The natural hedge of a gas-fired power plant
Abstract Electricity industries worldwide have been restructured in order to introduce competition. As a result, decision makers are exposed to volatile electricity prices, which are positively correlated with those of natural gas in markets with price-setting gas-fired power plants. Consequently, g...
Ausführliche Beschreibung
Autor*in: |
Guo, Xiaojia [verfasserIn] |
---|
Format: |
Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2014 |
---|
Schlagwörter: |
---|
Anmerkung: |
© The Author(s) 2014 |
---|
Übergeordnetes Werk: |
Enthalten in: Computational Management Science - Springer Berlin Heidelberg, 2004, 13(2014), 1 vom: 21. Okt., Seite 63-86 |
---|---|
Übergeordnetes Werk: |
volume:13 ; year:2014 ; number:1 ; day:21 ; month:10 ; pages:63-86 |
Links: |
---|
DOI / URN: |
10.1007/s10287-014-0222-x |
---|
Katalog-ID: |
OLC2075351449 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | OLC2075351449 | ||
003 | DE-627 | ||
005 | 20230502181949.0 | ||
007 | tu | ||
008 | 200820s2014 xx ||||| 00| ||eng c | ||
024 | 7 | |a 10.1007/s10287-014-0222-x |2 doi | |
035 | |a (DE-627)OLC2075351449 | ||
035 | |a (DE-He213)s10287-014-0222-x-p | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 330 |a 510 |q VZ |
082 | 0 | 4 | |a 330 |q VZ |
084 | |a 3,2 |2 ssgn | ||
100 | 1 | |a Guo, Xiaojia |e verfasserin |4 aut | |
245 | 1 | 0 | |a The natural hedge of a gas-fired power plant |
264 | 1 | |c 2014 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
338 | |a Band |b nc |2 rdacarrier | ||
500 | |a © The Author(s) 2014 | ||
520 | |a Abstract Electricity industries worldwide have been restructured in order to introduce competition. As a result, decision makers are exposed to volatile electricity prices, which are positively correlated with those of natural gas in markets with price-setting gas-fired power plants. Consequently, gas-fired plants are said to enjoy a “natural hedge.” We explore the properties of such a built-in hedge for a gas-fired power plant via a stochastic programming approach, which enables characterisation of uncertainty in both electricity and gas prices in deriving optimal hedging and generation decisions. The producer engages in financial hedging by signing forward contracts at the beginning of the month while anticipating uncertainty in spot prices. Using UK energy price data from 2006 to 2011 and daily aggregated dispatch decisions of a typical gas-fired power plant, we find that such a producer does, in fact, enjoy a natural hedge, i.e., it is better off facing uncertain spot prices rather than locking in its generation cost. However, the natural hedge is not a perfect hedge, i.e., even modest risk aversion makes it optimal to use gas forwards partially. Furthermore, greater operational flexibility enhances this natural hedge as generation decisions provide a countervailing response to uncertainty. Conversely, higher energy-conversion efficiency reduces the natural hedge by decreasing the importance of natural gas price volatility and, thus, its correlation with the electricity price. | ||
650 | 4 | |a Electricity markets | |
650 | 4 | |a Risk management | |
650 | 4 | |a Stochastic programming | |
700 | 1 | |a Beskos, Alexandros |4 aut | |
700 | 1 | |a Siddiqui, Afzal |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Computational Management Science |d Springer Berlin Heidelberg, 2004 |g 13(2014), 1 vom: 21. Okt., Seite 63-86 |w (DE-627)380021463 |w (DE-600)2136735-8 |w (DE-576)113850212 |x 1619-697X |7 nnns |
773 | 1 | 8 | |g volume:13 |g year:2014 |g number:1 |g day:21 |g month:10 |g pages:63-86 |
856 | 4 | 1 | |u https://doi.org/10.1007/s10287-014-0222-x |z lizenzpflichtig |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_OLC | ||
912 | |a SSG-OLC-WIW | ||
912 | |a SSG-OPC-MAT | ||
912 | |a GBV_ILN_26 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_267 | ||
912 | |a GBV_ILN_2018 | ||
912 | |a GBV_ILN_4277 | ||
951 | |a AR | ||
952 | |d 13 |j 2014 |e 1 |b 21 |c 10 |h 63-86 |
author_variant |
x g xg a b ab a s as |
---|---|
matchkey_str |
article:1619697X:2014----::hntrlegoaafrd |
hierarchy_sort_str |
2014 |
publishDate |
2014 |
allfields |
