Time-of-use Rates Research Articles

Do time-of-use prices deliver energy savings at the right time?

Time-of-use (TOU) electricity prices are increasingly being adopted to reduce consumption during the higher marginal cost afternoon hours. There is ample evidence that TOU rates reduce average consumption during the peak price hours of the day, but it is unknown how these energy savings are distributed across days. Using a unique dataset from households with smart thermostats, we find that adopting TOU rates causes large decreases in peak period AC usage, resulting in energy savings that are concentrated on the hottest, highest demand days when the benefits of conservation are the greatest.

Development of real-time energy monitoring system using IoT base

This paper aims to develop a real-time energy monitoring system based on smart metering to enhance energy efficiency in the single-phase residential sector. Based on the concept of low-cost IoT devices, this intelligent meter system is designed to monitor household energy consumption. It provides real-time information on a graphical Node-RED Dashboard, making it easy for households to track and manage their energy usage. The hardware of the power metering system included the Node MCU ESP8266, the PZEM-004T, and a cloud server built on the Raspberry Pi for storing electricity consumption data by using Node-RED to connect devices via an application programming interface system. This system can help analyze the electricity consumption behavior in the residential sector. It is a guideline for selecting the electricity rate between the TOD and TOU rates. The results found that in the electrical energy measurements of households 1 and 2, the mean deviations were 0.8758% and 0.5523%, respectively. The electricity cost-saving results when changing the electricity tariff from the TOD rate of households 1 and 2 to the TOU rate. It was found that electricity costs can be saved by 0.2807% and 1.0936%, respectively. The most critical variable is electricity consumption during peak periods. In the case study of household 1, if the electricity consumption during the peak period is less than 13%, it will be appropriate to select the Time of Use rate. In household 2, if there is electricity consumption during the peak period, less than 38% would be appropriate to choose the type of electricity user with a TOU rate.

Time-varying rates prompt different responses as a function of home energy efficiency

Spiking global energy prices make it all the more important to understand the distributional effects of commonly used energy transition policies such as financially-based demand management. We examine how building energy efficiency impacts the effect of time-of-use rates; peak prices could differently affect curtailment behaviour and bills in homes that require more continual energy use for comfort. Using a sample of 3,145 households in the Australian Capital Territory with panel data for up to five years, we use two-way fixed effects to estimate the effect of switching to a time-of-use (TOU) rate on household electricity use and bills, allowing for heterogeneous effects as a function of building Energy Efficiency Rating (EER). Analyses indicate that low-EER households dependent on electricity for heating curtail use when on TOU rates, while high-EER households do not. This does not appear to financially disadvantage low-EER households, but may impact comfort. Our findings suggest that the household responses to TOU rates can differ as a function of building energy efficiency for those households that are reliant on electricity for heating. This has implications for TOU rate design and demand management policies, particularly in the context of policies for electrification.

Electricity Retail Rate Design in a Decarbonizing Economy: An Analysis of Time-of-use and Critical Peak Pricing

Currently, the main component of most U.S. consumers’ electricity bills is based on a constant price per kWh consumed. As intermittent renewable resources and flexible loads that can be shifted within days (such as electric vehicle charging) gain prominence in the electricity system, the efficiency gains to be realized from basing bills instead on wholesale spot prices increase. There is little political support for this change, however. We focus on second-best alternatives: time-of-use (TOU) rates and critical peak pricing (CPP). We introduce alternative assessment criteria that focus on intra-day load shifting. Using historical data, we find that TOU rates can reasonably replicate the intra-day load-shifting incentives provided under spot pricing. Thus, TOU rates, especially when complemented with CPP involving load control during infrequent scarcity price events, can be considerably more socially valuable than previously estimated.

Analysis of Residential Electricity Usage Characteristics and the Effects of Shifting Home Appliance Usage Time under a Time-of-Use Rate Plan

Carbon reduction programs are being introduced for carbon neutrality and energy transition to clean energy sources in various sectors, such as energy, buildings, transportation, and agriculture. In the residential electricity energy of the energy sector, the time-of-use (TOU) rate plan, which employs dynamic rates depending on energy usage times based on the advanced metering infrastructure (AMI), is being implemented for efficient electricity energy consumption. For broad expansion of the TOU rate plan, customers need information about its benefits, such as potential savings on electricity bills. In this paper, we first analyze the statistical characteristics of electricity energy usage using the metering data collected from 10 apartment complexes through AMI and develop a model to calculate the electricity bill savings. We next introduce examples of major home appliances, of which usage times can be shifted, and offer projected bill savings from the developed model. We analyze the examples from both the perspectives of households and apartment complexes. The information from this analysis is helpful in practically investigating customers’ willingness to shift the usage time for a successful implementation of the demand response program.

