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Abstract A battery pack is produced by connecting the cells in series and/or in parallel to provide the necessary power for electric vehicles (EVs). Those parameters affecting cost and reliability of the EVs, including cycle life, capacity, durability and warranty are highly dependent on the thermal management system. In this work, computational fluid dynamic analysis is performed to investigate the air cooling system for a 38,120 cell battery pack. The battery pack contained 24 pieces of 38,120 cells, copper bus bars, intake and exhaust plenum and holding plates with venting holes. Heat generated by the cell during charging is measured using an accelerating rate calorimeter. Thermal performances of the battery pack were analyzed with various mass flow rates of cooling air using steady state simulation. The correlation between Nu number and Re number were deduced from the numerical modeling results and compared with literature. Additionally, an experimental testing of the battery pack at different charging rates is conducted to validate the correlation. This method provides a simple way to estimate thermal performance of the battery pack when the battery pack is large and full transient simulation is not viable.

Abstract In this work, the change of properties of empty fruit bunches (EFB), mesocarp fiber (PMF) and kernel shell (PKS) of oil palm when subjected to torrefaction process is reported. The properties include CHNS content, gross calorific value (GCV), mass and energy yields. These oil palm residues are torrefied at 200, 220 and 240, 260, 280 and 300 °C, respectively for duration of 2 h. In general, it has been found that the GCV and carbon content increase with the increase of torrefaction temperature but the O/C ratio, H and O content decrease for all residues. Also, we have shown that there are linear relationships between the mass loss with GCV and C content suggesting that it can be used as an indicator to monitor the severity of torrefaction process on these oil palm residues.

The benefits of more comprehensive energy use and promotion of renewable energy (RE) consumption enable large-scale microgrid deployment. However, the uncertainty of renewable energy sources and the diversity of load types pose a threat to the microgrid's stability. Recently, energy scheduling optimization for microgrids (MGs) has been primarily based on ideal models, but incorporating as many real-world characteristics as feasible can improve the reliability of the optimization outcome. This paper provides a multi-stage methodology for solving the energy management optimization (EMO) problem of MG under uncertainty considering carbon trading market and demand side response (DSR). To begin, scenario analysis method was used to address the uncertainty associated with RE in MG, and four typical scenarios of renewable energy were generated. Then, the flexible configuration and operational constraints of each power source in MG are dealt with under the premise of considering the carbon trading market. The third stage involved merging the characteristics of various load types and analyzing the response impacts of different percentage residential and industrial loads, respectively, using the price-based and load-transfer-based DSR approaches. Finally, quantum particle swarm optimization (QPSO) algorithm was used to obtain the optimal solution. The acquired results demonstrate the efficacy of the proposed multi-stage energy optimization framework, and support the following two conclusions: 1) The carbon trading market policy contributes to the reduction of carbon emissions and fossil fuel consumption. 2) A high load participation rate in DSR can increase MG operation economics by up to 27.48% compared to not considering DSR.

Abstract Transesterification of waste cooking oil with heterogeneous (heteropoly acid) catalyst and methanol has been investigated. Response Surface Methodology (RSM) and Artificial Neural Network (ANN) were employed to study the relationship between process variables and free fatty acid conversion and for predicting the optimal parameters. The highest conversion was 88.6% at optimum condition being 14 h, 65 °C, 70:1 and 10 wt% for reaction time, reaction temperature, methanol to oil molar ratio and catalyst loading, respectively. The RSM and ANN could accurately predict the experimental results, with R 2 = 0.9987 and R 2 = 0.985, respectively. Kinetics studies were investigated to describe the system. The reaction followed first-order kinetics with the calculated activation energy, Ea = 53.99 kJ/mol while the pre-exponential factor, A = 2.9 × 10 7 min −1 . These findings can help improve an environmentally friendly biodiesel process that conforms to ASTM D6751 standards.

