential customers with very distinct energy consumption patterns can decrease the overall financial risk associated with the aggregated revenue received from these customers. However, the results showed that adding customers to a customer base without consideration for their sociodemographic background can cause the overall financial risk associated with the aggregated revenue received to change erratically. Whilst previous studies have considered customer diversity and its influence on peak electricity demand, this research advances the state-of-the-art by showing the importance of customer diversity to the financial quantity risk experienced by electricity retailers. This finding has serious implications for electricity providers seeking to mitigate financial risk in the retail electricity market.     

Short- and long-run time-of-use price elasticities in Swiss residential electricity demand

This paper presents an empirical analysis on the residential demand for electricity by time-of-day. This analysis has been performed using aggregate data at the city level for 22 Swiss cities for the period 2000−2006. For this purpose, we estimated two log–log demand equations for peak and off-peak electricity consumption using static and dynamic partial adjustment approaches. These demand functions were estimated using several econometric approaches for panel data, for example LSDV and RE for static models, and LSDV and corrected LSDV estimators for dynamic models. The attempt of this empirical analysis has been to highlight some of the characteristics of the Swiss residential electricity demand. The estimated short-run own price elasticities are lower than 1, whereas in the long-run these values are higher than 1. The estimated short-run and long-run cross-price elasticities are positive. This result shows that peak and off-peak electricity are substitutes. In this context, time differentiated prices should provide an economic incentive to customers so that they can modify consumption patterns by reducing peak demand and shifting electricity consumption from peak to off-peak periods.     

Price elasticities of residential electricity demand: Estimates from household panel data in Bangladesh

We collect a household level panel dataset to estimate the price elasticities of electricity demand for different types of urban households in Bangladesh. We use an instrumental variable estimation strategy which exploits exogenous variation in average electricity prices induced by a value-added-tax shock. The results indicate significant heterogeneity in price elasticities by electricity consumption levels. We conduct a number of simulations under alternative policy scenarios to illustrate how incorporating the heterogeneous nature of price elasticities into pricing policy can help decrease electricity demand-supply mismatch and inequality in electricity consumption. The results have important policy implications for developing countries aiming to address major energy issues by implementing tariff reforms.     

Peak-load management in steel plants

Mini steel-plants in India, using electric-arc furnaces for steel manufacturing, are highly energy intensive. In the context of increasing electricity prices and the introduction of time varying electricity rates by utilities, mini steel-plants can reschedule their operations to reduce their electricity bills. This paper presents a load model, which incorporates the characteristics of batch-type loads common to any type of process industry. The model is coupled with an optimisation formulation utilising integer programming for minimising the total electricity-cost satisfying production, process flow and storage constraints for different tariff structures. The methodology proposed can be used for determining the optimal response for any industry under time varying tariffs. The case study of a steel plant shows that significant reductions in peak-period demand (about 50%) and electricity cost (about 5.7%) are possible with optimal-load schedules. The utility can also get significant reduction in the peak coincident demand if large industries optimally reschedule their productions in response to time-of-use (TOU) tariff.     

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.     

Reducing energy consumption by using self-organizing maps to create more personalized electricity use information

Identification of electricity use is one of the key elements to motivate customers to promote activities leading more efficient use of energy. Furthermore, electricity use comparisons with other similar customers give more interesting and concrete point of view to examine own consumption habits. In future, electricity providers and retailers are willing and probably forced by legislation to provide such information by the means of energy conservation and efficiency improvement. On the other hand, high number of customers set challenges to handle electricity use data and to create proper comparison information. In this study we present efficient and highly automated way to create comparison groups based on customers building characteristics. The main advantages of the data-based approach are that customer location is noticed, comparison groups are created using concrete building information, data processing is highly automated and also method is computationally efficient. Additionally, presented method provide tool to target and to create customer specific electricity saving guidance. The performance of suggested approach was tested using data set which contained electricity use and building information concerning almost 8000 customers.     

