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• The optimization of 10 important design parameters of a GCPV plant is introduced. • The IRR objective function includes both technical and economic aspects. • Genetic algorithm is suitable and effective to find the global optimum. • Optimal configurations are presented for six locations worldwide. • The effects of decreasing PV module costs on optimal plant design are analysed. The increasing penetration of photovoltaic (PV) technology calls for the development of an effective method for optimization of grid-connected photovoltaic power plants. This paper presents a simultaneous optimization method of ten important design parameters of a PV plant, including the module power, inverter sizing, support structure dimensions, cable losses, module orientation and row spacing. A mathematical PV performance model taking into account the important effects and losses and an economic cost model were developed and presented in detail. The objective function is the internal rate of return and the optimization is performed by a genetic algorithm. The results show that the proposed models and method are capable to optimize the grid-connected PV plant and provide reliable results after a 6–7 min calculation time. The method was demonstrated in detail for a Hungarian location, including the losses and cost structure of the optimal plant configuration. The optimization was also performed for 5 additional sites around the world to assess the effect of location and meteorology. The impact of the decreasing PV module prices on the optimal design is calculated to identify the expected future trends in PV plant design. The presented optimization method can be utilized to facilitate the optimal design of commercial PV plants and for research purposes. [ABSTRACT FROM AUTHOR]

Abstract Global warming, climate change, fossil fuel depletion and steep hikes in the price of environmentally friendly hydrocarbons motivate researchers to investigate CO 2 hydrogenation for hydrocarbons production. However, due to the reaction complexities and varieties of produced species, the process mechanism and subsequently estimation of the kinetic parameters have been controversial yet. Therefore, estimating the kinetic parameters using Artificial Bee Colony (ABC) and Differential Evolution (DE) optimization algorithms based on Langmuir-Hinshelwood-Hougen-Watson (LHHW) mechanism is proposed as a possible remedy to fulfil the requirements. To this end, a one-dimensional heterogeneous model comprising detailed reaction rates of reverse water gas shift (RWGS), Fisher-Tropsch (FT) reactions and direct hydrogenation (DH) of CO 2 is developed. It is observed that ABC exhibiting 6.3% error in predicting total hydrocarbons selectivity is superior to DE algorithm with 32.9% error. Therefore, the model employed the estimated kinetic parameters obtained via ABC algorithm, is exploited for products distribution analysis. Results reveal that maximum 73.21% hydrocarbons (C 1 C 4) selectivity can be achieved at 573 K and 1 MPa with 0.85% error compared to the experimental value of 72.59%. Accordingly, the proposed model can be exploited as a powerful tool for evaluating and predicting the performance of CO 2 hydrogenation to hydrocarbons process. Graphical abstract Image 1 Highlights • Studying kinetic parameters of CO 2 hydrogenation has remained controversial. • One-dimensional heterogeneous model including detailed reaction rates is developed. • Kinetic parameters are estimated via ABC and DE algorithms using experimental data. • Components distribution along reactor length are presented based on ABC algorithm. • Performance of the theoretical reactor is compared with available experimental data. [ABSTRACT FROM AUTHOR]

The progressive use of renewable energy sources to ensure a continuous and abundant energy supply is the significant target towards a sustainable and secure energy system. Previously, the countries that had relied on fossil fuel as a dominating energy source are now endorsing energy system transition towards renewable energy sources. In this study, a comparative assessment of the energy problems of South Asian countries is summarized. Nevertheless, there are many similarities and differences in the electricity supply system of these countries. Long-term planning for renewable energy development is suggested for a diverse population and dispersed geographical location considering all the significant challenges. However, appropriate schemes are imperative for integrating significant renewable energy sources. This study introduces a foresight plan of the electricity model according to the demand and supply balance for extensive technical analysis. The EnergyPLAN modeling tool was employed to work out a more ambitious VRE integration scenario than the official plans. The reference model was validated according to legitimate and authentic data, and then, the technically most feasible renewable energy-based alternate scenario was built. The additional grid integration cost of variable renewable energy was quantitatively investigated for comprehensive power system modeling for a real-time economic analysis. The results may be adapted and support developing more sustainable power generation serving 1787 million in South Asian countries. [ABSTRACT FROM AUTHOR]

• Modeling of CO 2 hydrogenation to hydrocarbons in CR, AFSR and RFSR are accomplished. • Kinetic parameters are estimated via heuristic DA optimization algorithm. • Performance of the theoretical CR is compared with available experimental data. • Effect of reactor configuration is investigated in terms of conversion and selectivity. Climate change and global warming, as well as growing global demand for hydrocarbons in industrial sectors, make great incentives to investigate the utilization of CO 2 for hydrocarbons production. Therefore, finding an in-depth understanding of the CO 2 hydrogenation reactors along with simulating reactor responses to different operating conditions are of paramount importance. However, the reaction mechanisms for CO 2 hydrogenation and their corresponding kinetic parameters have been disputable yet. In this regard, considering the previously proposed Langmuir-Hinshelwood-Hougen-Watson (LHHW) mechanism, which considered CO 2 hydrogenation as a combination of reverse water gas shift (RWGS) and Fischer-Tropsch (FT) reactions, and using a one-dimensional pseudo-homogeneous non-isothermal model, kinetic parameters of the rate expressions are estimated via fitting experimental and modelling data through a novel swarm intelligence optimization technique called dragonfly algorithm (DA). The predicted reactants conversion using DA algorithm are closer to the experimental data (with about 4% error) comparing to those obtained by the artificial bee colony (ABC) algorithm, and are in significant agreement with available literature data. The proposed model is used to assess the effect of reactor configuration on the performance and temperature fluctuations. Results show that axial flow spherical reactor (AFSR) and radial flow spherical reactor (RFSR) exhibiting the same surface area with that of the cylindrical reactor (CR), i.e., AFSR-2 and RFSR-2-i are the most efficient exhibiting hydrocarbons selectivity of 40.330% and 40.286% at CO 2 conversion of 53.763% and 53.891%. In addition, it is revealed that the location of the jacket has an essential role in controlling the reactor temperature. [ABSTRACT FROM AUTHOR]