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The exploitation of solar power for energy supply is of increasing importance. While technical development mainly takes place in the engineering disciplines, computer science offers adequate techniques for simulation, optimisation and controller synthesis. In this paper we describe a work from this interdisciplinary area. We introduce our tool for the optimisation of parameterised solar thermal power plants, and report on the employment of genetic algorithms and neural networks for parameter synthesis. Experimental results show the applicability of our approach.

With the success of CSP technology in the last years more players are active in the market, inducing the need for harmonization of technical terms and methodologies. The mission of the SolarPACES “guiSmo” project which was started in 2010 is to develop a guideline for CSP yield analysis [1]. Activities carried out so far have shown that people have different understandings of many terms used in daily CSP practice. Especially for the development of guidelines, the essential terms need to be clearly defined in order to avoid inconsistencies within the same project. A first version of a nomenclature has been compiled by the “guiSmo” team and will undergo final discussion. The aim is to come to a harmonized version by Summer 2013 which will then be presented at the ASME Energy Sustainability conference. The compilation so far includes essential definitions of terms like direct normal irradiance, incident angles, heat flows, and efficiencies on a system level. The definitions presented will be discussed together with existing standards like the ISO 80000 (physical quantities and units of measurement), the ISO 9488 (Solar energy-vocabulary) and other relevant sources. Although the list of terms is primarily put together for the work in the “guiSmo” project, it might serve as a basis for standardization in the official councils. An international group of solar experts is involved in the preparation of the document in order to ensure high quality and international support for the results.