Australians pay billions of dollars every year for an event that usually doesn’t happen: a critical demand peak on the electricity network. Electricity networks are designed to ensure continuous supply of electricity regardless of the demand placed on it. Every few years we are likely to experience a heat wave or cold snap that drives up simultaneous demand for energy across the network. The infrastructure required to cope with this peak in demand is very expensive; infrastructure that is not used except during these relatively rare events.
Shaving even just a very small amount of demand off these peak days has the potential to save up to $1.2b each year nationally according to a recent report by Deloitte. The tricky part is to try and target the peaks rather than drive down energy consumption in non-peak times. It’s this non-peak consumption that pays the bills for infrastructure investment. If distributors get less revenue and their peak infrastructure costs stay the same then prices have to go up. This is one of the big reasons why electricity prices have risen so steeply in recent years.
One way to do this is to send a price signal or incentive for consumers to moderate their demand on peak days. Last year at the ANZ Smart Utilities Conference in Sydney, Daniel Collins from Ausgrid gave an interesting presentation comparing the benefits for distributors of offering a critical peak pricing versus critical peak rebate. A critical peak price is where the network issues a very steep increase in electricity price on a handful of days each year. This price might be as much as ten times the usual electricity price. Under a critical peak rebate scheme consumers are charged the same amount on peak days but are given a rebate by the distributor if they keep their peak below a pre-defined threshold.
In electricity market where distributors cannot own retailers (the most common type of market in Australia) it is very difficult for price signals set by distributors to reach end consumers. This is because the distributors charge retailers and retailers then set the price and product options offered to consumers. Distributor price signals can get obscured in this process. In this type of market critical peak prices are unlikely to be mandated by government because it goes against a policy of deregulation and is highly politically unpalatable in an environment of rapidly increasing electricity prices. The only option for distributors then is an opt-in price.
The effectiveness of such a price then is highly dependent on the opt-in rate and given that only consumers who do stand to lose under such a price are the only one likely to opt in then the overall savings may be quite low.
A more interesting concept is critical peak rebate. For a start the rebate is given by the distributor directly which avoids the incentive being obscured by retail pricing. Such a scheme is also likely to attract a much greater uptake than opt-in peak pricing. The tricky part however is the design. How much rebate should be offered? Which consumers should be targeted and will they be interested? How do we set the upper demand limit?
It would be a mistake to offer the same deal to all consumers as it is very hard to offer a general incentive with significant return. A badly designed rebate could easily cost more to administer than it saves. There are four crucial elements that need to be considered in the design of a CPR.
How to measure the benefit?
This is quite tricky but by far the most important design element. There is a lag time between energy peaks on the network and infrastructure costs. This is because infrastructure spending is usually allocated on a five year cycle based on forecasts developed from historical peak demand data. It is vital that a scheme is deigned to capture the net savings in peak demand and that there is process to feed this data into the forecasting process. Unfortunately, I have never seen a demand management team feed data to a forecasting team.
Who do we target?
The first issue is to work out which consumers have high peaking demand and are likely to take up the incentive. There should also be consideration to how data will collected and analysed during the roll out of the program, and how this data is used to continually drive better targeting of the program. The problem with a one-size-fits-all scheme is that there may be a number of different groups who have different motivations for curtailing their peak demand. For example, the rebate financial incentive may be set for the average consumer but may not be high enough to appeal to a wealthy consumer. But there may be other ways to appeal to these customers such as offering donation to a charity if the peak demand saving target is reached. It is therefore to think about a segmentation approach to targeting the right customers with the right offer.
What price, demand threshold and event frequency do we set?
Pricing the incentive is a three dimensional problem: target demand threshold, price and frequency of events. Each of these effect the total benefit of the scheme and the consumer trade-offs need to be understood. The danger here again is relying on averages. Different cohorts of customer will have different trade-off thresholds and an efficient design is vital to the effectiveness of the incentive. It is unlikely that there is room to vary the rebate amount based on customer attributes but there is certainly room to design individualised demand thresholds and maybe also the frequency with which events are called for different cohorts of customers.
How do we refine the program?
In the rush to get new programs to market, response data and customer intelligence feedback is often not well considered. It is important that there is a system for holding data and routines for measuring response against control groups for each treatment group in the program so that incremental benefits can be measured but also so data can be fed back into improving models which select customers for the program. Incremental benefits of the program should also feed back into refining pricing of the rebate and target demand thresholds. Understanding which customers respond and the quantum of that response are valuable insights into customer behaviour which distributors usually do have the ability to capture in the normal course of their business. These are all good reasons for running a well-designed CPR program.