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Wind Farm Layout Optimization Based on Dynamic Opposite Learning-Enhanced Sparrow Search Algorithm
In recent years, the proportion of wind power in new energy generation has gradually increased. The natural wind in wind farms is subject to velocity attenuation by the wake effect, so improving the efficiency of wind farm power generation has become a problem that must be solved for wind power generation. Considering the uncertainty of wind farms, we regard wind farm layout optimization (WFLO) as a strongly nonlinear problem. In this paper, we improve the sparrow search algorithm (SSA) using dynamic opposite learning (DOL) strategy. Twenty-eight benchmark test results prove that compared with other algorithms, the improved algorithm DOLSSA has excellent robustness and the ability of searching for a better solution when solving a strongly nonlinear optimization problem, and the DOL strategy effectively improves the shortcomings of the original algorithm which is prone to local optimization and space limitation. In this paper, the authors establish the dynamic rotational coordinates of wind farms and set six different physical scenarios by considering the wind direction and wind speed variables, and the results prove that the performance of DOLSSA is optimal.
Optimal Sizing of Hybrid Renewable Energy System Using Two-Stage Stochastic Programming
Stochastic programming has become increasingly vital in energy applications, especially in the context of the growing need for renewable energy solutions. This paper presents a significant advancement in this field by introducing an efficient and robust algorithm for optimally sizing hybrid renewable energy systems. Utilizing a two-stage stochastic programming approach, the proposed algorithm addresses the challenges posed by the unpredictability of renewable energy sources. The proposed solution leverages the three-block alternating direction method of multipliers (ADMM), a cutting-edge technique that facilitates parallel computation and enhances computational efficiency. The distinctiveness of this method lies in its ability to solve complex stochastic optimization problems without compromising the mathematical integrity of the model. This is achieved by applying first-order optimality conditions, ensuring both robustness and efficacy. To demonstrate the practical applicability and superiority of the algorithm, a case study was conducted in a rural area of South Africa. The proposed algorithm was applied to design an optimal hybrid renewable energy system, and its performance was compared against traditional methods such as progressive hedging and Monte Carlo techniques. Results affirm the superiority of the approach, saving approximately 8.16% capital cost when compared to progressive hedging. In addition, the proposed algorithm outperforms the Monte Carlo method both in terms of CPU time and the number of cost function evaluations.
Hydrogen Storage Capacity of Lead-Free Perovskite NaMTH3 (MT=Sc, Ti, V): A DFT Study
Hydrogen is a promising clean energy carrier, but its storage is challenging. In this study, we investigate the potential of NaMTH3 (MT=Sc, Ti, V) hydride perovskite as solid-state hydrogen storage material. Using density functional theory (DFT), we comprehensively analyze their structural, hydrogen storage, phonon, electronic, elastic, and thermodynamic properties. Mechanical stability is assessed through calculation of lattice parameters, bulk and shear moduli, Poisson’s ratio, and Young’s modulus based on elastic constants. All three hydrides were found to be stable mechanically. Furthermore, the anisotropy factor was also investigated. Results show that the investigated hydrides are brittle and metallic. Their metallic character is due to the significant interplay between phonons and electrons. We also investigated their enthalpy, entropy, free energy, Debye temperatures, and specific heat capacities to investigate thermal stability.
Cu3P Nanoarrays Derived from 7,7,8,8-Tetracyanoquinodimethane for High-Rate Electrocatalytic Oxygen Reactions of Lithium-Oxygen Batteries
Efficient electrocatalysis at the cathode is crucial for addressing the challenges faced by lithium-oxygen batteries (LOBs), including limited stability and low-rate capability. To develop an efficient cathode for aprotic LOBs, self-supported copper phosphide nanoarrays on carbon cloth are prepared at different heating rates via a phosphidation process using CuTCNQ nanoarrays. Different ramping rates have effects on particle size and intrinsic interaction, which in turn affect their catalytic properties. When phosphidation is carried out at a slow rate, it results in the formation of smaller, evenly distributed Cu3P particles on the nanoarrays (SG-Cu3P NAs/CC) compared to fast rate phosphidation (FG-Cu3P NAs/CC). As a result, SG-Cu3P NAs/CC exhibits lower resistance and a higher concentration of active sites than FG-Cu3P NAs/CC. The SG-Cu3P NAs/CC demonstrates LOBs with a low overpotential of 1.51 V at a high current rate of 1 mA cm−2 and a long cycle life of 115 cycles at 0.1 mA cm-2. The in situ Raman spectroscopy supports that Li2O2 is uniformly formed and decomposed on the SG-Cu3P NAs/CC surface. This study provides a compelling approach for the precise fabrication and analysis of binder-free, self-supported copper phosphides as highly efficient and stable materials for bifunctional oxygen electrocatalysis.
Evaluation of Fe-Ni Composite Oxygen Carrier in Coal Chemical Looping Gasification
Although coal chemical looping gasification (CCLG) is a promising technology for the efficient utilization of coal, limited studies concerned about the industrial application of oxygen carrier in CCLG system owing to its performance requirements including high reactivity with solid fuel, high carbon conversion, and good mechanical property. To meet the requirements of oxygen carriers in CCLG system, novel Fe-Ni composite oxygen carrier (OC) samples were successfully prepared. The performances of these OCs were evaluated in a fixed bed reactor, where they were reduced by solid fuel and then fully oxidized by air. Both fixed bed tests and thermodynamic analysis showed that the prepare OCs exhibited high reactivity with coal and syngas selectivity, making them suitable for the CCLG process. Specifically, when the loading amount of NiO was 20 wt%, the Fe-Ni composite OCs achieved the highest carbon conversion (93.03%) and synthesis gas selectivity (73.29%). Additionally, the thermogravimetric data revealed that the Curie temperature of the OCs was higher than 550°C, making them suitable for magnetic separation of the OC particles and carbon residue in the CCLG system.
Probabilistic Energy Management of DGs and Electric Vehicle Parking Lots in a Smart Grid considering Demand Response
In this paper, a novel model of an energy management system (EMS) for a microgrid (MG) under uncertain conditions is proposed. The MG consists of renewable photovoltaic and wind sources along with electric vehicle parking lots. Hence, the model incorporates the uncertainties of renewable DGs, parking lots, and also load. In this study, the MG operation cost and voltage stability index are considered objective functions. A novel combined algorithm (hMOPSO-HS) is proposed for microgrid energy management. The hMOPSO-HS algorithm is a combination of the mutant multiobjective particle swarm optimization (MOPSO) algorithm and the harmony search (HS) algorithm. The simulations are performed in two parts, with and without considering the uncertainty. The comparative analysis involves evaluating the optimization outcomes achieved by the hMOPSO-HS algorithm in contrast to various other metaheuristic algorithms for multiobjective optimization. The simulation findings validate the superior efficacy of the hMOPSO-HS algorithm compared to other approaches. Also, the simulation results showed that in the conditions of uncertainty, the operating cost is 6.1% higher and the microgrid stability index is 6.8% lower. Also, considering the uncertainty has caused the penalty for energy not supplied (ENS) and demand response program (DRP) costs to increase by 3% and 4%, respectively.