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

作     者：Tim Mareda Ludovic Gaudard Franco Romerio 

作者机构：Institute for Environmental SciencesUniversity of Geneva Geneva School of Economics and Management(GSEM)University of Geneva 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2017年第4卷第2期

页      面：260-272页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 080802[工学-电力系统及其自动化] 0808[工学-电气工程] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by University of Geneva 

主　　题：Energy optimization grid integration genetic algorithm optimal spatial distribution power system modeling 

摘      要：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.