As the renewable energy sector grows, the electric power grid—in North America and all over the world—is becoming a much more dynamic and data-driven network. Some view this as a shift to volatility, uncertainty, and as a result, less reliability. Aside from volatility during the transition, reality is the opposite. Similar to our computer transition from mainframes to distributed networks, the result will be lower-cost, more intelligent and more reliable power infrastructure. It also will require a lot of data and advanced analytics from individual project design to grid management. In this new era, the geeks will rule the grid.
According to MIT’s recent study, Utility of the Future, flat volumetric tariffs are already no longer adequate for today’s power systems. They are responsible for inefficient investment, consumption, operational decisions, and pricing signals. Rather, there needs to be an increasing number of information technology solutions not only to monitor withdrawals and injections but also to create more efficient prices, charges and effective delivery of ancillary services. Granularity matters. To create efficient pricing and operations, the number of measurement nodes will increase by orders of magnitude.
However, those are current operations. That exploding quantity of data will also affect network design. Specifically, current and forecast demand data gives much more precision to transmission flows and forecasts, and as a result, optimal siting of new projects and their technology choices to optimize return on investment for developers and owners and to provide the most competitive offering for system operators and energy users. Where and what kind of new resources to develop will no longer be an artisanal trade by prospectors, but data driven investment decisions by many large players looking to create just a little more yield through identification of precise opportunities.
We already are seeing how development is evolving. It is less the art of land deals that then try to find a way to make a project succeed and more becoming more a science of quantitatively analyzing diverse inputs to find optimal sets of sites for projects, including demand, system configuration, equipment, labor, tax, interest rates, balance of plant costs, and interconnection. While electrical engineering and sales skills will continue to be fundamental in the power business, data science coupled with programming in python and R along with data integration and quality are becoming must-haves from trading to operations and all the way back to the development stage.