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This work builds a comprehensive North–West European Electricity Market model for the year 2020 and uses it to quantify the impacts of ambitious national renewable electricity targets. The geographical coverage of the model comprises Germany, France, Belgium, Netherlands, Luxemburg, Great Britain and Ireland. The model simulates the electricity market operation for the entire region at half hourly resolution and produces results in terms of electricity prices, cross border flows, emissions and associated total system costs. The impact of two carbon prices is examined within the model. Results highlight the policy challenges that arise when individual Member States formulate renewable energy plans in isolation in the absence of integrated modelling of interconnected regions as cross border power flows play a more significant role in market dynamics especially in the presence of geographically dispersed variable renewable generation sources such as wind and solar. From a policy perspective results suggest that based on these national plans, congestion will be present on a number of key lines at long periods during the year. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a model of the 2020 Irish electricity system which was developed and solved in a mixed integer programming, unit commitment and economic dispatch tool called PLEXOS®. The model includes all generators on the island of Ireland, a simplified representation of the neighbouring British system including proposed wind capacity and interconnectors between the two systems. The level of wind curtailment is determined under varying levels of three influencing factors. The first factor is the amount of offshore wind, the second is the allowed limit of system non-synchronous penetration (SNSP) and the third is inclusion or exclusion of transmission constraints. A binding constraint, resulting from the 2020 EU renewable energy targets, is that 37% of generation comes from wind. When the SNSP limit was increased from 60% to 75% there was a reduction in wind curtailment from 14% to 7%, with a further reduction when the proportion of wind capacity installed offshore was increased. Wind curtailment in the range of SNSP limit of 70–100% is influenced primarily by the inclusion of transmission constraints. Large changes in the dispatch of conventional generators were also evident due to the imposition of SNSP limits and transmission constraints. [Copyright &y& Elsevier]

Abstract: This paper describes a method for quantifying wind farm availability using two different approaches and comparing the results. Wind turbine suppliers regularly guarantee turbine availability in terms of time. A typical value of 97% is generally taken as the industry standard. This paper shows that this guarantee can potentially under-compensate the wind farm operator for losses sustained depending on when the period of non-availability occurs. Here we present an alternative method to quantify wind farm availability based on energy, which relates the energy losses in an Irish wind farm in 2007 to periods of turbine non-availability. It is shown in this analysis completed at this operational wind farm that while the technical non-availability as a percentage of time is 3%, the percentage of energy lost during downtimes is actually 11%. Based on the financial analysis above, the financial losses are significant. To answer the question should wind turbine availability be time or energy based, this paper shows that it can be advantageous for wind turbine owners to have energy-based calculations as long as the developers have sufficient monitoring of not only wind speed but also SCADA data. [Copyright &y& Elsevier]