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Stochastic Unit Commitment Study in a Power System with Flexible Load in Presence of High Penetration Renewable Farms
In this paper, a new hybrid multiobjective algorithm, namely, the modified bald eagle search Algorithm (MBES), integrated with the grasshopper optimization algorithm, is proposed to solve the unit commitment (UC) problem. We consider a standard 10-unit power system with two wind farms, two photovoltaic farms, and flexible loads for optimization purposes. The UC problem is tackled under uncertainties related to demand and renewable generation capacities. To account for these uncertainties, probability density functions (PDFs) are assigned to the sources of uncertainty, and Monte Carlo simulation (MCS) is employed to select several scenarios with specific probability coefficients. Additionally, two innovative objective functions based on operation cost and emissions are introduced, with each scenario weighted based on its occurrence probability. To assess the performance of the proposed MOGOA-MBES algorithm, simulations are conducted across three scenarios with varying conditions, and the results are compared against those obtained from several multiobjective algorithms. Our findings, supported by optimization results and the S-metric index, demonstrate that the proposed MOGOA-MBES algorithm outperforms other algorithms in terms of reducing operation cost and emissions. Furthermore, the simulation results reveal that uncertainties lead to an increase in cost and emissions, whereas the inclusion of flexible loads and their participation in the UC program can effectively mitigate cost and emission levels.
Smart Optimization of Semiconductors in Photovoltaic-Thermoelectric Systems Using Recurrent Neural Networks
In the relentless pursuit of sustainable energy solutions, this study pioneers an innovative approach to integrating thermoelectric generators (TEGs) and photovoltaic (PV) modules within hybrid systems. Uniquely, it employs neural networks for an exhaustive analysis of a plethora of parameters, including a diverse spectrum of semiconductor materials, cooling film coefficients, TE leg dimensions, ambient temperature, wind speed, and PV emissivity. Leveraging a rich dataset, the neural network is meticulously trained, revealing intricate interdependencies among parameters and their consequential impact on power generation and the efficiencies of TEG, PV, and integrated PV-TE systems. Notably, the hybrid system witnesses a striking 23.1% augmentation in power output, escalating from 0.26 W to 0.32 W, and a 20% ascent in efficiency, from 14.68% to 17.62%. This groundbreaking research illuminates the transformative potential of integrating TEGs and PV modules and the paramountcy of multifaceted parameter optimization. Moreover, it exemplifies the deployment of machine learning as a powerful tool for enhancing hybrid energy systems. This study, thus, stands as a beacon, heralding a new chapter in sustainable energy research and propelling further innovations in hybrid system design and optimization. Through its novel approach, it contributes indispensably to the arsenal of clean energy solutions.
General Modeling of Interconnected Hubs in Series and Parallel Structures
Energy decision-makers have considered solutions to meet the demand for affordable and highly reliable energy due to population growth and technological advancement. One of these possibilities is the utilization of several energy carriers in one system as an energy hub. Instead of optimizing a single energy carrier, the energy hub optimizes a system with many energy carriers, including electricity, natural gas, and local heat, by use of its converters and storages. In this paper, a novel and new general framework is proposed to evaluate and compare both series and parallel connections of hub to analyze cost and reliability aspects by considering coupling matrices. In order to compare the outcomes of the connections of several comparable hubs in both series and parallel modes, new indices are proposed and evaluated from the aspect of evaluating the amount of energy not supplied and the amount of energy input to each hub. Additionally, simulations are run for a variety of scenarios in order to better assess the proposed model and investigate each type of connection by evaluating the proposed performance indices. The results show that in all the examined scenarios, the total cost of the energy carriers in the series mode (link in the output) is lower than in the parallel mode (link in the input).