10.1007/s10287-014-0222-x doi (DE-627)OLC2075351449 (DE-He213)s10287-014-0222-x-p DE-627 ger DE-627 rakwb eng 330 510 VZ 330 VZ 3,2 ssgn Guo, Xiaojia verfasserin aut The natural hedge of a gas-fired power plant 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2014 Abstract Electricity industries worldwide have been restructured in order to introduce competition. As a result, decision makers are exposed to volatile electricity prices, which are positively correlated with those of natural gas in markets with price-setting gas-fired power plants. Consequently, gas-fired plants are said to enjoy a “natural hedge.” We explore the properties of such a built-in hedge for a gas-fired power plant via a stochastic programming approach, which enables characterisation of uncertainty in both electricity and gas prices in deriving optimal hedging and generation decisions. The producer engages in financial hedging by signing forward contracts at the beginning of the month while anticipating uncertainty in spot prices. Using UK energy price data from 2006 to 2011 and daily aggregated dispatch decisions of a typical gas-fired power plant, we find that such a producer does, in fact, enjoy a natural hedge, i.e., it is better off facing uncertain spot prices rather than locking in its generation cost. However, the natural hedge is not a perfect hedge, i.e., even modest risk aversion makes it optimal to use gas forwards partially. Furthermore, greater operational flexibility enhances this natural hedge as generation decisions provide a countervailing response to uncertainty. Conversely, higher energy-conversion efficiency reduces the natural hedge by decreasing the importance of natural gas price volatility and, thus, its correlation with the electricity price. Electricity markets Risk management Stochastic programming Beskos, Alexandros aut Siddiqui, Afzal aut Enthalten in Computational Management Science Springer Berlin Heidelberg, 2004 13(2014), 1 vom: 21. Okt., Seite 63-86 (DE-627)380021463 (DE-600)2136735-8 (DE-576)113850212 1619-697X nnns volume:13 year:2014 number:1 day:21 month:10 pages:63-86 https://doi.org/10.1007/s10287-014-0222-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_26 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 13 2014 1 21 10 63-86 |
spelling |
10.1007/s10287-014-0222-x doi (DE-627)OLC2075351449 (DE-He213)s10287-014-0222-x-p DE-627 ger DE-627 rakwb eng 330 510 VZ 330 VZ 3,2 ssgn Guo, Xiaojia verfasserin aut The natural hedge of a gas-fired power plant 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2014 Abstract Electricity industries worldwide have been restructured in order to introduce competition. As a result, decision makers are exposed to volatile electricity prices, which are positively correlated with those of natural gas in markets with price-setting gas-fired power plants. Consequently, gas-fired plants are said to enjoy a “natural hedge.” We explore the properties of such a built-in hedge for a gas-fired power plant via a stochastic programming approach, which enables characterisation of uncertainty in both electricity and gas prices in deriving optimal hedging and generation decisions. The producer engages in financial hedging by signing forward contracts at the beginning of the month while anticipating uncertainty in spot prices. Using UK energy price data from 2006 to 2011 and daily aggregated dispatch decisions of a typical gas-fired power plant, we find that such a producer does, in fact, enjoy a natural hedge, i.e., it is better off facing uncertain spot prices rather than locking in its generation cost. However, the natural hedge is not a perfect hedge, i.e., even modest risk aversion makes it optimal to use gas forwards partially. Furthermore, greater operational flexibility enhances this natural hedge as generation decisions provide a countervailing response to uncertainty. Conversely, higher energy-conversion efficiency reduces the natural hedge by decreasing the importance of natural gas price volatility and, thus, its correlation with the electricity price. Electricity markets Risk management Stochastic programming Beskos, Alexandros aut Siddiqui, Afzal aut Enthalten in Computational Management Science Springer Berlin Heidelberg, 2004 13(2014), 1 vom: 21. Okt., Seite 63-86 (DE-627)380021463 (DE-600)2136735-8 (DE-576)113850212 1619-697X nnns volume:13 year:2014 number:1 day:21 month:10 pages:63-86 https://doi.org/10.1007/s10287-014-0222-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_26 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 13 2014 1 21 10 63-86 |
allfields_unstemmed |
