There is ongoing discussion in the electric utility industry today on how to implement demand response programs that offer critical tier pricing (CTP) options to their customers. For the most part, the focus of these discussions has been on how to deploy demand response programs in an equitable manner across a customer base that has a broad range of energy usage habits. Missing from the discussions has been an objective look at the metering technology needed to implement critical tier pricing programs. This article looks at the pros and cons of two metering technologies available today that can be used to implement critical tier pricing.
One approach to implementing critical tier pricing is to collect interval data from a meter and derive critical tier pricing billing data at the head end system. This approach uses hourly load
profile (LP) data from an interval meter or a simple energy meter with a communications module attached that collects pulse data from the meter. Typically, the communications module will also be able to communicate with the head system through nearby collection devices or a communication network. The other approach uses TOU register data from the meter with a dynamically changing TOU register dedicated to the critical tier.
When critical tier pricing is in effect, data accumulates in the CTP register within the meter. The meter typically has communications built into the meter so it can be updated daily if CTP time slots change.
CTP Using Hourly Interval Data from Meter Modules
Historically, interval data has been the method used for collecting daily load usage data at energy delivery points. The most popular use of interval data recording has been on large commercial and industrial (C&I) utility accounts and at delivery point substations.
Typically, complex pricing can be implemented using hourly interval data directly from a meter or from a meter equipped with a communications module that creates interval data. If a customer has a residential monthly billing rate with CTP, the head end system must collect 24 hourly intervals daily from the meter or module to compute monthly billing data with CTP using hourly interval data. This results in 720 interval data collections per meter every 30 days.
When the interval data is collected it is stored in the head end system. Before the data can be stored as billing data or sent to customers, a data validation, editing, and estimation (VEE) process is required to ensure the energy consumption is valid and has no gaps in the data. If a
discrepancy is found, then a level of estimation and editing is required to account for the lost data. After the VEE process, the interval data is collapsed into the required CTP time structure based on the customer’s billing rate. The VEE process in some systems takes hours depending on the number of meters.
If a utility offers a demand response program that reports load usage to their customers daily, the head end system calculates daily CTP usage and packages it for delivery to their customers via the Internet or by other means. The head end system also continues to do hourly interval reads on the meters while it converts interval data into billing data. In all, the head end system must have the processing power to handle data collection, compute energy consumption, perform VEE, and deliver data to the customer.
CTP Using Time-of-Use Meters with CTP Register
Electronic meters that have an additional CTP register can also be used to implement demand response programs that offer critical tier pricing options. These meters are programmable electronic single phase meters manufactured for use in residential and small C&I accounts. They have communications built into the meter, so they do not need any additional modules installed.
These meters collect and store the billing data in the meter so there is no need to reconstruct the collected data in the head end system. As CTP rates change, the meter collects and stores billing data for each occurrence in the appropriate register within the meter (see Figure 1). Since the head end system has two-way communications with the meter, the time definition for critical tier pricing can be changed daily.
With this method, meters can be read daily or as required to provide data to the consumer. Since the data at the meter is billing data, the head end system is not required to create billing data from interval data. All the head end system needs to do is deliver the CTP and other billing data to the billing system.
Comparing the Technologies
When using hourly interval data to implement CTP there is a significant increase in the amount of data the head end system needs to collect and process. If a residential customer has a CTP billing rate it would require the system to collect 24 hourly intervals per day for 30 days. This equates to 720 meter reads per billing period. This level of data collection results in a significant amount of data storage required. Additionally, collected interval data requires VEE processing to convert it to billing data. Concern that interval data may be lost or have errors has encouraged regulators to impose stringent requirements on utilities that use interval data with validation, editing, and estimation to bill customers. These requirements protect the consumer and ensure the utilities provide fair and accurate billing data. The raw certified meter data must often be archived for long periods (three or more years) to protect against billing disputes. This requirement impacts the long-term data storage needed, since the raw certified data is interval data and all intervals must be archived.
Looking more closely at how the data collection requirements compare, take the example of a customer on a two-tier rate (on-peak usage, off-peak usage) with critical peak pricing allowed on up to 15 events a month. Assuming interval data is being collected, the hourly interval data involves 720 meter values for the month (24 hourly interval values times 30 days). If a TOU meter that supports critical tier is used, then 75 meter values are required per month (2 daily billing values times 30 days plus 15 critical peak pricing event values). In this instance, almost 10 times more data collection is required for the hourly interval data compared to daily TOU data
collection.
