Volume 19, Issue 1 (JIAEEE Vol.19 No.1 2022)
Journal of Iranian Association of Electrical and Electronics Engineers 2022, 19(1): 97-110 |
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khan ahmadi A, soleimani M M, ghaffarpour R, zamanian S. Optimal planning of renewable energy systems with considering uncertainty and LPSP concept: A case study. Journal of Iranian Association of Electrical and Electronics Engineers 2022; 19 (1) :97-110
URL: http://jiaeee.com/article-1-959-en.html
URL: http://jiaeee.com/article-1-959-en.html
Department of Electrical and Computer, Imam Hussain Comprehensive University
Abstract: (2072 Views)
In recent years, the tendency to use renewable energy resources in remote areas has been raised due largely to increase in the energy demand and fossil fuel costs. In this paper, the aim is to find the optimal number of components of the off-grid hybrid PV/wind/battery/diesel system, in the presence of the load and renewable power generation uncertainty. Optimization is based on minimizing the value of the annualized cost of system, which meets the requirements of the desired loss of power supply probability (LPSP).Three various optimization algorithms including PSO, CPSO, and Modified PSO are proposed to solve the optimization problem. To exam the efficiency of the proposed model and optimization strategies, the proposed approach has been applied to a stand-alone hybrid energy system in Sistan and Bluchestan, Iran. Furthermore, the effect of different desired LPSP and optimization algorithms on the economic operation have been discussed. The results demonstrate that the MPSO has a faster solution and better performance than the PSO and CPSO. In this paper, while previous research has mainly focused on theoretical results only, for practical implementation, the maximum allowed constraint for diesel fuel consumption is added to the optimization algorithm and practical implementation has been done based on the simulation results and actual data.
Keywords: Hybrid renewable energies, Annualized cost of system (ACS), Loss of power supply probability (LPSP), Uncertainty, Modified PSO algorithm, A case study.
Type of Article: Research |
Subject:
Power
Received: 2019/08/13 | Accepted: 2021/05/8 | Published: 2022/04/14
Received: 2019/08/13 | Accepted: 2021/05/8 | Published: 2022/04/14
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