Implementing of the multi-objective particle swarm optimizer and fuzzy decision-maker in exergetic, exergoeconomic and environmental optimization of a benchmark cogeneration system

Multi-objective optimization for design of a benchmark cogeneration system namely as the CGAM cogeneration system is performed. In optimization approach, Exergetic, Exergoeconomic and Environmental objectives are considered, simultaneously. In this regard, the set of Pareto optimal solutions known as the Pareto frontier is obtained using the MOPSO (multi-objective particle swarm optimizer). The exergetic efficiency as an exergetic objective is maximized while the unit cost of the system product and the cost of the environmental impact respectively as exergoeconomic and environmental objectives are minimized. Economic model which is utilized in the exergoeconomic analysis is built based on both simple model (used in original researches of the CGAM system) and the comprehensive modeling namely as TTR (total revenue requirement) method (used in sophisticated exergoeconomic analysis). Finally, a final optimal solution from optimal set of the Pareto frontier is selected using a fuzzy decision-making process based on the Bellman-Zadeh approach and results are compared with corresponding results obtained in a traditional decision-making process. Further, results are compared with the corresponding performance of the base case CGAM system and optimal designs of previous works and discussed.     

Multi-objective optimization of a joule cycle for re-liquefaction of the Liquefied Natural Gas

A LNG re-liquefaction plant is optimized with a multi-objective approach which simultaneously considers exergetic and exergoeconomic objectives. In this regard, optimization is performed in order to maximize the exergetic efficiency of plant and minimize the unit cost of the system product (refrigeration effect), simultaneously. Thermodynamic modeling is performed based on energy and exergy analyses, while an exergoeconomic model based on the total revenue requirement (TRR) are developed. Optimization programming in MATLAB is performed using one of the most powerful and robust multi-objective optimization algorithms namely NSGA-II. This approach which is based on the Genetic Algorithm is applied to find a set of Pareto optimal solutions. Pareto optimal frontier is obtained and a final optimal solution is selected in a decision-making process. An example of decision-making process for selection of the final solution from the available optimal points of the Pareto frontier is presented here. The feature of selected final optimal system is compared with corresponding features of the base case and exergoeconomic single-objective optimized systems and discussed.     

The optimal performance estimation for an unknown PEMFC based on the Taguchi method and a generic numerical PEMFC model

In this paper, a new approach to estimate the optimal performance of an unknown proton exchange membrane fuel cell (PEMFC) has been proposed. This proposed approach combines the Taguchi method and the numerical PEMFC model. Simulation results obtained using the Taguchi method help to determine the value of control factors that represent the tested unknown PEMFC. The objective of reducing both fuel consumption and operation cost can be achieved by determining the parameters for the unknown PEMFC. In addition, the optimal operation power for the tested unknown PEMFC can also be predicted. Experimental results on the test equipment show that the proposed approach is effective in optimal performance estimation for the tested unknown PEMFC, thus demonstrating the success achieved by combining the Taguchi method and the numerical PEMFC model.     

A dual objective global optimization algorithm based on adaptive weighted hybrid surrogate model for the hydrogen fuel utilization in hydrogen fuel cell vehicle

Current engineering optimizations mainly use surrogate models (SMs) to approximate complex black-box problems. However, SMs with different approximate characteristics may make the designers unable to accurately judge which type of SMs is more suitable for the actual optimization design. A reasonable combination of different SMs might be one of the solutions. To this end, a global optimization algorithm based on an adaptive weighted hybrid surrogate model (GOA-AWHS) is proposed. In each iteration, a hybrid model based on kriging and RBF is first constructed by adaptively selecting weight coefficients. Next, two objectives consisting of predicted objective, root mean square error and distance parameters are optimized to generate the Pareto frontier. Finally, further selection of data points on the Pareto front yields multiple promising optimal solutions. A series of standard numerical functions and hydrogen fuel utilization in hydrogen fuel cell vehicles are tested to demonstrate the effectiveness and robustness of the GOA-AWHS method.     

Multi‐objective exergoeconomic optimization of an Integrated Solar Combined Cycle System using evolutionary algorithms

In this study, a multi‐objective optimization scheme is developed and applied for an Integrated Solar Combined Cycle System that produces 400 MW of electricity to find solutions that simultaneously satisfy exergetic as well as economic objectives. This corresponds to a search for the set of Pareto optimal solutions with respect to the two competing objectives. The optimization process is carried out by a particular class of search algorithms known as multi‐objective evolutionary algorithms. An example of decision‐making has been presented and a final optimal solution has been introduced. The analysis shows that optimization process leads to 3.2% increasing in the exergetic efficiency and 3.82% decreasing of the rate of product cost. Finally, sensitivity analysis is carried out to study the effect of changes in the Pareto optimal solutions to the system important parameters, such as interest rate, fuel cost, solar operation period, and system construction period. Copyright © 2010 John Wiley & Sons, Ltd.     