Annual Effect of the VRF Control Algorithm in Response to the TOU Rate Plan

Many countries adopt a time-of-use (TOU) rate system, in which electricity rates vary by season and time of day, to reduce power usage during peak power consumption hours. South Korea offers a TOU rate plan that depends on the electricity usage of a building and its contracted power; in this plan, the electricity rate reaches up to 300% depending on the time of day. Hence, electrically powered variable refrigerant flow (VRF) systems are increasingly being installed in small- and medium-sized buildings requiring individual cooling and heating operations. This study aims to develop a new control algorithm to reduce electricity consumption and electricity rates for cooling and heating by VRF systems in university buildings adopting the TOU rate plan and apply it to actual buildings to verify the reduction effect. The proposed control algorithm primarily consists of a module that controls the refrigerant evaporation temperature (cooling) and high pressure (heating) according to the indoor heat load and a module that controls the indoor set temperature based on the hourly electricity rate. The developed algorithm was installed in the controller of a VRF system installed in an actual university building and the annual effect was verified using the method proposed by the International Performance Measurement and Verification Protocol. As a result, power consumption was reduced by 17.8% for heating and 4.0% for cooling due to the application of the control algorithm, and the electricity rates reduced by 19.2% and 7.3%, respectively.

Feasibility of low-cost energy management system using embedded optimization for PV and battery storage assisted residential buildings

In this study, an energy management system (EMS) focusing on low-cost hardware and embedded optimization has been built. A benchmark consisting of a residential photovoltaic (PV) and battery connected to the grid but without feed in power has been considered. The proposed EMS accepts input variables as building electrical load data, PV output data, the electricity time of use rates. The master EMS ensures the optimization of the battery charge-discharge profile to reach the lowest possible energy bill. Sensitivity analysis demonstrates that the presence of optimization systems leads to a more stable energy cost even though power demand and PV production vary during the day. In the cases studied, the bill reduction is 32% up to 50% depending on load or solar PV generation variations. By comparison, in the literature where more complex optimization in MATLAB environment were used, a bill reduction of 24%–34% was realized. The system cost is estimated to be around 30$ which is much lower than the typical 100$-600$ price for similar products. The system can be practically integrated in applications such as EMS of schools, residential or public buildings by inserting it through the power distribution panel where all protection devices are located.

Options for change: Restructuring California’s residential inclining rates for a better electricity future

Local distribution companies (LDCs) regulated by the California Public Utilities Commission use mandatory inclining rates (MIR) to bill their residential electricity customers. However, MIR hinder the California electricity industry’s provision of clean and reliable service at affordable rates. Hence, we consider the following options to restructure MIR: (1) adopting a flat energy rate and revised customer charges; (2) replacing the flat rate in (1) with time-of-use (TOU) rates; (3) replacing the TOU rates in (2) with real-time pricing; (4) replacing (3) with a Hopkinson tariff with reliability differentiation. Our assessment of these options recommends sequential implementation (1), (2) and (4), thus lessening the potentially large and adverse bill impacts on some residential end-users caused by a drastic rate design change. Our recommended implementation sequence is (2) and (4) when the potentially large bill impacts of TOU pricing on the small and poor end-users with relatively high peak kWh consumption are to be mitigated by income-based customer charges.

Growing PV generation and EV loads in a world of declining net metering: impacts on household-level economic, emission, and energy benefits

ABSTRACT While electric vehicle (EV) and residential solar photovoltaic (PV) panel capacities and adoption increase, US Federal states are rolling back their policy and technical support for net metering. This unfavourable situation is studied with a system dynamics model that simulates: 1) The impact of 5%–15% EV/PV capacity growth on net-metered electricity and economic, emission, and energy benefits; 2) The impact of 5%–15% Value of Solar (VoS) tariff decline on household economics; 3) The impact of introducing Time-Of-Use (ToU) rates on expenditures. The results show that net-metered electricity increases by up to 25% in the scenario where PV capacity grows by 15% and EV capacity – by 25%; emissions do not decline by more than 2–3% either. ToU, however, can mitigate revenue loss from declining VoS tariffs and reduce bills by 30% to 362%. The article recommends ToU rates and policies for energy storage and smart charging in the U.S.

