Optimal design of Microgrid’s network topology and location of the distributed renewable energy resources using the Harmony Search algorithm
Autor: | R. Mallol-Poyato, Silvia Jiménez-Fernández, Carlos Camacho-Gómez, Sancho Salcedo-Sanz, Javier Del Ser |
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Rok vydání: | 2018 |
Předmět: |
0209 industrial biotechnology
Mathematical optimization Optimization problem Computer science business.industry Node (networking) Computational intelligence 02 engineering and technology Solver HS algorithm Network topology Multi-objective optimization Theoretical Computer Science Renewable energy 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Harmony search 020201 artificial intelligence & image processing Geometry and Topology Microgrid business Software |
Zdroj: | Soft Computing. 23:6495-6510 |
ISSN: | 1433-7479 1432-7643 |
DOI: | 10.1007/s00500-018-3300-0 |
Popis: | In this paper, we tackle the joint optimization of the network topology and the optimal location of distributed renewable energy resources in a Microgrid (MG). The MG network topology optimization problem is focused on obtaining network deployments with minimal cost, whereas the location of distributed renewable generation is associated with the minimization of the electricity losses in the MG lines. In order to solve this joint optimization problem, we analyze the efficiency of the Harmony Search (HS), a novel meta-heuristic solver inspired by the music improvisation procedure observed in jazz bands. We consider two different approaches, the first one is a single-objective formulation of the problem, where the classical HS is applied with some adaptations. The second approach is to consider a multi-objective version of the HS algorithm, able to evolve a whole family of solutions in a Pareto front. Both approaches have been tested on two small-sized MGs: an 8 node MG and a 12 node MG, and results have been compared to an 8 node and a 12 node baseline scenario, respectively, obtaining improvements of up to 42%. |
Databáze: | OpenAIRE |
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