To BYOT or Not to BYOT: That is the Question

Itron is a long-time member of the Peak Load Management Alliance (PLMA), which was founded in 1999 as the voice of demand response practitioners. I serve on the Executive Committee of PLMA and co-chair the Thought Leadership planning group.

Over the past few years, PLMA has been very focused on sharing the expertise of its members to help demand response professionals better address changing industry dynamics. An example of this is a recent PLMA publication called “The Future of Utility ‘Bring Your Own Thermostat’ Programs” that was created to help members better understand the value of Bring Your Own Thermostat (BYOT) demand response programs. This paper—part of a new series call “Practitioner Perspectives,” where PLMA collects member insights on important industry trends—is a compendium of eight energy utility, manufacturer and solution provider viewpoints. The authors were selected in an open call for submissions and all article drafts were reviewed by a team of mentors that included the PLMA Thermostat Interest Group co-chairs prior to publication. Itron was pleased to be selected as one of the paper’s authors.

The perspective that Itron brings to the paper is that we have historically been focused on delivering operational demand response programs through the aggregation of a substantial amount of residential and small commercial load. As we outlined in the article, Itron today primarily views BYOT programs as a tremendous opportunity for utilities to better engage with their customers by rewarding those who have already purchased a Wi-Fi thermostat with the financial incentives that come with participating in a demand response programs. So as utilities look for ways to improve customer satisfaction scores, BYOT programs are a great vehicle to accomplish that. In terms of the BYOT role in demand response, for now, we see BYOT as a key component of a broader demand response/distributed energy resource strategy that may also include direct install devices, electric vehicle chargers, storage and more. And while we see the role of BYOT growing over time, due to limited penetration of these devices in today’s market, we don’t think it’s a great option to be the only load source when a large number of megawatts needs to be obtained.

To learn more, you can download the free paper here. PLMA also hosted a webinar on the paper that includes members of the Thermostat Interest Group, the Thought Leadership Planning Group and several of the authors. You can view a recording here.


eBook: How Utilities are Planning for Distributed Energy Resources

With the rapid integration of renewables and distributed technologies into the grid, utilities can no longer feasibly address the challenges of maintaining power quality, balancing supply and demand in real time and ensuring adequate distribution infrastructure capacity with their old systems. To gain a better understanding of what utilities think about a holistic distributed energy management strategy—and what challenges they face and benefits they expect—Itron and Zpryme conducted a survey of 170 electric utilities and collected the findings in a new eBook: Distributed Energy Management: The New Era of Demand Response.

The statistic that most resonated with me from this study is that 71 percent of utilities believe that distributed intelligence and computing power at the grid edge will be critical to effectively manage distributed energy resources (DER). At Itron, we couldn’t agree more. The proliferation of DER requires a distribution grid that responds to changing conditions in real time to meet demand and maintain grid stability. And this requires a network that enables distributed intelligence to make DERs at the grid edge capable of responding quickly and locally to real-time conditions as they occur.

With more than 400 downloads to date, it’s clear the eBook covers an important industry topic. You can download the eBook here.


Developing the Peak Day Dataset Using MetrixLT

I think of MetrixLT as a fancy load shape manipulator and calculator. While the software was originally designed to calibrate load shapes to monthly energy forecasts, newer features of MetrixLT allow users to calculate normal weather (including Rank and Average methods), add and subtract load shapes, and perform top-down calibrations of hourly loads. Within the myriad of features, I’ve found a hidden gem that helps me forecast peak loads. MetrixLT builds my monthly peak database.

The foundation for the monthly peak model is historic monthly peaks and the weather associated with those peaks. Monthly peaks are obtained from historic hourly temperatures. Once the peaks are identified, the dates of the peaks must be used to obtain the associated weather. Instead of culling through complex Excel formulas, MetrixLT creates this database from daily peak data in a single transformation.

An example of the results is shown below. In this figure, the January 2005 peak is 900 MW. The temperature that produced the 900 MW peak is 16.42 degrees. Likewise, the temperature on the day before peak is 33.13 degrees.

forecasting1

 

 

 

 

 

 

 

 

While this figure shows the temperature and prior day temperatures associated with the monthly peak, MetrixLT can provide any daily condition associated with the peak, such as dew point, wind speed or demand response estimates.

To create a monthly peak database, use the Frequency Transformation in MetrixLT. This transformation converts data of a different periodicity into monthly data. Begin by creating the Frequency Transformation and setting the frequency to “Monthly” as shown below.

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Within this Transformation, insert variables you want shown in the peak database. In this example, three variables are included.

1. Monthly Peak. The monthly peak value is obtained by creating the variable and placing the daily peak series in the “Source” box. Assign the method “Maximum” and Transform will return the monthly peak value.

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 2. Temperature on the day of the Peak. The temperature on the peak day is obtained by creating another variable and placing the daily temperature series in the “Source” box. Assign the “Coincident Max” method and insert the daily peak series in the Coincident Max box as shown below. This transformation will return the daily temperature coincident with the monthly peak value.

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3. Temperature on the day before the Peak. The temperature on the day before the peak is obtained with another variable. In this variable, assign the daily temperature series as the “Source”, select the “Coincident Max” method, and assign the daily peak series in the “Coincident Max” box. For this variable, assign “Coin Lag” the value of “1”. This transformation returns the daily temperature coincident with the monthly peak value, lagged one day.

forecasting5

 

 

 

 

 

 

 

 

 

 

 

While the three variables in this example result in the database shown at the top, the example is easily extended to obtain temperatures two days before the peak or daily wind speed on the peak day by defining more variables. Once all the desired variables are included in the monthly peak database, simply export the data, import the values into MetrixND, and build the monthly peak model.


Peak Survey

Itron’s forecasting group is currently conducting an Electric Peak Load Modeling Survey. The purpose of this survey is to gather information about methods used to forecast and weather adjust system peak loads. All survey participants will receive a summary report. Utility specific data will NOT be disclosed. If you would like to participate and learn more about peak forecasting method, contact Paige Schaefer at paige.schaefer@itron.com to participate.

Survey closes on Friday, August 21.

 


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