10.1007/s10287-014-0222-x doi (DE-627)OLC2075351449 (DE-He213)s10287-014-0222-x-p DE-627 ger DE-627 rakwb eng 330 510 VZ 330 VZ 3,2 ssgn Guo, Xiaojia verfasserin aut The natural hedge of a gas-fired power plant 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2014 Abstract Electricity industries worldwide have been restructured in order to introduce competition. As a result, decision makers are exposed to volatile electricity prices, which are positively correlated with those of natural gas in markets with price-setting gas-fired power plants. Consequently, gas-fired plants are said to enjoy a “natural hedge.” We explore the properties of such a built-in hedge for a gas-fired power plant via a stochastic programming approach, which enables characterisation of uncertainty in both electricity and gas prices in deriving optimal hedging and generation decisions. The producer engages in financial hedging by signing forward contracts at the beginning of the month while anticipating uncertainty in spot prices. Using UK energy price data from 2006 to 2011 and daily aggregated dispatch decisions of a typical gas-fired power plant, we find that such a producer does, in fact, enjoy a natural hedge, i.e., it is better off facing uncertain spot prices rather than locking in its generation cost. However, the natural hedge is not a perfect hedge, i.e., even modest risk aversion makes it optimal to use gas forwards partially. Furthermore, greater operational flexibility enhances this natural hedge as generation decisions provide a countervailing response to uncertainty. Conversely, higher energy-conversion efficiency reduces the natural hedge by decreasing the importance of natural gas price volatility and, thus, its correlation with the electricity price. Electricity markets Risk management Stochastic programming Beskos, Alexandros aut Siddiqui, Afzal aut Enthalten in Computational Management Science Springer Berlin Heidelberg, 2004 13(2014), 1 vom: 21. Okt., Seite 63-86 (DE-627)380021463 (DE-600)2136735-8 (DE-576)113850212 1619-697X nnns volume:13 year:2014 number:1 day:21 month:10 pages:63-86 https://doi.org/10.1007/s10287-014-0222-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_26 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 13 2014 1 21 10 63-86 |
allfieldsGer |
10.1007/s10287-014-0222-x doi (DE-627)OLC2075351449 (DE-He213)s10287-014-0222-x-p DE-627 ger DE-627 rakwb eng 330 510 VZ 330 VZ 3,2 ssgn Guo, Xiaojia verfasserin aut The natural hedge of a gas-fired power plant 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2014 Abstract Electricity industries worldwide have been restructured in order to introduce competition. As a result, decision makers are exposed to volatile electricity prices, which are positively correlated with those of natural gas in markets with price-setting gas-fired power plants. Consequently, gas-fired plants are said to enjoy a “natural hedge.” We explore the properties of such a built-in hedge for a gas-fired power plant via a stochastic programming approach, which enables characterisation of uncertainty in both electricity and gas prices in deriving optimal hedging and generation decisions. The producer engages in financial hedging by signing forward contracts at the beginning of the month while anticipating uncertainty in spot prices. Using UK energy price data from 2006 to 2011 and daily aggregated dispatch decisions of a typical gas-fired power plant, we find that such a producer does, in fact, enjoy a natural hedge, i.e., it is better off facing uncertain spot prices rather than locking in its generation cost. However, the natural hedge is not a perfect hedge, i.e., even modest risk aversion makes it optimal to use gas forwards partially. Furthermore, greater operational flexibility enhances this natural hedge as generation decisions provide a countervailing response to uncertainty. Conversely, higher energy-conversion efficiency reduces the natural hedge by decreasing the importance of natural gas price volatility and, thus, its correlation with the electricity price. Electricity markets Risk management Stochastic programming Beskos, Alexandros aut Siddiqui, Afzal aut Enthalten in Computational Management Science Springer Berlin Heidelberg, 2004 13(2014), 1 vom: 21. Okt., Seite 63-86 (DE-627)380021463 (DE-600)2136735-8 (DE-576)113850212 1619-697X nnns volume:13 year:2014 number:1 day:21 month:10 pages:63-86 https://doi.org/10.1007/s10287-014-0222-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_26 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 13 2014 1 21 10 63-86 |
allfieldsSound |