Design Analysis of a Helium Xenon-Printed Circuit Heat Exchanger for a Closed Brayton Cycle Microtransport Reactor
Microtransport with small size and a wide range of applications is very attractive for the utilization of future reactor modularization. A microreactor uses a closed Brayton cycle (CBC) to achieve high conversion efficiencies at low specific mass. The recuperator is one of the key components of CBC system which recovers heat exhausted from the turbine. A printed circuit heat exchanger (PCHE) is currently the preferred type of recuperator for the closed Brayton cycle due to its high heat transfer efficiency, high compactness, and high pressure and temperature resistance. This work intends to analyze different geometry configurations of a zigzag PCHE to increase its heat transfer efficiency while reducing its weight and size. The effect of geometric structure parameters such as channel diameter, zigzag pitch length, and zigzag angle on the Nusselt number and Fanning friction factor is investigated using a zigzag PCHE unit model. Based on the numerical simulation, the principle of least squares is employed to carry out a nonlinear fitting of the flow and heat transfer criterion correlation equations. Besides, the maximum value of the Nusselt number and minimum value of the Fanning friction factor are optimized as two conflicting objective functions using the nondominated sorting genetic algorithm-II (NSGA-II), with which a set of optimal solutions is obtained. Meanwhile, the shortest normalized distance is used to determine the compromised solution on the Pareto optimal points, and the independent variable’s sensitivity analysis is performed. Finally, a multiobjective optimization analysis is conducted for the PCHE to achieve lightweight and the high heat transfer efficiency design requirements of SIMONS.
Optimization of Integrated Solid Refuse Fuel and Solid Oxide Electrolyzer Cell System for Hydrogen Production
In this study, a performance predictive model for hydrogen production was developed for the commercialization of the integrated solid refuse fuel (SRF) and solid oxide electrolyzer cell (SOEC) system. A SRF system was developed, and reliability was verified in the steam conditions for the SOEC application. Systems optimization according to parametric analysis was conducted in the predictive model based on the experiments. When the steam temperature varies between 973 and 1,373 K, hydrogen production increases by 14% to 64 tons per year at 1,373 K; meanwhile, when the steam pressure varies between 0.1 and 0.7 MPa, the performance deteriorates significantly. Under optimal conditions (temperature: 1,373 K; pressure: 0.3 MPa; mass flow rate 200 kg/h), the amount of steam that can be produced by the integrated SRF–SOEC system is 1,752 tons per year, which can yield 87.6 tons of hydrogen per year. When SRF was used as a heat source, compared with the use of LNG, a total annual cost saving of approximately 2.6% was realized. The break-even point can be reduced by approximately 5 months, which reflects economic efficiency.
Reforming Methane with CO2 over Hierarchical Porous Silica-Supported Nickel Catalysts Modified with Lanthanum Oxide
Hierarchical porous silica-supported nickel catalysts modified with different amounts of lanthanum (La) were synthesized via “one-pot” method using cetyltrimethylammonium bromide as template, urea as precipitant, and tetraethyl orthosilicate as silica source. Their catalytic performances were evaluated in dry reforming with methane under different conditions (La loading, reaction temperature, and time on stream). The synthesized and spent catalysts were extensively characterized by ICP, physisorption, chemisorption, XRD, TPR, XPS, HAADF-TEM, TPH, Raman’s spectroscopy, and TG analysis. The impact of lanthanum amount on the catalytic performance, sintering, and carbon deposition was discussed. Compared to unmodified catalyst, La promoter induced the nickel nanoparticles with larger crystallite sizes and weakened the metal-support interaction as well as the formation of 1 : 1 nickel-phyllosilicate, leading to the metal sintering increasing in the order Ni1.5La/SiO2 < Ni3.0La/SiO2 < Ni4.5La/SiO2. The modified catalysts exhibited better carbon resistance, which was significantly enhanced with increasing La content. Despite this, the stability increased following the sequence of Ni3.0La/SiO2 < Ni4.5La/SiO2 < Ni1.5La/SiO2. Ni1.5La/SiO2 displayed the best stability at 750°C within 10 h stability test, with CH4 conversion dropping from 61.3 to 58.0%. The deactivation reason for Ni1.5La/SiO2 was mainly the carbon deposition, while that for Ni3.0La/SiO2 and Ni4.5La/SiO2 was the metal sintering. These results emphasized that the activity and stability in the NiLa/SiO2 catalysts for the dry reforming of methane depended on two important factors, the metal-support interaction and the particles size of nickel, providing the necessity and sufficiency to balance two attributes.