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This paper aims to provide a concentrated photovoltaic thermal (CPVT) system regarding the high potential of receiving solar energy in Iran. Generated thermal energy of the system supplies the average thermal load of a commercial building and also its generated electricity is sold to the grid according to Iran's feed-in tariff (FiT). In order to calculate the system profitability, an economic evaluation is done in 20 years that is regarded as a novel approach. Furthermore, sensitivity analyses are performed to develop the results to the other locations with different economic conditions and various potential of energy resources, and also to present an appropriate financial outlook. The results demonstrate that the system is highly profitable given net present value (NPV) of 551.55 k$, internal rate of return (IRR) of 150.79%, benefit to cost ratio (BCR) of 10.32, payback time (PBT) of 0.51 years, and levelized cost of energy (LCOE) of 0.1293 $/MWh. Moreover, sensitivity analyses show that the system profitability is greatly appropriate even regarding the variety of unpredictable parameters. For instance, if the generated energy decreases by 20%, IRR and PBT will equal 120.63% and 0.63 years respectively, and the system can still maintain its high profitability. Moreover, it has been revealed that the enhancement of FiT can increase the system's economic efficiency. According to the results, it is noticeably profitable to use the CPVT systems to produce electrical and thermal power in countries with a high potential of receiving solar energy (especially middle-eastern countries). [ABSTRACT FROM AUTHOR]

The exponential growth of population and industries has brought about an increase in energy consumption, causing severe climatic and environmental problems. Therefore, the move towards green renewable energy is being ever more intensified. This study aims at estimating the rooftop solar power production for Tehran, the capital city of Iran, using a Geospatial Information System (GIS) to assess the big data of city building parcels. Tehran is faced with severe air pollution due to its excessive fossil fuel usage, and its electricity demand is increasing. As a result, this paper attempts to provide the quantified solar power potential of city roof tops for policymakers and authorities in order to facilitate decision-making in relation to integrating renewable energies into the power production infrastructure. The results shows that approximately 3000 GWh (more than 14% of the total electric energy consumption) of solar power can be produced by the rooftop PV installations in Tehran. The potential nominal power of rooftop PV installations is estimated to be more than 2000 MW, which is four times the current installed PV capacity of the whole country. The findings of the study suggest that there is great potential hidden on the rooftops of the city, which can be utilized to assist the power systems of the city in the longer run for a more sustainable future. [ABSTRACT FROM AUTHOR]

Iran is one of the most potent energy exporters and fastest‐growing energy consumers in the world. Its large amount of energy exported can directly impact the economy of importer countries. Iran's energy matrix mostly consists of hydrocarbons, while the remaining portion is compounded by a blend of biofuels, hydropower, wind, solar, and other renewable sources. Even though the country has an excellent potential to reach a large amount of renewable power, it is most likely that Iran will deal with a shortage in its electricity segment during the upcoming years. Besides wind and solar energy, bioenergy appears to be a good alternative for enhancing the country's energy matrix and transit Iran's energy consumption pattern from a high‐level usage of hydrocarbons to a more renewable scenario. Therefore, in the present paper, the role of Iran in global energy production is discussed, and its potential for renewable energy generation is described. The review presents a vast set of data related to environmental, infrastructural, economic, and social aspects to clarify the potential and benefits of Iran's transition to a more renewable energy matrix. The investigations revealed the country's extremely favourable conditions to use bioenergy resources as a key solution to avoiding future energy crises, besides improving quality of life. The context can help researchers, power suppliers, and energy consumers know how renewable energies, especially bioenergy, can modify the energy matrix of a country rich in hydrocarbon resources. [ABSTRACT FROM AUTHOR]

Policy-makers should focus on solar energy due to the increasing energy demand and adverse consequences such as global warming. Conflicting criteria influence choosing the most desirable place to construct a Solar Power Plant (SPP). Researchers have popularized multicriteria decision-making (MCDM) methods because of the potential. Although the simultaneous use of several methods increases the robustness and accuracy of the results, existing methods to integrate MCDM methods mainly consider the same weight for all methods and utilize the alternatives ranking for the final comparison. This paper presents a hybrid decision-making framework to determine the best location for SPPs in Iran using a set of criteria extracted from the literature and expert opinions. An initial list of decision-making alternatives is prepared and evaluated using GIS software in terms of criteria. Decision-makers prioritized the identified alternatives using the MCDM methods, including SWARA and different ranking methods (TOPSIS, TODIM, WASPAS, COPRAS, ARAS, and MULTIMOORA). Finally, the CCSD method aggregates the results and identifies the best location. Results highly correlate with the results of previous methods and demonstrate the robustness of the proposed approach and its capability to overcome the limitations of previous methods. [ABSTRACT FROM AUTHOR]

