Power Plants In Iran Research Articles

Techno-economic assessment and environmental impact of concentrating solar power plants in Iran

This study aims to assess the technical, economic, and environmental aspects of parabolic trough and solar tower power plants under the climatic conditions of south-central regions of Iran with an average direct normal irradiation of about 6 kWh/m2/day. The effects of power plant capacity, cooling system, solar multiple, and hours of thermal energy storage on the techno-economic performance of the power plants are examined. In addition, the environmental metrics including reduction of CO2 emissions, fossil fuel saving, and energy payback time are evaluated. Four power plant capacities of 20, 50, 100, and 200 MWe along with two cooling options, including dry and wet are investigated. Besides, the solar multiple and capacity of thermal energy storage are varied from 1.0 to 4.0 with a step of 0.1, and from 0 to 18 h with a step of 1 h, respectively. The ground weather data from the meteorological organization of Iran are used as input data in the System Advisor Model software. Based on the results, the optimal configurations for the studied power plants concerning the techno-economic parameters are determined. It is revealed that the dry-cooled solar tower power plant with a capacity of 100 MWe, 14 h storage system, and solar multiple of 3.0 is the most efficient configuration under the studied climatic conditions. For this concentrating solar power plant, the levelized cost of electricity and solar-to-electricity efficiency are 11.3 ¢/kWhe and 14.7%, respectively. Furthermore, the energy payback time is about 15 months, annual CO2 emissions reduction is 399 kilotons, and annual fossil fuel saving is 190 million cubic meters of natural gas.

Investigation of accurate location planning for wind farm establishment: a case study

Purpose Wind as a major source of renewable energy has received tremendous attentions due to its unique features to reduce carbon emission and also to keep the environment safe. Nevertheless, to use wind energy properly, the environmental circumstances and geographical location related to wind intensity should be considered as a priority. Different factors may affect the selection of a suitable location for developments of wind power plants; thus, these factors should be considered concurrently to identify the optimum location of wind plants. Design/methodology/approach In this study, first, basic data envelopment analysis (DEA) was used, then dual DEA was used and, finally, Anderson Petersen (AP) model of dual DEA was selected to prioritize cities or decision-making units (DMUs). Numerical Taxonomy (NT) method was also used to assess the validity of AP dual model in DEA. The prescribed approach was applied for five cities in East Azerbaijan province of Iran. Findings The results indicate that wind power as a renewable energy can be harnessed in few cities, and the ranking by DEA illustrated that the city of Tabriz is the first priority. Practical implications Low environmental degradation effects in comparison to other methods and the ability to utilization at a widespread level include the benefits of using wind energy in the generation of electricity. In this regard, the study of relevant potentials and finding suitable locations for the deployment of wind energy utilization equipment are essential. Using DEA method helps us to choose optimal locations according to different criteria. Social implications Wind energy is justifiable in reducing social costs in comparison with fossil fuel plants, which includes negative effects, and its electricity can be used as a sustainable energy in the country's economic, social and cultural development. Originality/value For identifying the most proper location for development of wind power plants in Iran, DEA is applied for the first time to prioritize the suitable locations for installations of wind turbines among five different cities in the East Azerbaijan region. A number of crucial factors including land price, distance to power, rate of natural hazards, wind speed and topography are considered for location optimization of wind turbines for the first time. Also, to validate the results of DEA method, NT method is used to assess the validity of AP dual model in DEA.

An optimization of energy cost of clean hybrid solar-wind power plants in Iran

ABSTRACTGiven the available capacities in Iran in terms of wind and solar renewable energies and regarding the fact that, to achieve a high economic growth, Iran needs to rise its power generation capacity, as well as considering that Iran suffers from severe shortage of water sources and necessarily needs to produce electricity without consuming water, this study aimed to find the lowest cost of energy generation by hybrid solar-wind system in 103 stations in Iran, using data taken from NASA and HOMER. Since building sector accounts for 45% of energy consumption in Iran, our purpose was to supply the electricity of an off-grid residential building. Results revealed that there is a high potential for using solar and wind renewable energies in Iran, so that the lowest and highest percentages of using renewables were recorded at Darab with 87% and Jask stations with 100% usage. Furthermore, the highest and lowest price per kWh of power generated were associated with a solar-diesel generator-battery system at Darab station with a price of $0.75/kWh and a wind-diesel generator-battery system at Bandarabbass station with a price of $0.586/kWh. Authors hope these results could play an effective role in development of using renewable energies in Iran.