Variability of electricity load patterns and its effect on demand response: A critical peak pricing experiment on Korean commercial and industrial customers

To the extent that demand response represents an intentional electricity usage adjustment to price changes or incentive payments, consumers who exhibit more-variable load patterns on normal days may be capable of altering their loads more significantly in response to dynamic pricing plans. This study investigates the variation in the pre-enrollment load patterns of Korean commercial and industrial electricity customers and their impact on event-day loads during a critical peak pricing experiment in the winter of 2013. Contrary to conventional approaches to profiling electricity loads, this study proposes a new clustering technique based on variability indices that collectively represent the potential demand–response resource that these customers would supply. Our analysis reveals that variability in pre-enrollment load patterns does indeed have great predictive power for estimating their impact on demand–response loads. Customers in relatively low-variability clusters provided limited or no response, whereas customers in relatively high-variability clusters consistently presented large load impacts, accounting for most of the program-level peak reductions. This study suggests that dynamic pricing programs themselves may not offer adequate motivation for meaningful adjustments in load patterns, particularly for customers in low-variability clusters.     

The cost impacts of a mandatory move to time-of-use pricing on residential customers: an Ontario (Canada) case-study

Using 2008 hourly electricity data from 1,020 households in Milton, Ontario (Canada), this article asks and answers two questions: ‘How do residential customers’ total costs change as the result of a mandatory move from a traditional, flat-rate pricing structure, to a time-of-use one?’; and ‘Are particular “kinds” of customers either “winners” or “losers” as a result of this move?’ In response to the first question, 45% of customers have lower bills under a time-of-use regime (as compared with what they would have paid, had the previous two-tier regime continued, with their ‘new’ consumption patterns), while 55% of customers have higher bills. For 98.2% of customers, the difference in total cost is less than 5% (either way), and the average relative change is a 0.233% increase. In response to the second question, customers that have a relatively high level of consumption in either peak periods or wintertime are, in the absence of other differences, more likely to have higher bills under a time-of-use regime. Those households that consume higher quantities of electricity are more likely to have lower bills under a time-of-use regime, as compared with the two-tier regime. The article concludes by highlighting the equity implications of this finding and by identifying areas for future research.     

带追溯功能的窃电侦查多维离群点分析方法及应用

针对现有方法对历史月已开始的潜在持续性窃电的搜索能力较差的问题,提出一种带追溯功能的窃电侦查多维离群点分析方法。首先从集抄数据中提取出月用电量水平、日温度相关性、月温度相关性、日用电量波动率、环比增长率、谷电比重6项指标,采用多维离群点分析方法,找出当月开始窃电的疑似用户;随后,对其余用户进行历史月的多维离群点分析,找出存在历史疑似窃电起始月的用户,从最近的疑似窃电起始月至当前月,运用累计和值法对这些用户实施日用电量序列的向上水平迁移分析,将不存在向上水平迁移的情况判定为潜在的持续性窃电。对某小区的实例分析结果表明,所提方法可在不明显增加误判率的同时显著提升对窃电可疑用户的搜索率。     

峰谷分时电价对用户满意度影响的系统动力学分析

峰谷分时电价作为电力需求侧管理的一种有效调峰手段,其实施会对用户的用电方式满意度和电费支出满意度产生一定的影响,从而最终影响用户对峰谷分时电价的综合满意度。从系统和运动的观点出发,分析了不同影响因素间的因果关系,基于系统动力学方法构建了峰谷分时电价对用户满意度影响的系统流程图,从峰谷电量变化、用户电费节约及用户满意度3个方面分析了峰谷分时电价对用户满意度的影响,并对不同峰谷电价比和响应敏感型用户进行了敏感性分析。结果表明,模型合理、有效。     

Causal responsibility and peak load pricing

This article clarifies the basis for peak load pricing when some customers have a steady demand and others have a specific peak period demand. Causal responsibility for peak capacity rests on both types of customers. Peles, in this journal, has proposed that electricity consumption beyond the off-peak period monthly average be priced at a higher rate than other consumption. Customers consuming only on the peak would face higher prices than those who consumed relatively less during the peak. However, such a price structure can discourage economic off-peak consumption and encourage uneconomic peak period consumption. Both steady and nonsteady demanders must face the same price signal during peak periods for the achievement of efficiency, so long as each group imposes the same costs on the system.     