In this paper, the efficient amounts of radiation received by a flat‐plate collector and top heat loss coefficient along with affecting parameters are investigated using measured data in different climates of Iran. Coded program in MATLAB software is then distributed to calculate the optimum tilt angle for extracting the highest solar radiation in different months of the year for five cities of Iran with different geographic latitudes. An optimal annual slope is obtained for maximum utilization and for conditions where the collector can only be adjusted at an angle during the year. Using an interpolation, an equation has been derived for obtaining the optimal tilt angle of collector for areas with latitude between 27° and 37°. Inasmuch as the top heat loss coefficient is required for evaluating the thermal performance of solar collectors, the value for different temperatures is obtained at ambient temperature up to 200°C for the absorber plate. Effects of key parameters such as collector slope, number of covers, wind speed, emittance coefficient of adsorption plate, and cover spacing on top heat loss coefficient are investigated. In the end, the particle swarm optimization (PSO) method is employed to minimize the top heat loss coefficient. The minimum amount of top heat loss coefficient is obtained with the amount of 0.99 W/m2K. [ABSTRACT FROM AUTHOR]

The reason for utilizing PV-T systems is their capability to generate both electricity and heat at the same time, which enhances energy efficiency and decreases the need for multiple energy sources. Considering the potential of solar energy and different climates in Iran, in this research, the feasibility, modeling, and comparison of the PV-T system to supply electricity and heat to residential units were investigated in 28 cities with 9 climates of Iran. A thorough evaluation of performance was conducted using the software Polysun 9.0 and PVsyst 6.6.8 for a duration of one year. The scientific novelty of this study relies on the simultaneous utilization of two analytical software, the thoroughness of the research, and taking into account the impact of climate on the findings. Investigations showed that the best performance of the designed system is related to Bandar Abbas and Zahedan, and the worst performance is related to Rasht and Sari. In general, according to the amount of electricity and heating produced by the PV-T system, it showed that Shiraz and Rasht stations have the highest and lowest production of energy. In terms of environmental effects, Birjand station will have the most and Rasht the least reduction in CO2 emissions. In terms of heating energy demand for the investigated building, Ardabil has the highest amount and Bandar Abbas has the lowest amount. [ABSTRACT FROM AUTHOR]

This study analyses the expansion of solar energy in Iran, considering political, economic, social, and technological factors. Due to the prolonged sanctions on Iran, the development of clean energy power plants has been either halted or significantly reduced. Hence, this study aims to identify barriers to the expansion of solar energy power plants and simulate solar power plants using PVsyst (Photovoltaic system) software. The study is unique in its approach of combining technical analysis with social sciences to facilitate the implementation of solar energy expansion in remote areas. This study focuses on two specific areas with high solar radiation, namely Darab and Meybod, which are located in Fars and Yazd provinces, respectively. Solar energy can be generated in these two areas due to their unique location with high levels of solar irritation. To achieve this goal, the technical analyses focuson simulating the performance of a 9 kWp (kilowatt 'peak' power output of a system) grid-connected polysilicon(poly-Si) photovoltaic plant for Darab and a 9.90 kWp plant for Meybod. The simulation is carried out to obtain maximum electricity production and evaluate parameters such as incident radiation, performance ratio, energy into the grid, energy output at the array, and losses. The produced energy for Darab was 20.40 MWh/year, with specific production of 2061 kWh/kWp/year, and the performance ratio (PR) was 81.26%. For Meybod, production was 20.70 MWh/year, with specific production of 2091 kWh/kWp/year, and the performance ratio (PR) was 80.88%. Through the PEST analysis, it is evident that strategic planning and appropriate actions are crucial at the provincial, national, and local levels for energy systems' development. This indicates that both governments and citizens should play an active role in supporting the expansion of energy systems by planning and creating awareness among the public to embrace and adopt energy systems. [ABSTRACT FROM AUTHOR]