Estimation of the employment rate of wind power plants in Iran in the horizon of 2050

The current capacity of electricity production by renewable sources in Iran is 660, and 420 MW is under construction. The wind power plants are responsible for more than 40% of total renewable electricity production. One of the substantial advantages of utilising renewable energies is job creation. In this study, the employment information is collected from Iranian wind power plants. Then, collected information is compared with the result of another research carried out at Kahak wind power plant. After that, direct and indirect job coefficients are calculated and compared with previous studies. Thereafter, using the statistical data of electricity consumption from 1967 to 2016, the required electricity generation capacity in 2050 is predicted. Finally, three energy use scenarios are defined, and job creation rate by wind energy is calculated. Based on the results, it is realised that 3 million direct and indirect jobs will be created if only 2.5% of the nominal capacity is allocated to the wind power plants in 2050.

Effect of environmental capabilities on environmental strategies and performances of thermal power plants in Iran

The electricity industry has an important role in the economic growth and industrial dynamics in all countries. However, it is one of the main sources of environmental pollutants as well. Hence, environmental experts are always trying to reduce the amount of pollutants. This study aims to realize the relations between environmental capabilities and environmental strategies with environmental, financial and operational performances. Accordingly, questionnaires, by 39 questions, distributed among 103 participants, including environmental experts of the thermal power plants and thermal power company. Among 103 questionnaires, 70 qualified questionnaires were collected. The reliability of questioners was tested by Cronbach alpha criterion and the amount of this criterion (0.845) verified the reliability of the questionnaire. Also, the validity of the questionnaire confirmed through the Fornell and Locker methods and other factors. For testing the hypotheses of research, the method of structural equation modelling analysis was used. The empirical results show that: 1. The environmental capabilities have a positive impact on environmental strategies 2. The environmental strategies have positive impacts on financial, operational and environmental performances 3. The environmental capabilities can have positive effects on environmental, financial and operational performance through environmental strategies. These results confirm that focusing on environmental issues of thermal power plant activities, not only improve environmental performance, but also improve their operational and financial performances.

Improvement of Identification Procedure Using Hybrid Cuckoo Search Algorithm for Turbine-Governor and Excitation System

In this paper a new method is introduced in order to modify identification process of a gas power plant using a metaheuristic algorithm named Cuckoo Search (CS). Simulations play a significant role in dynamic analyses of power plants. This paper points out to a practical approach in model selection and parameter estimation of gas power plants. The identification and validation process concentrates on two subsystems: governor-turbine and exciter. Standard models GGOV1 and STB6 are preferred for the dynamical structures of governor-turbine and exciter respectively. Considering definite standard structure, main parameters of dynamical model are pre estimated via system identification methods based on field data. Then obtained parameters are tuned carefully using an iterative Cuckoo algorithm. Models must be validated by results derived via a trial and error series of simulation in comparison to measured test data. The procedure gradually yields in a valid model with precise estimated parameters. Simulation results show accuracy of identified models. Besides, a whiteness analysis has been performed in order to show the authenticity of the proposed method in another way. Despite various detailed models, practical attempts of model selection, identification, and validation in a real gas unit could rarely be found among literature. In this paper, Chabahar power plant in Iran, with total install capacity of 320 MW, is chosen as a benchmark for model validation.

