Time Of Use Tariff Research Articles

Is demand side response a woman's work? Domestic labour and electricity shifting in low income homes in the United Kingdom

This paper discusses a utility-led research project which piloted smart meters and DSR products (a time of use tariff and a critical peak rebate scheme) with 500 low income households in London. As households set about the task of adjusting their electricity use in response to shifting prompts, they revealed the importance of managing domestic labour to generate value from DSR products and the role of women in carrying this out. The experience is at odds with the smart future more typically imagined in which chore-doing is handed over to feminized AI assistants who orchestrate IoT appliances to create comfort and capture value. Strengers has cautioned against constructing a smart future to serve `Resource Man'. Drawing on trial participants' experiences, the paper develops the concept of `Flexibility Woman' in order to bring the realities of domestic labour more sharply into focus. The paper argues that chore-doing needs to become a narrative in the smart future to understand the burdens and opportunities for `Flexibility Woman' to create value from her labour. It suggests that women unable to afford a surrogate AI wife may find themselves becoming `Flexibility Woman' or else excluded from accessing the cheaper, greener electricity of the future. It also suggests that ignoring gender risks undermining the impacts that policy makers and network operators hope to achieve through DSR.The paper makes a unique contribution to our understanding of how DSR relates to gender roles and what the implications are for the effectiveness and inclusivity of flexibility products.

Integration of Optimal Time-of-Use Pricing in Stochastic Programming for Energy and Reserve Management in Smart Micro-grids

In this paper, the optimal operation problem of smart micro-grids integrated with the pricing of Time-of-Use (TOU) demand response (DR) program is modeled as a two-stage stochastic programming problem with the aim of minimizing the cost of MG operation and running TOU in the presence of renewable resources and incentive-based DR programs. Here, TOU as the most common type of time-based DR programs is implemented using a linear function based on the concept of self- and cross-price elasticity of load demand. In the presented model, the forecasting errors of generation of renewable resources are modeled by probability density functions. The operator of MG decides on two stages for optimal management of its network; the first stage refers to the operation of base condition of MG and the second one is pertaining to after the realization of different scenarios for generation of renewable resources. The base condition of MG refers to the situation in which the active power productions of renewables are equal to the predicted values. The proposed model is solved by Particle Swarm Optimization algorithm. A typical MG is employed to investigate and analyze the different features of the method. By varying the demand response potential of MG consumers, TOU tariffs are determined, and their impact on the results of energy and reserve cost as well as voltage and load profiles of MG are analyzed. Numerical results show the efficiency of DR in reducing costs as well as covering the uncertainty resulting from renewables.

Consumers’ Willingness to Accept Time-of-Use Tariffs for Shifting Electricity Demand

Time-of-use (TOU) electricity tariffs represent an instrument for demand side management. By reducing energy demand during peak times, less investments in otherwise necessary, costly, and CO2 intensive redispatch would be required. We use a choice experiment (CE) to analyze private consumers’ acceptance of TOU tariffs in Germany. In our CE, respondents choose between a fixed rate tariff and two TOU tariffs that differ by peak time scheme and by a control of appliances’ electricity consumption during that time. We use a mixed logit model to account for taste heterogeneity. Moreover, investigating decision strategies, we identify three different strategies that shed light on drivers of unobserved taste heterogeneity: (1) Always choosing the status quo, (2) always choosing the maximum discount, and (3) choosing a TOU tariff but not always going for the maximum discount. Overall, about 70% of our 1398 respondents would choose a TOU tariff and shift their electricity demand, leading to a decline in energy demand during peak times. Rough estimates indicate that this would lead to significant savings in electricity generation, avoiding up to a mid to large-sized fossil-fuel power plant.

