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Personne# Xavier Pelet

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Resources both physical and financial are scarce in most developing countries while environmental concerns are growing requiring a greater effort to rationally plan investments. Nowadays the methods for designing, planning and managing integrated energy systems, while holistically considering the major economic and environmental factors, are still embryonic. However, the first phase of the design is often crucial if we want to aim at a better resource management and at the reduction of energy consumption together with reduced environmental impact. Considering integrated energy systems implies dealing with complex systems in which the synergy between the various components is exploited at best. The context of isolated communities further increases the difficulties when considering the long distance of transportation required to supply fossil fuels, the constraints of not being connected to a national network and the locations in very precarious environments, with limited or inexistent resources except for solar. This paper illustrates a holistic method to rationalize the design of energy integrated systems accounting for Life Cycle Analysis parameters (CST method). It is based on a superstructure (collection of models of all envisaged technologies) using a multi-modal and multi-objective evolutionary algorithm. The models include both solar power technologies as well as conventional Diesel electric generators. The approach is applied to the supply of energy services to an oasis in the Sahara desert. The study shows that the economic implementation of renewable energy (solar) is even more difficult, compared to Diesel based solutions, in cases of isolated communities with high load variations.

2007The paper discusses the interest of the multi-objective optimization approaches for the design of complex energy systems and the basics of the original evolutionary algorithm used. The specific problems linked to the design of the concentrators of solar tower power plants are then introduced to illustrate the objective of the work presented. The first analysis deals with the optimization of the x-y positioning of each of the 500 rectangular reflectors of an existing predesigned field. In spite of the large number of variables (1000) the algorithm successfully calculated an optimum positioning of each concentrators and the main induced variations are shown. The second part of the paper describes a new program, which was made and coupled with the same algorithm to optimize the whole design in a two-objective approach (specific energy cost versus investment cost). The main hypothesis made, to keep the number of variables within a reasonable domain, was to distribute identical reflectors along a set of concentric ellipses around the tower. The interest of the method is illustrated for one given period with the Pareto curve showing all the optimum solutions (lowest specific energy cost at any given investment). The investment cost includes the cost of the heliostats themselves, the cost of land, the cost of the tower, etc. Variables include the x-y positioning, the height of the support and the dimensions of each rectangular reflectors as well as the height of the tower and the number of reflectors. These decision variables being given for each solution, the extracted energy is calculated by a program of CIEMAT [Sanchez, Romero, 2003]. Details of the design parameters are illustrated for selected optima along the Pareto curve References M. Sanchez, M. Romero Optimisation of heliostat field layout in central receiver systems based on yearly normalized energy surfaces. ISES, 2003, Goteborg

2006Solar thermal power plants have a large potential, particularly in the sun belt countries around the world. The objectives of this study are to identify the heliostat fields for solar tower plants with the lowest specific energy cost and to investigate the impact of field size on the energy obtained. The design of heliostat fields is based on a radial staggered layout, which ensures no mechanical touching and less blocking losses. The field is optimised with a multi-objective optimiser (MOO) based on two objectives (specific energy cost versus investment cost). The resulting Pareto curve shows all the optimum solutions, from small fields with small investment cost to large and costly fields. Results show that with the method chosen, solutions with lower specific costs compared to published test cases can be achieved. Examples of fields along the Pareto curve are shown and the relationships between the decision variables and the energy concentrated are discussed.

2007