The hourly income elasticity of electricity

Using a detailed data set on appliance-level electricity consumption at the hourly level, we provide the first estimates of hourly and end-use-specific income elasticities for electricity. Such estimates are informative about how consumption patterns in general, and peak demand in particular, will develop as households' income changes. We find that the income elasticities are highest during peak hours for kitchen and lighting, with point estimates of roughly 0.4, but insignificant for space heating.     

The impact of retail rate structures on the economics of commercial photovoltaic systems in California

This article examines the impact of retail electricity rate design on the economic value of grid-connected photovoltaic (PV) systems, focusing on commercial customers in California. Using 15-min interval building load and PV production data from a sample of 24 actual commercial PV installations, we compare the value of the bill savings across 20 commercial-customer retail electricity rates currently offered in the state. Across all combinations of customers and rates, we find that the annual bill savings from PV, per kWh generated, ranges from $0.05 to $0.24/kWh. This sizable range in rate-reduction value reflects differences in rate structures, revenue requirements, the size of the PV system relative to building load, and customer load shape. The most significant rate design issue for the value of commercial PV is found to be the percentage of total utility bills recovered through demand charges, though a variety of other factors are also found to be of importance. The value of net metering is found to be substantial, but only when energy from commercial PV systems represents a sizable portion of annual customer load. Though the analysis presented here is specific to California, our general results demonstrate the fundamental importance of retail rate design for the customer-economics of grid-connected, customer-sited PV.     

Smart Integration

Electric utilities in the United States and globally are heavily investing to upgrade their antiquated delivery, pricing, and service networks including investments in the following areas: -- smart grid, which generally includes improvements upward of the meters all the way to the transmission network and beyond -- smart metering, sometimes called advanced metering infrastructure (AMI), which usually includes control and monitoring of devices and appliances inside customer premises -- smart pricing including real-time pricing (RTP) or, more broadly, time-variable pricing, sometimes including differentiated pricing -- smart devices and in-home energy management systems such as programmable controllable thermostats (PCTs) capable of making intelligent decisions based on smart prices -- peak load curtailment, demand-side management (DSM), and demand response (DR) -- distributed generation, which allows customers to be net buyers or sellers of electricity at different times and with different tariffs, for example, plug-in hybrid electric vehicles (PHEVs), which can be charged under differentiated prices during off-peak hours. The main drivers of change include: -- insufficient central generation capacity planned to meet the growing demand coupled with the increasing costs of traditional supply-side options -- rising price of primary fuels including oil, natural gas, and coal -- increased concerns about global climate change associated with conventional means of power generation -- demand for higher power quality in the digital age.     

基于广义Leontief函数的峰谷分时电价用户响应研究

基于广义Leontief函数建立了电价替代弹性模型,利用峰谷分时电价用户历史数据求解不同高峰时段电价替代弹性,并进一步分析了替代弹性和高峰时段时长与划分的关系。结果表明,高峰时段较短时替代弹性较大,说明用户对电价响应较好,随着高峰时段的延长,替代弹性下降比较明显,实例亦验证了替代弹性与高峰时段划分的相关性。     

基于短期均衡模型的动态电价经济效率研究

针对动态电价是实现电力市场资源优化配置的重要途径问题,基于微观经济学理论建立了动态电价的短期均衡模型,通过实际算例分析了峰荷、用电总量、用户用电成本、消费者剩余、生产者剩余及社会剩余对动态电价的经济有效性。结果表明,动态电价能有效反映供电成本,引导用户有序用电,减少用电成本,具有良好的经济效率。