Today, the real challenge with the emerging Smart Grids businesses is not the technology itself, it is the the business model and earning logic for the service that the technology is deployed for. I do not want to sound like I am oversimplifying the technical challenges, but in all honesty they are fairly straightforward engineering challenges. The business model can not be worked out so easily, especially since no existing model can be directly applied on price levels, pricing parameters or customer behavior. Therefore a different approach is needed.
The approach that I advocate starts with the seemingly simple task of learning. There is a reason for the use of the word “seemingly” as there are some challenges with it in a completely new business. In an existing business this is much easier since some of the variables are well known (e.g. competition, volumes, price levels, customer preferences), therefore reducing the amount of possible outcomes and the overall complexity of the problem.
In a new, untried business the learning needs to be based on a different principle. The most effective way of learning is by experimenting with the parameters of the business and then measuring the outcome. In order to be effective this approach needs some structure, most importantly the assumptions need to be clearly documented and then adjusted as the measurements are collected.
An example on charging point location selection illustrates this principle better. While using our planning tool one needs to have an idea on the principles behind the charging point locations. For example, my preference is to look for locations where people will park their vehicles in any case (retail locations, offices, etc) and locations where multiple user groups can make use of a single location (commuters, shoppers, etc). As an initial set of charging points is installed, a set of Key Performance Indicators (KPIs) needs to be set up to measure the usage of the poles, track changes in the patterns and warn of pending capacity issues. The KPIs will fairly quickly tell, if the chosen locations work as assumed in the planning stage or whether changes are needed.
The learning is based on the documented assumptions and measurement of the outcome. This allows the next round of installations to be done more intelligently, either saving money on avoiding erroneous locations or hitting the right locations. Without the documented assumptions or the measurements the learning will not happen.
This link to an article in the UtilityDive publication illustrates the learning process in practice with a couple of utilities learning the most effective deployment scenario for their EV chaging infrastructure.