Other factors to consider are:
Meters that have a dedicated CTP register eliminate much of the processing needed by the head end system because billing data is stored in the meter’s registers. Less communication bandwidth is needed so communication costs are reduced. If the meter is using a CTP register, only one meter read is required per day to collect the data. Since the CTP data in the meter is billing data and no VEE processing or data conversion is needed, data can be sent to the customer in a more timely manner. Additionally, the logistics of providing near real-time energy usage data to home automation devices such as programmable thermostats and control devices that monitor high-energy use appliances is simplified. These devices can receive real-time data directly from the meter.
Simple meters with add-on modules require additional manipulation of interval data by the head end system. As the number of meters in the system increases, the need for the head end system to process more hourly load profile data introduces lag time in the system. This lag time could potentially diminish a system's near real-time capabilities as the number of meters serviced by the system grows. Smart electronic meters with a CTP register can provide billing data directly to the system in near real-time. The consumer can read their billing data at the meter and the data is readily available to home automation systems at the point of energy delivery.
What is the Cost?
The cost of purchasing a meter module for a simple energy meter is about the same as the cost of a smart meter with a CTP register. Additional fixed costs come into play if a utility company needs to add to or modify the communications infrastructure needed to support the metering technology. But the recurring costs using hourly interval data could be significant.
The following characteristics of using hourly interval data for CTP programs impact recurring costs.
Additional Impacts of VEE
While it is widely accepted by utilities that billing data can be computed from interval data, there is some inherent risk in labeling the computed results as billing data. When discrepancies are found between the collected interval data and the energy consumption seen at the meter, a level of estimation and editing is required to account for the lost data, and this is not typically viewed as billing data, but is considered modified data. There is also a time element of interval data. Any skew in the real-time relationship of interval data can have a dramatic impact on the computed demand and CTP data. Additional attention for validation, editing, and estimation is given to interval data in instances where the stream of data is interrupted somewhere in the process. Such interruptions may occur because of a problem with the meter, the pulse initiator, the recording medium, or in the data transmission from the field site to the central office system. Also, power outages must be distinguished from net zero consumption periods.
Validation verifies that the collected data matches the energy difference between the start and stop meter readings, and that the number of intervals collected matches the reported time between data collections. If any of the validation procedures fail, estimating and editing of the data is required. Very complex procedures are used by utilities to estimate lost data.
Same-time data from similar accounts, same-time data from previous days for the same account, the average of the last several days’ data and the same-date data from a year ago, plus other variations, have all been used to estimate the missing data.
Conclusion
There is a mutual understanding in the industry that utilities must be able to measure and track each participating customer’s energy consumption to successfully implement demand response programs. The meter must be capable of knowing what critical tier pricing rate is in effect so that it can accurately “bucket” the energy usage. Implementing near real-time pricing (RTP) for customers that have home energy management systems requires that the customer’s meter have the energy usage data readily available.
Obviously, CTP, demand response, and real-time pricing programs can only be successful if the technology can deliver energy usage data in near real-time. This may warrant a closer look at each of these technologies to see if they can perform to both the energy provider’s and the energy consumer’s expectations.
About the Author
Sharon Allan has responsibility for strategic initiatives, third party relations, product management, regulatory affairs, and new business development for Elster Electricity, LLC. In addition, she is a member of the division patent committee overseeing the protection of intellectual property. In 2002, Sharon was named one of the ‘50 Key Women in Energy’ for her global leadership in the area of innovation and creativity. Sharon has been a presenter for Edison Electric Institute, Metering International, and AMRA meetings, as well as other energy industry forums. She is a board member for Metering Americas and National Energy Marketers. Sharon can be reached at sharon.s.allan@us.elster.com
Sharon has an MBA from the Fuqua School of Business at Duke University. She earned a degree in electrical engineering from the University of Florida. Sharon has completed post-graduate electrical engineering courses at NCSU and University of South Carolina.
