Photovoltaic/Hydrokinetic/Hydrogen Energy System Sizing Considering Uncertainty: A Stochastic Approach Using Two-Point Estimate Method and Improved Gradient-Based Optimizer

Mustafa Kamal, Renzon Daniel Cosme Pecho, Hassan Falah Fakhruldeen, Hailer Sharif, Vedran Mrzljak (), Saber Arabi Nowdeh () and Igor Poljak
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Mustafa Kamal: Department of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, Dammam 32256, Saudi Arabia
Renzon Daniel Cosme Pecho: Salutem Diagnostic Imaging Center, Lima 15498, Peru
Hassan Falah Fakhruldeen: Computer Techniques Engineering Department, Faculty of Information Technology, Imam Ja’afar Al-Sadiq University, Baghdad 10011, Iraq
Hailer Sharif: Medical Technical College, Al-Farahidi University, Baghdad 10001, Iraq
Vedran Mrzljak: Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
Saber Arabi Nowdeh: Power and Energy Group, Institute of Research Sciences, Johor Bahru 81310, Malaysia
Igor Poljak: Department of Maritime Sciences, University of Zadar, Mihovila Pavlinovića 1, 23000 Zadar, Croatia

Sustainability, 2023, vol. 15, issue 21, 1-30

Abstract: In this paper, stochastic sizing of a stand-alone Photovoltaic/Hydrokinetic/Hydrogen storage energy system is performed with aim of minimizing the cost of project life span (COPL) and satisfying the reliability index as probability of load shortage (POLS). The stochastic sizing is implemented using a novel framework considering two-point estimate method (2m+1 PEM) and improved gradient-based optimizer (IGBO). The 2m+1 PEM is used to evaluate the impact of uncertainties of energy resource generation and system demand on sizing problem. The 2m+1 PEM utilizes the approximate method to account for these uncertainties. In order to avoid premature convergence, the gradient-based optimizer (GBO), a meta-heuristic algorithm influenced by Newtonian concepts, is enhanced using a dynamic lens-imaging learning approach. The size of the system devices, which is determined utilizing the IGBO with the COPL minimization and optimally satisfying the POLS, is one of the optimization variables. The results of three hPV/HKT/FC, hPV/FC, and hHKT/FC configurations of the system are presented in two situations of deterministic and stochastic sizing without and with taking uncertainty into consideration. The findings showed that the hPV/HKT/FC configuration and the IGBO performed better than other configurations and techniques like conventional GBO, particle swarm optimization (PSO), and artificial electric field algorithm (AEFA) to achieve the lowest COPL and POLS (higher reliability) in various cases. Additionally, the COPL for the hPV/HKT/FC, hPV/FC, and hHKT/FC configurations increased by 7.63%, 7.57%, and 7.65%, respectively, while the POLS fell by 5.01%, 4.48%, and 4.59%, respectively, contrasted to the deterministic sizing, according to the results of stochastic sizing based on 2m+1 PEM. As a result, the findings indicate that in the deterministic sizing model, the quantity of output and energy storage is insufficient to meet demand under unknown circumstances. Applying stochastic sizing while taking into account the volatility of both supply and demand can, therefore, be an economically sound way to meet demand.

Keywords: stochastic sizing; hydrogen energy storage; cost of project life span; probability of load shortage; dynamic lens-imaging learning; improved gradient-based optimizer (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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