Comprehensive Financial Modeling of Solar PV Systems

Baschieri, Davide; Magni, Carlo Alberto; Marchioni, Andrea

doi:10.2139/ssrn.3722442

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…There are a total of three estimated parameters for the IOM (R c , C, and L); the IOM is represented by Equations ( 4) and (5).…”

Section: Dynamic Pv Modelmentioning

confidence: 99%

“…The total number of FOM parameters is five (R c , C, L, α, and β); the FOM is represented by Equations ( 6) and ( 7). There are a total of three estimated parameters for the IOM (R c , C, and L); the IOM is represented by Equations ( 4) and (5).…”

Section: Dynamic Pv Modelmentioning

confidence: 99%

“…Modeling of solar systems is one of the contemporary research topics, although it is not a modern research idea, emphasizing its importance, as well as the importance of the improvement algorithms that have begun to intervene in many topics, changing old views on them [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

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Accurate Photovoltaic Models Based on an Adaptive Opposition Artificial Hummingbird Algorithm

et al. 2022

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The greater the demand for energy, the more important it is to improve and develop permanent energy sources, because of their advantages over non-renewable energy sources. With the development of artificial intelligence algorithms and the presence of so many data, the evolution of simulation models has increased. In this research, an improvement to one recent optimization algorithm called the artificial hummingbird algorithm (AHA) is proposed. An adaptive opposition approach is suggested to select whether or not to use an opposition-based learning (OBL) method. This improvement is developed based on adding an adaptive updating mechanism to enable the original algorithm to obtain more accurate results with more complex problems, and is called the adaptive opposition artificial hummingbird algorithm (AOAHA). The proposed AOAHA was tested on 23 benchmark functions and compared with the original algorithm and other recent optimization algorithms such as supply–demand-based optimization (SDO), wild horse optimizer (WHO), and tunicate swarm algorithm (TSA). The proposed algorithm was applied to obtain accurate models for solar cell systems, which are the basis of solar power plants, in order to increase their efficiency, thus increasing the efficiency of the whole system. The experiments were carried out on two important models—the static and dynamic models—so that the proposed model would be more representative of real systems. Two applications for static models have been proposed: In the first application, the AOAHA satisfies the best root-mean-square values (0.0009825181). In the second application, the performance of the AOAHA is satisfied in all variable irradiance for the system. The results were evaluated in more than one way, taking into account the comparison with other modern and powerful optimization techniques. Improvement showed its potential through its satisfactory results in the tests that were applied to it.

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“…There are a total of three estimated parameters for the IOM (R c , C, and L); the IOM is represented by Equations ( 4) and (5).…”

Section: Dynamic Pv Modelmentioning

confidence: 99%

Section: Dynamic Pv Modelmentioning

confidence: 99%

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Accurate Photovoltaic Models Based on an Adaptive Opposition Artificial Hummingbird Algorithm

et al. 2022

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Average internal rate of return for risky projects

Hazen

Magni

2021

The Engineering Economist

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Investing in Green Energy: Profitability Analysis of Solar Energy for Household Consumption in Albania

Kapidani,

Numani

2023

Wseas Transactions on Business and Economics

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Exhaustion of conventional non-renewable energy sources together with climate change mitigation presents challenges for policy makers globally. An alternative to address the increased demand for energy with less negative impacts on the environment is using renewable energy sources whose reserves are supplemented by natural ecosystems. Furthermore, EU directives impose actions on Member States for the reduction of pollution from conventional sources. Countries must aim to increase the share of renewable energy sources in energy consumption, as significant measures towards sustainable development. However, several factors affect the succession of green energy adoption, where financial considerations remain key determinants in decision-making by companies, families, and institutions. The study conducts a cost-benefit analysis using methods of capital budgeting to evaluate the profitability of solar energy for household consumption in Albania. The paper aims to provide insights into the potential of green energy investment in Albania, focusing on the solar energy sector and financial factors that are relevant to these investments’ succession. Data analysis and forecasting are conducted for a lifespan of 30 years, assessing average data of electricity prices, the productivity of solar panels, direct costs of investment, interest rates, exchange rates, and inflation rates. The study concludes that investing in solar panels for household consumption in Albania is profitable, as it is reported that discounted benefits exceed the investment cost.

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Comprehensive Financial Modeling of Solar PV Systems

Cited by 3 publications

References 9 publications

Accurate Photovoltaic Models Based on an Adaptive Opposition Artificial Hummingbird Algorithm

Accurate Photovoltaic Models Based on an Adaptive Opposition Artificial Hummingbird Algorithm

Average internal rate of return for risky projects

Investing in Green Energy: Profitability Analysis of Solar Energy for Household Consumption in Albania

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