One approach to implementing critical tier pricing is to collect interval data from a meter and derive critical tier pricing billing data at the head end system. This approach uses hourly load
profile (LP) data from an interval meter or a simple energy meter with a communications module attached that collects pulse data from the meter. Typically, the communications module will also be able to communicate with the head system through nearby collection devices or a communication network. The other approach uses TOU register data from the meter with a dynamically changing TOU register dedicated to the critical tier.
When critical tier pricing is in effect, data accumulates in the CTP register within the meter. The meter typically has communications built into the meter so it can be updated daily if CTP time slots change.
CTP Using Hourly Interval Data from Meter Modules
Historically, interval data has been the method used for collecting daily load usage data at energy delivery points. The most popular use of interval data recording has been on large commercial and industrial (C&I) utility accounts and at delivery point substations.
Typically, complex pricing can be implemented using hourly interval data directly from a meter or from a meter equipped with a communications module that creates interval data. If a customer has a residential monthly billing rate with CTP, the head end system must collect 24 hourly intervals daily from the meter or module to compute monthly billing data with CTP using hourly interval data. This results in 720 interval data collections per meter every 30 days.
When the interval data is collected it is stored in the head end system. Before the data can be stored as billing data or sent to customers, a data validation, editing, and estimation (VEE) process is required to ensure the energy consumption is valid and has no gaps in the data. If a
discrepancy is found, then a level of estimation and editing is required to account for the lost data. After the VEE process, the interval data is collapsed into the required CTP time structure based on the customer’s billing rate. The VEE process in some systems takes hours depending on the number of meters.
If a utility offers a demand response program that reports load usage to their customers daily, the head end system calculates daily CTP usage and packages it for delivery to their customers via the Internet or by other means. The head end system also continues to do hourly interval reads on the meters while it converts interval data into billing data. In all, the head end system must have the processing power to handle data collection, compute energy consumption, perform VEE, and deliver data to the customer.
CTP Using Time-of-Use Meters with CTP Register
Electronic meters that have an additional CTP register can also be used to implement demand response programs that offer critical tier pricing options. These meters are programmable electronic single phase meters manufactured for use in residential and small C&I accounts. They have communications built into the meter, so they do not need any additional modules installed.
These meters collect and store the billing data in the meter so there is no need to reconstruct the collected data in the head end system. As CTP rates change, the meter collects and stores billing data for each occurrence in the appropriate register within the meter (see Figure 1). Since the head end system has two-way communications with the meter, the time definition for critical tier pricing can be changed daily.
With this method, meters can be read daily or as required to provide data to the consumer. Since the data at the meter is billing data, the head end system is not required to create billing data from interval data. All the head end system needs to do is deliver the CTP and other billing data to the billing system.
Comparing the Technologies
When using hourly interval data to implement CTP there is a significant increase in the amount of data the head end system needs to collect and process. If a residential customer has a CTP billing rate it would require the system to collect 24 hourly intervals per day for 30 days. This equates to 720 meter reads per billing period. This level of data collection results in a significant amount of data storage required. Additionally, collected interval data requires VEE processing to convert it to billing data. Concern that interval data may be lost or have errors has encouraged regulators to impose stringent requirements on utilities that use interval data with validation, editing, and estimation to bill customers. These requirements protect the consumer and ensure the utilities provide fair and accurate billing data. The raw certified meter data must often be archived for long periods (three or more years) to protect against billing disputes. This requirement impacts the long-term data storage needed, since the raw certified data is interval data and all intervals must be archived.
Looking more closely at how the data collection requirements compare, take the example of a customer on a two-tier rate (on-peak usage, off-peak usage) with critical peak pricing allowed on up to 15 events a month. Assuming interval data is being collected, the hourly interval data involves 720 meter values for the month (24 hourly interval values times 30 days). If a TOU meter that supports critical tier is used, then 75 meter values are required per month (2 daily billing values times 30 days plus 15 critical peak pricing event values). In this instance, almost 10 times more data collection is required for the hourly interval data compared to daily TOU data
collection.
Other factors to consider are:
- With daily TOU meter data register collection, the head end system does not have to do VEE processing on interval data and convert the interval data into rate tier usage buckets daily for display to customers.
- Data storage requirements are significantly less with daily TOU meter data register collection. Only the TOU register data that is collected from the meter every month for billing needs to be archived. With hourly interval data, 720 data values are required to derive the monthly CTP billing data and all must be archived.