10.1007/s10287-014-0222-x doi (DE-627)OLC2075351449 (DE-He213)s10287-014-0222-x-p DE-627 ger DE-627 rakwb eng 330 510 VZ 330 VZ 3,2 ssgn Guo, Xiaojia verfasserin aut The natural hedge of a gas-fired power plant 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2014 Abstract Electricity industries worldwide have been restructured in order to introduce competition. As a result, decision makers are exposed to volatile electricity prices, which are positively correlated with those of natural gas in markets with price-setting gas-fired power plants. Consequently, gas-fired plants are said to enjoy a “natural hedge.” We explore the properties of such a built-in hedge for a gas-fired power plant via a stochastic programming approach, which enables characterisation of uncertainty in both electricity and gas prices in deriving optimal hedging and generation decisions. The producer engages in financial hedging by signing forward contracts at the beginning of the month while anticipating uncertainty in spot prices. Using UK energy price data from 2006 to 2011 and daily aggregated dispatch decisions of a typical gas-fired power plant, we find that such a producer does, in fact, enjoy a natural hedge, i.e., it is better off facing uncertain spot prices rather than locking in its generation cost. However, the natural hedge is not a perfect hedge, i.e., even modest risk aversion makes it optimal to use gas forwards partially. Furthermore, greater operational flexibility enhances this natural hedge as generation decisions provide a countervailing response to uncertainty. Conversely, higher energy-conversion efficiency reduces the natural hedge by decreasing the importance of natural gas price volatility and, thus, its correlation with the electricity price. Electricity markets Risk management Stochastic programming Beskos, Alexandros aut Siddiqui, Afzal aut Enthalten in Computational Management Science Springer Berlin Heidelberg, 2004 13(2014), 1 vom: 21. Okt., Seite 63-86 (DE-627)380021463 (DE-600)2136735-8 (DE-576)113850212 1619-697X nnns volume:13 year:2014 number:1 day:21 month:10 pages:63-86 https://doi.org/10.1007/s10287-014-0222-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_26 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 13 2014 1 21 10 63-86 |
language |
English |
source |
Enthalten in Computational Management Science 13(2014), 1 vom: 21. Okt., Seite 63-86 volume:13 year:2014 number:1 day:21 month:10 pages:63-86 |
sourceStr |
Enthalten in Computational Management Science 13(2014), 1 vom: 21. Okt., Seite 63-86 volume:13 year:2014 number:1 day:21 month:10 pages:63-86 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Electricity markets Risk management Stochastic programming |
dewey-raw |
330 |
isfreeaccess_bool |
false |
container_title |
Computational Management Science |
authorswithroles_txt_mv |
Guo, Xiaojia @@aut@@ Beskos, Alexandros @@aut@@ Siddiqui, Afzal @@aut@@ |
publishDateDaySort_date |
2014-10-21T00:00:00Z |
hierarchy_top_id |
380021463 |
dewey-sort |
3330 |
id |
OLC2075351449 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2075351449</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502181949.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2014 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10287-014-0222-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2075351449</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10287-014-0222-x-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">330</subfield><subfield code="a">510</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">330</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">3,2</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Guo, Xiaojia</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">The natural hedge of a gas-fired power plant</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2014</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Electricity industries worldwide have been restructured in order to introduce competition. As a result, decision makers are exposed to volatile electricity prices, which are positively correlated with those of natural gas in markets with price-setting gas-fired power plants. Consequently, gas-fired plants are said to enjoy a “natural hedge.” We explore the properties of such a built-in hedge for a gas-fired power plant via a stochastic programming approach, which enables characterisation of uncertainty in both electricity and gas prices in deriving optimal hedging and generation decisions. The producer engages in financial hedging by signing forward contracts at