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The transition to renewable energy is key for sustainable development. Therefore, one of the sustainable development goals (SDG) of United Nations (UN) is to shift towards clean energy. However, in many resource-rich countries several factors can stand as barriers towards the transition process. In this regard, this paper examines the influence of higher oil price, CO 2 emissions, and income on renewable energy consumption in Iran from 1980 to 2019, employing General to Specific (Gets) modeling approach. Estimation results indicated that oil price and CO 2 emissions both have statistically significant and negative impact on renewable energy consumption. The negative influence of higher oil prices on renewable energy consumption may be interpreted as sign of satisfaction coming from higher oil prices, which postpones the transition from traditional energy sources to renewable for Iran. Furthermore, we concluded that income does not have a statistically significant influence on renewable energy use. The paper discusses alternative policies to promote renewable energy consumption. • The effect of higher oil price on renewable energy consumption was studied. • A novel version of General to Specific (Gets) modeling approach was employed. • We found significant, negative impact of oil price on renewable energy use in Iran. • The paper discusses alternative policies for Iran to promote renewable energy use. [ABSTRACT FROM AUTHOR]

This paper investigates the sustainability of Tabriz City focusing on its decarbonization. Statistical, quantitative, and comparative research methods are used to analyze the total energy consumed, conversion of primary energy, final exergy, and losses analysis according to the renewable and carbon-based energies. Then, the renewable resource potentials of the city are simulated using Grasshopper plug-in-Rhino simulator, THERM, PVSyst photovoltaic software, and ArcMap. In terms of the socio-economic assessment, non-oil Per Capita GDP, emissions, and energy intensities are analyzed concerning the B2DS scenario by 2050. The per capita GDP of the city is 71% of the country's average value. Tabriz's real GDP growth averaged −1.11% per year, primary energy demand increased by an average of 6.1%, CO 2 emissions from energy use grew by an average of 6% annually and the emission intensity of the economy increased by a total of 52.02%, in the period from 2011 to 2019. Finally, it is shown that the city consumes 58050 GWh/year of different carbon-based resources that only produced 20110 GWh/year of useful work with 5.63 MT CO 2 emissions, while the same work could locally be supplied by 30000 GWh/year RE with a minimum rate of 0.18 MT CO 2 emissions. The city Landfill receives 1000 tons/day of MSW, but two-thirds of this amount can be recycled. Regarding different sectors, buildings produce half of the CO 2. The cement industry with dual pollutants (17%) is the most polluting factory. Carbon-based electricity (66%) is the most polluting form of energy. In terms of carbon sequestration requirements with trees, shifting from fossil fuels toward green energies, EE, and recycling MSW can plunge required trees from 81.9 to 1.16 million trees. This paper highlights the importance of electrification of transportation based on RE, energy performance of buildings, and all sectors. • Large city decarbonization strategies • Application of RE according to geographic and climatic factors. • Electrification of sectors based on RE locally. • MSW recycling and sequestration by trees for decarbonization regarding the LULC and NDVI analysis. • Conversion and storage efficiencies of carbon-based fuels vs RE. [ABSTRACT FROM AUTHOR]

Hydropower plants play an integral role in energy supply and are one of the main renewable energy sources. The efficiency of hydropower reservoir systems is controlled by operating rules that need to be optimized to maximize energy production. Here, a novel optimization algorithm, influential flower pollination algorithm (IFPA), is developed in this paper. IFPA has three enhancements: an efficient exploration mechanism based on an adaptive coefficient, a new exploitation mechanism around the best solution, and a rank-based strategy to smoothly transition from exploration to exploitation. IFPA is tested in two benchmark multi-reservoir systems and a real-world five-reservoir hydropower system in Iran. The results show IFPA outperforms other advanced methods in terms of convergence speed and preciseness to achieve the global solution. The tested algorithms include adaptive guided differential evolution (AGDE), composite DE (CODE), hybridizing sum-local search optimizer (HSLSO), improved teaching learning-based optimization (ITLBO), self-adapting control parameters in DE (jDE), self-adaptive TLBO (SATLBO), and a multi-strategy hybrid of DE and particle swarm optimization (MS-DEPSO). IFPA also achieved better total power production in the real-world case study compared to other algorithms. Overall, IFPA can provide better solutions with less power deficit and shows as a promising method for optimizing hydropower reservoir problems. [ABSTRACT FROM AUTHOR]

Improving the accuracy of photovoltaic (PV) power forecasting is crucial to ensure more effective use of energy resources. Improvements are especially important for regions for which historical solar radiation data do not exist. This paper proposes a cyber-secure forecasting model called federated deep learning (FDL) to forecast PV power generation in various regions across Iran. The training process in each client is done by a convolutional neural network (CNN). Then, a generalizable global supermodel is generated based on the features extracted in each client, which has the ability to generalize and forecast for regions where there is no training data. Preserve data privacy and ideal performance against cyber-attacks are prominent features of the proposed method. The use of the proposed method is illustrated with a case study for Iran. The proposed FDL network is designed with 9 clients and three different scenarios were developed to test the robustness of the suggested method. In the first scenario, the PV power generation forecasting is done via the proposed technique and other conventional methods. The performance accuracy (R 2) of the generated global supermodel in this scenario for PV power generation forecasting in the regions of Khomein, Meybod, Varzaneh, Taleghan, and Shiraz are obtained as 0.981, 0.989, 0.986, 0.983, and 0.987 respectively. However, it was observed that other conventional deep learning-based models such as CNN and long short-term memory were not able to provide any forecasting for these regions. The second scenario models the scaling attack as a specific pattern of false data injection attack, to evaluate the performance of forecasting models against the data integrity attack. In the third scenario, cyber-attack detection is performed based on data visualization and image processing procedures. The results presented in different scenarios emphasize the high accuracy and generalizability of the global cyber-secure supermodel in PV power generation forecasting in different regions of Iran. [ABSTRACT FROM AUTHOR]