Impact of Electric Vehicle customer response to Time-of-Use rates on distribution power grids

Electric Vehicles (EV) present a unique challenge to electric power system (EPS) operations because of the potential magnitude and timing of load increases due to EV charging. Time-of-Use (TOU) electricity pricing is an established way to reduce peak system loads. It is effective at shifting the timing of some customer-activated residential loads – such as dishwashers, washing machines, or HVAC systems – to off-peak periods. EV charging, though, can be larger than typical residential loads (up to 19.2 kW) and may have on-board controls that automatically begin charging according to a pre-set schedule, such as when off-peak periods begin. To understand and quantify the potential impact of EV charging’s response to TOU pricing, this paper simulates 10 distribution feeders with predicted 2030 EV adoption levels. The simulation results show that distribution EPS experience an increase in peak demand as high as 20% when a majority of the charging begins immediately after on-peak times end, as might occur if EV charging is automatically scheduled. However, if charging start times are randomized within the off-peak period, EV charging is spread out and the simulations showed a decrease in the peak load to be 5% lower than results from simulations that did not implement TOU rates.

Changes in hourly electricity consumption under COVID mandates: A glance to future hourly residential power consumption pattern with remote work in Arizona

The transition to remote work brings uncertainty to the future power consumption pattern. The COVID mandates in 2020 have accelerated the transition to remote work, generating major uncertainty regarding how residential power consumption patterns will shift. Understanding these shifts is vital for regional operators who will need to implement long-term planning strategies if companies continue to adopt remote work practices. Additionally, if new COVID variants prompt extended stay-at-home mandates, the resulting behavior shifts will decide the optimal combination of power generation in a region. Our study examines changes in hourly residential power consumption patterns resulting from COVID mandates in Arizona. We estimate how the COVID mandates and subsequent remote work practices could change the power consumption patterns using individual-consumer-level hourly power consumption data for 6,309 consumers and a machine learning framework. We also simulate how the hourly power consumption pattern will change with increasing penetration of remote work under winter and summer temperature settings. We then use our simulations to test the policy effectiveness of changing time-of-use (TOU) rates. Our results show that COVID mandates likely increase the power consumption in the afternoon by 13%, and can change the power consumption pattern in winter from a two-peaked shape to a one-peaked shape. Furthermore, we show that the residents' income, race, and house size are significantly correlated with the changes in power consumption, and the correlation is not linear. We find that, by increasing the peak hour prices and decreasing the off-peak hour prices by 10% of the TOU pricing, the peak electricity demand could be reduced by 10%. Our results show under the new remote work era: (1) the need for modifying previous energy generation combination planning due to changing peak demand hours; (2) equity concerns regarding TOU pricing and the inability of vulnerable groups to shift electricity consumption; (3) the ability of governments and utilities to lower the maximum load of power consumption by modifying the TOU rates.

Home Energy Management System for Price-Responsive Operation of Consumer Technologies Under an Export Rate

Dynamic tariffs such as time-of-use (TOU) were introduced to motivate consumers to change the operation of their behind-the-meter (BTM) resources to reduce their power consumption during peak demand times. Recently, export rates have been implemented that reimburse consumers for electricity exported to the grid at a rate less than the retail rate to reduce reverse power flow in distribution power systems with high photovoltaic penetrations. Export rates are expected to be employed more widely, and therefore it is important to understand their impact on consumers, the operation of BTM resources, and the distribution grid. This paper presents a home energy management system (HEMS) capable of optimally scheduling BTM resources under a tariff with export rates. The proposed HEMS is formulated as a multi-objective model predictive control problem. In the paper, we analyze the performance of the proposed HEMS under an export rate including the operation of each BTM resource and the impact on the cost and comfort of homeowners. We also compare the performance of the proposed HEMS under an export rate with the HEMS under a TOU rate. Simulation results are presented for a small power system with several homes that employ the proposed HEMS to respond to an export rate. The simulation results show that the estimated energy cost savings for houses with HEMS and energy storage under an export rate is 20% compared to the baseline scenario with no HEMS. The results also demonstrate that the proposed HEMS responding to an export rate increases the self-consumption of buildings more than HEMS responding to a TOU rate.

