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.