Abstract In the quest for alternatives for fossil fuels, phase change materials (PCMs) have attracted considerable attention due to their ability to store renewable thermal energy. Compared to other storage systems, PCM systems are of low cost and capable of the storage of a high density of energy. However, few drawbacks hinder their practical application at an industrial scale. Among the drawbacks, supercooling problem affecting all types of PCMs is crucial. Supercooling as a shortcoming in PCM applications limits their practical applications. However, a comprehensive discussion or review articles have not been published. A PCM can exists in the liquid form below the phase change temperature or its freezing point, without fully freezing, due to supercooling. Thus, practical applications are limited by major problems such as the temperature variations and the increase of energy consumption. In this paper, most of the reported supercooling mitigation techniques for various types of PCMs and nanofluids are reviewed. These techniques are based mainly on adding nucleating agents (such as carbon nanotubes, fine salt particles, and nanoaditives), thickeners (such as carboxy methyl cellulose), and macroporous structures. The mitigation of phase separation and thermal cycling effects on supercooling are also discussed. The mitigation of supercooling in encapsulated organic PCMs, which is an important issue that is not very well understood, too is briefly addressed. Recommendations and future challenges to enhance the application of PCMs are discussed.

Abstract This study deals with the development of an optimal power scheduling controller for energy management of distributed energy resources in the microgrid system. The developed optimized controller is implemented using lightning search algorithm to overcome the uncertainties of microgrid energy management and to provide an optimum power delivery to loads with minimum cost. The primary objectives of the proposed optimized controller are to: (i) develop an optimized controller for microgrids energy management, (ii) minimize the total operating cost of the distributed energy resources units, (iii) reduce the environmental emission, and (iv) solve the complicated constraint optimization problems. The proposed optimization algorithm is implemented in the modified IEEE 14-bus test system to optimize the microgrid power management schedule. The optimized controller is executed based on the real load varying conditions recorded in Perlis, Malaysia. It is observed that the optimized controller successfully reduced the amount of power consumption from 971.65 MW to 364.3 MW which in turn saving cost of RM 265432.06. The proposed scheduling optimized controller performance is compared with the recent reported work of backtracking search algorithm optimization for validation. Result shows that the lightning search algorithm based MG controller produced a cost-effective system with 62.5% of cost saving and 61.98% of carbon dioxide emission reduction which is much higher compared to with MG and backtracking search algorithm based MG optimization, respectively. The effectiveness of the proposed approach outperformed other techniques in terms of minimum total operating cost of distributed energy resources and solving complicated constraints in optimization problems.

Malaysia has abundant potentials of renewable energy resources mainly because of its rich agriculture that makes high potential in bio-power and its tropical climate, which provides sufficient sunlight for utilization of solar systems. Feed in Tariff mechanism has been applied since 2011 in Malaysia to expand utilization of renewable energy for electricity generation. In this study, a broad range of data is gathered to develop a comprehensive system dynamics model to evaluate the impacts of Feed in Tariff mechanism on the generation mix of Malaysia during a 20-year period between 2011 and 2030. Results demonstrate that although the policy may lead to a satisfactory level of target achievement but the Malaysian government may face an increasing shortage in its RE fund budget starting around 2019 unless it increases its income sources by rising the surcharges on electricity bills or decreases its expenditures by optimizing the amount of FiT payments in different periods. The sensitivity analysis illustrates that the more funding will not lead to a more sustainable generation mix unless it is paid in the right time and in the right direction. Using this model, policymakers can carry out analysis to determine the amount of money that must be collected from the electricity consumers through the surcharges on electricity bills as well as the amount of feed in tariff to be paid for different renewable resources in different periods.

Abstract We have investigated, for the first time, the alkaline pre-treatment of microalgal biomass, from the species Chlorococcum infusionum , using NaOH for bioethanol production. This pre-treatment step aims to release and breakdown entrapped polysaccharides in the microalgae cell walls into fermentable subunits. Three parameters were examined here; the concentration of NaOH, temperature and the pre-treatment time. The bioethanol concentration, glucose concentration and the cell size were studied in order to determine the effectiveness of the pre-treatment process. Microscopic analysis was performed to confirm cell rupturing, the highest glucose yield was determined to be 350 mg/g, and the maximum bioethanol yield obtained was 0.26 g ethanol/g algae using 0.75% (w/v) of NaOH and 120 °C for 30 min. Overall, the alkaline pre-treatment method proved to be promising option to pre-treat microalgal biomass for bioethanol production.

A graphical pinch approach for analysis of water footprint constraints on biofuel production systems is presented. The technique is based on the composite curve method which was originally developed for carbon-constrained energy planning, which is extended in this paper based on the underlying similarities of source-sink allocation problems. The pinch analysis approach enables limiting water footprint conditions to be identified, and provides insights that are useful for planning the large-scale cultivation of biofuel crops. An illustrative case study based on the bioethanol program of the Philippines is solved using the proposed approach.