The CLIMIX model: A tool to create and evaluate spatially-resolved scenarios of photovoltaic and wind power development. (February 2015) - Jerez, S.; Thais, F.; Tobin, I.; Wild, M.; Colette, A.; Yiou, P.; Vautard, R.
Abstract: Renewable energies arise as part of both economic development plans and mitigation strategies aimed at abating climate change. Contrariwise, most renewable energies are potentially vulnerable to climate change, which could affect in particular solar and wind power. Proper evaluations of this two-way climate–renewable energy relationship require detailed information of the geographical location of the renewable energy fleets. However, this information is usually provided as total amounts installed per administrative region, especially with respect to future planned installations. To help overcome this limiting issue, the objective of this contribution was to develop the so-called CLIMIX model: a tool that performs a realistic spatial allocation of given amounts of both photovoltaic (PV) and wind power installed capacities and evaluates the energy generated under varying climate conditions. This is done over a regular grid so that the created scenarios can be directly used in conjunction with outputs of climate models. First, we used the 0.44° resolution grid defined for the EURO-CORDEX project and applied the CLIMIX model to spatially allocate total amounts of both unreported 2012 and future 2020 PV and wind power installations in Europe at the country level. Second, we performed a validation exercise using the various options for estimating PV and wind power production under the created scenarios that are included in the model. The results revealed an acceptable agreement between the estimated and the recorded power production values in every European country. Lastly, we estimated increases in power production derived from the future deployment of new renewable units, often obtaining non-direct relationships. This latter further emphasizes the need of accurate spatially-resolved PV and wind power scenarios in order to perform reliable estimations of power production.