Development of Operational Strategies of Energy Storage System Using Classification of Customer Load Profiles under Time-of-Use Tariffs in South Korea

This study proposes a methodology to develop adaptive operational strategies of customer-installed Energy Storage Systems (ESS) based on the classification of customer load profiles. In addition, this study proposes a methodology to characterize and classify customer load profiles based on newly proposed Time-of-Use (TOU) indices. The TOU indices effectively distribute daily customer load profiles on multi-dimensional domains, indicating customer energy consumption patterns under the TOU tariff. The K-means and Self-Organizing Map (SOM) sophisticated clustering methods were applied for classification. Furthermore, this study demonstrates peak shaving and arbitrage operations of ESS with current supporting polices in South Korea. Actual load profiles accumulated from customers under the TOU rate were used to validate the proposed methodologies. The simulation results show that the TOU index-based clustering effectively classifies load patterns into ‘M-shaped’ and ‘square wave-shaped’ load patterns. In addition, the feasibility analysis results suggest different ESS operational strategies for different load patterns: the ‘M-shaped’ pattern fixes a 2-cycle operation per day due to battery life, while the ‘square wave-shaped’ pattern maximizes its operational cycle (a 3-cycle operation during the winter) for the highest profits.

Economic feasibility of battery energy storage systems for replacing peak power plants for commercial consumers under energy time of use tariffs

Abstract This work assesses the economic feasibility of replacing conventional peak power plants, such as Diesel Generator Sets (DGS), by using distributed battery energy storage systems (BESS), to implement Energy Time Shift during peak hours for commercial consumers, whose energy prices vary as a function of energy time of use (ToU tariffs). The economic analysis is performed by calculating the break-even point (BEP) for four different battery technologies: (i) lead-acid (OPzS); (ii) nickel-cadmium (NiCd); (iii) lithium-ion (Li-NCA); and (iv) Redox Flow (FeCr), using the HOMER Energy software in the simulations. One case study is analyzed for a commercial consumer connected to the MV network (Campinas/Brazil). The results show that, considering the updated 2018 BESS costs, none of the types of battery analyzed is economically attractive as a replacement for DGS. However, it can be observed that a 31%, 38% and 26% reduction in the costs of OPzS, Li-NCA and FeCr batteries, respectively, makes the installation of BESS viable. It is estimated that, in a 5- to 6-year horizon, these technologies would become economically attractive as a result of the strong decrease of the expected costs for the years to come.

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.

Why Have Voluntary Time‐of‐Use Tariffs Fallen Short in the Residential Sector?

We investigate the causes behind the underwhelming adoption of voluntary Time‐of‐Use (TOU) tariffs in the residential electricity market. TOU tariffs are deployed by utilities to better match electricity generation capacity with market demand by giving consumers price incentives to reduce their consumption when electricity demand is at its peak. However, consumers in residential electricity markets are heterogeneous in their consumption preferences. Hence, utilities face a trade‐off when deploying voluntary TOU tariffs—to provide aggressive price incentives that will only appeal to consumers with flatter profiles or milder incentives to appeal to a larger proportion of the market. Using a game‐theoretic model, we identify the key factors that determine the viability of voluntary TOU tariff deployment. On the supply side, the gap between wholesale prices in the peak and off‐peak periods determines how much the utility stands to benefit by inducing demand response. On the demand side, heterogeneity within target consumer sets determines how much demand response the utility can induce with a certain price incentive. We show that misaligned incentives between utilities and regulators lead to underwhelming TOU tariff adoption compared to the socially desirable level, and that this under‐adoption is worse when consumption preferences are uniformly distributed. We also evaluate the degree of cross‐subsidization across tariff structures to identify their implications for equity among the different consumer types, and find that low levels of voluntary TOU adoption are less equitable than the default tariffs.

