Personalized Feedback-based Customer Incentives in Automated Demand Response

Abstract

Automated Demand Response (ADR) can facilitate residential customers to effectively reduce their energy demand and make savings in a simple way, provided that appropriate incentives are offered to them. Most often, incentives involved in ADR contracts are statically defined and assume full customer rationality, thus hindering sustained customer enrollment to them of customers with other characteristics (e.g. altruism). In this paper, we derive appropriate (and personalized) incentives for ADR contracts, so that non-fully rational customers are compensated even when information for consumer utilities is not available. In case such information is hidden, we assume that customers provide feedback on their satisfaction from direct endowments, albeit sustaining energy-consumption reduction. Moreover, we consider the case where customers may strategically lie on their satisfaction from ADR incentives, so as to self-optimize. We mathematically model the customer and the utility company’s problems and solve them algebraically or in a distributed manner. Furthermore, based on customer feedback on appropriate endowments for different energy-consumption reductions, we propose an algorithm that can find the optimal set of satisfied targeted customers, which achieve the total desired energy-consumption reduction at the minimum endowment cost. Based on numerical evaluation and simulation experiments, we showcase the validity of our analytical framework in realistic scenarios and that, for the case of hidden information, customer feedback is adequate for calculating incentives that can lead to successful DR campaigns.