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A Parametric Genetic Algorithm Approach to Assess Complementary Options of Large Scale Wind-solar Coupling

IEEE/CAA Journal of Automatica Sinica 2017年第2期4卷 260-272页

作者： Tim Mareda Ludovic Gaudard Franco Romerio Institute for Environmental Sciences University of Geneva Geneva School of Economics and Management(GSEM) University of Geneva

The transitional path towards a highly renewable power system based on wind and solar energy sources is investigated considering their intermittent and spatially distributed characteristics. Using an extensive weather-driven simulation of hourly power mismatches between generation and load, we explore the interplay between geographical resource complementarity and energy storage strategies. Solar and wind resources are considered at variable spatial scales across Europe and related to the Swiss load curve, which serve as a typical demand side reference. The optimal spatial distribution of renewable units is further assessed through a parameterized optimization method based on a genetic algorithm. It allows us to explore systematically the effective potential of combined integration strategies depending on the sizing of the system, with a focus on how overall performance is affected by the definition of network boundaries. Upper bounds on integration schemes are provided considering both renewable penetration and needed reserve power capacity. The quantitative trade-off between grid extension, storage and optimal wind-solar mix is *** paper also brings insights on how optimal geographical distribution of renewable units evolves as a function of renewable penetration and grid extent.

关键词： Energy optimization grid integration genetic algorithm optimal spatial distribution power system modeling

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