Internet of things based metaheuristic reliability centered maintenance of distribution transformers

The transformer is a vital component of the power system. Continuous stress on the transformer due to overload, transient and faults will lead to physical damages. The isolation of the transformer causes significant revenue loss and inconvenience to the consumers at the distribution level. This invites the need to achieve a reliable power supply to the consumers and to perform maintenance activity appropriately. Optimized and predictive maintenance strategies are evolved to improve power availability for consumers. The model considers dispersive generation at the customer end, namely solar photovoltaics standalone system, diesel generation, and vehicle to load capabilities. Incipient or critical status of transformers’ functional parameters are observed through the transformer terminal unit and sent to the internet of things platform. The remote processing unit acquires the information from all the distribution transformer and generates the optimized and reliability-centered maintenance schedule. In the proposed work, new reliability indices concerning the consumer dispersive generation are defined. The maximization of the reliability problem is solved using the coconut tree optimization technique. The highest reliability of power supply to the consumer and maintenance schedule are obtained. Economic facet of the estimated maintenance schedule exhibit benefit for both utility and consumer as it encapsulate time of use tariff. The heuristic dataset is used to synthesize the trained model by the machine learning algorithm and future maintenance schedule is predicted. The comparative study is made for the outcome of time-based optimized and predicted maintenance schedules against reliability.

Householders’ readiness for demand-side response: A qualitative study of how domestic tasks might be shifted in time

Domestic demand-side response (DSR) involves having householders shift energy-consumptive behaviours forwards or backwards in time to avoid peak-demand periods. Time of Use tariffs, which vary the cost of energy depending on when it is used, are a proposed mechanism for DSR. However, we do not know which behaviours, if any, are amenable to being brought forward or postponed, nor what information people would need to do this. Here we presented people with hypothetical variable financial costs and, for the first time, carbon costs for future energy consumption. People's think-aloud decisions about when they would perform consumptive activities were qualitatively analysed. We show that non-daily high-consumption activities (e.g., ironing, vacuuming) are perceived as moveable in response to price changes or, notably, information about environmental consequences; but barriers to moving other activities, even in the face of higher costs or environmental harm, include household dynamics (difficulties negotiating consumption across cohabitants), social roles, community living (not wishing to disturb neighbours), lack of energy literacy and the routine nature of many tasks. This study provides a framework for understanding which behaviours might or might not be shifted and what guidance and information may be needed by householders if tariffs are to provide effective DSR.

Predicting the demand flexibility of wet appliances at national level: The case of France

The manual shift of residential wet appliances (dishwashers and washing machines) in French households that have subscribed to a Time of Use (ToU) tariff represents a flexibility potential available already today and at zero-cost. In this paper we quantify, for the first time, this potential by building a national representative synthetic population for France and using a novel bottom-up model. This includes a stochastic occupant behaviour model for predicting the use of the wet appliances and an overlying agent-based model for predicting their manual shifting under a ToU tariff system. The two models are calibrated and validated using empirical data collected over two recent monitoring campaigns, each carried out in more than 100 French households. By using the new model we observe that a 100% penetration rate of the ToU tariff together with an improved (but practically feasible) households’ flexibility could lead, between 19:00 and 20:00, to a mean hourly load reduction of 0.48 GW, which represents around 0.5% of the typical French annual peak load. This flexibility potential, which is today only partially exploited, could be achieved with zero additional costs for new infrastructures, by encouraging eco practices and/or making the ToU tariff more economically attractive for the households.

Multi-objective energy storage capacity optimisation considering Microgrid generation uncertainties

High penetration of renewable energy sources (RES) in distributed Microgrid (MG) systems provides significant economic and environmental benefits. Nevertheless, the intermittent nature of RESs is the greatest challenge in the efficient utilisation of these resources. Using Battery Energy Storage Systems (BESS) in MGs is proposed as an effective solution to mitigate the fluctuations of RESs power generation. However, BESSs are expensive components with a short lifetime and therefore, they should be optimally sized according to the characteristics and requirements of MGs. This paper proposes a BESS sizing approach to determine the capacity and power rating of the BESS in a Grid-Connected MG, taking economic and technical criteria as well as the uncertainty of dispatchable generators (DG) and RESs into consideration. The technical requirements include the degree of reliability, BESS degradation, and operating constraints of the DGs. On the other hand, the cost factors associated with the utility grid including peak cost, valley cost, and Time of Use tariff (TOU) are taken into account. In this paper, a multi-objective mixed-integer quadratic model is developed to include the aforementioned parameters in the BESS capacity optimisation algorithm. Inspecting the impact of the considered parameters on the optimal size of the BESS and total cost of the MG, four case scenarios are studied. The first two scenarios evaluate the effect of the BESS depreciation, whereas the third and fourth scenarios investigate the impact of grid volatility (GV) and reliability indices on the optimal solution. The obtained results from the case studies verify the effectiveness of the proposed BESS sizing method.