Assessment and optimization of an integrated energy system with electrolysis and fuel cells for electricity,cooling and hydrogen production using various optimization techniques

In this research study, a novel integrated solar based combined, cooling, heating and, power (CCHP) is proposed consisting of Parabolic trough solar collectors (PTSC) field, a dual-tank molten salt heat storage, an Organic Rankine Cycle (ORC), a Proton exchange membrane fuel cell (PEMFC), a Proton exchange membrane electrolyzer (PEME), and a single effect Li/Br water absorption chiller. Thermodynamics and economic relations are used to analyze the proposed CCHP system. The mean of Tehran solar radiation as well as each portion of solar radiation during 24 h in winter is obtained from TRNSYS software to be used in PTSC calculations. A dynamic model of the thermal storage unit is assessed for proposed CCHP system under three different conditions (i.e., without thermal energy storage (TES), with TES and with TES + PEMFC). The results demonstrate that PEMFC has the ability to improve the power output by 10% during the night and 3% at sunny hours while by using TES alone, the overnight power generation is 86% of the power generation during the sunny hours. The optimum operating condition is determined via the NSGA-II algorithm with regards to exergy efficiency and total cost rate as objective functions where the optimum values are 0.058 ($/s) and 80%, respectively. The result of single objective optimization is 0.044 ($/s) for the economic objective in which the exergy efficiency is at its lowest value (57.7%). In addition, results indicate that the amount of single objective optimization based on exergetic objective is 88% in which the total cost rate is at its highest value (0.086 $/s). The scattered distribution of design parameters and the decision variables trend are investigated. In the next step, five different evolutionary algorithms namely NSGA-II, GDE3, IBEA, SMPSO, and SPEA2 are applied, and their Pareto frontiers are compared with each other.     

Optimization of PEMFC system operating conditions based on neural network and PSO to achieve the best system performance

PEMFC system is a complex new clean power system. Based on MATLAB/Simulink, this paper develops a system-level dynamic model of PEMFC, including the gas supply system, hydrogen supply system, hydrothermal management system, and electric stack. The neural network fits the electric stack model to the simulation data. The effects of different operating conditions on the PEMFC stack power and system efficiency are analyzed. Combining the power of the reactor and the system efficiency to define the integrated performance index, the particle swarm optimization (PSO) algorithm is introduced to optimize the power density and system efficiency of the PEMFC with multiple objectives. The final optimal operating point increases the power density and system efficiency by 1.33% and 12.8%, respectively, which maximizes the output performance and reduces the parasitic power.     

A novel multiobjective charging optimization method of power lithium‐ion batteries based on charging time and temperature rise

Power lithium‐ion batteries have been widely utilized in energy storage system and electric vehicles, because these batteries are characterized by high energy density and power density, long cycle life, and low self‐discharge rate. However, battery charging always takes a long time, and the high current rate inevitably causes great temperature rises, which is the bottleneck for practical applications. This paper presents a multiobjective charging optimization strategy for power lithium‐ion battery multistage charging. The Pareto front is obtained using multiobjective particle swarm optimization (MOPSO) method, and the optimal solution is selected using technique for order of preference by similarity to ideal solution (TOPSIS) method. This strategy aims to achieve fast charging with a relatively low temperature rise. The MOPSO algorithm searches the potential feasible solutions that satisfy two objectives, and the TOPSIS method determines the optimal solution. The one‐order resistor‐capacitor (RC) equivalent circuit model is utilized to describe the model parameter variation with different current rates and state of charges (SOCs) as well as temperature rises during charging. And battery temperature variations are estimated using thermal model. Then a PSO‐based multiobjective optimization method for power lithium‐ion battery multistage charging is proposed to balance charging speed and temperature rise, and the best charging stage currents are obtained using the TOPSIS method. Finally, the optimal results are experimentally verified with a power lithium‐ion battery, and fast charging is achieved within 1534 s with a 4.1°C temperature rise.     