the beginning of the month while anticipating uncertainty in spot prices. Using UK energy price data from 2006 to 2011 and daily aggregated dispatch decisions of a typical gas-fired power plant, we find that such a producer does, in fact, enjoy a natural hedge, i.e., it is better off facing uncertain spot prices rather than locking in its generation cost. However, the natural hedge is not a perfect hedge, i.e., even modest risk aversion makes it optimal to use gas forwards partially. Furthermore, greater operational flexibility enhances this natural hedge as generation decisions provide a countervailing response to uncertainty. Conversely, higher energy-conversion efficiency reduces the natural hedge by decreasing the importance of natural gas price volatility and, thus, its correlation with the electricity price.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Electricity markets</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Risk management</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Stochastic programming</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Beskos, Alexandros</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Siddiqui, Afzal</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Computational Management Science</subfield><subfield code="d">Springer Berlin Heidelberg, 2004</subfield><subfield code="g">13(2014), 1 vom: 21. Okt., Seite 63-86</subfield><subfield code="w">(DE-627)380021463</subfield><subfield code="w">(DE-600)2136735-8</subfield><subfield code="w">(DE-576)113850212</subfield><subfield code="x">1619-697X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:13</subfield><subfield code="g">year:2014</subfield><subfield code="g">number:1</subfield><subfield code="g">day:21</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:63-86</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10287-014-0222-x</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-WIW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_267</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">13</subfield><subfield code="j">2014</subfield><subfield code="e">1</subfield><subfield code="b">21</subfield><subfield code="c">10</subfield><subfield code="h">63-86</subfield></datafield></record></collection>
|
author |
Guo, Xiaojia |
spellingShingle |
Guo, Xiaojia ddc 330 ssgn 3,2 misc Electricity markets misc Risk management misc Stochastic programming The natural hedge of a gas-fired power plant |
authorStr |
Guo, Xiaojia |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)380021463 |
format |
Article |
dewey-ones |
330 - Economics 510 - Mathematics |
delete_txt_mv |
keep |
author_role |
aut aut aut |
collection |
OLC |
remote_str |
false |
illustrated |
Not Illustrated |
issn |
1619-697X |
topic_title |
330 510 VZ 330 VZ 3,2 ssgn The natural hedge of a gas-fired power plant Electricity markets Risk management Stochastic programming |
topic |
ddc 330 ssgn 3,2 misc Electricity markets misc Risk management misc Stochastic programming |
topic_unstemmed |
ddc 330 ssgn 3,2 misc Electricity markets misc Risk management misc Stochastic programming |
topic_browse |
ddc 330 ssgn 3,2 misc Electricity markets misc Risk management misc Stochastic programming |
format_facet |
Aufsätze Gedruckte Aufsätze |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
nc |
hierarchy_parent_title |
Computational Management Science |
hierarchy_parent_id |
380021463 |
dewey-tens |
330 - Economics 510 - Mathematics |
hierarchy_top_title |
Computational Management Science |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)380021463 (DE-600)2136735-8 (DE-576)113850212 |
title |
The natural hedge of a gas-fired power plant |
ctrlnum |
(DE-627)OLC2075351449 (DE-He213)s10287-014-0222-x-p |
title_full |
The natural hedge of a gas-fired power plant |
author_sort |
Guo, Xiaojia |
journal |
Computational Management Science |
journalStr |
Computational Management Science |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
300 - Social sciences 500 - Science |
recordtype |
marc |
publishDateSort |
2014 |
contenttype_str_mv |
txt |
container_start_page |
63 |
author_browse |
Guo, Xiaojia Beskos, Alexandros Siddiqui, Afzal |
container_volume |
13 |
class |
330 510 VZ 330 VZ 3,2 ssgn |
format_se |
Aufsätze |
author-letter |
Guo, Xiaojia |
doi_str_mv |
10.1007/s10287-014-0222-x |
dewey-full |
330 510 |
title_sort |
the natural hedge of a gas-fired power plant |
title_auth |
The natural hedge of a gas-fired power plant |
abstract |
