Energy Economics
Mohammad Sayadi
Abstract
Given the 95% share of electricity generation from non-renewable energies, implementing effective policies to motivate electricity generation from sustainable energy resources is essential. Since the current Feed-in Tariff (FiT) policy increases the government’s expenditures to support renewable ...
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Given the 95% share of electricity generation from non-renewable energies, implementing effective policies to motivate electricity generation from sustainable energy resources is essential. Since the current Feed-in Tariff (FiT) policy increases the government’s expenditures to support renewable energies, a real options (RO) model is proposed to estimate solar power generation incentive subsidy. Moreover, establishing a carbon emission trading (CET) scheme under uncertainty is proposed, and sensitivity analysis is conducted for the project value, threshold value, and subsidy. Our results show that establishing a CET market could significantly reduce the economic costs of achieving renewable energy promotion goals. Based on the net present value (NPV) and RO criteria, in the case “with the possibility of CET,” the amount of incentive subsidy that should be paid to electricity generation from a solar project (case of a 5 kW plant) are 37.49 and 42.42 million Rials/kW, indicating 20% and 12% reduction compared to the base case (without the possibility of CET), respectively. The results also indicate that more electricity price volatility can increase the incentive subsidy while enhancing the market price of electricity can slightly decrease the required subsidy, which triggers solar investment.
Mohammad Sayadi; Meysam Omidi
Abstract
This study applied a prediction-based portfolio optimization model to explore the results of portfolio predicament in the Tehran Stock Exchange. To this aim, first, the data mining approach was used to predict the petroleum products and chemical industry using clustering stock market data. Then, some ...
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This study applied a prediction-based portfolio optimization model to explore the results of portfolio predicament in the Tehran Stock Exchange. To this aim, first, the data mining approach was used to predict the petroleum products and chemical industry using clustering stock market data. Then, some effective factors, such as crude oil price, exchange rate, global interest rate, gold price, and S&P 500 index, were used to estimate each industry index using Radial Basis Function and Multi-Layer Perceptron neural networks. Finally, by comparing the validation ratios in a bullish market using K-Means, SOM, and Fuzzy C-means clustering algorithms, the best algorithm was employed to predict indicators for each industry. The sample was collected between December 15, 2008, and April 25, 2018. The results revealed that the Multi-Layer Perceptron algorithm had the highest accuracy and was the best option for portfolio predicament. However, the Fuzzy C-means algorithm produced the best clusters. Practical results showed that Sepahan oil and Kharg petrochemical stocks were the most important stocks in the short term while Kharg petrochemical, Fannavaran petrochemical, and Tehran oil refinery stocks made higher contributions in a stock portfolio in the medium- or long-term.
