  The customer baseline is required to assign rebates to participants in
baseline-based demand response (DR) programs. The average baseline method has
been widely accepted in practice due to its simplicity and reliability.
However, the customer's baseline manipulation is little-known in the
literature. We start from a customer's perspective and establish a Markov
decision process to model the customer's payoff-maximizing problem. The
behavior of a rational customer's underconsumption on DR days and
overconsumption on non-DR days are revealed. Furthermore, we propose an
approximated baseline method and show how the consumption distribution and
program parameters affect the results. Due to the curse of dimensionality, a
linear policy-based rollout algorithm is introduced to obtain a practical
approximate solution. Finally, a case study is carried out to illustrate the
baseline manipulation, where the simulation results confirm the effectiveness
of the proposed methods and shed light on how to properly design baseline
methods.