Demand Response Model Development for Smart Households Using Time of Use Tariffs and Optimal Control—The Isle of Wight Energy Autonomous Community Case Study

Residential variable energy price schemes can be made more effective with the use of a demand response (DR) strategy along with smart appliances. Using DR, the electricity bill of participating customers/households can be minimised, while pursuing other aims such as demand-shifting and maximising consumption of locally generated renewable-electricity. In this article, a two-stage optimization method is used to implement a price-based implicit DR scheme. The model considers a range of novel smart devices/technologies/schemes, connected to smart-meters and a local DR-Controller. A case study with various decarbonisation scenarios is used to analyse the effects of deploying the proposed DR-scheme in households located in the west area of the Isle of Wight (Southern United Kingdom). There are approximately 15,000 households, of which 3000 are not connected to the gas-network. Using a distribution network model along with a load flow software-tool, the secondary voltages and apparent-power through transformers at the relevant substations are computed. The results show that in summer, participating households could export up to 6.4 MW of power, which is 10% of installed large-scale photovoltaics (PV) capacity on the island. Average carbon dioxide equivalent (CO2e) reductions of 7.1 ktons/annum and a reduction in combined energy/transport fuel-bills of 60%/annum could be achieved by participating households.

Industrial Energy Load Profile Forecasting under Enhanced Time of Use Tariff (ETOU) using Artificial Neural Network

The demand response program involves consumers to mitigate peak demand and reducing global CO2 emission. In sustaining this effort, energy provider such as Tenaga Nasional Berhad (TNB) in Peninsular Malaysia has introduced Enhance Time of Use (ETOU) tariff. However, since 2015, small numbers join the ETOU program due to less confidence in managing their energy consumption profile. Thus, this study provides an optimum forecasting load profile model for TOU and ETOU tariffs using Artificial Neural Network (ANN). An industry's average energy profile has been used as a case study, while the forecasting technique has been conducted to find the optimum energy load profile congruently. The load shifting technique has been adopted under ETOU tariff price while integrating to the ANN procedure. A significant comparison in terms of cost reduction between TOU and ETOU electricity tariffs has been made. In contrast, ANN performance results in searching for the best-shifted load profile have been analyzed accordingly. From the proposed method, the total electricity cost saving has been founded to be saved for about 7.9% monthly. It is hoped that this work will benefit the energy authority and consumers in future action, respectively.

Activating current and future flexibility potential in the distribution grid through local energy markets

Increasing shares of volatile generation and additional peak demand through electric vehicles and heat–pumps challenge the stable operation of current and especially future distribution grids. In order to cope with these challenges, several approaches to reduce peak feed-in and demand are possible. For example, the curtailment of renewable generation or peak loads or the activation of demand-side flexibility through, e.g., time of use tariffs. Local energy markets (LEMs) are a promising alternative concept to combine various of these measures and activate untapped flexibility potential of the demand- and generation-side as well as storages. In this study, the authors propose a linear optimisation-based market mechanism that can explicitly consider demand, generation and storage flexibility in the form of flexibility orders. Through the simulation of a LEM in a rural region of Germany with a high share of renewable feed-in, they demonstrate the effects of the proposed LEM for the current situation and a future scenario with additional load through electric vehicles and heat pumps. The results show that, compared to a business as usual case, the LEM can provide both peak-shaving of PV feed-in during midday and a significant reduction of evening demand peaks of up to 23%.