Iran's energy consumption is constantly and rapidly increasing. Due to the country's geographical placement on the Sun Belt of the Earth, there is a great potential for photovoltaic (PV) energy to be used; however, it necessitates an evidence-based selection of appropriate locations to develop solar farms. In this paper, we used a new approach to prioritize potential PV plant sites employing geographical information system (GIS)-based Fermatean Fuzzy (FF) TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). We considered economic, geographic, climatic, infrastructures, and demographic criteria in GIS-based FF-TOPSIS and compared outputs with earlier versions of the TOPSIS method (crisp and triangular fuzzy numbers (TFN)). Although all three versions indicated high correlations of >0.89, 20.88% of pixels' priority differed depending on the chosen method's logic. Regarding the sensitivity analysis, the GIS-based FF-TOPSIS in terms of criteria's weight was substantially more resistant to uncertainty than the TNF-TOPSIS. Besides, the GIS-based FF-TOPSIS has rational sensitivity (i.e., tolerance) to the accuracy of input data. The GIS-based FF-TOPSIS method demonstrated sensitivity or robustness depending on the degree of uncertainty in the numbers, which can be altered depending on the expertise and mastery of experts. Our findings indicated that approximately 13.81% of the entire country has the potential for solar power plants. According to the results of the GIS-based FF-TOPSIS approach, the provinces of Sistan and Baluchestan, Hormozgan, Fars, and Khuzestan have the largest potential for solar power farms in Iran. • Prioritizing potential areas to localize solar farms on a national scale. • Evaluating GIS-based TOPSIS with and without considering fuzzy principles. • Integrating Fermatean fuzzy sets (FFs) concept with spatial data and GIS concepts. • Proposing a GIS-based FF-TOPSIS as a practical method for uncertainty conditions. [ABSTRACT FROM AUTHOR]

The purpose of this study is the fabrication and performance evaluation of a new type of solar humidification–dehumidification (HD) desalination unit to supply sufficient fresh water for a seawater greenhouse in the MAKRAN coast in southeast Iran. In the proposed design, a particular type of air-to-air condenser is used. The cold air coming out of the greenhouse ventilation system (fan and pad) in summer and the cold ambient air in winter is used to supply the required cooling of the system. In this way, when cold air passes over the pipes in air-to-air condensers, condensation of water vapor occurs in the moist air inside the pipes, and fresh water is produced. Greenhouse fans, which have an air flow rate of around 20,000 m3/hr, are used to create this air flow. By fabricating two condensers, each using 42 rows of PVC pipes with a diameter of 75 mm, it is possible to produce 400 L of fresh water per day in a 400 m2 greenhouse. The required heating is provided by the solar farm, which includes 96 square meters of flat plate collectors. The steps of unit fabrication are described in detail in this research. However, the effect of greenhouse air temperature and circulating seawater flow rate on freshwater production, energy consumption, and energy intensity are also investigated. By increasing the flow rate of circulating seawater and decreasing the greenhouse air temperature, the production rate of the system increases. When the hot seawater and greenhouse air temperature are 61.7 °C and 26 °C, respectively, the maximum instantaneous production is estimated to be 80 L/h. The energy intensity of the HD desalination unit is varied between 3192 and 4382 kJ/L, and the gain output ratio of the system is around 0.6. The proposed system can be easily paired with conventional greenhouses employing a fan and pad cooling system and produces around 1.25 (L/m2·day) fresh water. [ABSTRACT FROM AUTHOR]

