Risk–return incentives in liberalised electricity markets

Tol, Richard S.J.; Lynch, Muireann Á.; Shortt, Aonghus; O’Malley, Mark

doi:10.1016/j.eneco.2013.08.015

Cited by 24 publications

(9 citation statements)

References 12 publications

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“…The FAST algorithm mimics the input-output relationship of a mixed-integer unit commitment model but does so in orders of magnitude faster, which is of practical relevance when simulating many scenarios. The algorithm is described in Lynch et al (2013) and Shortt and O'Malley (2014) and seeks to determine least-cost schedules for generation dispatch, considering start-up and no load costs, as well as variable costs and technical constraints. The FAST solution produces unit-commitment and economic dispatch schedules whose costs are on par with those from the MIP under a relatively tight optimality gap.…”

Section: 4mentioning

confidence: 99%

“…Flexible units (which tend to be numerous, small and more exible) are represented by linear costs. FAST solutions bear a strong degree of similarity across a number of metrics with equivalent mixed-integer programmes except for computation time, where FAST on average determines schedules several thousand times more quickly (Lynch et al, 2013). FAST's computational eciencies are achieved through a number of simplications.…”

Section: 4mentioning

confidence: 99%

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The impact of the North Atlantic Oscillation on electricity markets: A case study on Ireland

2016

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. www.econstor.eu ESRI working papers represent un-refereed work-in-progress by researchers who are solely responsible for the content and any views expressed therein. Any comments on these papers will be welcome and should be sent to the author(s) by email. Papers may be downloaded for personal use only. Terms of use: Documents in The Impact of the North Atlantic Oscillation on Electricity Markets: A Case Study on IrelandJohn Curtis a,b , Muireann Á . Lynch a,b , Laura Zubiate c Abstract: The North Atlantic Oscillation (NAO) is a large-scale circulation pattern driving climate variability in north-western Europe. As the deployment of wind-powered generation expands on electricity networks across Europe the impacts of the NAO on the electricity system will be amplified. This study assesses the impact of NAO, via wind-power generation, on the electricity market considering thermal generation costs, wholesale electricity prices and wind generation subsidies. A Monte Carlo approach is used to model NAO phases and generate hourly wind speed time-series data, electricity demand and fuel input data. A least-cost unit commitment and economic dispatch model is used to simulate an island electricity system, modelled on the all-island Irish electricity system. The impact of NAO obviously depends on the level of wind capacity within an electricity system. Our results indicate that NAO phases can affect thermal generation costs by up to 8%, wholesale electricity prices by as much as €1.5/MWh, and that wind power generators receive on average 12% higher remuneration.

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Section: 4mentioning

confidence: 99%

Section: 4mentioning

confidence: 99%

The impact of the North Atlantic Oscillation on electricity markets: A case study on Ireland

2016

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“…There is no explicit limit on the maximum level of instantaneous wind generation but FAST will curtail wind energy where doing so will reduce total costs. Policy applications using the FAST algorithm to date include Lynch et al (2013), which investigates risk-return incentives in electricity markets.…”

Section: Fast : Flexible Algorithm For Scheduling Technologiesmentioning

confidence: 99%

The effects of wind generation capacity on electricity prices and generation costs: a Monte Carlo analysis

Lynch

Curtis

2015

Applied Economics

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may Muireann Á Lynch, John CurtisAbstract: We use Monte Carlo analysis to examine the potential of increased renewable generation to provide a hedge against variability in energy prices and costs. Fuel costs, electricity demand and wind generation are allowed to vary and a unit commitment and economic dispatch algorithm is employed to produce costminimising generation schedules under different levels of installed wind capacity. Increased wind capacity reduces the mean and the variance of production costs but only the variance of electricity prices. Wind generators see their market revenues increase while consumer payments and fossil generator profits do not considerably vary as wind capacity increases. Risk aversion is captured by considering the Conditional Value-at-Risk for both consumers and producers. The optimal level of wind generation increases as risk aversion increases due to the potential of wind to act as a hedge against very high electricity prices in high fuel price scenarios.

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“…For an electricity dispatch model we use the Flexible Algorithm for Scheduling Technologies (FAST ), which was developed as a response to the problem of providing electricity generation schedules that mimic system-operator decisions in real time, while meeting demand and respecting technical constraints (Shortt and O'Malley, 2014;Lynch et al, 2013). Several approaches are used in the literature to simulate electricity generation schedules depending on the application.…”

Section: 4mentioning

confidence: 99%

“…The electricity output of exible units (which tend to be numerous, small and more exible) are represented by linear variables. FAST solutions bear a strong degree of similarity across a number of metrics with equivalent mixed-integer programmes except for computation time, where FAST on average determines schedules several thousand times more quickly (Lynch et al, 2013). FAST's computational eciencies are augmented through a number of simplications.…”

Section: 4mentioning

confidence: 99%

Carbon dioxide (CO2) emissions from electricity: The influence of the North Atlantic Oscillation

2016

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. In recent years there has been an increasing deployment of wind-powered generation technology, i.e. wind farms, on electricity networks across Europe. As this deployment increases it is important to understand how climate variability will affect both windpowered and non-renewable power generation. This study extends the literature by assessing the impact of NAO, via wind-power generation, on carbon dioxide emissions from the wider electricity system. A Monte Carlo approach is used to model NAO phases, generate hourly wind speed timeseries data, electricity demand and fuel input data. A unit commitment, least-cost economic dispatch model is used to simulate an entire electricity system, modelled on the all-island Irish electricity system. Our results confirm that the NAO has a significant impact on monthly mean wind speeds, wind power output, and carbon dioxide emissions from the entire electricity system. The impact of NAO on emissions obviously depends on the level of wind penetration within an electricity system but our results indicate that emissions intensity within the Irish electricity system could vary by as much as 10% depending on the NAO phase within the next few years. The emissions intensity of the electricity system will vary with the NAO phase. Terms of use: Documents in

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Risk–return incentives in liberalised electricity markets

Cited by 24 publications

References 12 publications

The impact of the North Atlantic Oscillation on electricity markets: A case study on Ireland

The impact of the North Atlantic Oscillation on electricity markets: A case study on Ireland

The effects of wind generation capacity on electricity prices and generation costs: a Monte Carlo analysis

Carbon dioxide (CO2) emissions from electricity: The influence of the North Atlantic Oscillation

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