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ACCOUNTING FOR LEARNING CURVES

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ACCOUNTING FOR LEARNING CURVES
Kyoto´s Protocol signed by European Countries in 1997 made Eurozone commits itself to reducing their CO2 emissions by 5% (referred to 1990 figures) [1].
In order to achieve such a reduction all countries were forced to their own abatement figures and also to incorporate environmental policies to their legislations. The EU Commision´s 2007 and 2008 communications and proposals were the basis of Directive 2009/28/EC where Strategy 2020 was established. The basic core of this strategy is 20/20/20: 20% reduction in CO2 emissions, 20% renewable energy consumption and 20% efficiency on energy management in buildings [2].
Achieving those ambitious targets is based on selecting a choice of instruments to provide the investors with stable regulatory policies as huge inversions are needed, in fact by 2020 the financial cost of such a big re-designing has been estimated at 70 M Euro/per year [3] [4].
This paper analyzes the different feed in tariff policies (FIT policies) used by the EU27 countries and identifies the best practices among them. The aim of the paper is to design a Pareto-superior feed in tariff structure for long term optimal photovoltaic development which is able to converge to a grid-parity situation and therefore with no need for incentives.
FIT is an energy supply policy created for supporting the development of new renewable energy projects by offering guaranteed prices over fixed periods of time for energy produced from Renewable Energy Sources [5] it is also defined by a long term purchase agreement ([6], [7], [8]) offered within contracts over a period of time ranging from 10 to 25 years for every kilowatt per hour produced [9]; the growing question is: how can we fix the price per Kw/h? shall the price be linked to market electricity price?
Premium FIT (linked to market price) and Fixed FIT (not linked) are two different approaches to FIT design policies; but not only the linkage to electricity market price is needed, there are also 3 main

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