Residential Electricity Rate Plans and Their Selections Based on Statistical Learning

Demand response (DR) is one of the major benefits that utility companies can derive from the advanced metering infrastructure (AMI). In particular, the dynamic rate plan with DR is attracting attention as an electricity rate system suitable for the future power environment. In order for electricity consumers to select an appropriate electricity rate plan, it is necessary to provide information such as whether electricity bills are reduced by the plan and the estimated amount of electricity bill savings. In this paper, we first comparatively analyze the current progressive rate plan and a dynamic rate plan of the time-of-use (TOU). We next propose several prediction methods for households to provide information on whether the electricity bill amount can be reduced in advance when changing to the TOU rate plan from the progressive rate plan by using only the current monthly electricity usages and bills. In order to develop three different prediction methods based on statistical learning, we use the support vector machine, linear regression, and deep neural network techniques. As a ground truth training sequence, we use hourly electricity usages and bills obtained from ten apartment complexes through AMI, and an apartment complex is used for testing the designed methods. The decision accuracy for the test complex was more than 0.98 and the root mean square error of the saving prediction was 1.7%.

Application of model predictive control to grid flexibility provision by distributed energy resources in residential dwellings under uncertainty

Owing to the high penetration of renewable energy sources, power systems require more flexibility to respond to the fluctuation of variable renewable energy sources. However, flexibility provision by distributed energy resources (DERs) installed on the demand side is a cost-effective measure. In this study, we proposed a scheme that utilizes time-of-use (TOU) electricity rates as an incentive to encourage residential prosumers to provide more flexibility by controlling the DERs. In particular, we developed an energy management system model that applies model predictive control. The TOU rates were designed by an aggregator who traded with markets and prosumers. The DERs of residential prosumers were fuel cells with combined heat and power, CO2 heat pump water heaters, or batteries. Numerical simulations using measured energy demand and insolation data were performed to evaluate the performance of the developed model. According to the results, the developed model could integrate prediction errors and provide flexibility to the grid. Finally, the economies of the aggregator and prosumers were also evaluated.

Optimal Configuration with Capacity Analysis of PV-Plus-BESS for Behind-the-Meter Application

As the cost of photovoltaic (PV) systems and battery energy storage systems (BESS) decreases, PV-plus-BESS applied to behind-the-meter (BTM) market has grown rapidly in recent years. With user time of use rates (TOU) for charging and discharging schedule, it can effectively reduce the electricity expense of users. This research uses the contract capacity of an actual industrial user of 7.5 MW as a research case, and simulates a PV/BESS techno-economic scheme through the HOMER Grid software. Under the condition that the electricity demand is met and the PV power generation is fully used, the aim is to find the most economical PV/BESS capacity allocation and optimal contract capacity scheme. According to the load demand and the electricity price, the analysis shows that the PV system capacity is 8.25 MWp, the BESS capacity is 1.25 MW/3.195 MWh, and the contract capacity can be reduced to 6 MW. The benefits for the economical solution are compared as follows: 20-year project benefit, levelized cost of energy (LCOE), the net present cost (NPC), the internal rate of return (IRR), the return on investment (ROI), discounted payback, total electricity savings, renewable fraction (RF), and the excess electricity fraction. Finally, the sensitivity analysis of the global horizontal irradiation, electricity price, key component cost, and real interest rate will be carried out with the most economical solution by analyzing the impacts and evaluating the economic evaluation indicators. The analysis method of this research can be applied to other utility users to program the economic benefit evaluation of PV/BESS, especially an example for Taiwan’s electricity prices at low levels in the world.

Viability of the store-on Grid Scheme model for grid-tied rooftop solar photovoltaic systems in Sub-Saharan African countries

The viability of the store-on grid (SoG) scheme model across 13 selected Sub-Saharan African (SSA) countries was investigated. The individual country's datasets applicable to the SoG scheme and a typical industrial building as a baseline prosumer were used in the evaluation. Two SoG scheme scenarios (A and B) based on solar photovoltaic (PV) module prices were used in this analysis. The electricity tariffs under the SoG scheme were compared with the time of use rates for the selected SSA countries. The analysis results revealed that the scheme could be viable in only nine and four out of 13 countries under scenario A and scenario B, respectively, with high lending interest rate in some countries being the key hindrance. Under the SoG scheme model, solar PV systems in all the selected countries could feed about 15.65–39.25% of the generated energy to the grid. The government tax in the range of 1.95–3.38 and 2.48–4.31 c$/kWh could be collected from the energy fed to the grid under SoG scheme scenario A and scenario B, respectively. Overall, the SoG scheme exhibits greater potential as an energy business model to foster rooftop solar PV technology adoption in developing countries.

