Accurate Data for Settlement and Wholesale Hedging
In the electricity market, suppliers have to purchase an amount of electricity that matches the amount they provide to their end customers. This is done through a process known as settlement. Suppliers have to “settle” on a half-hourly basis with higher charges at peak times.
At present suppliers only know total consumption on an aggregated basis, which is then apportioned across half-hourly periods based on typical “profiles”. These remain estimates. The energy suppliers will have little incentive to move their customers to time-of-use tariffs unless their settlement charges are based on the actual usage of their customers, rather than the average national profile figures.
The industry recognises that smart meters could allow accurate setllement, but again the conventional view is that actual half-hourly consumption data will have to be exported from each meter and processed on the server side by the settlement authorities. As well as the privacy implications of this, there are also the considerable costs of moving the data to the setlement authorities and processing it centrally.
Settlement Algorithm Executed Locally
A “settlement applet” can be sent to the smart meter. This applet can access the actual 30-minute data, and it can apply a settlement algorithm. A single figure of merit can be sent to the settlement authority, perhaps monthly, that accurately reflects the real consumption at that meter.
Data transfer and processing costs can be dramatically reduced and almost no personal information is exported from the home. The settlement bodies would only require the data in aggregated form, not the raw data.
As an example, the algorithm would multiply actual half-hourly consumption by a weightng curve and accumulate the result. At the end of the month the total would be a measure of how that household’s consumption varied from a national average – a single figure is sufficient for an accurate settlement calculation.
Wholesale Hedging
Wholesale hedging is a commercial activity linked to the settlement issue discussed above. Suppliers will typically buy a large proportion of their energy needs in advance and will need to do so for half-hourly slots. The better the information they have on their customers’ usage, the better they will be able to forecast their future energy demands and buy ahead what they need and manage their costs.
Energy suppliers argue that they need customers’ 30-minute data for this.
It is easy to envisage some local processing algorithm that could be used to provide the neccessary data to the energy suppliers without divulging all of the half-hourly data.
For example, a single 30-minute reading could be selected at random once a month by a “hedging applet” and sent to the energy supplier. When averaged over their millions of customers this would build a statistically reliable picture of aggregated consumption patterns, with almost no loss of privacy. As an additional protection, perhaps the random measurement could be sent to the settlement authority who would perform the aggregation.