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Comparative study of metaheuristic algorithms for optimal sizing of standalone microgrids in a remote area community

  • Special Issue on Computational Intelligence-based Control and Estimation in Mechatronic Systems
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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..

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Abbreviations

\({\mathcal{R}}_{f}\) :

Fuel consumption cost (AU$)

\({\mathcal{R}}_{n}^{c}\) :

Capital cost of component n (AU$)

\({\mathcal{R}}_{n}^{m}\) :

Minor maintenance cost of component n (AU$)

\({\mathcal{R}}_{n}^{r}\) :

Major maintenance cost of component n (AU$)

\({\mathcal{R}}_{lgc}\) :

LGC cost (AU$)

\({\mathcal{M}}\) :

Project lifetime (year)

\(NPV\) :

Total NPV of system configurations (AU$

\({\mathcal{L}}_{T}\) :

LCOE of system configurations (AU$)

:

Interest rate (%)

\({\mathcal{N}}_{n}\) :

Number of components n

n :

Type of component

\({\mathcal{D}}_{on}\) :

Online DGs’ capacity (kW)

\(E_{l}\) :

Annual load demand of microgrid (MWh)

:

Annual load growth of microgrid (%)

\({\mathcal{O}}_{b}\) :

State-of-charge of battery (%)

\({\mathcal{P}}_{b}^{ch} , {\mathcal{P}}_{b}^{dis}\) :

Charging/discharging power of BES (kW)

\({\mathcal{P}}_{b}^{in} , {\mathcal{P}}_{b}^{out}\) :

Available input/output power limit of BES (kW)

\({\mathcal{P}}_{g}\) :

Diesel generators’ power (kW)

\({\mathcal{P}}_{rg}\) :

Rated power of DG (kW)

\({\mathcal{P}}_{l}\) :

Microgrid’s load power (kW)

\({\mathcal{P}}_{l,0}\) :

Microgrid’s present load power (kW)

\({\mathcal{P}}_{p}\) :

Generated power by PV (kW)

\({\mathcal{P}}_{n}\) :

Generated power by component n (kW)

\({\mathcal{P}}_{w}\) :

Generated power by WT (kW)

\({\mathcal{P}}_{out}\) :

Available output power of battery (kW)

\({\mathcal{S}}_{d}\) :

Dynamic reserve power of microgrid (kW)

\({\mathcal{S}}_{s}\) :

Static reserve power of microgrid (kW)

\({\mathcal{S}}_{t}\) :

Total reserve power of microgrid (kW)

\(\eta_{b}\) :

Battery’s efficiency (%)

\(\xi_{l}\) :

Load consumption forecast error (%).

\(\xi_{p}\) :

Wind turbine forecast error (%).

\(\xi_{w}\) :

Solar photovoltaic forecast error (%)

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Authors and Affiliations

  1. Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia

    Mohammad Fathi

  2. College of Science and Engineering, Flinders University, Adelaide, SA, 5042, Australia

    Rahmat Khezri & Amin Mahmoudi

  3. College of Science, Health, Engineering and Education, Murdoch University, Perth, WA, 6150, Australia

    Amirmehdi Yazdani

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  1. Mohammad Fathi
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  3. Amirmehdi Yazdani
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  4. Amin Mahmoudi
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Correspondence to Amin Mahmoudi.

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Appendix

Appendix

Table

Table 5 Initial parameters of the metaheuristic optimization algorithms
Full size table

5 presents the initial parameters of each metaheuristic algorithm applied for optimal sizing in this study.

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Fathi, M., Khezri, R., Yazdani, A. et al. Comparative study of metaheuristic algorithms for optimal sizing of standalone microgrids in a remote area community. Neural Comput & Applic 34, 5181–5199 (2022). https://doi.org/10.1007/s00521-021-06165-6

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