[01] Abtin Ataei, Department of Energy Engineering, Graduate School of the Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran, Iran. [02] Seyed Mehdi Ebadi, Department of Energy Engineering, Graduate School of the Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Iran is known as one of the richest countries in fossil fuel resources. This country has also great resources of renewable energy. Qazvin province is one of the best areas in Iran for developing wind power plants thanks to its strong wind resource. In this paper, a new method for calculation of optimum combination of thermal and renewable energy power plants in year of 2030 for Qazvin province with the aim of reducing environmental pollutants was developed. To achieve the aim, the optimization tool of Long-range Energy Alternatives Planning system (LEAP) software was employed considering three different scenarios; direct cost, direct and external cost and carbon limitation. The year of 2013 was considered as the base year. The electricity demand in the base year was 11,288,216 MWh which its annual growth rate was estimated as 5.51%. Thus, the electricity demand in 2030 is estimated to be 25,586,700 MWh. Although the share of renewable power plants is almost zero at the current account, the optimization model showed that the share of wind power plants may reach to 78% due to direct cost and externalities consideration. The energy optimization model indicated not only a potential of 150 million tons of CO₂ saving but also possibility of 1.3 billion U.S. dollars saving in power generation until 2030 by introducing the optimal combination of power plants for Qazvin province. According to the results of the energy optimization model, in spite of rich fossil fuel resources in Iran, the renewable energy power plants should have a key role in electricity generation to reduce the power generation cost and environmental pollutants.

Renewable Energy, LEAP Model, Optimization, Qazvin Province, Electricity Generation

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