Challenge Description

In the traditional operation of electrical energy systems, production always shifts to meet demand with a number of large power plants that ramp up and down operation to meet consumers requirements. As more and more inflexible renewable energy loads, such as solar and wind, come online, this traditional operation is facing new challenges.

With the high costs of large-scale storage, flexibility on the demand side of the equation is predicted to be an essential part of the solution. Demand side management is a concept where flexible consumption as a service can be compensated to balance the grid. Certain loads, such as heat pumps, electric vehicle charging, and batteries, can be shifted to times of peak production without any impact on the consumer. In order to evaluate the potential energy which could be shifted, this task proposes to evaluate user consumption profiles in order to identify the specific flexible loads and the total quantifiable flexible potential. In addition, the analysis should include a prediction of how much peak energy consumption can be reduced with this flexibility and how much money could be saved in energy costs by offering up this flexibility to utilities.

Another step would be to evaluate the installation of major appliances and flexible loads in a household, including how much money could be saved by updating inefficient appliances and introducing flexible generation.

Data

Several datasets can be used which include real information sampled form households at high frequency. Some of these data sets are listed below:

• TUM data set
• BLUED data from Carnegie Mellon
• Belkin data set
• AMPd data set
• Dutch Residential Energy Dataset
• COOLL data set