Jaya Learning-Based Optimization for Optimal Sizing of Stand-Alone Photovoltaic,Wind Turbine,and Battery Systems

Renewable energy sources(RESs)are considered to be reliable and green electric power generation sources.Photovoltaics(PVs)and wind turbines(WTs)are used to provide electricity in remote areas.Optimal sizing of hybrid RESs is a vital challenge in a stand-alone environment.The meta-heuristic algorithms proposed in the past are dependent on algorithm-specific parameters for achieving an optimal solution.This paper proposes a hybrid algorithm of Jaya and a teaching–learning-based optimization(TLBO)named the JLBO algorithm for the optimal unit sizing of a PV–WT–battery hybrid system to satisfy the consumer’s load at minimal total annual cost(TAC).The reliability of the system is considered by a maximum allowable loss of power supply probability(LPSPmax)concept.The results obtained from the JLBO algorithm are compared with the original Jaya,TLBO,and genetic algorithms.The JLBO results show superior performance in terms of TAC,and the PV–WT–battery hybrid system is found to be the most economical scenario.This system provides a cost-effective solution for all proposed LPSPmax values as compared with PV–battery and WT–battery systems.

Industrial Preparation and Acid Resistance of Ultra-stable Y Zeolite with Small Cell Size Produced by Gas-phase Method

Small-cell HSY-S zeolite prepared by the gas-phase ultra-stable method had been researched and developed,and industrial preparation tests of HSY-S have been successfully carried out for the first time.The acid resistance of industrially prepared HSY-S was investigated by acid solutions with different pH values.The structures and properties of HSY-S and its acid-treated samples were characterized by XRD,XRF,BET,and IR.Results show that the HSY-S samples have the characteristics of high crystallinity,good stability,large specific surface area,and good acid resistance.

Cost projections for microwave plasma CO production using renewable energy

Successful deployment of renewable fuel production requires substantial cost reduction along the entire value chain of the underlying manufacturing routes.To improve their performance,renewable fuel production technologies should follow a cost-reducing learning curve.In this article,we adopt recent evidence that learning-by-doing is directly influenced by the technology unit size and explore three scenarios for microwave plasma CO_(2)conversion in which the learning rate varies between 10%,15%,and 20%.Our projections reveal that the total investments required to deploy this CO_(2)conversion technology at an exajoule scale decline from 83 down to 23 billion euros under a 10%increase in the value of the learning rate.The CO_(2) production costs in 2050 amount to 247–346€(2019)/t CO_(2),in which the range is determined by the value of the learning rate.Even under substantial learning until 2050 the levelized CO production cost is unlikely to become competitive with conventional natural gas-based CO_(2) production processes,except when a CO_(2)tax is applied of up to 150€(2019)/t CO_(2).To optimally exploit effects of learning-by-doing,we recommend developing several CO production technologies simultaneously with multiple unit sizes,so as to improve the chance of ultimately selecting the process with the highest learning rate.