Abstract Electricity industries worldwide have been restructured in order to introduce competition. As a result, decision makers are exposed to volatile electricity prices, which are positively correlated with those of natural gas in markets with price-setting gas-fired power plants. Consequently, gas-fired plants are said to enjoy a “natural hedge.” We explore the properties of such a built-in hedge for a gas-fired power plant via a stochastic programming approach, which enables characterisation of uncertainty in both electricity and gas prices in deriving optimal hedging and generation decisions. The producer engages in financial hedging by signing forward contracts at the beginning of the month while anticipating uncertainty in spot prices. Using UK energy price data from 2006 to 2011 and daily aggregated dispatch decisions of a typical gas-fired power plant, we find that such a producer does, in fact, enjoy a natural hedge, i.e., it is better off facing uncertain spot prices rather than locking in its generation cost. However, the natural hedge is not a perfect hedge, i.e., even modest risk aversion makes it optimal to use gas forwards partially. Furthermore, greater operational flexibility enhances this natural hedge as generation decisions provide a countervailing response to uncertainty. Conversely, higher energy-conversion efficiency reduces the natural hedge by decreasing the importance of natural gas price volatility and, thus, its correlation with the electricity price. © The Author(s) 2014 |
abstractGer |
Abstract Electricity industries worldwide have been restructured in order to introduce competition. As a result, decision makers are exposed to volatile electricity prices, which are positively correlated with those of natural gas in markets with price-setting gas-fired power plants. Consequently, gas-fired plants are said to enjoy a “natural hedge.” We explore the properties of such a built-in hedge for a gas-fired power plant via a stochastic programming approach, which enables characterisation of uncertainty in both electricity and gas prices in deriving optimal hedging and generation decisions. The producer engages in financial hedging by signing forward contracts at the beginning of the month while anticipating uncertainty in spot prices. Using UK energy price data from 2006 to 2011 and daily aggregated dispatch decisions of a typical gas-fired power plant, we find that such a producer does, in fact, enjoy a natural hedge, i.e., it is better off facing uncertain spot prices rather than locking in its generation cost. However, the natural hedge is not a perfect hedge, i.e., even modest risk aversion makes it optimal to use gas forwards partially. Furthermore, greater operational flexibility enhances this natural hedge as generation decisions provide a countervailing response to uncertainty. Conversely, higher energy-conversion efficiency reduces the natural hedge by decreasing the importance of natural gas price volatility and, thus, its correlation with the electricity price. © The Author(s) 2014 |
abstract_unstemmed |
Abstract Electricity industries worldwide have been restructured in order to introduce competition. As a result, decision makers are exposed to volatile electricity prices, which are positively correlated with those of natural gas in markets with price-setting gas-fired power plants. Consequently, gas-fired plants are said to enjoy a “natural hedge.” We explore the properties of such a built-in hedge for a gas-fired power plant via a stochastic programming approach, which enables characterisation of uncertainty in both electricity and gas prices in deriving optimal hedging and generation decisions. The producer engages in financial hedging by signing forward contracts at the beginning of the month while anticipating uncertainty in spot prices. Using UK energy price data from 2006 to 2011 and daily aggregated dispatch decisions of a typical gas-fired power plant, we find that such a producer does, in fact, enjoy a natural hedge, i.e., it is better off facing uncertain spot prices rather than locking in its generation cost. However, the natural hedge is not a perfect hedge, i.e., even modest risk aversion makes it optimal to use gas forwards partially. Furthermore, greater operational flexibility enhances this natural hedge as generation decisions provide a countervailing response to uncertainty. Conversely, higher energy-conversion efficiency reduces the natural hedge by decreasing the importance of natural gas price volatility and, thus, its correlation with the electricity price. © The Author(s) 2014 |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_26 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 |
