Interactive Time-of-use demand response for industrial electricity customers: A case study

Demand response (DR) programs are regarded as one of the most reliable and reasonable methods to benefit electricity suppliers and consumers. This paper presents a modified approach to DR based on an Interactive Time-of-Use (ITOU) model by which volunteer industrial customers and their electricity suppliers obtain the best possible performance. Owing to this aim, when studying the region's electrical load profile to determine the peak-hours, the one-year production and sales profile of industrial customers is also studied to select off-peak hours for industrial subscribers. The results of program implementation for the selected and volunteer industrial customers at the sub-transmission level are presented to evaluate the performance of the proposed model. Considering the electricity bills of the subscribers in the program based on reduced energy consumption at peak hours (selected by the utility), and increased energy consumption at the off-peak price (selected by industrial customers), the economic benefits to industrial customers are calculated and verified. Plotting new load curves confirms load shifting from the peak to the valley of the load curve. The obtained results of the conventional TOU and ITOU models indicate that the proposed ITOU is more effective in achieving program goals.     

Domestic electricity use: A high-resolution energy demand model

The pattern of electricity use in an individual domestic dwelling is highly dependent upon the activities of the occupants and their associated use of electrical appliances. This paper presents a high-resolution model of domestic electricity use that is based upon a combination of patterns of active occupancy (i.e. when people are at home and awake), and daily activity profiles that characterise how people spend their time performing certain activities. One-min resolution synthetic electricity demand data is created through the simulation of appliance use; the model covers all major appliances commonly found in the domestic environment. In order to validate the model, electricity demand was recorded over the period of a year within 22 dwellings in the East Midlands, UK. A thorough quantitative comparison is made between the synthetic and measured data sets, showing them to have similar statistical characteristics. A freely downloadable example of the model is made available and may be configured to the particular requirements of users or incorporated into other models.     

Time-based pricing and electricity demand response: Existing barriers and next steps

Interest in Demand Response (DR) is increasing due to its potential to improve reliability and save costs for electricity systems. DR can provide a sustainable and cost-effective option for supply balancing, especially in a scenario with more volatile inflows from renewable energy sources. End-users can be incentivized to provide DR through time-based pricing in general and dynamic pricing in particular. This paper provides a theoretic framework and practice-oriented review of the status of DR in Europe, outlining the major challenges currently hampering further DR development. Important challenges involve the split-incentive issue for investments in enabling technologies, traditional market rules for flexibility that favor large generation units and the need for electricity market and network operation coordination.     

Heterogeneity in own-price residential customer demand elasticities for electricity under time-of-use rates: Evidence from a randomized-control trial in the United States

Regulators, policymakers, and stakeholders in several states have raised concerns about the price elasticity of residential customers’ electricity demand, especially as related to different customer subpopulations, management of critical end-uses, and enrollment approaches. This analysis sought to quantify the diversity of residential customer price elasticity in a time-of-use rate across these different dimensions using data generated from a utility pricing experiment. Customers were found to be more elastic during the peak period of critical peak days, who were predicted to own and use air conditioning, and who volunteered for time-of-use (TOU) rates (but those defaulted on were 25% as elastic).     

Temporal aggregation: Time use methodologies applied to residential electricity demand

Understanding what constitutes peaks and identifying areas of effective load shifting intervention becomes vital to the balancing of demand and supply of electricity. Whilst there is information about the aggregate level of consumption of electricity, little is known about residential peak demand and what levels of flexibility might be available. Specifically, methodologies linking people's activities and residential electricity load profiles are typically under-investigated. The overall aim of this paper is to introduce methodologies which capture the variation in sequences of activities taking place at times of peak electricity demand. The paper introduces a set of analytical tools which can be deployed when examining time use survey data in energy demand research. It presents the state of the art with modelling load profiles based on time use data and design methodological modifications to improve modelling around peak periods. It is demonstrated how the methodologies presented in the paper can be applied to specific understanding of distributional effects of Time of Use tariffs. The paper discusses issues associated with validation between synthetic data, survey data and electricity metered data and concludes with policy implications and some observations for future research.     

