Optimal Microgrid Topology Design and Siting of Distributed Generation Sources Using a Multi-Objective Substrate Layer Coral Reefs Optimization Algorithm

Research areas:

• Methodology - Machine learning

Year:

2019

Type of Publication:

Article

Authors:

• Jiménez-Fernández, Silvia
• Camacho-Gómez, Carlos
• Mallol-Poyato, Ricardo
• Fernández, Juan Carlos
• Ser, Javier Del
• Portilla-Figueras, Antonio
• Salcedo-Sanz, Sancho

Journal:

Sustainability

Volume:

11

Number:

1

Pages:

169

ISSN:

2071-1050

BibTex:

@article{Fernandez2019a, author = "Silvia Jim{\'e}nez-Fern{\'a}ndez and Carlos Camacho-G{\'o}mez and Ricardo Mallol-Poyato and Juan Carlos Fern{\'a}ndez and Javier Del Ser and Antonio Portilla-Figueras and Sancho Salcedo-Sanz", abstract = "In this work, a problem of optimal placement of renewable generation and topology design for a Microgrid (MG) is tackled. The problem consists of determining the MG nodes where renewable energy generators must be optimally located and also the optimization of the MG topology design, i.e., deciding which nodes should be connected and deciding the lines’ optimal cross-sectional areas (CSA). For this purpose, a multi-objective optimization with two conflicting objectives has been used, utilizing the cost of the lines, C, higher as the lines’ CSA increases, and the MG energy losses, E, lower as the lines’ CSA increases. To characterize generators and loads connected to the nodes, on-site monitored annual energy generation and consumption profiles have been considered. Optimization has been carried out by using a novel multi-objective algorithm, the Multi-objective Substrate Layers Coral Reefs Optimization algorithm (Mo-SL-CRO). The performance of the proposed approach has been tested in a realistic simulation of a MG with 12 nodes, considering photovoltaic generators and micro-wind turbines as renewable energy generators, as well as the consumption loads from different commercial and industrial sites. We show that the proposed Mo-SL-CRO is able to solve the problem providing good solutions, better than other well-known multi-objective optimization techniques, such as NSGA-II or multi-objective Harmony Search algorithm.", awards = "JCR(2019): 2.576 Position: 120/265 (Q2) Category: ENVIRONMENTAL SCIENCES", comments = "JCR(2019): 2.576 Position: 120/265 (Q2) Category: ENVIRONMENTAL SCIENCES", doi = "https://doi.org/10.3390/su11010169", issn = "2071-1050", journal = "Sustainability", note = "JCR(2019): 2.576 Position: 120/265 (Q2) Category: ENVIRONMENTAL SCIENCES", number = "1", pages = "169", title = "{O}ptimal {M}icrogrid {T}opology {D}esign and {S}iting of {D}istributed {G}eneration {S}ources {U}sing a {M}ulti-{O}bjective {S}ubstrate {L}ayer {C}oral {R}eefs {O}ptimization {A}lgorithm", url = "doi.org/10.3390/su11010169", volume = "11", year = "2019", }

Note:

JCR(2019): 2.576 Position: 120/265 (Q2) Category: ENVIRONMENTAL SCIENCES

Abstract:

In this work, a problem of optimal placement of renewable generation and topology design for a Microgrid (MG) is tackled. The problem consists of determining the MG nodes where renewable energy generators must be optimally located and also the optimization of the MG topology design, i.e., deciding which nodes should be connected and deciding the lines’ optimal cross-sectional areas (CSA). For this purpose, a multi-objective optimization with two conflicting objectives has been used, utilizing the cost of the lines, C, higher as the lines’ CSA increases, and the MG energy losses, E, lower as the lines’ CSA increases. To characterize generators and loads connected to the nodes, on-site monitored annual energy generation and consumption profiles have been considered. Optimization has been carried out by using a novel multi-objective algorithm, the Multi-objective Substrate Layers Coral Reefs Optimization algorithm (Mo-SL-CRO). The performance of the proposed approach has been tested in a realistic simulation of a MG with 12 nodes, considering photovoltaic generators and micro-wind turbines as renewable energy generators, as well as the consumption loads from different commercial and industrial sites. We show that the proposed Mo-SL-CRO is able to solve the problem providing good solutions, better than other well-known multi-objective optimization techniques, such as NSGA-II or multi-objective Harmony Search algorithm.

Comments:

JCR(2019): 2.576 Position: 120/265 (Q2) Category: ENVIRONMENTAL SCIENCES