Optimal positioning of wind turbines on Gökçeada using multi‐objective genetic algorithm

This paper addresses the problem of optimal placement of wind turbines in a farm on Gokçeada Island located at the north‐east of Aegean Sea bearing full potential of wind energy generation. A multi‐objective genetic algorithm approach is employed to obtain optimal placement of wind turbines by maximizing the power production capacity while constraining the budget of installed turbines. Considering the speed and direction history, wind with constant intensity from a single direction is used during optimization. This study is based on wake deficit model mainly because of its simplicity, accuracy and fast calculation time. The individuals of the Pareto optimal solution set are evaluated with respect to various criteria, and the best configurations are presented. In addition to best placement layouts, results include objective function values, total power output, cost and number of turbines for each configuration. Copyright © 2009 John Wiley & Sons, Ltd.     

Thermodynamic multi-objective optimization of a solar-dish Brayton system based on maximum power output,thermal efficiency and ecological performance

Solar-dish Brayton system driven by the hybrid of fossil fuel and solar energy is characterized by continuously stable operation, simplified hybridization, low system costs and high thermal efficiency. In order to enable the system to operate with its highest capabilities, a thermodynamic multi-objective optimization was performed in this study based on maximum power output, thermal efficiency and ecological performance. A thermodynamic model was developed to obtain the dimensionless power output, thermal efficiency and ecological performance, in which the imperfect performance of parabolic dish solar collector, the external irreversibility of Brayton heat engine and the conductive thermal bridging loss were considered. The combination of NSGA-II algorithm and decision makings was used to realize multi-objective optimization, where the temperatures of absorber, cooling water and working fluid, the effectiveness of hot-side heat exchanger, cold-side heat exchanger and regenerator were considered as optimization variables. Using the decision makings of Shannon Entropy, LINMAP and TOPSIS, the final optimal solutions were chosen from the Pareto frontier obtained by NSGA-II. By comparing the deviation index of each final optimal solution from the ideal solution, it is shown that the multi-objective optimization can lead to a more desirable design compared to the single-objective optimizations, and the final optimal solution selected by TOPSIS decision making presents superior performance. Moreover, the fitted curve between the optimal power output, thermal efficiency and ecological performance derived from Pareto frontier is obtained for better insight into the optimal design of the system. The sensitivity analysis shows that the optimal system performance is strongly dependent on the temperatures of absorber, cooling water and working fluid, and the effectiveness of regenerator. The results of this work offer benefits for related theoretic research and basis for solar energy industry.     

A novel strategy based on salp swarm algorithm for extracting the maximum power of proton exchange membrane fuel cell

Cell temperature and water content of the membrane have a significant effect on the performance of fuel cells. The current-power curve of the fuel cell has a maximum power point (MPP) that is needed to be tracked. This study presents a novel strategy based on a salp swarm algorithm (SSA) for extracting the maximum power of proton-exchange membrane fuel cell (PEMFC). At first, a new formula is derived to estimate the optimal voltage of PEMFC corresponding to MPP. Then the error between the estimated voltage at MPP and the actual terminal voltage of the fuel cell is fed to a proportional-integral-derivative controller (PID). The output of the PID controller tunes the duty cycle of a boost converter to maximize the harvested power from the PEMFC. SSA determines the optimal gains of PID. Sensitivity analysis is performed with the operating fuel cell at different cell temperature and water content of the membrane. The obtained results through the proposed strategy are compared with other programmed approaches of incremental resistance method, Fuzzy-Logic, grey antlion optimizer, wolf optimizer, and mine-blast algorithm. The obtained results demonstrated high reliability and efficiency of the proposed strategy in extracting the maximum power of the PEMFC.     

Adaptive real-time optimal energy management strategy based on equivalent factors optimization for hybrid fuel cell system

Fuel cell, a new kind of energy supply equipment, has several advantages such as high efficiency, low noise, and no emission. Proton exchange membrane fuel cell (PEMFC) is considered to have the potential to take the place of the conventional engine on unmanned underwater vehicle (UUV). Besides the power sources in the hybrid power system, the energy management system (EMS) is crucial to operating performance. In this paper, an on-line adaptive equivalent hydrogen consumption minimization strategy (ECMS) is proposed to solve the problem of prior knowledge demand and poor adaptability of current energy management algorithms. In this presented method, a battery state of charge (SOC) constituted penalty term is designed to calculate the equivalent factor (EF), and then the equivalent factor obtained by optimization is substituted into the original objective equation to realize the real-time energy regulation. In this paper, a typical UUV load curve is used to verify the control effect under different working conditions, and the performance is compared with three conventional algorithms’. Simulation results show that the hydrogen consumption of proposed algorithm is close to the optimal solution obtained in offline environment, and it is reduced by more than 3.79% compared with the traditional online methods.     

