How Time of Use Rates Work Samuel Adeyemo THIS ARTICLE WAS ORIGINALLY PUBLISHED ON AURORA SOLAR’S BLOG What do the prices of movie tickets to Hollywood’s latest blockbuster and the cost of energy in California have in common? First, in most theaters, you will find that ticket prices change based on the time of day you want to see your show. This is similar to energy rates in California, where the cost of electricity varies based on the time of day it is consumed (an approach known as Time of Use, or TOU, rates). Second, even for a specific time (take a matinee for example), movie tickets often cost less for different types of consumers. Students and seniors, who generally have less income than regular moviegoers, pay less for their tickets than others do. We see a similar phenomenon in California where for a given time period, consumers who use less than a baseline amount of energy, will pay less than those who use more than the baseline amount of energy, even if it is for the exact same time of day. The illustration below shows how California’s Pacific Gas and Electric utility varies the cost of energy based on time of day and cumulative consumption. For this article, let us focus on the first of these energy rate factors, where the cost of energy varies according to the time of day it is consumed. In subsequent articles we will look how rates vary based on level of energy consumption, and eventually look at how utility rate changes will change the returns for solar customers (and how you can optimize your designs to take advantage of it). Figure 1: E-TOU rates option A during the summertime. Source: w ww.pge.com . While California is among the first states to make this type of utility rate structure mandatory for residential customers, it is not alone: in 2015, the Massachusetts Department of Public Utilities adopted default Time of Use rates for residential customers, and the Tennessee Valley Authority (the nation’s largest federally-owned electric utility which serves nine million customers in Tennessee and other southern states) has also proposed transitioning to TOU rates. Time-variable rate programs are already offered on a voluntary basis in nearly every state. Although participation in these opt-in programs has been low to date, as many states consider efforts to modernize the electrical grid and reduce peak energy consumption, TOU rates (and other related time-based rate structures) are expected to become increasingly prevalent . There are multiple approaches that utilities take when it comes to time-varying energy costs, but Time of Use (TOU) rates , in which customers are charged higher rates for the energy they use during specified peak demand times, are one of the most common. According to Pacific Gas and Electric (PG&E), TOU rates help consumers save money by making the cost of energy low during the time when demand is low. In Figure 1, you can see that the cost of energy is higher between 3pm – 8pm on weekdays, than it is for any other time. During a weekday in summertime, as of March 1st, 2017, rates range from $.317/kWh, to $.393/kWh (for now we are ignoring “baseline” quantities, we will explore that in Part 2 of this series). That is almost a 25% variation between the lowest and highest cost energy! Let us use Aurora to look at a case study where we compare two households to fully capture how this can affect their monthly utility bill. Household A lives in Bakersfield, CA. We used Aurora’s Consumption Profile to automatically pull up the typical hourly summertime energy consumption for a house in Bakersfield. According to US Climate Data , Bakersfield temperatures range between 64 and 97 degrees in the summertime. Consequently, Aurora by default generated a load profile where air conditioning was a large portion of Household A’s energy consumption (see Figure 2). Unfortunately for this household, about 43% of their summertime energy consumption is going to occur during the time when electricity is most expensive. Figure 2: Load Profile for a house (“Household A”) in Bakersfield, CA generated automatically in Aurora. Let us now consider Household B, also located in Bakersfield, CA. In this case, instead of using Aurora to automatically generate a typical load profile, we obtained actual measured interval data for a house in Bakersfield from PG&E. We uploaded this into Aurora, which generated the plot below. To make the case more interesting, we assumed that the homeowner has an Electric Vehicle (a Tesla) which they drive about 20 miles per day. This type of homeowner is only spending about 10% of their energy consumption during the peak 3pm – 8pm time zone. Figure 3: Load Profile for a house (“Household B”) in Bakersfield, CA generated in Aurora, based on uploaded energy use data. If you have been following along, your intuition would suggest that for the same energy consumption, Household A should have a higher electricity bill than Household B, because they are consuming energy during the high cost period. Before we evaluate the financial implications of Time of Use Rates, let us recap the two households’ information: Household A Household B Location Bakersfield, CA Bakersfield, CA Utility Rate PG&E, E-TOU A- Residential TOU Region W PG&E, E-TOU A- Residential TOU Region W Energy Consumption (July) 1,873 kWh 1,873 kWh % of weekday consumption during peak hours (3pm-8pm) 43% 10% Running Aurora’s utility bill calculator, we find that Household A (high peak consumption) had a bill of $591 for the month of July. We find that Household B (low peak consumption) had a July bill of $561. So despite consuming the exact same amount of electricity, Household A’s bill was about 5.5% higher in July than Household B. Figure 4: Household A’s July electric bill is $591. Figure 5: Household B’s July electric bill is $561. TOU Bill Difference = (DaysTOU/7) * (ConsumptionpeakA– ConsumptionpeakB) * (URpeak – URoffpeak) / URoffpeak Equation 1: Rough estimate of the TOU effect on energy bills. Let us plug some numbers into Equation 1. Term Definition Value Days TOU Days of the week that TOU values apply 5* Consumption_peakA Energy consumption (kWh) during peak hours for household A 43% Consumption_peakB Energy consumption (kWh) during peak hours for household B 10% UtilityRate_peak Peak period utility rate ** $.393/kWh UtilityRate_offpeak Off peak period utility rate** $.317/kWh *TOU rates in this region only apply to weekdays ** For simplicity we are assuming that this is for the above baseline energy consumption TOU Bill Difference = (5/7) * (43%-10%) * (.393- .317) / .317= 5.64% You can see our quick estimate came pretty close to the actual difference between Household A’s (high peak consumption) and Household B’s (low peak consumption) energy bills. In Part 2 of this series, we will extend this case study to consider how increasing the total amount of energy consumed affects energy bills. Key Takeaways: Similar to movie tickets, in California, the cost of energy varies based on the time of day you use it. California tends to be a bellwether for the US solar energy market; Time of Use energy rates are already available on a voluntary basis in almost all states and utilities are increasingly considering their expansion as a means to reduce peak energy demands, so it is a good idea to understand how they work. You can use the following simple formula to calculate how much of an impact the difference between high-cost electricity and low-cost electricity has on a homeowner’s bill: TOU Bill Difference = (DaysTOU/7) * (ConsumptionpeakA– ConsumptionpeakB) * (URpeak – URoffpeak)/URoffpeak* Certification Solar Solar Design & Installation Solar Finance Solar Plus Storage Solar Sales & Marketing Utility-Scale Solar Originally posted on March 22, 2017 Written by Samuel Adeyemo Samuel Adeyemo is the chief operating officer at Aurora Solar. Immediately prior to Aurora, Samuel was with Vituo, a company that designed and installed small commercial PV systems in developing countries. Previously, Samuel was a Vice President in JPMorgan's Chief Investment Office, where he was responsible for investing the bank's assets in various public debt markets. He holds MBA and an MSc from Stanford, and is co-chair of the SunSpec Alliance’s Finance Technical Working Group. More posts by Samuel