No abstract

Wind capacity credit is usually defined in relation to LOLE (Loss of load expectation). For instance, the credit can be estimated as the additional conventional thermal capacity that would be necessary to maintain the same LOLE level if wind was not in place. This indicator however encapsulates no risk aversion: it allows to compensate a LOLE increase in some winters by an identical LOLE decrease in some others winters with different meteorology and electricty demand. Such a change in the risk distribution is precisely what can happen when a large amount of wind is installed in the system, compared to a system based only on conventional capacity. This paper relies on a sample of 100 meteorological years with Great Britain characteristics. It is found that at constant overall LOLE, the system with large wind capacity displays a larger number of high-LOLE winters. This may be unwelcome for risk-adverse consumers, regulators or decision-makers. We therefore propose to account for extreme risks better by rooting the definition of the wind capacity credit on the 5%-worst years (with the highest LOLE). The case studied suggests that the reduction of the credit is small, about -1 point reduction from a credit of 20%.

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 SummaryOne of the main issues in the climate policy agenda, the timing of abatement efforts, hinges on the uncertainties of climate change risks and technological evolution. We use a stochastic optimization framework and jointly explore these two features. First, we embed in the model future potential large-scale availability of Carbon Capture and Storage (CCS) technologies. While non-CCS mitigation that reduces fossil energy use is modelled as exerting inertia on the economic system, mainly due to the durability of the capital in energy systems and to technology lock-in and lock-out phenomena, the implementation of CCS technologies is modelled as implying less resilience of the system to changes in policy directions. Second, climate uncertainty is related in the model to the atmospheric temperature response to an increase in GHGs concentration.Performing different simulation experiments, we find that the environmental target, derived from a cost-benefit analysis, should be more ambitious when CCS is included in the picture. Moreover, the possible future availability of CCS is not a reason to significantly reduce near-term optimal abatement efforts. Finally, the availability of better information on the climate cycle is in general more valuable than better information on the CCS technological option.

No abstract

No abstract