A hybrid approach based on inverse neural network to determine optimal level of energy consumption in electrical power generation

Power plants are a strategic infrastructure industry in each country and provide a strong driving force for the development of other industries. Hence, the performance of a power plant should be optimized by adjusting input parameters to maximize efficiency. This paper mainly proposes a novel hybrid approach that aims to present an optimal level of input parameters of a power plant integrating Data Envelopment Analysis (DEA), Artificial Neural Network and Inverse Problem in a new way. The inputs for a power plant system include internal electricity, fossil fuel and water consumption which are used to generate electrical power. First, the collected data are evaluated using a DEA model to identify and determine the days that operated efficiently. Then, the efficient vectors including resources consumption and generated power in the days evaluated in the previous stage are entered into the artificial neural network. Afterward, the weights and biases of the trained network are extracted for use in the inverse neural network. The inverse neural network model is presented as a nonlinear optimization problem with Gaussian constraints. Because of the severely ill-posed nature of the problem, to solve and obtain reasonable solutions, an innovative approach is used to determine the optimal level of inputs in less time and more accuracy. Based on this approach, for solving the nonlinear model and reducing the number of candidate solutions, the optimal interval is computed for each input and each day. A real case study of the thermal power plant in Iran is presented to show the abilities of the proposed approach.

Integrating neuro-fuzzy system and evolutionary optimization algorithms for short-term power generation forecasting

PurposeThis study aims to short-therm forecasting of power generation output for this purpose, an adaptive neuro-fuzzy inference system (ANFIS) is designed to forecast the output power of power plant based on climate factors considering wind speed and wind direction simultaneously.Design/methodology/approachSeveral methods and algorithms have been proposed for systems forecasting in various fields. One of the strongest methods for modeling complex systems is neuro-fuzzy that refers to combinations of artificial neural network and fuzzy logic. When the system becomes more complex, the conventional algorithms may fail for network training. In this paper, an integrated approach, including ANFIS and metaheuristic algorithms, is used for increasing forecast accuracy.FindingsPower generation in power plants is dependent on various factors, especially climate factors. Operating power plant in Iran is very much influenced because of climate variation, including from tropical to subpolar, and severely varying temperature, humidity and air pressure for each region and each season. On the other hands, when wind speed and wind direction are used simultaneously, the training process does not converge, and the forecasting process is unreliable. The real case study is mentioned to show the ability of the proposed approach to remove the limitations.Originality/valueFirst, ANFIS is applied for forecasting based on climate factors, including wind speed and wind direction, that have rarely been used simultaneously in previous studies. Second, the well-known and more widely used metaheuristic algorithms are applied to improve the learning process for forecasting output power and compare the results.

Developing a mathematical model of location decision optimization for solar cells using genetic algorithm

ABSTRACTWith growing proliferation of environmental pollution and reduction of energy sources, human being has been always looking for a solution to reduce pollution. One of these solutions is to use solar energy. In this research, a mathematical model is developed for optimization location decision of solar cells to minimize the operational and investment costs. The location of placement and the type of solar technology is planned and optimized by Genetic Algorithm (GA). To show the validity of the model, an accurate solving method is applied for small dimensions. Then, the GA efficiency is shown by comparing GAMS and GA results in large dimensions using the optimum values of the GA parameters. Finally, the applicability of the proposed model is presented for Jarghoyeh power plant in Iran. The results indicate that GA with spending less time could find better answers in comparison with GAMS. The results show the effectiveness of the proposed model.

Determination of optimum hybrid cooling wet/dry parameters and control system in off design condition: Case study

