Modelling Irrational Behaviour of Residential End Users using Non-Stationary Gaussian Processes
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866909150496161792 |
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| author | Dinh, Nam Trong Karimi-Arpanahi, Sahand Yuan, Rui Pourmousavi, S. Ali Guo, Mingyu Liisberg, Jon A. R. Lemos-Vinasco, Julian |
| author_facet | Dinh, Nam Trong Karimi-Arpanahi, Sahand Yuan, Rui Pourmousavi, S. Ali Guo, Mingyu Liisberg, Jon A. R. Lemos-Vinasco, Julian |
| contents | 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%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_09012 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Modelling Irrational Behaviour of Residential End Users using Non-Stationary Gaussian Processes Dinh, Nam Trong Karimi-Arpanahi, Sahand Yuan, Rui Pourmousavi, S. Ali Guo, Mingyu Liisberg, Jon A. R. Lemos-Vinasco, Julian Systems and Control Information Theory 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%. |
| title | Modelling Irrational Behaviour of Residential End Users using Non-Stationary Gaussian Processes |
| topic | Systems and Control Information Theory |
| url | https://arxiv.org/abs/2309.09012 |