The cost of providing operational flexibility from distributed energy resources

Abstract

Flexible household devices, such as heat pumps combined with thermal energy storage or battery energy storage units, can provide flexibility to the electricity sector. However, to make flexibility available to the market, it has to be correctly quantified, and its cost has to be estimated. In this work, a methodology for generic flexibility quantification is proposed and developed in a Python environment using model predictive control. The chosen methodology allows to quantify the adjustable power, and also to determine the corresponding cost of the flexibility provision. It was observed that the available flexibility and its cost is influenced by many factors such as system components, human behavior, building thermal parameters, and price signals. Also, the inclusion of even a low share of households with batteries or electric vehicles smoothens the aggregated flexibility profile, and a considerable amount of flexibility is available at almost any point in time.