The consequences of water crisis in terms of political, social and environmental impacts have been motivated in this paper to recognize the hybrid wet/dry cooling systems of power plant in Iran. The existence of temperature constraints for consumers connected to the hybrid cooling tower, Space constraints for the construction of auxiliary system, devoting attention to high Net Present Value (NPV) of the project over the course of 25 years, Returning the appropriate capital, Optimal Design of air cooler and it's desirable performance over the course of a year were the reasons led to use of the genetic algorithm (GA) in this paper. The GA should optimize the design of the air cooler, according to that, at one functional point of a year, the lowest investment costs and the lowest cost of utilization in one year will happen. The cost of utilization in this project including: water consumption (makeup water of the cycle), fan power consumption (wet and dry block of hybrid cooling system), as well as chemical additives to water. The results illustrated that the proposed algorithm usage, applying a proper control system, taking into account the standards in the written code and determining the optimization intervals according to the manufacturer's data could conclude to the design of an air cooler, which can be constructed by inquiring from the manufacturing companies. The results also indicated that optimized design of hybrid cooling tower could reduce water consumption about 63% over the course of one year, return on investment by 5 years, develop NPV up to 40 M€ and enhance steam turbine power.

The hydrogeochemistry and geothermometry of the thermal waters in the Mouil Graben, Sabalan volcano, NW Iran

The emerging temperature of thermal springs within the Mouil Graben in the Sabalan volcano, a primary prospect for a geothermal power plant in Iran, varies from about 24 to 80 °C. The investigation was carried out by analyzing hydrogeochemistry (i.e., main cations and anions, trace elements and heavy metals) and environmental isotopes (i.e., δ18O, δ2H, tritium, and δ18C) of water from cold and thermal springs, deep wells and the Khiavchay River over the period from 1979 to 2016. The results were also combined with those from a geo–structural survey. The findings reveal that the ascending geothermal fluids around the summit of the Sabalan volcano primarily move into the Mouil Graben fault system where the intersection of the caldera rim faults and the main oblique-slip faults occur at the lowest topographic elevation around the volcano as an emergent dome. The waters in the graben can be categorized into four clusters G1 to G4. The thermal springs which are categorized into groups G1 and G2 result from the contribution of geothermal waters and/or the condensation of the geothermal steam into near–surface waters. The spring waters of group G1 are characterized by being of the chloride water type with the highest values for temperature and EC. The waters of this group predominantly result from the contribution of geothermal water and are then more appropriate for geothermometry. The spring waters of group G2 are characterized by acidic–sulfate water type with lower values for temperature and EC. The waters of this group which predominantly arise from the condensation of geothermal steam, are mainly cold and enriched by H2S into near–surface waters or steam heated waters and are not appropriate for thermometry. Notably, δ18O of the G1 water samples has a significant positive shift while in the G2 water samples there is either no shift or a small positive shift. The cold spring waters with a low δ18O ratio (G3 and G4) originate from precipitation percolated into the surrounding highlands. Moreover, it can be concluded that the controlling silica phase in the Mouil reservoir is quartz and the average temperature calculated by the quartz geothermometer (maximum steam loss) for the water samples from the deep wells is about 225–230 °C which is near the range of values determined in the deep wells (230–242 °C). The temperatures calculated for the water samples from the deep wells by the cation geothermometers of Na–K (169–349 °C), Na–K–Ca 174 to higher than 300 °C), Na–K–Ca–Mg (268 to higher than 350 °C), Mg–Li (167–266 °C), and Na–Li (226–258 °C) are higher than the temperature range determined in the deep wells (230–242 °C). The average temperature calculated for the water samples from the springs of group G1 using all the silica geothermometers (<196 °C) and the cation geothermometers of Na–K (236–290 °C), Na–K–Ca (194–221 °C), Na–K–Ca–Mg (more than 350 °C), and Mg–Li (∼64–126 °C) is different from the temperature range measured in the deep wells except that calculated by the Na–Li geothermometer (173–250 °C and 226 °C on average) which is relatively close to the measured range. The common discharging gas from the deep wells is CO2 (about 98% of the total gas). It seems that the volatile gases discharging from the deep wells come from a mixture of crustal/mantle and meteoric gas. The deep well gas samples lie well below the air saturation water line.

