Demand response (DR) plays a critical role in ensuring efficient electricity consumption and optimal use 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 apply 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 end-user price-responsive behaviour when 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. Lastly, the business model reduces the electricity costs of solar end users by 11%.

翻译：需求响应（DR）在确保电力高效消费和电网资产优化利用中起着关键作用。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。本研究提出了一种价格响应模型，该模型整合了终端用户非理性的核心特征，具体包括损失厌恶、时间不一致性和有限理性。为此，我们首先构建了一个框架，利用基于局部加权回归的多季节趋势分解（MSTL）和非平稳高斯过程来模拟住宅用户的电力消费随机性。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，我们应用了一种机会约束优化模型来管理CBS运行，以应对终端用户非理性行为带来的不可预测性。基于真实世界数据的仿真结果表明，所提出的DR模型在考虑非理性行为时能更真实地估计终端用户的价格响应行为。与无法完全考虑终端用户非理性行为的确定性模型相比，机会约束CBS运行模型额外带来了19%的收益。最后，该商业模式使太阳能终端用户的电力成本降低了11%。