Iran is one of the most energy-consuming countries, especially in the construction sector, and more than 40% of its energy consumption is in the construction sector. Therefore, considering the very high potential of Iran in the field of solar energy, the need to pay attention to providing part of the energy required by buildings by solar energy seems necessary. The study of the effect of climate on the performance of a BIPV has not been done in Iran so far. Also, the use of ranking methods using the weighting of parameters affecting the performance of BIPV has not been done so far. The purpose of this study is to investigate the power supply of BIPV connected to the grid in the eight climates of Iran. Technical–economic–environmental energy analyses were performed by HOMER 2.81 software. In order to study different types of BIPV, four angles of 0°, 30°, 60°, and 90° were considered for the installation of solar cells. The effective output parameters of HOMER software were weighted by the Stepwise Weight Assessment Ratio Analysis (SWARA) method based on the opinion of experts, and it was observed that payback time (year) has the highest weight among the studied criteria. Then, different cities were ranked using the evaluation based on distance from the average solution (EDAS) method. The results showed that Jask is the most suitable and Ramsar is the most unsuitable city. Also, the results of the EDAS method were confirmed by Additive Ratio Assessment (ARAS), Weighted Aggregates Sum Product Assessment (WASPAS), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. [ABSTRACT FROM AUTHOR]

Presently, photovoltaic systems are an essential part of the development of renewable energy. Due to the inherent dependence of solar energy production on climate variations, forecasting power production using weather data has a number of financial advantages, including dependable proactive power trading and operation planning. Megacity electricity generation is regarded as a current research problem in the modern features of urban administration, particularly in developing nations such as Iran. Machine learning could be used to identify renewable resources like transformational participation (TP) and photovoltaic (PV) technology; based on resident motivational strategies, the smart city concept offers a revolutionary suggestion for supplying power in a metropolitan region. The sustainable development agenda is introduced at the same time as this approach. Therefore, the article's goals are to estimate Mashhad, Iran's electrical power needs using machine learning technologies and to make innovative suggestions for motivating people to generate renewable energy based on the expertise of experts. The potential of solar power over the course of a year is then assessed in our research study in Mashhad, Iran, using the solar photovoltaic modelling tool. The present idea in this research uses linear regression techniques to forecast utilising artificial neural networks (ANN). The most important factor in sizing the installation of solar power producing units is the daily mean sun irradiation. The amount of power that will be produced by solar panels can be estimated using the mean sun irradiance at a particular spot. A precise prediction can also be used to determine the complexity of the system, return on investment (ROI), and system load metrics. Several regression techniques and solar irradiance-related metrics have been combined to forecast the mean sun irradiation in terms of kilowatt hours per square metre. Azimuth and zenith factors considerably enhance the performance of the model, as demonstrated by the proposed method. The results of this study demonstrate 99.9% reliability rate for ANN model prediction of the electrical power usage during the summer and winter seasons. Thus, the maximum of power requirement during the hottest and coolest periods can be managed by using the photovoltaic system's renewable power projections. [ABSTRACT FROM AUTHOR]

Nowadays, due to restrictions on fossil fuels, the use of renewable energies is increasing day by day. Among renewable energies, solar energy has received more attraction due to its availability in all places. Among solar energy technologies, the solar tower has been welcomed due to its high power generation of electrical energy. For accurate modeling of the studied system, each component of the system has been evaluated and modeling has been done. Therefore, in this research, solar tower modeling has been conducted to achieve high electrical energy production, and to better compare the production rate, 5 cities in Iran with different weather conditions have been considered. According to the results, it can be mentioned that the highest production power by the studied system is related to Shiraz city with an average production of 20 kW/m2, and among the cities studied, the lowest rate is related to Mashhad with a production power of 15 kW/m2. [ABSTRACT FROM AUTHOR]

The consumption of fossil fuels has risen to be the main source of energy generation in most countries since the start of the industrial revolution around the world. The greenhouse gases generated from this use produce significant negative effects on the environment and human health. As a result of these disadvantages, the world needs to search for alternative energy sources that are low in carbon. The article focused on the amount of renewable energy generation in Turkey, Iran and Iraq from the collection of statistical data for the period from 2010-2020. Turkey occupied the clear progress to benefit from renewable energy sources, followed by Iran and Iraq. Where the percentage of annual growth of power generation based on hydropower in Turkey, Iran and Iraq (71.4%, 28.5% and 0.1%) respectively. With regard to other sources of renewable energy, Turkey had the largest share in generating energy from wind and solar energy, with an annual growth rate of (88.03%, 98.22%), respectively. Finally, renewable energy can be considered as a promising source of energy for future generations when correct planning policies are determined by countries. [ABSTRACT FROM AUTHOR]