Inclusive Design and Implementation of Online Load Angle Measurement for Real-Time Transient Stability Improvement of a Synchronous Generator in a Smart Grid

Smart grid applications have been extended to perform supervisory functions within a short and efficient time. Insecure operating conditions are likely to happen in power systems. Reducing the risk associated with this condition, adaptive countermeasures, and self-healing actions are made using the infrastructures of the smart grids. Real-time transient stability assessment is classified into these kinds of applications. Corrective control actions could be taken based on the postfault rotor angle deviations. In this paper, a novel real-time digital signal processing (DSP)-based load angle measurement instrument for synchronous generators is discussed. This real-time instrument is designed for high-precision online applications. In this approach, input signals contain the rotor's shaft position and zero crossing of generator terminal voltage is applied to a DSP board to achieve the online load angle. The proposed load angle measuring instrument is installed and used in many power plants in Iran. The experimental results demonstrate the high accuracy and fast response of the proposed instrument.

Mathematical modeling and validation of a 320 MW tangentially fired boiler: A case study

The main step to evaluate the effect of various parameters on optimal operation conditions and pollution reduction of a boiler is to create a detailed model. In this paper, a steady state integrated model of boiler has been developed. Besides implementing main components of boiler applying thermodynamics laws, heat transfer and pressure drop correlations, following aspects have been considered: equilibrium combustion to predict flue gas composition and temperature, recirculation gas in the furnace, furnace exit gas temperature, radiation of the flue gases to the waterwall and superheaters, desuperheaters and rotary regenerative air heater (Ljungström). Heat transfer surfaces are considered as heat exchangers including direct and inter-tube radiation energy. The model was successfully applied to 320 MW tangentially fired boiler of Bandar Abbas power plant in Iran at six different loads 240, 250, 260, 270, 280 and 290 MW. Inlet/outlet temperature of working fluids for each heat transfer surfaces of the boiler are presented, compared and validated against the actual measured data from the power plant with maximum relative error of 8.72%. Effect of each heat transfer surfaces of boiler on its efficiency is presented and analyzed by averaged energy distribution in the boiler.

Construction of an indirect solar dryer with a photovoltaic system and optimised speed control

With a radiant energy of 4.5 kWh/m2 (International Power Plant in Iran), Iran has a good potential for using solar thermal energy to dehumidify products in the agricultural and pharmaceutical industry. In fact, drying provides the possibility of restoring surplus products due to its longer shelf life, for future use. Indirect drying results are better results (nutrition and taste) than direct drying. Indirect solar dryer today works in many countries in industrial and lower levels of humidity and is quite satisfactory. It should be noted that the electronic and control system is embedded in data creation and air flow when necessary. The solar dryer is a device that performs the drying of different products with the use of renewable energies, in other words, performs dehumidifying operations without even using 1 W of electricity from the city.

Finding the optimal combination of power plants alternatives: A multi response Taguchi-neural network using TOPSIS and fuzzy best-worst method

With increasing growth of electricity consumption in developed and developing countries, the necessity of constructing and developing of power plants is inevitable. There are two main resources for electricity generation includes fossil and renewable energies which have some different characteristics such as manufacturing technology, environmental issues, accessibility and etc. In developing plans, it is important to consider and address the policy makers' indicators such as environmental, social, economic and technical criteria. In this paper, an integrated multi response Taguchi-neural network-fuzzy best-worst method (FBWM) -TOPSIS approach is applied to find an optimal level of five different power plants including: gas, steam, combined cycle, wind and hydroelectric. Taguchi method is used to design combinations and calculate some of the signal to noise (S/N) ratios. Then, neural network is applied to estimate the rest of S/N ratios. Finally, FBWM and TOPSIS methods are used for weighing sub-indicators and selecting the best combination, respectively. To illustrate the usefulness of the proposed approach, a case study on the development of power plants in Iran is considered and the results are discussed. According to the results, in general, small size power plants for fossil resources are preferable. In contrast, medium and larger size power plants for renewable resources are preferable.

