Demand response (DR) plays a critical role in ensuring efficient electricity consumption and optimal usage of network assets. Yet, existing DR models often overlook a crucial element, the irrational behaviour of electricity end users. In this work, we propose a price-responsive model that incorporates key aspects of end-user irrationality, specifically loss aversion, time inconsistency, and bounded rationality. To this end, we first develop a framework that uses Multiple Seasonal-Trend decomposition using Loess (MSTL) and non-stationary Gaussian processes to model the randomness in the electricity consumption by residential consumers. The impact of this model is then evaluated through a community battery storage (CBS) business model. Additionally, we propose a chance-constrained optimisation model for CBS operation that deals with the unpredictability of the end-user irrationality. Our simulations using real-world data show that the proposed DR model provides a more realistic estimate of price-responsive behaviour considering irrationality. Compared to a deterministic model that cannot fully take into account the irrational behaviour of end users, the chance-constrained CBS operation model yields an additional 19% revenue. In addition, the business model reduces the electricity costs of end users with a rooftop solar system by 11%.

翻译：需求响应（DR）在确保高效用电和优化网络资产使用中起着关键作用。然而，现有DR模型往往忽略了一个关键因素——电力终端用户的非理性行为。本文提出了一种价格响应模型，该模型整合了终端用户非理性行为的关键方面，具体包括损失厌恶、时间不一致性和有限理性。为此，我们首先开发了一个框架，该框架利用基于Loess的多重季节趋势分解（MSTL）和非平稳高斯过程来建模居民用电的随机性。随后，通过社区电池储能（CBS）商业模式评估了该模型的影响。此外，我们还提出了一种应对终端用户非理性行为不可预测性的机会约束优化模型用于CBS运营。基于真实数据的仿真结果表明，所提出的DR模型在考虑非理性行为的情况下，能更真实地估计价格响应行为。与无法充分考虑终端用户非理性行为的确定性模型相比，采用机会约束的CBS运营模型可额外增加19%的收益。同时，该商业模式使拥有屋顶太阳能系统的终端用户电费降低了11%。