Optimal Sizing of Standalone Photovoltaic System Using Improved Performance Model and Optimization Algorithm

Tamer Khatib and Dhiaa Halboot Muhsen
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Tamer Khatib: Department of Energy Engineering and Environment, An-Najah National University, Nablus 97300, Palestine
Dhiaa Halboot Muhsen: Department of Computer Engineering, University of Mustansiriyah, Baghdad 10001, Iraq

Sustainability, 2020, vol. 12, issue 6, 1-18

Abstract: A standalone photovoltaic system mainly consists of photovoltaic panels and battery bank. The use of such systems is restricted mainly due to their high initial costs. This problem is alleviated by optimal sizing as it results in reliable and cost-effective systems. However, optimal sizing is a complex task. Artificial intelligence (AI) has been shown to be effective in PV system sizing. This paper presents an AI-based standalone PV system sizing method. Differential evolution multi-objective optimization is used to find the optimal balance between system’s reliability and cost. Two objective functions are minimized, the loss of load probability and the life cycle cost. A numerical algorithm is used as a benchmark for the proposed method’s speed and accuracy. Results indicate that the AI algorithm can be successfully used in standalone PV systems sizing. The proposed method was roughly 27 times faster than the numerical method. Due to AI algorithm’s random nature, the proposed method resulted in the exact optimal solution in 6 out of 12 runs. Near-optimal solutions were found in the other six runs. Nevertheless, the nearly optimal solutions did not introduce major departure from optimal system performance, indicating that the results of the proposed method are practically optimal at worst.

Keywords: photovoltaic; standalone; sizing; optimization; DEMO (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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