The role of user interaction and acceptance in a cloud-based demand response model

Demand response management is one of the key applications of future energy systems. Most of the corresponding models and algorithms require a certain amount of user interaction to define appliances and rules for these processes. In this paper we implemented a cloud-based demand response approach to evaluate its efficiency depending on users input data. For simulation we used results of an online survey, mainly focusing on selection of devices and temporal flexibility concerning possible switch-off times. Contrary to our expectations the costs per load reduction (incentive prices to be paid by the utility) does not decrease when users have more than the day/night option to define switch-off timeslots. A high effect on cost cutting can be identified in the amount of participating users which underlines the relevance of user acceptance concerning demand response solutions.