If we consider the telecom business, over the last several years they have moved away from a real time billing model to a model of monthly packages and, in many cases unlimited usage. Clearly, unlimited usage is not a suitable model for energy usage since the objective of the smart grid initiative is to reduce consumption not to promote more consumption. However, as with the telecom example, I believe that the utility industry can and should develop various pricing models that abstract usage to a higher level and allow for real time pricing. Some examples of possible models would include:

• Monthly usage packages similar to those employed by the telecom industry. In these models, there could be several tiers of usage with each tier having a different per kWH price. Reduced consumption could be encouraged by charging premium prices for consumption above a specified level. An individual plan could have one or more tiers with the first N kWH being charged at the lowest base rate, additional tiers charged at progressively higher rates and any usage above those tiers charged at a significant premium. To be effective, the lowest tier would have to offer a significant discount over the current fixed rates. In this case, the pricing signals that would be sent to the consumer would indicate their current consumption against their monthly plan and could even predict the total bill based on consumption trends. Warnings could be sent when a tier threshold is crossed. The home energy management system could have a role to play here by maintaining data on average daily consumption and using this to forecast total usage for the current billing cycle. This would not be exact but would provide a signal that the consumer could act on and, over time, the consumer would get a feeling for how accurate the projections are. In fact the intelligence could be built into the systems to allow them to self calibrate over a period of time to become increasingly accurate.

• Variable rates based on historical data. The utilities could develop models of demand based on historical data that predict, for a given set of attributes such as; time of day, day of the week, temperature, humidity, air quality etc, what mix of generation sources they need to call upon to meet demand and the actual cost of that particular mix of generating assets. Based on these models, they could derive estimated price points. These could be discrete based on certain thresholds or continuously variable as appropriate. These price signals would then be sent to consumers or to their smart meters/ home energy management systems to effect demand response.

Other models are possible too. The important point is that moving to delivery of real time pricing does not necessarily mean that the utilities need to completely overhaul their current offline billing systems and make those real time.

The lack of finalized standards for the smart grid is another piece that comes into play. No utility is terribly willing to make drastic changes to their systems with the fear that they may have to change them again once DOE standards are set in the future.

Another issue is whether or not the consumer is ready to see this kind of rapidly-changing data. The swings from base load to peak power can be great and are constantly changing with the market. If this information is fed too quickly, customers with good intentions of being conscientious of their energy usage may experience information overload. The result is discouragement and cessation of use of these great tools.

As to real-time data, what’s so difficult? If you are interested in real-time data, you most likely already have Internet access, the utility doesn’t need to provide it. A wireless webcam pointed at the meter and some OCR software would provider real-time data for under $50 per meter.