Power grid balancing of energy systems with high renewable energy penetration by demand response

It is generally accepted that the integration of intermittent energy resources like wind energy and photovoltaics into an electricity system cannot exceed a limit of around 20% or 25%, see, e.g. [EWEA, 2005. Large-scale integration of wind energy in the European power supply: analysis, issues and recommendations. The European Wind Energy Association]. However, the decoupling of electricity generation and consumption cannot be implemented only by use of electricity storage. In the end, electricity is converted into many different energy services – quite often into thermal energy – which is better suited for storage. This article presents the results of investigations which studied the potential of those demand response activities for Germany. The investigations are based on both modelling of thermal storage devices and laboratory tests.     

Quantifying distribution-system operators' economic incentives to promote residential demand response

Demand response (DR) from end-users is widely investigated as a power-system flexibility resource in a European smart-grid environment. Limited knowledge exists on the added value such flexibility can bring to actors in the electricity value chain. This work investigates the economic effect of consumption flexibility under current regulatory remuneration on distribution-system operators with a Swedish case study. Results indicate DR leads to savings for the distribution-system operator, which might be used towards smart-grid investments. Peak demand is and will continue to be a main driver for grid costs and therefore should be a focal point in tariff design.     

Asymmetric price response of industrial electricity demand in India

This paper analyses the asymmetric effects of electricity prices on industrial electricity demand in India from 1981 to 2016 using the two-threshold nonlinear autoregressive distributed lag (NARDL) model. The results show that customers react more strongly to a large price cut than a large price increase, whereas a small price change does not affect electricity consumption. A large price decrease, which raises the quantity demanded by a higher percentage, makes it more challenging to reach net-zero emissions as electricity generation relies mainly on fossil fuels. Since demand is inelastic for price increases, a large price hike minimally reduces electricity consumption.     

Climate change impacts on electricity demand: The case of a Southern European country

We study the relationship between average temperatures and electricity consumption in Portugal. Control variables include electricity prices and the economic activity. Our results indicate that there is U-shaped relationship between temperature and electricity consumption. While changes in average temperature do not affect electricity consumption by a large extent, extreme temperatures have stronger impacts. Given the mild weather in Portugal, electricity demand has not changed significantly over the last decades. Larger impacts can only be expected if extreme weather events become more frequent. Our results show the importance of considering country specificities in the analysis and of comparing several model specifications.     

Gas and electricity demand in Spanish manufacturing industries: An analysis using homogeneous and heterogeneous estimators

A comparative analysis of electricity and gas demand in the industrial sector over a long period of time appears to be absent in the literature. In fact, unlike electricity demand, natural gas demand in the industrial sector has not been well researched. Our paper aims to cover this gap. It analyses electricity and gas consumption patterns by the Spanish manufacturing sector, between 1995 and 2010. A novel and innovative quantitative approach based on, both, homogenous and heterogeneous estimators was used for this purpose. The results of the no-spurious estimations (the Augmented Mean Group Estimator) show that the price elasticity of gas demand is significantly negative and within the −0.44 to −0.48 range. In contrast, the price elasticity of electricity demand is not statistically significant. The income elasticities show the opposite pattern: those of natural gas are not statistically significant, whereas the income elasticities for electricity are statistically significant and within the 0.22 to 0.29 range. Compared to previous findings, our preferred estimation shows some variation regarding price elasticities of natural gas demand.     

