|تعداد مشاهده مقاله||106,292,803|
|تعداد دریافت فایل اصل مقاله||83,185,366|
A Simulation-Optimization Model for Solar PV Panel Selection Under Solar Irradiance and Load Uncertainty
|Advances in Industrial Engineering|
|دوره 54، شماره 2، تیر 2020، صفحه 141-164 اصل مقاله (1.21 M)|
|نوع مقاله: Research Paper|
|شناسه دیجیتال (DOI): 10.22059/jieng.2021.323127.1760|
|Maedeh Motalebi1؛ Mohammad Mahdi Nasiri* 1؛ Hamed Shakouri G.2؛ Hosein Taghaddos3|
|1School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran|
|2ctrical and Computer Engineering (ECE) Department, University of British Columbia, Canada|
|3School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran|
|In this reserach, a multi-objective model is presented considering simulated behavior of high-efficiency rooftop solar PV panels in factory, which are among the largest producers of green-house gases. The paper proposes a simulation-optimization approach is used to maximize the net present value (NPV) of economic benefits along with minimizing the payback period (PBP) of the investment, and maximizing solar energy consumption rate (SECR). In addition, the solar PV panels degradation and maintenance cost, as well as the uncertainty in solar irra-diance and demand load, are also considered. The study consists of two scenarios, in the first of which both electricity tariffs and feed-in-tariffs (FiT) are fixed by a long-term contract. The second scenario investigates the situation in which subsidies on electricity tariff are removed. The best type of panels are found in each scenario considering trade-off between objective functions. The preferred trade-off solution in the first scenario, with 2% increase in PBP, achieves more than 10% growth in NPV which is about $15000 in a year. In the second sce-nario, with only about 0.2% decrease in NPV and 3% increase in PBP, the preferred solution attains 9% increase in SECR.|
|Simulation؛ Rooftop solar PV panels؛ Electricity tariffs policy؛ Maintenance؛ Uncertainty|
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