Setting the Time-of-Use Tariff Rates With NoSQL and Machine Learning to a Sustainable Environment

The electricity consumption will continue to increase despite the overall efforts and tendencies of changing the old appliances to less energy intensive ones. The advancements of Electric Vehicles (EV) and public mobility, electric heating, and the abundance of smart appliances that enhance the comfort of modern life lead to an increasing consumption trend. On the other hand, prosumers raising the quota of distributed generation and storage capacity will balance the electricity consumption trend. These changes at the consumption and generation level lead to the necessity to increase the awareness and incentive the consumers' behavior to flatten the consumption curve and improve the savings. Such objectives could be reached by properly setting the Time-of-Use (ToU) tariff rates to encourage the consumption at off-peak hours when the rates are lower and unstress the grid loading. In this paper, we propose a methodology for setting the Time-of-Use (ToU) tariff rates and peak/off-peak intervals using big data technologies and machine learning, and verify the assumptions considering the large volume of consumption data of over 4200 residential consumers recorded in a smart metering implementation trail period that took place in Ireland from January to December 2010. We calculate the contribution to the peak/off-peak of the total consumption and use it in setting the ToU tariff rates starting from the flat tariff. Then, the consumers' sensitivity to tariff change from flat to ToU is considered to identify the consumption change. The results show that using ToU instead of flat tariff, the peak is reduced in average by 5 to 7.5% and annual savings are around 4%. Also, by clustering the consumers a better allocation of the tariffs is possible. Thus, clustering is proposed considering the importance of the tariff allocation in Demand Side Management (DSM).

Holistic analysis of the effect on electricity cost in South Africa’s platinum mines when varying shift schedules according to time-of-use tariffs

In the past the cost of electricity was not a significant concern and was not common practice for mining companies to consider peak time-of-use (TOU) tariffs for their shift schedules. It has become more prevalent, as TOU tariffs continue increasing, to consider energy saving important. A study was carried out to analyse the mining operation of a South African deep-level platinum mine in respect of integrated load management, shift changes and TOU schedules. This was achieved by thoroughly analysing energy consumers, mine operational schedules and their interconnectedness. A specific mining system was analysed as a case study and a maximum savings scenario was determined, using the methodology formulated. The maximum savings scenario schedule change resulted in a 1.3% cost reduction. System improvements had an additional potential reduction effect of 8.4%, which was primarily the result of a reduction in compressors’ power consumption. The implications of the proposed schedule adjustments necessitated a realistic scenario. The realistic scenario had an effective financial reduction of 0.7%. The realistic schedule change, however, opened the door for large system operational improvements, which could increase the reduction potential by 7.6%. The study methods described illustrate the potential implications of integrated load management and operational schedule optimisation on the power demand and cost savings in the mining industry, specifically focusing on deep-level platinum mines.

Markov Model-Based Energy Storage System Planning in Power Systems

Battery energy storage systems (BESSs) can regulate the supply of energy from an electric utility, and improve the reliability and stability of the power system, which has high penetration of renewable power generation. This paper determines the optimal sizes (MWh) and capacities (MW) of BESSs in a distribution system that is composed of photovoltaic (PV) arrays. The objective is to minimize the investment cost, replacement cost, operation/maintenance cost, and the net present values of both the energy loss cost and to maximize the annual arbitrage benefit (AAB). The energy loss cost and AAB incorporate time-of-use (TOU) tariffs. The objective is subject to equality constraints (power flow equations and chronological ESS balance constraints) and inequality constraints (voltage, line flow, and BESS operational limits). Because the studied problem involves many constraints and variables in the chronological domain, traditional optimization methods cannot be applied directly to this large-scale problem. Assume that the chronological PV powers and loads are stochastic process, where the description of present state fully contains the information for the future evolution. Thus, Markov models are implemented by considering probability/duration/frequency of PV powers and loads to reduce the numbers of constraints and variables. The Markov states are attained using a clustering algorithm that is based on fuzzy-c-means. Simulation results obtained from a 33-bus system consisting of a 1 MW PV farm and six BESSs reveal the applicability of the proposed method. Simulation results indicate that the TOU tariff markedly affects the allocation of BESSs and the AAB benefit is generally smaller than the costs of the BESSs and energy losses.

