Impact of electricity price forecasting errors on bidding: a price-taker's perspective

• Published in: IET generation, transmission & distribution Vol. 14; no. 25; pp. 6259 - 6266
• Main Authors: Zheng, Kedi, Wen, Bojian, Wang, Yi, Chen, Qixin
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 22.12.2020; Wiley
• Subjects: bidding curves; deregulated market; deterministic bidding models; electricity market; electricity price forecasting; forecasted prices; linear programming; multiparametric linear programming; power markets; predefined forecasting; price forecasting error level; pricing; real‐market price data; Research Article; stochastic bidding models; stochastic programming; electricity market; electricity price forecasting; forecasted prices; stochastic programming; real-market price data; stochastic bidding models; predefined forecasting; linear programming; deregulated market; bidding curves; deterministic bidding models; power markets; multiparametric linear programming; price forecasting error level; pricing
• ISSN: 1751-8687; 1751-8695
• DOI: 10.1049/iet-gtd.2020.1188

Electricity price forecasting is very important for market participants in a deregulated market. However, only a few papers investigated the impact of forecasting errors on the market participants' behaviours and revenues. In this study, a general formulation of bidding in the electricity market is considered and the participant is assumed to be a price-taker which is general for most of the participants in power markets. A numerical method for quantifying the impact of forecasting errors on the bidding curves and revenues based on multiparametric linear programming is proposed. The forecasted prices are regarded as exogenous parameters for both deterministic and stochastic bidding models. Compared with the existing method, the proposed method can calculate how much improvement will be achieved in the cost or revenue of the bidder if he reduces the price forecasting error level, and such calculation does not require any predefined forecasting results. Numerical results and discussions based on real-market price data are conducted to show the application of the proposed method.