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Acute shortage of potable water and energy supplies is expected to raise in developing countries in the near future. One solid way to address these issues is to exploit renewable energy resources efficiently. Hence, this study aims to investigate wind and solar energy use in the coastal areas of southern Iran for renewable-powered seawater desalination and hydrogen production systems.

To accomplish the aforementioned purpose, five areas most prone to the problems in Iran, namely, Mahshahr, Jask and Chabahar ports and Kish and Hormoz islands were scrutinized. To ascertain the amount of wind and solar energy available in the areas, Weibull distribution function, Angstrom–Prescott equation and HOMER software were used.

The findings indicated that wind energy density in Kish was 2,014.86 (kWh/m².yr) and solar energy density in Jask equaled to 2,255.7 (kWh/m².yr) which possessed the best conditions among the areas under study. Moreover, three commercial wind turbines and three photovoltaic systems were examined for supplying energy needed by the water desalination and hydrogen production systems. The results showed that application of wind turbines with rated power of 660, 750 and 900 kWh in Kish could result in desalting 934,145, 1,263,339 and 2,000,450 (m³/yr) of seawater or producing 14,719, 20,896 and 31,521 (kg/yr) of hydrogen, respectively. Additionally, use of photovoltaic systems with efficiency of %14.4, %17.01 and %21.16 in Jask could desalinate 287, 444 and 464 (m³/yr) of seawater or generate 4.5, 7 and 7.3 (kg/yr) of hydrogen, respectively.

Compared to the huge extent of water shortage and environmental pollution, there has not been conducted enough studies to obtain broader view regarding use of renewable energies to solve these issues in Iran. Therefore, this study tries to close this gap and to give other developing nations the idea of water desalination and hydrogen production via renewable energies.

Every day, the sun provides by far more energy than the amount necessary to meet the whole world’s energy demand. Solar energy, unlike fossil fuels, does not suffer from depleting resource and also releases no greenhouse gas emissions when being used. Hence, using solar irradiance to produce electricity via photovoltaic (PV) systems has significant benefits which can lead to a sustainable and clean future. In this regard, the purpose of this study is first to assess the technical and economic viability of solar power generation sites in the capitals of the states of Canada. Then, a novel integrated technique is developed to prioritize all the alternatives.

In this study, ten provinces in Canada are evaluated for the construction of solar power plants. The new hybrid approach composed of data envelopment analysis (DEA), balanced scorecard (BSC) and game theory (GT) is implemented to rank the nominated locations from techno-economic-environmental efficiency aspects. The input data are obtained using HOMER software.

Applying the proposed hybrid approach, the order of high to low efficiency locations was found as Winnipeg, Victoria, Edmonton, Quebec, Halifax, St John’s, Ottawa, Regina, Charlottetown and Toronto. Construction of ten solar plants in the ten studied locations was assessed and it was ascertained that usage of solar energy in Winnipeg, Victoria and Edmonton would be economically and environmentally justified.

As to novelty, it should be clarified that the authors propose an effective hybrid method combining DEA, BSC and GT for prioritizing all available scenarios concerned with the construction of a solar power plant.

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.

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.

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.

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.

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.

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.

This paper aims to investigate the techno-economic feasibility of wind power potential for a tribal region located in Gachsaran in the South-West of Iran.

Techno-economic feasibility study and analysis of data were conducted by HOMER v2.68 software. Simulations and calculations were performed for 10 kW turbines, 8 Trojan L16P batteries, 12 kW converter and 12 kW generator. To anticipate the pay back period (PBP) or the time required to reach profitability, an engineering economic method named net equivalent uniform annual was applied.

The power plant construction cost, including the initial cost, installing, replacing and project operating costs for useful life of 20 years was equal to $40970. The net income of the project for each year was $8538 and the calculations were carried out using interest rate of 18%. Results indicated that PBP was 13 years which is lower than 20 years useful life of the turbine. Therefore, it is economically feasible to use this type of turbine for the nominated region.

There has not been conducted a research regarding remote areas in Iran; therefore, this study aims at closing this research gap. Moreover, this method could be used for any remote areas in any other developing country.

Rural areas are one of the effective regions in economy and self-sufficiency field especially in agricultural and livestock section. Planning in the rural section and the effort in solving the problems of farmers lead to increase their interest in farming and manufacturing in the villages and decrease their migration to the cities and metropolitans. Therefore, the present study aimed at feasibility of electricity to a rural household in Iran using off-grid solar-based hybrid system.

In renewable energy projects, a successful evaluation requires suitable criteria so that one can properly analyze the operational behavior of all feasible scenarios. In the present paper, HOMER software has been used for this purpose for a village with no access to electricity grid (Bar Aftab-e Jalaleh, Iran). Due to drastic fluctuation of fossil fuel prices and varied solar radiations in various years because of climate change, sensitivity analysis has been performed using HOMER.

In the optimum status economically, 70% of needed energy is provided by solar cells at the price 0.792 $/kWh. The comparison between the optimum condition economically and the condition that only use fossil fuels revealed that the return on investment will occur after less than 2 years and have remained profitable over 23 years.

The authors hope that the results of this study can be used in planning of the authorities to realize the interests of people in this village.