Generating Electricity Using Geothermal Energy in Iran

Given that fossil fuels will end one day and these types of fuels have a lot of pollution, Each country should look for new ways to generate energy that is needed by its people in proportion to its energy resources . Given that Iran is geographically located in an appropriate region of the earth ,it has great potential for using of renewable energy . In this study, sources and uses of Iranians geothermal energy has been studied , All of which indicates that Iran has a very good potential for electricity production using geothermal energy . According to the study, Iran has one geothermal energy plant in Meshkin shahr city and this plant power with capacity of 100 megawatts is an active power plant in Iran. Also The potential of geothermal power generation was verified in Khoy located in Azarbaijan Gharbi province, Sabalan Ardebil province, Sahand in Azarbaijan Sharghi and Damavand in Tehran province Examination verified that around 8.8% of total land in Iran is capable of geothermal energy production

Decrease in CO2 emission per capita as a result of the reduction in power grid losses in Iran

ABSTRACTIn power grids, a significant amount of the generated power in power plants is wasted in the grid between power plants and consumers. The amount depends on different factors. With regard to the vital role of electricity in economic and social development alongside with high investment costs in electricity generation, transmission and distribution, practical solutions for decreasing grid losses and enhancing the efficiency of existing facilities are crucial. Due to the global environmental situation and significant effects of energy consumption on the environment, the general trend is towards the deployment of methods which are more efficient and less fuel consuming. In addition, more implementation of monitoring and controlling equipment and instalment of more efficient equipment in electricity transmission and distribution sectors reveal the effort towards the reduction of pollutants. These facts reveal the importance of this issue amongst authorities in the power industry. In this research through the assessment of power grid losses and studying different types of power plants in Iran, the amount of electricity generation and grid loss are studied. In addition, the impact of different power plants on the amount of environmental pollutants especially CO2 is presented. Also, some solutions to reduce CO2 emission are provided.

Short-term power output forecasting of hourly operation in power plant based on climate factors and effects of wind direction and wind speed

Short-term power output forecasting is a fundamental and mandatory task in the power plants. Since the restructuring and privatization of power plants in Iran are in progress, this is particularly important in the operational planning and cost control in the power plants. Numerous factors affect power output forecasting especially climate factors. In this paper, besides several climate factors, two factors including wind speed and wind direction that have been rarely considered simultaneously for power output forecasting in previous studies, have been used. These two factors have many fluctuations and usually create a significant noise in forecasting models. To illustrate this claim, the mechanical simulations are used to demonstrate the necessity of these factors for short-term power output forecasting. For this purpose, a neural network-based approach is proposed using six variables. This approach uses the mixture models of Kohonen's self-organizing map (SOM) as clustering method and radial basis function (RBF) as classification method for accurate power output forecasting in a power plant. Furthermore, the real case study in Iranian power plant is used to show the ability of the proposed approach. Furthermore, the statistical tests are provided to indicate the advantages and capabilities of the proposed approach.

Multi-criteria evaluation and priority analysis of different types of existing power plants in Iran: An optimized energy planning system

Abstract The energy assurance and electricity supply are significant factors for progress and development of all countries. Low investment costs, reducing greenhouse emission, and high output efficiency for good performance of power plants are key factors in evaluating energy parameters for governments. This study is intended on determining and proving the compatibility of existing power plants in the country of Iran by applying observational data through the application of Multi-Criteria Decision-Making Analysis (MCDA). The obtained compromise solutions applied through a MATLAB code that helped in specifying which power plants are particularly suitable for establishment in the future work. Furthermore, it supports the generalization and validation of applying VIKOR method for thorough evaluation of power systems by comparing the results with real condition. The different types of power plants have been considered as alternatives. Multi-criteria evaluations of these diverse power plants were also carried out with respect to the environmental, technological, and economic criteria. It was concluded that hydropower plant is the best possible case for establishment, and the VIKOR method is a reliable technique for evaluating energy systems which can help to rank alternatives and determines the solution named compromise that is the closest to the ideal case.

Corrigendum: Performance evaluation of floating solar chimney power plant in Iran: estimation of technology progression and cost investigation

Corrigendum: Performance evaluation of floating solar chimney power plant in Iran: estimation of technology progression and cost investigation