Net Metering–Morphing Customers Who Self Generate

The U.S. Public Utilities Regulatory Policy Act of 1978 started a flood of non-utility generation, initially a few very large cogeneration plants and recently a large number of small roof top solar generation.[1]  The rapid growth in the number of small roof top solar generators requires the electric industry to develop a pricing plan that is fair to traditional customers as well as to the hybrid customers, those still connected to the grid but with some self generation.

Electric utilities support their pricing decisions with class cost of service studies  (CCOSS).  The CCOSS allocates the utility’s revenue requirement[2] to groups of customers, called classes.  Classes of customers are claimed to be homogeneous, such as being of a similar size, but more often as having similar load patterns.

Some costs in a CCOSS are allocated based on the number of customers, perhaps weighted by the cost of meters and services.  Fuel costs are allocated based on energy through the meter, though often weighted by the losses incurred to reach the  meter.  A large portion of the costs are allocated based on demand, the amount of energy used by the class during the times of maximum stress on the utility, or at times of maximum stress upon portions of the utility, such as on the generation, the transmission, distribution.  Utilities are concerned about recovering these demand related costs as customers morph from being a full requirements customer to being hybrid customers.

Electric utilities have long alleged that the homogeneity of residential load patterns allowed the utility to use energy meters, often called watt-hour meters, to determine how much each residential customer should pay each month.  The logic is that the allocation process brought costs into the rate class based on the customer’s demand.  Further, homogeneity means that the amount of energy through the meter is proportional to the customer’s demand.  The utility could collect roughly the right amount of money from each residential customer by charging residential customers based on their energy consumption[3] instead of charging residential customers based on the demand.

Charging customers based on energy allowed utilities to reduce substantially the cost of owning and reading meters without significantly distorting how the revenue to cost ratio from each customer.  At least until roof top solar substantially reduced the amount of energy that goes through the meter without necessarily reducing the customer demand.  Thus, with roof top solar, the revenue collected from the customer goes down greatly while the costs brought in by the customer demand goes down only slightly.

The growth in roof top solar coincides with the growth of Advanced Metering Infrastructure (AMI).  AMI often includes automatic meter reading and interval metering .  Automatic meter reading generally means replacing the person walking door to door with equipment.  The carrying cost of the equipment is often less than the cost of the human meter reader, allowing AMI to pay for itself.  Interval metering means collecting the amount of energy delivered during small time intervals, generally one hour (24×7), though sometimes on an intra-hour basis.  These interval readings are the demands in the CCOSS.

The intra-hour meter readings made possible by AMI would allow electric utilities to charge all residential customers based on their maximum demands, the determinant used in CCOSS to allocate costs to customer classes.  No longer would the utility have to rely on homogeneity assumptions in regard to residential customers.  The demand charge permitted by AMI would reduce the disparity between the lower revenue to cost ratio for residential customers with roof top solar relative to the revenue to cost ratio of standard residential customers.



[1] See

[2] the amount of money the utility needs to collect each year to continue functioning

[3] with a very inexpensive watt-hour meter

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Mark Lively earned a BS in Electrical Engineering from MIT in 1969 and a MS in Management from MIT Sloan School in 1971. He worked for American Electric Power Service Corporation in New York City from 1971 to 1976 and at Ernst & Ernst, Ernst & Whinney, Ernst & Young in its Washington Utility Group from 1976 to 1991.

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