In this study, the comprehensive thermodynamic, exergoeconomic and environmental performance of two multigeneration systemsfuelled by biomass and solar energy is surveyed. The multigeneration system A utilizes municipal solid waste and solar energy to produce power, heating, cooling, fresh water, and hydrogen which is considered to be located in the north of Iran with a moderate climate. Whereas, the multigeneration system B consumes bagasse and solar energy to supply power, heating, cooling, liquefied natural gas, and freshwater which is assumed to be located in the south of Iran with a hot climate. The results of the study show thatsystem B provides better performance from a thermodynamic viewpoint with energy and exergy efficiencies of 82.45% and 15.75%. Moreover, according to the outputs of exergoeconomic modelling, system B presents better performance because of lower capital costs. Finally, environmental profit is attained by accomplishing system B because of avoiding 1.14 million tons of NOx and 0.31 million tons of CO2 depletion to the atmosphere per year. In the end, through conducting a parametric study, the effect of key parameters on the thermodynamic, economic, and environmental performances of two systems is discussed. [ABSTRACT FROM AUTHOR]

Hydrogen is a clean and environmentally friendly energy vector that can play an important role in meeting the world's future energy needs. Therefore, a comprehensive study of the potential for hydrogen production from solar energy could greatly facilitate the transition to a hydrogen economy. Because by knowing the exact amount of potential for solar hydrogen production, the cost-effectiveness of its production can be compared with other methods of hydrogen production. Considering the above, it can be seen that so far no comprehensive study has been done on finding the exact potential of solar hydrogen production in different stations of Iran and finding the most suitable station. Therefore, in the present work, for the first time, using the HOMER and ArcGIS softwares, the technical-economic study of solar hydrogen production at home-scale was done. The results showed that Jask station with a levelized cost of energy equal to $ 0.172 and annual production of 83.8 kg of hydrogen is the best station and Darab station with a levelized cost of energy equal to $ 0.286 and annual production of 50.4 kg of hydrogen is the worst station. According to the results, other suitable stations were Bushehr and Deyr, and other unsuitable stations were Anzali and Khalkhal. Also, in 102 under study stations, 380 MW of solar electricity equivalent to 70.2 tons of hydrogen was produced annually. Based on the geographic information system map, it is clear that the southern half of Iran, especially the coasts of the Persian Gulf and the sea of Oman, is suitable for hydrogen production, and the northern, northeastern, northwestern, and one region in southern of Iran are unsuitable for hydrogen production. The authors of this article hope that the results of the present work will help the energy policymakers to create strategic frameworks and a roadmap for the production of solar hydrogen in Iran. [ABSTRACT FROM AUTHOR]

Renewable energy technologies have been developed in recent years due to the limited sources of fossil fuels, the possibility of depletion of fossil fuels and the related environmental issues. In these types of systems, it is crucial to reach optimum sizing in order to have an affordable system based on solar and wind energy and energy storage. In this study, a powerful optimization scheme based on tabu search, called discrete tabu search, has been proposed for sizing three stand-alone solar/wind/energy storage (battery) hybrid systems. For validating the applied algorithm effectiveness, the results are compared with the results found by the discrete harmony search. The obtained outcomes are compared on the basis of total annual cost. The components of the scheme are analyzed in different operating conditions by applying meteorological data in addition to real time information from three typical regions of Iran. According to the obtained data, applying 'discrete tabu search' leads to better outputs on the basis of mean, standard deviation and worst indexes. [ABSTRACT FROM AUTHOR]

Optimizing the location of wind and photovoltaic solar power plants is a significant environmental management problem. The effectiveness of the site selection process for renewable energy systems (RES) could be strengthened by flexible spatial and environmental planning strategies using decision support systems (DSS) to critically identify the most productive, environmentally friendly and acceptable sites for the production of sustainable and reliable wind and solar energy. This study discusses hybrid DSS, using multi-criteria evaluation based on the analytical hierarchy process (AHP), a geographical information system (GIS), fuzzy logic, and a weighted linear combination (WLC) approach to determine optimal locations for renewable energy generation infrastructure. In the first stage, the most decisive factors for evaluating the site suitability were identified, based on experts' opinions. Next, raster layers of ecological and socioeconomic sub-criteria were prepared GIS software. After incorporating the raster maps of each parameter, fuzzy membership functions were applied to normalize each raster layer between 0 and 1. The relative weights of different indicators were calculated using super decision software. Prioritizing vital elements were performed using AHP. In the final stage, the WLC approach was utilized to amalgamate layers in the GIS environment, which afforded the final site suitability maps. In Isfahan Province, Iran, 26% of the land area was found to be highly suitable for solar farms with 18% being highly suitable for wind farms. The results illustrate that using and comparing the results from combinations of computer-based DSS are more likely to result in better decisions than using individual DSS tools for the determination of the most suitable sites for RES location. [ABSTRACT FROM AUTHOR]