Harnessing TOU Rate Complexity to Reduce Residential EV Charging Costs

Time-of-use (TOU) electricity rates have been proposed as a tool to reduce electric vehicle (EV) charging costs and shift consumption away from periods of system stress. TOU rates can be complicated, however, and the potential benefits of that complexity have not been comprehensively investigated. In this paper, I assess the value of two kinds of rate complexity on EV charging costs. Scope complexity relates to the number of pricing periods on the rate, while scale complexity relates to the fraction of costs given a time-varying dimension within the rate. Looking across 17 efficient, cost-based rates, I find that adding complexity can consistently reduce EV charging costs. The most complex rates can reduce costs by half, compared to flat rates. I also find that increasing scope complexity can reduce EV charging costs more than increasing scale complexity, all else equal. A high-scope/low-scale rate triples the benefits compared to a low-scope/high-scale rate. So, instead of trying to create retail rates with many pricing periods, regulators can spur large benefits by promoting simple two-season/two-period TOU rates which span energy, capacity, transmission, and distribution cost categories.

Effects of a Battery Energy Storage System on the Operating Schedule of a Renewable Energy-Based Time-of-Use Rate Industrial User under the Demand Bidding Mechanism of Taipower

Due to the increased development of the smart grid, it is becoming crucial to have an efficient energy management system for a time-of-use (TOU) rate industrial user in Taiwan. In this paper, an extension of the direct search method (DSM) is developed to deal with the operating schedule of a TOU rate industrial user under the demand bidding mechanism of Taipower. To maximize the total incentive obtained from the Taiwan Power Company (TPC, namely Taipower), several operational strategies using a battery energy storage system (BESS) are evaluated in the study to perform peak shaving and realize energy conservation. The effectiveness of the proposed DSM algorithm is validated with the TOU rate industrial user of the TPC. Numerical experiments are carried out to provide a favorable indication of whether to invest in a BESS for the renewable energy-based TOU rate industrial user in order to execute the demand bidding program (DBP).

Managing residential solar photovoltaic-battery systems for grid and life cycle economic and environmental co-benefits under time-of-use rate design

The residential time-of-use (TOU) rates have been increasingly discussed or implemented by the US power utilities. The TOU rate design can potentially promote residential battery installations targeting increased selling or utilization of solar energy during the on-peak hours. However, our understanding in terms of the design and management of solar photovoltaic (PV)-battery systems for economic, environmental, and grid co-benefits under the TOU design remains limited. This study integrated system dynamics modeling with life cycle assessment to investigate the peak load reduction, life cycle cost, as well as life cycle climate change, water depletion, and fossil fuel depletion effects of residential grid-connected PV-battery systems under a TOU rate design. A residential prototype house in the Boston-Logan area, MA was selected for model simulation. Our study found solar PV-battery systems that maximize the on-peak grid selling can achieve the highest on-peak load reduction and economic benefits. However, they may not result in the highest environmental benefits, as on-peak hours have lower carbon emission and fossil fuel depletion factors as compared with the mid-peak hours in the New England grid. This suggests a potential tradeoff between the need of on-peak load reduction, economic saving, and environmental protection. Installing a PV system alone presents relatively strong economic and environmental performances, but its on-peak load reduction is limited. Installing a battery system but without an effective control strategy might result in relatively weak peak-load reduction, economic, and environmental outcomes. This highlights the importance of effective battery control in the implementation of solar PV-battery systems.

Cost/comfort-oriented clustering-based extended time of use pricing

Real-time pricing (RTP), as the most effective electricity pricing method, has recently attracted significant attention in tackling the challenges of the evolving power grid such as peak shaving and shifting, as well as the emergence of energy prosumers. However, the realization of RTP schemes require an advanced metering infrastructure (AMI). Therefore, time-of-use (ToU) pricing is still in use in residential sectors. This paper presents a novel clustering-based extended time of use pricing (ETOU) algorithm as an intermediate solution, which combines the advantages of both RTP and ToU pricing methods, without a need for AMI, while considering consumers’ desired comfort level. The proposed ETOU is designed based on analyzing the consumption patterns and the real cost of energy generation. The proposed rates are defined considering the dominant consumption and energy cost values extracted by clustering. The effectiveness of the proposed ETOU is verified through numerical analysis of a residential building with an electrical thermal system modeled as a controllable load and an electrical vehicle modeled as a delay-tolerant demand. Results show that the proposed tariffs, in comparison with RTP and ToU rates, noticeably increase the load factor and energy flexibility and decrease the maximum power ramp and electricity costs, while providing the desired comfort level. Additionally, implementation of the proposed ETOU does not require AMI despite the RTP method.