Renewable Energy Economics, Policies and Planning
Mohammad Hossein Jahangir; Arash Kargarzadeh; Mohammad Montazeri
Abstract
As one of the main consumers of electricity, industries account for in releasing a large amount of emission. Using renewable energies to feed factories is not an easy task and they should be economically viable to compete with fossil fuels. The goal of this study is to analyze the possibilities of using ...
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As one of the main consumers of electricity, industries account for in releasing a large amount of emission. Using renewable energies to feed factories is not an easy task and they should be economically viable to compete with fossil fuels. The goal of this study is to analyze the possibilities of using energy local area networks in off-grid and on-grid modes in an industrial project by considering and calculating all primary and deferrable loads in detail for the first time. The industrial project is sensitive and all possibilities should be considered closely to avoid economic losses. In this case, changes in electrical loads during the project, degradation of components, environmental risks, and economic risks of the investment (for each scenario) are considered and determined too. The results indicate that component degradation can cause 24,000 kWh drop in total electricity production at the end of the project and the total biogas consumption increases from 742 kg/yr to 9330 kg/yr. The results also show that the on-gird scenario (solar/battery) with the Net Present Cost of 200,000$ will be an easy and low-risk choice for investment, but has high environmental risks. On the other hand, the stand-alone scenario (solar/wind/bio/battery) with Net Present Cost of 598,000$ minimizes the environmental risks at the expense of high investment risk. A proper comparison between the multi-year and single-year modes at the end of the project ensures the high accuracy of techno-economic analysis in terms of optimum system types, emissions, and economics.
Renewable Energy Economics, Policies and Planning
Ali Khatibi; Mohammad Hossein Jahangir; Fatemeh Razi Astaraiea
Abstract
Land-use change is one of the most important spatial phenomena that can affect the usage of energy technologies. In this study, land-use change in barren and residential areas in Alborz province in Iran was modeled using the cellular automata combined with the Markov Chain from 2001 to 2031. Due to adaptability ...
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Land-use change is one of the most important spatial phenomena that can affect the usage of energy technologies. In this study, land-use change in barren and residential areas in Alborz province in Iran was modeled using the cellular automata combined with the Markov Chain from 2001 to 2031. Due to adaptability to the environmental considerations, all protected areas were removed from the study area. Then, an economical and performance-based optimization model was developed; then, by using the status of the two land-use classes in 2031, an optimum scenario was identified for generating solar electricity. Based on the results, the optimum scenario involves installing distributed photovoltaic modules in 18.37 % of residential areas and setting up concentrated solar systems in 0.74 % of barren areas, simultaneously. Economic investigation of the optimum scenario showed that although there were some environmental and political benefits for using the solar electricity such as reduction of air pollutants and more energy safety, the optimum scenario will be costly and non-economical without the government’s financial supports.
Environmental Impacts and Sustainability
Mohammad Hossein Jahangir; Mahnaz Abolghasemi; Seyedeh Mahsa Mousavi Reineh
Abstract
Drought is considered as a destructive disaster that can have irreversible effects on different aspects of life. In this study, artificial neural network was used as a powerful means of modeling nonlinear and indefinite processes in order to simulate drought intensities at 7 synoptic stations of Khorasan ...
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Drought is considered as a destructive disaster that can have irreversible effects on different aspects of life. In this study, artificial neural network was used as a powerful means of modeling nonlinear and indefinite processes in order to simulate drought intensities at 7 synoptic stations of Khorasan Razavi from more than 35 years ago up to the year 2014. Input data were the calculations of the two indicators of PNPI and SPI by DIC software, and the output layer (drought intensity) was taken to the Matlab software and employed as the teaching data (from 25 years), experiment (from 5 years), and validation (from another 5 years). The 3-9-1 structure of the network of layers had the maximum accuracy with the error rate of less than 2 % and high correlation (more than 90 %). After trial and error for each station through sigmoid stimulation function in the Perceptron network, it was observed that the stations of Mashhad and Quchan had the minimum error and the maximum error was related to the station of Neyshabur. The results of comparisons and observations showed that the artificial neural network had high efficiency in simulation of the data. The obtained correlation amount of 0.999 for the base station represented the small error of the model in prediction. Drought forecasting was performed in this study by the trained algorithm in the artificial neural network without using the observation data. The results showed that rainfall, temperature, and speed models had a positive role in forecasting the provinces that would experience drought. Due to its lower amount of error, SPI indicator was selected for mapping, the findings of which showed that the highest drought intensity belonged to the near normal to normal wet lands.
Renewable Energy Economics, Policies and Planning
Seyed Mohammad Emami Razavi; Mohammad Hossein Jahangir; Soroush Mousavi
Abstract
The renewable energy can be utilized to satisfy the energy demand. Moreover, the solar energy as the most abundant energy resource among renewable energies plays a crucial role to provide the energy demand. The BIPV (building integrated photovoltaics) systems can be considered to supply the required ...
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The renewable energy can be utilized to satisfy the energy demand. Moreover, the solar energy as the most abundant energy resource among renewable energies plays a crucial role to provide the energy demand. The BIPV (building integrated photovoltaics) systems can be considered to supply the required energy demand from renewable sources. The essential advantage of BIPV systems is that they can be utilized as building component such as roof, window, shading systems and building façade and they can generate electricity simultaneously. Even though the photovoltaic technologies have been improved within past few years, however the utilization of the BIPV systems will be considered expensive. For this reason, the payback period calculation is considered a vital parameter in evaluating the BIPV systems. In this study, the overall energy consumption for producing one m2 of a mono-crystalline photovoltaic module is calculated 1334 kWh. Additionally, the photovoltaic module data for three companies were investigated and the annual energy productions for one m2 of each company’s product were obtained. The results showed that the average energy payback time for 270 and 280 watt modules are 5.565 and 5.254 respectively. Moreover, the energy payback time for 290, 325 and 340 watt modules were calculated 4.903, 5.437 and 4.965 respectively.