Reduced grid operating costs and renewable energy curtailment with electric vehicle charge management

Widespread adoption of plug-in electric vehicles (PEVs) and renewable energy (RE) can help to jointly decarbonize the transportation and electricity sectors. Previous studies indicate strategies to manage PEV charging facilitate integration of RE into electricity grids, but the value of such strategies at scale is unclear because electricity markets and PEV charging have been inadequately represented together. This analysis focuses on the state of California in 2025, and improves on prior work by linking high-resolution mobility and grid dispatch models to quantify the value of managed charging under a 50% RE grid and PEV adoption scenarios up to California's 5 million vehicle target. Even after accounting for practical charging and grid constraints, 0.95 to 5 million “smart” charging PEVs avoid $120 to $690 million in California grid operating costs annually (up to 10% of total costs) and reduce RE curtailment up to 40% relative to unmanaged PEVs. Overnight time-of-use (TOU) charging provides similar cost savings but increases curtailment. Both of these managed strategies defer system infrastructure expansion at the 5 million PEV deployment. The results suggest residential smart charging complemented by TOU tariffs with added daytime periods are policies with most potential to advance California's dual PEV and RE goals.

Smart residential electricity distribution system (SREDS) for demand response under smart grid environment

The present research work attempts to develop a Smart Residential Electricity Distribution System (SREDS) for enabling Smart Residences (SR) to participate in Demand Response (DR) schemes of Power Distribution Company (PDC) with either Flat Rate tariff, Time of Use tariff or Real Time Pricing. SREDS includes a Smart Energy Meter (SEM) enabled with Internet of Things, an Intelligent Residential Load Management System (IRLMS) with Wi-Fi connectivity, distributed Wi-Fi enabled Smart Load Nodes (SLN) and Smart Battery Charing/Discharging Controller (SBCDC). The SEM receives the key parameters such as electricity pricing, maximum demand limit, etc. as per the DR schemes from the PDC through Meter Data Management System (MDMS) using Internet. IRLMS receives the required operational timings of the loads as per the desire and comfort of the user either through the user interface or from the SLN connected to the appliances participating in DR. If the SR is equipped with battery storage system, then the battery units are interfaced with the IRLMS through SBCDC. The present status of the battery is communicated to IRLMS by SBCDC. The communication between SEM, IRLMS, SLNs and SBCDC is established through Home Area Network using Wi-Fi. The IRLMS processes the PDC parameters and load requirements, and executes an optimization algorithm to find the best possible scheduling of appliances in order to get minimized electricity bill. The SEM communicates back the energy consumption data to the PDC through MDMS.

Electrical load tracking scheduling of steel plants under time-of-use tariffs

Electrical load tracking is a critical strategy for energy intensive industries to provide demand response (DR) in today’s electricity markets. It can take advantage of time-of-use (TOU) tariffs in steelmaking-refining-continuous casting (SRCC) scheduling. In this paper, we develop a continuous-time mixed integer nonlinear programming (MINLP) model subject to energy-awareness and TOU tariffs to manage the electrical load tracking scheduling of SRCC. Due to the complex cases among load intervals of electrical load tracking, time-slots of TOU tariffs and processing cycles of jobs result from different time granularities of the electrical load tracking and TOU tariffs, we formulate the objective functions with derived general formulations that can apply to all cases. An improved strength Pareto evolutionary algorithm 2, AHSPEA2, is developed to solve this proposed model, whose search ability and population diversity are enhanced greatly by two strategies, the arithmetic crossover operator and the improved hybrid self-adaptive mutation operators. The computational results demonstrate that AHSPEA2 is far superior and prove its effectiveness in providing high-quality scheduling plans which follow the pre-contracted load curve carefully to decrease deviations and reduce electricity costs simultaneously.