Numerical investigation on the performance of proton exchange membrane fuel cells with channel position variation

Such factors as mole fractions of species, water generation, and conductivity influence the performance of proton exchange membrane fuel cells (PEMFCs). The geometrical shape of the fuel cells also should be considered a factor in predicting the performance because this affects the species' reaction speed and distribution. Specifically, the position between the channel and rib is an important factor influencing PEMFC performance because the current density distribution is affected by the channel and rib position. Three main variables that decide the current density distribution are selected in the paper: species concentration, overpotentials, and membrane conductivity. These variables should be considered simultaneously in deciding the current density distribution with the given PEMFC cell voltage. In addition, the inlet relative humidity is another factor affecting current density distribution and membrane conductivity. In this paper, two channel‐to‐rib models, namely, channel‐to‐channel and the channel‐to‐rib, are considered for comparing the PEMFC performance. Thorough performance comparisons between these two models are presented to explain which is better under certain parameters. A three‐dimensional numerical PEMFC model is developed for obtaining the current density distribution. Water transfer mechanism because of electro osmotic drag and concentration diffusion also is presented to explain the PEMFC performance comparison between the two models. Copyright © 2012 John Wiley & Sons, Ltd.     

Behaviour modelling of a PEMFC operating on diluted hydrogen feed

The polymer electrolyte membrane fuel cell (PEMFC) using reformate gas as fuel is regarded as an attractive solution for the near‐term introduction of fuel cells in stationary or mobile power generation market. With respect to hydrogen feeding, the reformate gas fuelling involves additional polarization losses because of the hydrogen dilution and the impurities contained in the gas. In this paper a one‐dimensional model has been developed to investigate the behaviour of a PEMFC operating with reformate gas mixture. The model, based on a semi‐empirical approach, considers the kinetic reactions in the anode side taking into account the effect of reverse water–gas shift (RWGS) due to the presence of CO₂ in the fuel. As it is well known, the exhaust stream from fuel reformers can contain a high carbon dioxide concentration (>20%) that can have a detrimental effect on the fuel cell performance because of the combination of the dilution and the formation of CO by the RWGS reaction. The numerical simulation results have been compared with the experimental data, obtained in the test room of Industrial Engineering Department of Cassino University, and a good match has been observed. The model has been developed by using a simplified approach that, nevertheless, can allow to obtain a good numerical prediction of the fuel cell performance reducing the simulation time and computational efforts. Copyright © 2008 John Wiley & Sons, Ltd.     

PEMFC电极孔隙率的优化研究

对质子交换膜燃料电池单体建立了三维稳态电化学模型,考察了气体扩散层孔隙率对电池性能的影响,验证了扩散层孔隙率及层厚的变化反映从气体通道到扩散层和催化剂层的反应气体扩散量,进而影响电化学反应的活跃程度;以膜与阴极催化剂层界面处获得的最大电压为目标函数,采用鲍威尔搜索法对气体扩散层孔隙率进行数值优化,得到了扩散层孔隙率和层厚的最优值。通过优化前后氧气浓度和电流密度的对比显示,这些参数可以显著改善电极的传质性能,使燃料电池获得最佳性能。     

A novel optimization model for combined wind power accommodation and electric boiler with thermal storage

As the installed capacity of wind power continues to increase, the problem of curtailed wind power is becoming serious in China, especially in the northern region during the winter heating season. To solve the problem of wind‐heat conflict during the heating period in the Three North area, an electric boiler with thermal storage (EBTS) is installed at the end of the grid where wind power is difficult to accommodate and using curtailed wind power to supply heat promotes local accommodation. In this paper, a multi‐objective optimization model of wind power accommodation based on the wind power–EBTS system for heating is established. The goals of maximizing wind power accommodation, minimizing the number of times EBTS must be adjusted, and minimizing operating costs are presented, and a bi‐level optimization scheme is designed. An improved multi‐objective particle swarm optimization algorithm is used to solve these functions, and an optimal compromise solution from the generated Pareto solution set is filtered using the fuzzy membership method. Based on actual data from a demonstration project in China's Jilin Province, the simulation results verify that this method can effectively reduce operating costs and improve wind power accommodation.     