According to the surveys, despite Iran being the first country in terms of providing solar power to the villages, so far no socio-economic-environmental assessment has been done for a solar cell-based micro-grid in an off-grid mode for a remote village that is deprived of electricity from a national electricity grid. In addition, for the first time in Iran, the effect of the fuel price and solar radiation parameters variability on the performance of system have been investigated.

In hot and dry climates, air conditioning accounts for a large portion of total energy consumption; therefore, this paper aims to investigate the impact of sol-air temperature and ground temperature on the loss of cooling energy in hot and dry regions of Iran.

In line with this objective, the values of sol-air temperature along different directions and ground temperature at different depths were assessed with respect to climatic data of Yazd City. The impact of sol-air temperature and ground temperature on the rate of heat loss was investigated. So, energy loss of the walls aligned to four primary directions was calculated. This process was repeated for a 36 m² building with three different shape factors. All analyses were conducted for the period from May to September, during which buildings need to be cooled by air conditioners.

Numerical analyses conducted for hot and dry climate show that sol-air temperature leads to a 41-17 per cent increase in the wall’s energy loss compared with ambient temperature. Meanwhile, building the wall below the surface leads to a significant reduction in energy loss. For example, building the wall 400 cm below the surface leads to about 74.8-79.2 per cent energy saving compared with above ground design. The results also show that increasing the direct contact between soil and building envelope decreases the energy loss, so energy loss of a building that is built 400 cm below the surface is 53.7-55.3 per cent lower than that of a building built above the surface.

The impact of sol-air temperature and ground temperature on the cooling energy loss of a building in hot and dry climate was investigated. Numerical analysis shows that solar radiation increases heat loss from building envelope. Soil temperature fluctuations decrease with depth. Heat loss from building envelope in an underground building is lower than that from building envelope in a building built above the ground. Three different shape factors showed that sol-air temperature has the maximum impact on square-shaped plan and minimal impact on buildings with east-west orientation.

The purpose of this paper is to forecast wind power generation in an area through different methods, and then, recommend the most suitable one using some performance criteria.

The Box–Jenkins modeling and the Neural network modeling approaches are applied to perform forecasting for the last 12 months.

The results indicated that among the tested artificial neural network (ANN) model and its improved model, artificial neural network-genetic algorithm (ANN-GA) with RMSE of 0.4213 and R2 of 0.9212 gains the best performance in prediction of wind power generation values. Finally, a comparison between ANN-GA and ARIMA method confirmed a far superior power generation prediction performance for ARIMA with RMSE of 0.3443 and R2 of 0.9480.

Performance of the ARIMA method is evaluated in comparison to several types of ANN models including ANN, and its improved model using GA as ANN-GA and particle swarm optimization (PSO) as ANN-PSO.

To design, control and evaluate photovoltaic (PV) systems, an accurate model is required. Accuracy of PV models depends on model parameters. This study aims to use a new algorithm called improved social spider algorithm (ISSA) to detect model parameters.

To improve performance of social spider algorithm (SSA), an elimination period is added. In addition, at the beginning of each period, a certain number of the worst solutions are replaced by new solutions in the search space. This allows the particles to find new paths to get the best solution.

In this paper, ISSA is used to estimate parameters of single-diode and double-diode models. In addition, effect of irradiation and temperature on I–V curves of PV modules is studied. For this purpose, two different modules called multi-crystalline (KC200GT) module and polycrystalline (SW255) are used. It should be noted that to challenge the performance of the proposed algorithm, it has been used to identify the parameters of a type of widely used module of fuel cell called proton exchange membrane fuel cell. Finally, comparing and analyzing of ISSA results with other similar methods shows the superiority of the presented method.

Changes in the spider’s movement process in the SSA toward the desired response have improved the algorithm’s performance. Higher accuracy and convergence rate, skipping local minimums, global search ability and search in a limited space can be mentioned as some advantages of this modified method compared to classic SSA.

The purpose of this paper is to propose a framework of green strategies as a combination of energy-efficiency measures and solutions towards environmental impact reduction for improving environmental sustainability at logistics sites. Such measures are examined by discussing the related impacts, motivations and barriers that could influence the measures' adoption. Starting from the framework, directions for future research in this field are outlined.

The proposed framework was developed starting from a systematic literature review (SLR) approach on 60 papers published from 2008 to 2022 in international peer-reviewed journals or conference proceedings.

The framework identifies six main areas of intervention (“green strategies”) towards green warehousing, namely Building, Utilities, Lighting, Material Handling and Automation, Materials and Operational Practices. For each strategy, specific energy-efficiency measures and solutions towards environmental impact reduction are further pinpointed. In most cases, “green-gold” measures emerge as the most appealing, entailing environmental and economic benefits at the same time. Finally, for each measure the relationship with the measures' primary impacts is discussed.

From an academic viewpoint, the framework fills a major gap in the scientific literature since, for the first time, this study elaborates the concept of green warehousing as a result of energy-efficiency measures and solutions towards environmental impact reduction. A classification of the main areas of intervention (“green strategies”) is proposed by adopting a holistic approach. From a managerial perspective, the paper addresses a compelling need of practitioners – e.g. logistics service providers (LSPs), manufacturers and retailers – for practices and solutions towards greener warehousing processes to increase energy efficiency and decrease the environmental impact of the practitioners' logistics facilities. In this sense, the proposed framework can provide valuable support for logistics managers that are about to approach the challenge of turning the managers' warehouses into greener nodes of the managers' supply chains.