For the last couple of years the commonly accepted view is that load factor growth is a major threat to the economical operation of electricity utilities in Australia. The problem is that peak demand has been growing but consumption has not been growing at the same rate. For utilities, the main capital expenditure cost is providing enough network capacity for maximum demand, but revenue from sales of electricity comes from what is sold.
Part of the hypothesis for why load is growing is that consumers are buying more energy efficient devices and are bombarded with energy conservation messages which means that ordinarily they use less electricity but on peak hot days they forgo conservation for comfort and contribute to peak demand. The result is that electricity utilities still have high capital expenditure but the revenue base shrinks due to non-peak energy saving by consumers. But is this really true or is it a false assumption based on a subtle variation of the Khazzoom-Brookes postulate?
In terms of an energy paradox, there are a couple of observations that I can make from the analytics we have been doing recently. As one industry leader told me recently, network forecasting was very easy until about 2006. The period from about 2003 to 2008 did not only include some of the hottest years on record but also was notable for record population growth.
Then everything changed.
Since the late-2000s many substation forecasts have started to break down. This has been a problem common to a number of utilities, but it has been of particular interest to networks that have been experiencing growth in connections. It is compounded by growing political pressure to reduce capital expenditure.
Firstly, we had a global downturn which depressed economic activity and caused widespread job losses that encouraged consumers to save money. In Australia, we also had what is starting to look like a long cycle change in the climate with a series of wetter, cooler summers which means that peak air conditioner use has fallen in many places. And in mid-2008 the rate of population growth started to slow. Even more curiously, the pattern of consumption by housing age starts to change. From about 1980 until the mid-2000s, the newer the house the higher the consumption, but around 2005-2008 all that changed. After this date, fewer houses start to consume relatively less electricity and possibly also less peak demand. Daniel Collins at Ausgrid has done some interesting analysis in this regard which he presented at the 2011 ANZ Smart Utilities Conference.
And then there are solar photo voltaic panels which have been subsidised by government and in effect increase load factor and therefore electricity prices. Solar panels reduce net consumption by feeding electricity back into the grid, and this usually happens during the middle of the day when the sun is brightest but peak demand happens in the early evening on hot days when people get home to a hot house and turn on the air conditioning. This leads to lower revenue but the same or higher network costs.
Electricity is perhaps the oldest post-Industrial revolution technology still in widespread use and this means that there is a long body of thought and experience in understanding its consumption and the mechanisms for its delivery. This strength may also however prove to be a weakness. Many ideas have been around for a long time and are no longer routinely challenged. The energy paradox is one of them. I am not saying that the underlying of the premise is wrong, but there is certainly room to reinterpret this idea in relation to the current situation in Australia. Challenging orthodox thinking. You need to be very sure of your facts. This is where analytics and a wide spread of both data and data mining methodologies can help. As I have said, analytics is at its strongest when it is not hypothesis driven but is working without an explicit hypothesis or trying to decide between many competing ones.
The answer is that I don’t really know what is going on with load factor. I have yet to be convinced that anyone (including myself) has worked how to properly account for the climatic variance of peak demand, or fully understand the relationship between housing age and consumption, or what the true relationship is with population growth, or how many speeds our multi-speed economy has, but something is definitely happening and investigative analytics has excellent potential in being able greatly develop our understanding of how all of these effects interact.