Solving electric power distribution uncertainty using deep learning and incentive-based demand response

Recent Demand Response (DR) struggles with the end user's uncertainty in Electric Power Consumption (EPC), which affects the system's generation costs and stability. Incentive-based DR has offered to be an effective technique for mitigating supply and demand imbalances. However, it presents complex issues, such as electricity consumption uncertainty. This article proposes an incentive-based integrated DR model for Demand Side Management (DSM) program to handle the EPC uncertainty. In addition, the applicability of DR has been enhanced by the deep learning-based Bi-directional Long Short Term Memory (B-LSTM) model to forecast and curtail the load of the participated end users in the DSM program. Finally, results indicate that the proposed DSM program can achieve a win-win situation in reducing end-user uncertainty, lowering costs, and enhancing system stability.     

Demand response,market design and risk: A literature review

Different market designs may create distortions on spot pricing equilibrium, thus providing different signals for market agents and investment decisions on new generation assets. Also, demand flexibility market value may differ according to market characteristics. This paper aims to study the demand response (DR) – market design – risk nexus. According to our findings, despite the design differences among MIBEL, EEX and Nord Pool, some variables are common to all while those associated with risk premia might differ. This is due to the mismatch between the specific market structure characteristics and premises, particularly in what concerns the supply generation portfolio composition in the day-ahead markets.     

Quantitative effects of drivers and barriers on networking strategies in public construction projects

Past studies have found that network strategies can contribute to better company and project performance. The adoption of network strategies is motivated by a set of factors (i.e., drivers), but also faces numerous challenges (i.e., barriers). The appreciation of the factors motivating and deterring networking strategies is beneficial to the successful implementation of network strategies. In the context of public construction projects, this study aims to examine the quantitative effect of drivers and barriers on three network strategies (i.e., trust, information sharing and joint problem solving). The results of a questionnaire-survey of 104 public projects show that the adoption of network strategies in public construction is mainly cost-driven. The results indicate that four barriers impede network strategies in public projects: (i) a lack of continuity and (ii) ethos of public services are harmful to trust; (iii) institutional constraints hinder information sharing; and (iv) a lack of capability is a hurdle to information sharing and trust. It is also found that one barrier (i.e., ethos of public services) has a positive influence on trust between clients and consultants. Recommendations on enhancing network strategies through the appreciation of drivers and barriers are provided.     

Enabling independent flexibility service providers to participate in electricity markets: A legal analysis of the Belgium case

Ambitious renewables targets of the EU call for creating a flexible electricity system accommodating and facilitating the integration of large-scale intermittent renewable electricity. To further exploit the considerable potential of demand flexibility, final customers need to be fully mobilised to offer their flexibility to electricity markets. The independent flexibility service provider (IFSP) operates independently from the electricity supplier and has a separate balance responsible party (BRP), which plays a key role to that end. However, in activating demand flexibility, suppliers and BRPs are affected and thus likely to impose barriers or restrictions on market entry of IFSPs. This analysis utilizes legal-empirical research to closely examine how to promote broader market participation of IFSPs by regulating their relationships with affected suppliers and BRPs. In doing so, the Belgian regulatory framework is used as a case study. This article further analyses the remaining regulatory gaps and challenges of the Belgian framework.     

How should price-responsive electricity tariffs evolve? An analysis of the German net demand case

This research proves, using daily data, that net demand has a higher impact on the German wholesale market price than the traditional concept of electricity demand. The aim of this paper is to develop a broader framework for the design of new tariffs for German consumers, taking net demand as its primary reference. To accomplish this, a classification of net demand by level was developed. The findings of this research provide new insights about the occurrence of net valley and net peak periods, something which is useful for designing tariffs, affecting how the price should be differentiated during the day.     

Feasibility of zero carbon homes in England by 2016: A house builder's perspective

The UK government set itself a 60% reduction of carbon dioxide emissions target on 2000 levels by 2050. This commitment will require carbon reductions to be made by all industries including the housing sector which presently accounts for 27% of carbon dioxide emissions. The house building industry is the subject of numerous government policies and legislation, but none are as demanding as the Code of Sustainable Homes, which set a ‘world-beating’ target for all new homes to be zero carbon by 2016. This paper sets out to investigate the feasibility of building zero carbon homes in England by 2016 from a house builder's perspective. A comprehensive opinion of the feasibility of zero carbon homes is gathered through a questionnaire survey and in-depth semi-structured interviews with the major UK housing developers. The research found that there are currently numerous legislative, cultural, financial and technical barriers facing house builders to deliver zero carbon homes in England by 2016. The house builders surveyed concurred that these challenges are not insurmountable provided that a swift, all-embracing and above all realistic strategy is adopted and implemented across the supply chain.     

