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.

中文翻译：

---

需求响应程序中基线操纵的分析和评估

客户基线需要为基于基线的需求响应（DR）计划的参与者分配回扣。平均基线方法由于其简单性和可靠性已在实践中被广泛接受。但是，客户的基准操作在文献中鲜为人知。我们从客户的角度出发，建立一个马尔可夫决策过程来对客户的收益最大化问题进行建模。揭示了理性客户在灾难恢复日的消费不足和非灾难恢复日的过度消费的行为。此外，我们提出了一种近似的基线方法，并显示了消耗量分布和程序参数如何影响结果。由于维数的诅咒，引入了基于线性策略的卷展算法，以获得实用的近似解。最后，