Empowering consumers with actionable intelligence about their power will not be the outcome of the deployment of smart meters. Rather, it will be exactly what the utilities intend for it to be: a cost-effective way to implement real-time pricing, demand side management and distribution system monitoring.In essence, his argument is that the data from smart meters is not granular enough for consumers to make informed decisions about electricity usage . We, consumers, can control our usage, and ultimately the electricity bill, by turning on and off specific electric appliances, e.g. TVs, washers, dryers, etc. A smart meter is only going to give us data about electricity consumption for the whole house, not a particular device. The data would still have to be processed further (how?) to help us figure out when and which device ought to be turned on or off to save money.
Remarkably, depending on the system scale, the usefulness of the same data is drastically different. While the electricity supplier can use it directly to make power generation and distribution decisions, the consumer is stuck with more information overload.
No wonder, Google wants a piece of the smart meter action. The greater information overload, the greater the need for data processing services.
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Notes for the class:
1. A good exercise for the Magicians. The case shows how a change in the point of view (Climb-on-the-Roof Magician) allows us to identify problems in the super-system (power generation & distribution) vs the sub-system ( house & house appliances).
2. Five-element analysis (recursive up and down): House as a Tool in the super-system; Appliance as a Tool in the sub-system (House). The role of Control, both on super- and sub- system levels. The difference between data and information.
3. Assuming that smart meters are deployed on a mass scale (short-term), formulate detection and control problems on the sub-system (House) level. Propose solutions ( tech & business).
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