As the destructive impacts of unbridled use of fossil fuels threaten the quality of life, moving towards renewable energy like solar power has precipitated. While such movements are commendable, environmental impacts of its infrastructure development should not be overlooked. In this sense, this paper addresses a multi-objective robust mathematical model to study an environmentally conscious solar photovoltaic supply chain under correlated uncertainty. The proposed model aims at minimizing the environmental impacts of included activities in the concerned supply chain in addition to minimizing the conventional cost objective. To quantify and assess the relevant environmental impacts, a life cycle assessment-based model is accomplished employing the ReCiPe 2008‏ approach embedded in the SimaPro software tool. The augmented ϵ -constraint procedure is deployed to optimize the proposed multi-objective model, which is able to attain compromise solutions from the Pareto-optimal set. Correlated uncertainty whose importance is underlined in the real-life situations of the photovoltaic industry is also tackled exploiting a robust optimization method. Ultimately, the validity of the presented model is vindicated through discussing a case study. The acquired results endorse the effectiveness and usefulness of the model and offer prominent practical and managerial insights. • An optimization model to study an environmentally conscious solar photovoltaic supply chain is presented. • To quantify the environmental impacts, a life cycle assessment-based model is performed. • The augmented ϵ -constraint procedure is deployed to optimize the proposed model. • The model is able to consider the correlated uncertainty of data. • The validity of the model is vindicated througha case study. [ABSTRACT FROM AUTHOR]

High costs of the conventional energy systems' operation are one of the main challenges for isolated energy systems. Problems and costs of the fuel transferring to the islands for using in the diesel generator systems is another challenge for the operators. In this paper, a solar-based hybrid energy system (SHES) is proposed to satisfy the demands of an isolated industrial complex in Kish Island, an island in the south of Iran, in an optimal way. The proposed SHES contains various energy systems such as diesel generator, energy storage and rooftop solar power generation systems to provide demands of the isolated industrial complex's different users. This work tries to solve the optimization problem in a more optimal way by both energy saving arrangements and optimal sizing and operation of energy components. The load's consumption of the proposed isolated energy system is reduced optimally by using the construction operations related to the buildings' walls and windows insulation. After that, the optimal sizing and operation of the energy components are applied to the isolated energy system to satisfy the reduced demands. In other words, insulating walls and windows, with a low investment cost, will reduce the amount of the proposed isolated energy system's load and the load reduction makes a more cost reduction in the investment cost of energy generation systems. An Information gap decision theory-based (IGDT-based) optimization method is applied to the proposed model to find a robust solution against load uncertainty for the proposed problem. Finally, the proposed method is implemented to a comprehensive test case to demonstrate the practicability and applicability of the proposed approach. [ABSTRACT FROM AUTHOR]

Growing energy demand, rising greenhouse gas emissions and the depletion of fossil fuels necessitates the development of renewable energy sources. In order to improve renewable energy utilization, it is necessary to determine the most important factors influencing energy strategy. The energy system of Iran is highly dependent on fossil fuels; however, Iran has a high potential for solar energy development and several policies are being pursued by the government to develop power generation by renewable energy resources. This study presents and discusses several important parameters of energy policy and security which make the installation of photovoltaic (PV) systems crucial for the future. Installation of solar PV will improve energy security, foster job creation and reduce environmental problems. It also introduces and investigates those institutions and organizations involved in developing renewable energies, especially solar energy. These include the Ministry of Energy and universities, both of which play key roles in the development of solar energy. [ABSTRACT FROM AUTHOR]

