How to engage households in energy demand response solutions?

Within the smart energy and smart grid field, Demand Response (DR) is expected to play an important role in balancing energy consumption with intermittent energy production from renewable sources in the future. This DR vision, in many cases, relies on active involvement of households in actions aimed at time shifting their energy demand. However, DR solutions targeted households have had a rather limited diffusion and impact so far, and it has proven difficult to ensure sustained user engagement.

Based on practice-theoretical approaches to residential energy demand, this paper takes as its starting point that new methods to involve households are required if we are going to design DR solutions that will be successful in engaging households in DR actions. As a contribution to this, the paper explores what role learning might play in realizing DR solutions based on active involvement of households. Here, learning is understood as both the self-reflection about own habits and the appropriation of new practices. The role of learning is explored by, first, identifying and discussing the types of situations and dynamics in everyday life that can initiate processes of learning. Such situations or dynamics are termed “initiators of cultivation”, and examples include social feedback, encountering others’ ways of doing, embodied sensory feedback, etc. Secondly, we discuss to what extent existing DR approaches are employing these situations or dynamics in order to foster instances of learning in relation to moving everyday practices in time. The conclusion is that the scope of present DR approaches is quite narrow and only addresses a limited number of possible initiators of cultivation. Therefore, the paper provides some first ideas on how to make DR designs better at initiating processes of learning, which could lead to more engaging and successful DR solutions. This part informs policy-makers as well as DR solution designers.

The paper is based on a literature review. We are not necessarily implying that new learning approaches “by default” result in efficient DR solutions that are successful in changing the energy consumption profiles of households. This is an open question, which needs further theoretical and empirical exploration to be answered, but we hope that our paper contributes to the foundation of such further investigation.