Modelling Irrational Behaviour of Residential End Users using Non-Stationary Gaussian Processes

TL;DR

This paper introduces a novel demand response model that incorporates irrational end-user behaviors like loss aversion and bounded rationality using non-stationary Gaussian processes, improving realism and economic outcomes.

Contribution

It develops a new framework combining MSTL and non-stationary Gaussian processes to model irrational consumption patterns and applies chance-constrained optimization for better CBS operation.

Findings

Proposed model yields more realistic demand estimates.

Chance-constrained CBS operation increases revenue by 19%.

Business model reduces solar end-user costs by 11%.

Abstract

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%.