Adaptive nesting of evolutionary algorithms for the optimization of Microgrid’s sizing and operation scheduling
- 134 Downloads
This paper proposes a novel adaptive nesting Evolutionary Algorithm to jointly optimize two important aspects of the configuration and planning of a Microgrid (MG): the structure’s design and the way it is operated in time (specifically, the charging and discharging scheduling of the Energy Storage System, ESS, elements). For this purpose, a real MG scenario consisting of a wind and a photovoltaic generator, an ESS made up of one electrochemical battery, and residential and industrial loads is considered. Optimization is addressed by nesting a two-steps procedure [the first step optimizes the structure using an Evolutionary Algorithm (EA), and the second step optimizes the scheduling using another EA] following different adaptive approaches that determine the number of fitness function evaluations to perform in each EA. Finally, results obtained are compared to non-nesting 2-steps algorithm evolving following a classical scheme. Results obtained show a 3.5 % improvement with respect to the baseline scenario (the non-nesting 2-steps algorithm), or a 21 % improvement when the initial solution obtained with the Baseline Charge and Discharge Procedure is used as reference.
KeywordsMicrogrids Microgrid design Microgrid operation Energy storage system scheduling Evolutionary algorithms Nesting algorithms
- Haesen E, Espinoza M, Pluymers B, Goethals I, Thong VV, Driesen J, Belmans R, Moor BD (2005) Optimal placement and sizing of distributed generator units using genetic optimization algorithms. J Electr Power Qual Util XI(1):97–104Google Scholar
- Real Decreto 1164/2001, October 26th, that establishes access tariffs for electric energy transport and distribution. https://www.boe.es/boe/dias/2001/11/08/pdfs/A40618-40629
- Reference power demand and consumption profiles (2014) Red Eléctrica de Espana. http://www.ree.es/es/actividades/operacion-del-sistema/medidas-electricas