Meters that have a dedicated CTP register eliminate much of the processing needed by the head end system because billing data is stored in the meter’s registers. Less communication bandwidth is needed so communication costs are reduced. If the meter is using a CTP register, only one meter read is required per day to collect the data. Since the CTP data in the meter is billing data and no VEE processing or data conversion is needed, data can be sent to the customer in a more timely manner. Additionally, the logistics of providing near real-time energy usage data to home automation devices such as programmable thermostats and control devices that monitor high-energy use appliances is simplified. These devices can receive real-time data directly from the meter.
Simple meters with add-on modules require additional manipulation of interval data by the head end system. As the number of meters in the system increases, the need for the head end system to process more hourly load profile data introduces lag time in the system. This lag time could potentially diminish a system's near real-time capabilities as the number of meters serviced by the system grows. Smart electronic meters with a CTP register can provide billing data directly to the system in near real-time. The consumer can read their billing data at the meter and the data is readily available to home automation systems at the point of energy delivery.
What is the Cost?
The cost of purchasing a meter module for a simple energy meter is about the same as the cost of a smart meter with a CTP register. Additional fixed costs come into play if a utility company needs to add to or modify the communications infrastructure needed to support the metering technology. But the recurring costs using hourly interval data could be significant.
The following characteristics of using hourly interval data for CTP programs impact recurring costs.
- The communication bandwidth needed to transfer hourly residential interval data over the communication system.
- System processing power and time needed for VEE processing.
- System processing power and time needed to convert interval data into CTP data for the billing period.
- Data storage requirements for both on-line data and archival storage of raw certified meter data.
Additional Impacts of VEE
While it is widely accepted by utilities that billing data can be computed from interval data, there is some inherent risk in labeling the computed results as billing data. When discrepancies are found between the collected interval data and the energy consumption seen at the meter, a level of estimation and editing is required to account for the lost data, and this is not typically viewed as billing data, but is considered modified data. There is also a time element of interval data. Any skew in the real-time relationship of interval data can have a dramatic impact on the computed demand and CTP data. Additional attention for validation, editing, and estimation is given to interval data in instances where the stream of data is interrupted somewhere in the process. Such interruptions may occur because of a problem with the meter, the pulse initiator, the recording medium, or in the data transmission from the field site to the central office system. Also, power outages must be distinguished from net zero consumption periods.
Validation verifies that the collected data matches the energy difference between the start and stop meter readings, and that the number of intervals collected matches the reported time between data collections. If any of the validation procedures fail, estimating and editing of the data is required. Very complex procedures are used by utilities to estimate lost data.
Same-time data from similar accounts, same-time data from previous days for the same account, the average of the last several days’ data and the same-date data from a year ago, plus other variations, have all been used to estimate the missing data.
Conclusion
There is a mutual understanding in the industry that utilities must be able to measure and track each participating customer’s energy consumption to successfully implement demand response programs. The meter must be capable of knowing what critical tier pricing rate is in effect so that it can accurately “bucket” the energy usage. Implementing near real-time pricing (RTP) for customers that have home energy management systems requires that the customer’s meter have the energy usage data readily available.
Obviously, CTP, demand response, and real-time pricing programs can only be successful if the technology can deliver energy usage data in near real-time. This may warrant a closer look at each of these technologies to see if they can perform to both the energy provider’s and the energy consumer’s expectations.
About the Author
Sharon Allan has responsibility for strategic initiatives, third party relations, product management, regulatory affairs, and new business development for Elster Electricity, LLC. In addition, she is a member of the division patent committee overseeing the protection of intellectual property. In 2002, Sharon was named one of the ‘50 Key Women in Energy’ for her global leadership in the area of innovation and creativity. Sharon has been a presenter for Edison Electric Institute, Metering International, and AMRA meetings, as well as other energy industry forums. She is a board member for Metering Americas and National Energy Marketers. Sharon can be reached at sharon.s.allan@us.elster.com
Sharon has an MBA from the Fuqua School of Business at Duke University. She earned a degree in electrical engineering from the University of Florida. Sharon has completed post-graduate electrical engineering courses at NCSU and University of South Carolina.
