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


METADATA ONLY
Loading...

Date

2020-12

Publication Type

Journal Article

ETH Bibliography

yes

Citations

Altmetric
METADATA ONLY

Data

Rights / License

Abstract

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. © 2020 The Institution of Engineering and Technology

Permanent link

Publication status

published

Editor

Book title

Volume

14 (25)

Pages / Article No.

6259 - 6266

Publisher

Institution of Engineering and Technology

Event

Edition / version

Methods

Software

Geographic location

Date collected

Date created

Subject

pricing; power markets; linear programming; stochastic programming; electricity price forecasting; deregulated market; electricity market; power markets; bidding curves; multiparametric linear programming; forecasted prices; deterministic bidding models; stochastic bidding models; price forecasting error level; real‐market price data; predefined forecasting

Organisational unit

Notes

Funding

Related publications and datasets