Unveiling bidding uncertainties in electricity markets: A Bayesian deep learning framework based on accurate variational inference

Shengyang Wu, Zhaohao Ding, Jingyu Wang and Dongyuan Shi

Energy, 2023, vol. 276, issue C

Abstract: With the ever-increasing level of bidding freedom bestowed to participants in deregulated electricity markets, bidding strategies have become more diversified and complicated, inevitably giving rise to the growth of market uncertainties. Some researchers have developed tools to predict the bidding behaviors of generation companies (GENCOs) considering uncertainties. However, there still remains a gap in enhancing the performance of probabilistic Bidding Behavior Forecasting (BBF) and understanding the sources of bidding uncertainties in electricity markets. This paper proposes a holistic Bayesian Deep Learning (BDL) framework based on Accurate Variational Inference (AVI) to capture both aleatoric uncertainty and epistemic uncertainty, two important uncertainties in bidding behaviors. The introduced framework also procures higher BBF accuracy and reasonable computation cost compared with existing techniques. Derivatives of GENCOs’ bidding uncertainty are ascertained by implementing a new metric to quantify the impact of influence factors. A numerical experiment is conducted using data from National Electricity Market (NEM) in Australia to demonstrate the performance of the proposed framework.

Keywords: Bidding behavior forecasting; Uncertainty analysis; Bayesian deep learning; Influence factor analysis; Accurate variational inference (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:276:y:2023:i:c:s0360544223006801

DOI: 10.1016/j.energy.2023.127286

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