The response of large industrial energy consumers to four coincident peak (4CP) transmission charges in the Texas (ERCOT) market

Large industrial energy consumers served at transmission voltage in the ERCOT market reduce their consumption up to 4% during intervals in which consumers are charged for transmission services. The response normally lasts two to three hours, since consumers do not know exactly which interval will set one of the four summer coincident peaks (CPs), which are the basis for transmission charges. Thus, the design of transmission prices in ERCOT has been successful in eliciting demand response from that market's largest industrial energy consumers. However, there is no noticeable response during some CPs, reflecting the difficulties in predicting the actual timing of the peak. The response by industrials served at primary voltage to the price signals is insignificant.     

Evaluation of multi-family housing systems in Poland

On the basis of selected statistical data, the authors briefly discuss the housing construction situation in Poland, focusing on two periods — that preceding the transition to a market economy (up to the end of 1988) and the period 1989–1994). Comparative analysis of selected systems of multi-family housing, based on specific criteria, has been presented. The analysis comprises systems which have been used for many years and continue to be used. The entropy method, one of the multi-criteria optimization methods, has been used in the analysis.     

Study on the demand and driving factors of urban underground space use

For sustainable development of Urban Underground Space (UUS¹), determination of the desirable volume and function share of UUS is of vital importance. However, prediction of demand for UUS remains a rather challenging task. Very little quantifiable information, which is a basis for comparative study and improvement of prediction methods, is currently available pertaining to UUS. Xinjiekou, the center of Nanjing city, is called the first commercial center of China. Employing precision mapping on site and inquiring with a variety of organizations concerned, all the developed UUS in the area of Xinjiekou has been investigated in detail. On the basis of the quantitative investigation, the development modes and functional ratios are compared with other Chinese and foreign cases. By quantitative and comparative studies, the demand and driving factors of UUS have been identified. It is also found that the study area has the potential to be further developed and improved. It is hoped that this paper will enhance knowledge about the quantitative characteristics of UUS use, and be helpful for the demand prediction and planning of UUS development.     

Evaluating the performance of combining neural networks and genetic algorithms to forecast construction demand: the case of the Singapore residential sector

In recent years, forecasting demand for residential construction in Singapore has become more vital, since it is widely perceived that the next trough of the real estate cycle is approaching. This paper evaluates the use of a combination of neural networks (NNs) and genetic algorithms (GAs) to forecast residential construction demand in Singapore. Successful applications of NNs, especially in solving complex non-linear problems, have since stimulated interest in exploring the capabilities of other biological-based methods such as GAs, and in exploiting the synergy of these two techniques to create more problem-solving power. In the study, a basic NN model is used as a benchmark to gauge the performance of the combined NN-GA model. A relative measure of forecasting accuracy, known as the mean absolute percentage error (MAPE), is used for the comparison. The models are checked also for internal validity by allowing each to be trained twice and having a set of forecasts generated after each training. Both models are found to produce accurate forecasts, because their MAPE values consistently fall within the acceptable limit of 10%. However, the combined model out-performs the basis model remarkably by reducing the average MAPE from about 6% to a mere 1%. For each model, the marginal difference in the MAPE values (i.e., 0.5% for the NN model and 0.06% for the NN-GA model) of its two forecasts indicates consistency in performance, hence establishing internal validity as well. The findings reinforce the reliability of using NNs to model construction demand and reveal the benefit of combining NNs and GAs to produce more accurate models.