  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%.
