Development of optimization algorithms for the Leaf Community microgrid. (February 2015)

• Record Type: Journal Article
• Authors: Provata, Elena; Kolokotsa, Dionysia; Papantoniou, Sotiris; Pietrini, Maila; Giovannelli, Antonio; Romiti, Gino
• Is Part Of: Renewable energy. Volume 74(2015)
• Page Start: 782
• Page End: 795
• Publication Date: 2015-02
• Subjects: Microgrid -- Optimization -- Genetic algorithms -- Neural networks; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2014.08.080
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: The aim of this work is the development of an optimization model in order to minimize the cost of Leaf Community microgrid. This cost is a sum of energy cost and the maintenance cost of the energy storage system (ESS). The developed objective function is constrained and the problem here is solved by using the method of genetic algorithms at Matlab. The genetic algorithm decides about the transportation of the energy from or to the ESS and it calculates an optimum cost. The optimization time horizon is 24 h ahead, thus the prediction of energy production and consumption was necessary. This was achieved by using neural networks. In order to verify the performance of the developed model, some scenarios were tested. This study concludes that a management of a microgrid can achieve energy and money savings. Highlights: We model the Leaf Community microgrid's operation and energy fluxes via Matlab. Power generation and consumption is predicted via neural networks. Genetic algorithms are utilised to optimise the microgrid's operation. The total energy cost is minimised. The hydroelectric power creates profits for the Leaf Community.