This paper focuses on hybrid renewable energy systems (HRES) as an alternative for conventional energy systems, especially fossil fuels. The problem is to find the optimal number of components of a standalone hybrid system, based on minimizing the value of the annualized cost of the system (ACS), in the presence of the constraints. To reduce the CO2 particles as one of the most threatening particles for the planet, an additional constraint is added to the objective function. For solving the problem of system sizing, Harmony Search and Ring Theory-based Evolutionary Algorithm have drawn attention to be preferred to numerous techniques. In addition,a combination of both, which has never been used before, is proposed. The proposed hybrid algorithm shows its ability to mitigate both the emissions and costs of the system promisingly in comparison with the others. Clearly, with Carbond = 15 and different values of LPSPMax as well as using RTHS, RTEA and HS algorithms for PV/WT/BSS/DG configuration, the best performance in terms of ACS minimization belongs to RTHS algorithm with the minimum cost of $1528.2. Also, considering LPSP = 0 and changing carbon from 0 to 20 tons/year, the ACS of the PV/WT/BSS/DG configuration is reduced from $38586.3 to $21153.5 (45% reduction). • Using a hybrid algorithm called RTHS to minimize the annual costs of the system. • Considering the LPSP constraint to supply the load demand at an acceptant level. • Reducing CO 2 emissions to increase the renewables penetration. • Considering uncertainty on both the generation and demand sides. • Validating of the proposed method based on real data for a case study in Iran.. [ABSTRACT FROM AUTHOR]

Although the socio-economic determinants of terrorism are conducted by many researchers using different samples and time spans, the impact of renewable energy consumption on terrorism has been ignored for a long time. To bridge the gap, this paper aims to investigate the impact of renewable energy consumption on terrorism regarding economic growth, non-renewable energy consumption, and income inequality over the period straddling 1990 and 2019 for a panel of the USA, China, India, Iran, Germany, and Russia. We employ the CUP-FM and CUP-BC estimators to reveal the long-run relationship between variables. The empirical findings suggest that renewable energy consumption has a negative impact on terrorism. A 1% increase in renewable energy consumption decreases terrorism by 0.315% and 0.631%, according to the CUP-FM and CUP-BC estimators, respectively. Economic growth also has a negative impact on terrorism, whereas non-renewable energy consumption and income inequality positively affect terrorism. Our empirical findings highlighted that renewable energy consumption and economic growth are crucial for combating terrorism in these countries. Therefore, these countries need to improve comprehensive policies in terms of renewable energy consumption and simultaneously decrease non-renewable energy consumption and income inequality. • Renewable energy consumption helps to lower the terrorism index. • Inequality of income expedites the rise of terrorism. • Terrorism is exacerbated as a result of fossil fuel consumption. [ABSTRACT FROM AUTHOR]

The costs of gas transportation and its environmental/security impacts in Iran have led to evaluating geothermal/solar renewable sources instead. Regarding the high potential of geothermal/solar in Sistan/Baluchestan province, Iran, which have still no access to the global gas network, these resources create a desirable plan. This paper attempted to present/compare four possible solar-assisted ground source heat pumps to supply four purposes of electricity demand, heating/cooling, and domestic hot water. First, the energy needed in the region was designed with DesignBuilder. Then, each proposed system was modeled in Polysun software using a photovoltaic-thermal and a vertical geothermal heat exchanger. Afterward, all probable plans were evaluated and compared technically, economically, and environmentally. The results indicated that the maximum/minimum values of the coefficient of heat pump performance for direct heat exchanger and series systems were 4.39/3.87. Also, the optimal plan was introduced with 4957/10667 kWh electricity consumption/production. Besides, an economic analysis was estimated for the optimal strategy with an investment return of 7years. The designed system prevented 5089kgCO 2 emissions annually. By means of this integrating hybrid system, Sistan/Baluchestan province would become a significant renewable power plant globally, and a considerable techno-economic and environmental challenge would be dissolved in Iran. • A novel geothermal/solar hybrid system was introduced and analyzed technically, economically and environmentally. • The proposed hybrid system could supply heating/cooling/electricity demands. • One of significant challenge, non-renewable resources usage, would be dissolved in Iran by using a renewable hybrid system. • The proposed plan could reduce CO2 emissions significantly. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive framework for evaluating renewable and non-renewable power plants' performance using the Life Cycle Analysis (LCA) and emergy analysis. The emergy analysis is used to consider the free ecosystem services in the sustainability of the systems as a supplement to the LCA. The results indicate that the wind and photovoltaic power plants have the best performance in terms of the LCA analysis, while the wind and combined cycle power plants have the highest emergy sustainability index. The best scenario is chosen under a two-objective optimization problem, including the single score and emergy sustainability as objective functions. Here, the wind power plant is the most interesting option while the combined cycle power plant with CO 2 capture is the least interesting alternative. In this study, a novel framework is developed to assess the Cost of Avoided Carbon emissions (CACe) using the integrated LCA-emergy analysis for the combined cycle power plant in which its value is evaluated to be about $151.65/ton of CO 2. Eventually, an uncertainty analysis is performed on the solar transformities using Monte Carlo simulation. The framework presented in this paper provides insights to increase power plants' sustainability for managers and authorities. • The sustainability of power plants is assessed using life cycle and emergy analyses. • A novel method is described to appraise the cost of avoided carbon emissions. • Uncertainty in the sustainability results is assessed using Monte Carlo simulation. • The genetic algorithm is applied to optimize the sustainability of power plants. • The fossil fuel power plant is less sustainable even with pollution control systems. [ABSTRACT FROM AUTHOR]