Zur Kurzanzeige

dc.contributor.author
Zheng, Kedi
dc.contributor.author
Wen, Bojian
dc.contributor.author
Wang, Yi
dc.contributor.author
Chen, Qixin
dc.date.accessioned
2021-03-03T10:36:12Z
dc.date.available
2021-03-03T08:29:15Z
dc.date.available
2021-03-03T10:36:12Z
dc.date.issued
2020-12
dc.identifier.issn
1751-8695
dc.identifier.issn
1751-8687
dc.identifier.other
10.1049/iet-gtd.2020.1188
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/472628
dc.description.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
en_US
dc.language.iso
en
en_US
dc.publisher
Institution of Engineering and Technology
dc.subject
pricing
en_US
dc.subject
power markets
en_US
dc.subject
linear programming
en_US
dc.subject
stochastic programming
en_US
dc.subject
electricity price forecasting
en_US
dc.subject
deregulated market
en_US
dc.subject
electricity market
en_US
dc.subject
power markets
en_US
dc.subject
bidding curves
en_US
dc.subject
multiparametric linear programming
en_US
dc.subject
forecasted prices
en_US
dc.subject
deterministic bidding models
en_US
dc.subject
stochastic bidding models
en_US
dc.subject
price forecasting error level
en_US
dc.subject
real‐market price data
en_US
dc.subject
predefined forecasting
en_US
dc.title
Impact of electricity price forecasting errors on bidding: a price‐taker's perspective
en_US
dc.type
Journal Article
dc.date.published
2021-01-21
ethz.journal.title
IET Generation, Transmission & Distribution
ethz.journal.volume
14
en_US
ethz.journal.issue
25
en_US
ethz.journal.abbreviated
IET Gener. Transm. Distrib.
ethz.pages.start
6259
en_US
ethz.pages.end
6266
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Stevenage
ethz.publication.status
published
en_US
ethz.date.deposited
2021-03-03T08:29:22Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-03-03T10:36:23Z
ethz.rosetta.lastUpdated
2024-02-02T13:13:32Z
ethz.rosetta.versionExported
true
ethz.COinS
ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.atitle=Impact%20of%20electricity%20price%20forecasting%20errors%20on%20bidding:%20a%20price%E2%80%90taker's%20perspective&rft.jtitle=IET%20Generation,%20Transmission%20&%20Distribution&rft.date=2020-12&rft.volume=14&rft.issue=25&rft.spage=6259&rft.epage=6266&rft.issn=1751-8695&1751-8687&rft.au=Zheng,%20Kedi&Wen,%20Bojian&Wang,%20Yi&Chen,%20Qixin&rft.genre=article&rft_id=info:doi/10.1049/iet-gtd.2020.1188&
 Printexemplar via ETH-Bibliothek suchen

Dateien zu diesem Eintrag

DateienGrößeFormatIm Viewer öffnen

Zu diesem Eintrag gibt es keine Dateien.

Publikationstyp

Zur Kurzanzeige