George Danner, Business Laboratory
Most of today’s commercially available renewable energy technologies are subject to the whims of nature: sunshine intensity fluctuates and wind strength and direction is difficult to predict. Storing renewable energy often is not feasible — kinetic storage like hydroelectric pumped storage is rarely close by the renewable source — making the supply less controllable than fossil fuels.
Electric utility companies are using dated models to predict demand that assume peak usage at certain times of day. Technology advances are increasing the use of electric vehicles and information-communication technologies, such as wireless networks used to access cloud services, which means electricity consumption in the future will be far different from the traditional load shape we have seen in the last 50 years. More importantly, those models do not adequately consider input from renewable resources, preventing grid companies from including them into the supply mix.
Increased adoption of smart meters creates a wealth of new data that can be collected and analyzed to identify true usage patterns, more accurately forecast demand, and optimize supply. This includes input from renewable resources, and how they can be incorporated in the future. My own experience in demand forecasting in the UK suggests that smart meters combined with demographic data give us a much sharper picture of energy consumers of tomorrow.
Computing advances provide capability to simulate and model a wide range of plausible futures. The resolution rests on regulators, utility companies and others to recognize the need for our energy models to adapt, with smarter logic and comprehensive scenario planning. It is then that we will be able to visualize the proper role of renewable technologies in the energy mix, both now and in the long run.
George Danner has 29 years of experience in corporate strategy across multiple industries. At Business Laboratory, he utilizes simulation modeling to improve organizational performance through problem solving, optimization and advanced forecasting. George holds a bachelor’s degree in mechanical engineering from Texas A&M University and a master’s degree in management from MIT.
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