Journal article
Comparative study of metaheuristic algorithms for optimal sizing of standalone microgrids in a remote area community
Neural Computing and Applications
2021
Abstract
This paper evaluates the performance and suitability of four different metaheuristic algorithms for optimal sizing of standalone microgrids in remote area. The studied metaheuristic algorithms are particle swarm optimization, differential evolution, water cycle algorithm and grey wolf optimization. These algorithms are applied to optimize the capacity of diesel generator, fuel tank, solar photovoltaic, wind turbine, and battery energy storage in four different AC-coupled standalone microgrids for a remote area community in South Australia. The objective function is selected as the net present value of electricity over a 20-year lifetime. The optimisation study is conducted based on the real data of annual load consumption, ambient temperature, solar insolation, and wind speed of the site. Capital, replacement, and maintenance costs of components in Australian market are incorporated for the economic analysis. An operating power reserve is maintained based on the static and dynamic reserve concepts. Uncertainty analysis based on 10-year real data of renewable energies and load consumption is conducted. Sensitivity analysis is provided for variations of the battery price and capacity. The performance of the applied algorithms is evaluated by comparing the economic and operational results, as well as the computational time and optimization convergence. It is found that differential evolution algorithm is unreliable for optimal sizing problem of the studied standalone microgrids..
Details
- Title
- Comparative study of metaheuristic algorithms for optimal sizing of standalone microgrids in a remote area community
- Authors/Creators
- M. Fathi (Author/Creator) - University of Technology MalaysiaR. Khezri (Author/Creator) - Flinders UniversityA. Yazdani (Author/Creator) - Murdoch UniversityA. Mahmoudi (Author/Creator) - Flinders University
- Publication Details
- Neural Computing and Applications
- Publisher
- Springer London
- Identifiers
- 991005539985307891
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
- Additional Information
- Special Issue on Computational Intelligence-based Control and Estimation in Mechatronic Systems
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites
Metrics
50 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.18 Power Systems & Electric Vehicles
- 4.18.204 Smart Grid Optimization
- Web Of Science research areas
- Computer Science, Artificial Intelligence
- ESI research areas
- Engineering