A dynamic MOPSO algorithm for multiobjective optimal design of hybrid renewable energy systems
Author | Sharafi, Masoud |
Author | Elmekkawy, Tarek Y. |
Available date | 2016-05-26T12:20:01Z |
Publication Date | 2014-12 |
Publication Name | International Journal of Energy Research |
Resource | Scopus |
Citation | Sharafi M., and ElMekkawy T. Y. (2014), "A dynamic MOPSO algorithm for multiobjective optimal design of hybrid renewable energy systems", Int. J. Energy Res., 38, pages 1949-1963, |
ISSN | 0363-907X |
Abstract | In this paper, a dynamic multiobjective particle swarm optimization (DMOPSO) method is presented for the optimal design of hybrid renewable energy systems (HRESs). The main goal of the design is to minimize simultaneously the total net present cost (NPC) of the system, unmet load, and fuel emission. A DMOPSO-simulation based approach has been used to approximate a worthy Pareto front (PF) to help decision makers in selecting an optimal configuration for an HRES. The proposed method is examined for a case study including wind turbines, photovoltaic (PV) panels, diesel generators, batteries, fuel cells, electrolyzer, and hydrogen tanks. Well-known metrics are used to evaluate the generated PF. The average spacing and diversification metrics obtained by the proposed approach are 1386 and 4656, respectively. Additionally, the set coverage metric value shows that at least 67% of Pareto solutions obtained by DMOPSO dominate the solutions resulted by other reported algorithms. By using a sensitivity analysis for the case study, it is found that if the PV panel and wind turbine capital cost are decreased by 50%, the total NPC of the system would be decreased by 18.8 and 3.7%, respectively. |
Language | en |
Publisher | John Wiley and Sons Ltd |
Subject | CO2 emission Hybrid renewable energy systems Optimization Particle Swarm Optimisation (PSO) Simulation |
Type | Article |
Pagination | 1949-1963 |
Issue Number | 15 |
Volume Number | 38 |
Files in this item
Files | Size | Format | View |
---|---|---|---|
There are no files associated with this item. |
This item appears in the following Collection(s)
-
Mechanical & Industrial Engineering [1396 items ]