container_issue |
1 |
title_short |
The natural hedge of a gas-fired power plant |
url |
https://doi.org/10.1007/s10287-014-0222-x |
remote_bool |
false |
author2 |
Beskos, Alexandros Siddiqui, Afzal |
author2Str |
Beskos, Alexandros Siddiqui, Afzal |
ppnlink |
380021463 |
mediatype_str_mv |
n |
isOA_txt |
false |
hochschulschrift_bool |
false |
doi_str |
10.1007/s10287-014-0222-x |
up_date |
2024-07-04T01:05:14.092Z |
_version_ |
1803608511316230144 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2075351449</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502181949.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2014 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10287-014-0222-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2075351449</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10287-014-0222-x-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">330</subfield><subfield code="a">510</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">330</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">3,2</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Guo, Xiaojia</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">The natural hedge of a gas-fired power plant</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2014</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Electricity industries worldwide have been restructured in order to introduce competition. As a result, decision makers are exposed to volatile electricity prices, which are positively correlated with those of natural gas in markets with price-setting gas-fired power plants. Consequently, gas-fired plants are said to enjoy a “natural hedge.” We explore the properties of such a built-in hedge for a gas-fired power plant via a stochastic programming approach, which enables characterisation of uncertainty in both electricity and gas prices in deriving optimal hedging and generation decisions. The producer engages in financial hedging by signing forward contracts at the beginning of the month while anticipating uncertainty in spot prices. Using UK energy price data from 2006 to 2011 and daily aggregated dispatch decisions of a typical gas-fired power plant, we find that such a producer does, in fact, enjoy a natural hedge, i.e., it is better off facing uncertain spot prices rather than locking in its generation cost. However, the natural hedge is not a perfect hedge, i.e., even modest risk aversion makes it optimal to use gas forwards partially. Furthermore, greater operational flexibility enhances this natural hedge as generation decisions provide a countervailing response to uncertainty. Conversely, higher energy-conversion efficiency reduces the natural hedge by decreasing the importance of natural gas price volatility and, thus, its correlation with the electricity price.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Electricity markets</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Risk management</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Stochastic programming</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Beskos, Alexandros</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Siddiqui, Afzal</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Computational Management Science</subfield><subfield code="d">Springer Berlin Heidelberg, 2004</subfield><subfield code="g">13(2014), 1 vom: 21. Okt., Seite 63-86</subfield><subfield code="w">(DE-627)380021463</subfield><subfield code="w">(DE-600)2136735-8</subfield><subfield code="w">(DE-576)113850212</subfield><subfield code="x">1619-697X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:13</subfield><subfield code="g">year:2014</subfield><subfield code="g">number:1</subfield><subfield code="g">day:21</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:63-86</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10287-014-0222-x</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-WIW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_267</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">13</subfield><subfield code="j">2014</subfield><subfield code="e">1</subfield><subfield code="b">21</subfield><subfield code="c">10</subfield><subfield code="h">63-86</subfield></datafield></record></collection>
|
score |
7.3998337 |