Multiobjective optimization for exergoeconomic analysis of an integrated cogeneration system

In this paper, a novel cogeneration system integrating Kalina cycle, CO₂ chemical absorption, process, and flash‐binary cycle is proposed to remove acid gases in the exhaust gas of solid oxide fuel cell (SOFC) system, improve the waste heat utilization, and reduce the cold energy consumed during CO₂ capture. In the CO₂ chemical absorption process, the methyldiethanolamine (MDEA) aqueous solution is utilized as a solvent, and feed temperature and absorber pressure are optimized via Aspen Plus software. The single‐objective and multiobjective optimization are carried out for the flash‐binary cycle subsystem. Results show that when the multiobjective optimization is applied to identify the exergoeconomic condition, the cogeneration system can simultaneously satisfy the high thermodynamic cycle efficiency and also the low product unit cost. The optimal results of the exergy efficiency, product unit cost, and normalized CO₂ emissions obtained by Pareto chart were 75.84%, 3.248 $/GJ, and 13.14 kg/MWhr, respectively.     

Optimal energy management strategy of fuel-cell battery hybrid electric mining truck to achieve minimum lifecycle operation costs

Proton exchange membrane fuel cell (PEMFC) electric vehicle is an effective solution for improving fuel efficiency and onboard emissions, taking advantage of the high energy density and short refuelling time. However, the higher cost and short life of the PEMFC system and battery in an electric vehicle prohibit the fuel cell electric vehicle (FCEV) from becoming the mainstream transportation solution. The fuel efficiency-oriented energy management strategy (EMS) cannot guarantee the improvement of total operating costs. This paper proposes an EMS to minimize the overall operation costs of FCEVs, including the cost of hydrogen fuel, as well as the cost associated with the degradations of the PEMFC system and battery energy storage system (ESS). Based on the PEMFC and battery performance degradation models, their remaining useful life (RUL) models are introduced. The control parameters of the EMS are then optimized using a meta-model based global optimization algorithm. This study presents a new optimal control method for a large mining truck operating on a real closed-road operation cycle, using the combined energy efficiency and performance degradation cost measures of the PEMFC system and lithium-ion battery ESS. Simulation results showed that the proposed EMS could improve the total operating costs and the life of the FCEV.     

Effects of the humidity and the land ratio of channel and rib in the serpentine three‐dimensional PEMFC model

The construction of a reliable numerical model and the clarification of its operational conditions are necessary for maximizing fuel cell operation. Numerous operating factors, such as mole fractions of species, pressure distribution, overpotential, and inlet relative humidity, affect the performance of proton exchange membrane fuel cells (PEMFCs). Among these operational parameters, geometrical shape and relative humidity are investigated in this paper. Specifically, the land ratio of the gas channel and rib is an important parameter affecting PEMFC performance because current density distribution is influenced by this geometrical characteristic. Three main variables determine the current density distribution, namely, species concentration, pressure, and overpotential distributions. These distributions are considered simultaneously in assessing fuel cell performance with a given PEMFC cell‐operating voltage. In this paper, three different land ratio models are considered to obtain better PEMFC performance. Similarly, three different inlet relative humidity variations are studied to achieve an enhanced operating condition. A three‐dimensional numerical PEMFC model is developed to illustrate the current density distribution as the determining factor for PEMFC performance. Copyright © 2012 John Wiley & Sons, Ltd.     

Influence of sloping baffle plates on the mass transport and performance of PEMFC

Sloping baffle plates are installed numerically in the flow channel of proton exchange membrane fuel cell (PEMFC) to promote the mass transport in the porous electrode and the fuel cell performance. The sloping angle of baffle plate on the mass transport and performance of PEMFC are investigated and optimized. The numerical results show that the sloping angle of baffle plate influences the velocity distribution, flow resistance in the flow channel, and the intensity of mass transport between the channel and porous electrode. Larger sloping angle increases the velocity in the vertical direction which brings stronger squeeze effect between the channel and porous electrode, but it also reduces the squeeze area and increases the flow resistance. An optimization for the sloping angle of baffle plate is carried out. The baffle plate with the sloping angle of 45° shows the best performance in PEMFC net power considering the pumping power caused by the pressure loss. The effect of the baffle plate number is also investigated and optimized. The fuel cell current density increases with the baffle plate number, but the increment rate is decreased. The pumping power increases almost linearly with the baffle plate number. The PEMFC with six sloping baffle plates installed in the channel is found to be optimal in terms of the net power.