Micro-CHP systems are designed to reduce energy consumption in the present era. In this study, micro-CHP systems with a solar-powered Stirling engine are designed for use in similar buildings in three different climates of Iran. These solar systems are highly dependent on weather conditions in different cities. Modeling and optimizing to reduce costs, the economic dimension of the system, increasing energy storage and reducing the amount of emissions, especially CO2, have been achieved and acceptable results have been obtained. [ABSTRACT FROM AUTHOR]

Lowering uncertainty in crude oil volume measurement has been widely considered as one of main purposes in an oil export terminal. It is found that crude oil temperature at metering station has big effects on measured volume and may cause big uncertainty at the metering point. As crude oil flows through an aboveground pipeline, pick up the solar radiation and heat up. This causes the oil temperature at the metering point to rise and higher uncertainty to be created. The amount of temperature rise is depended on exterior surface paint color. In the Kharg Island, there is about 3 km distance between the oil storage tanks and the metering point. The oil flows through the pipeline due to gravity effects as storage tanks are located 60m higher than the metering point. In this study, an analytical model has been conducted for predicting oil temperature at the pipeline exit (the metering point) based on climate and geographical conditions of the Kharg Island. The temperature at the metering point has been calculated and the effects of envelope color have been investigated. Further, the uncertainty in the measurement system due to temperature rise has been studied. [ABSTRACT FROM AUTHOR]

The world's electricity generation has increased with renewable energy technologies such as solar (solar power plant), wind energy (wind turbines), heat energy, and even ocean waves. Iran is in the best condition to receive solar radiation due to its proximity to the equator (25.2969° N). In 2020, Iran was able to supply only 900 MW (about 480 solar power plants and 420 MW home solar power plants) of its electricity demand from solar energy, which is very low compared to the global average. Yazd, Fars, and Kerman provinces are in the top ranks of Iran, with the production of approximately 68, 58, and 47 MW using solar energy, respectively. Iran also has a large area of vacant land for the construction of solar power plants. In this article, the amount of electricity generation using solar energy in Iran is studied. In addition, the construction of a 10 MW power plant in the city of Sirjan is economically and technically analyzed. The results show that with US$16.14 million, a solar power plant can be built in the Sirjan region, and the initial capital will be returned in about four years. The results obtained using Homer software show that the highest maximum power generation is in July. [ABSTRACT FROM AUTHOR]

The estimation of solar radiation for planning current and future periods in different fields, such as renewable energy generation, is very important for decision makers. The current study presents a hybrid model structure based on a multi-objective shark algorithm and fuzzy method for forecasting and generating a zone map for solar radiation as an alternative solution for future renewable energy production. The multi-objective shark algorithm attempts to select the best input combination for solar radiation (SR) estimation and the optimal value of the adaptive neuro-fuzzy inference system (ANFIS) parameter, and the power parameter of the inverse distance weight (IDW) is computed. Three provinces in Iran with different climates and air quality index conditions have been considered as case studies for this research. In addition, comparative analysis has been carried out with other models, including multi-objective genetic algorithm-ANFIS and multi-objective particle swarm optimization-ANFIS. The Taguchi model is used to obtain the best value of random parameters for multi-objective algorithms. The comparison of the results shows that the relative deviation index (RDI) of the distributed solutions in the Pareto front based multi-objective shark algorithm has the lowest value in the spread index, spacing metric index, favorable distribution, and good diversity. The generated Pareto solutions based on the multi-objective shark algorithm are compared to those based on the genetic algorithm and particle swarm algorithm and found to be the optimal and near ideal solutions. In addition, the determination of the best solution based on a multi-criteria decision model enables the best input to the model to be selected based on different effective parameters. Three different performance indices have been used in this study, including the root mean square error, Nash–Sutcliffe efficiency, and mean absolute error. The generated zone map based on the multi-objective shark algorithm-ANFIS highly matches with the observed data in all zones in all case studies. Additionally, the analysis shows that the air quality index (AQI) should be considered as effective input for SR estimation. Finally, the measurement and analysis of the uncertainty based on the multi-objective shark algorithm-ANFIS were carried out. As a result, the proposed new hybrid model is highly suitable for the generation of accurate zone mapping for different renewable energy generation fields. In addition, the proposed hybrid model showed outstanding performance for the development of a forecasting model for the solar radiation value, which is essential for the decision-makers to draw a future plan for generating renewable energy based solar radiation. [ABSTRACT FROM AUTHOR]