A multi-objective optimization model for sizing decisions of a grid-connected photovoltaic system

Ahmed M. Attia, Ahmad Al Hanbali, Haitham H. Saleh, Omar G. Alsawafy, Ahmed M. Ghaithan and Awsan Mohammed

Energy, 2021, vol. 229, issue C

Abstract: Greenhouse gasses are by-products of using fossil fuels to generate energy. It is known that climate change is caused by an increase in greenhouse gasses in the Earth's atmosphere. Solar energy is a renewable energy source that is an environmentally friendly resource for generating power. The photovoltaic (PV) system is a well-established technology for transforming solar energy into electric power. Sizing-decisions of a PV system is a strategic decision-making process that has a long-term impact on the project. This work proposes a multi-objective optimization (MOO) model for sizing-decisions of a grid-connected PV system. The objectives consider the economic aspect in terms of minimizing the project lifespan costs and non-economic aspects, including maximizing system reliability and reducing CO2 emissions. The sizing decisions are the number of PV panels, the amount of energy purchased from the main grid, and the amount of energy supplied to the main grid. In addition to a binary variable for an either-or decision, either purchase or supply from the grid. The model generates Pareto-optima to assist the decision-maker in assessing trade-offs between alternatives. A case study based on monthly demand for the residential area at King Fahd University of Petroleum & Minerals (KFUPM) is provided to clarify the practicality of the model. It is found that 1713 PV modules are required to meet annual demand at an annual cost of M$1.61 over 25 years.

Keywords: Photovoltaic system; Capacity planning; Multi-objective optimization; Multi-criteria decision making (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:229:y:2021:i:c:s0360544221009786

DOI: 10.1016/j.energy.2021.120730

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