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.     

Emission, reserve and economic load dispatch problem with non-smooth and non-convex cost functions using the hybrid bacterial foraging-Nelder-Mead algorithm

In this paper, a new approach is proposed to solve the economic load dispatch (ELD) problem. Power generation, spinning reserve and emission costs are simultaneously considered in the objective function of the proposed ELD problem. In this condition, if the valve-point effects of thermal units are considered in the proposed emission, reserve and economic load dispatch (ERELD) problem, a non-smooth and non-convex cost function will be obtained. Frequency deviation, minimum frequency limits and other practical constraints are also considered in this problem. For this purpose, ramp rate limit, transmission line losses, maximum emission limit for specific power plants or total power system, prohibited operating zones and frequency constraints are considered in the optimization problem. A hybrid method that combines the bacterial foraging (BF) algorithm with the Nelder-Mead (NM) method (called BF-NM algorithm) is used to solve the problem. In this study, the performance of the proposed BF-NM algorithm is compared with the performance of other classic (non-linear programming) and intelligent algorithms such as particle swarm optimization (PSO) as well as genetic algorithm (GA), differential evolution (DE) and BF algorithms. The simulation results show the advantages of the proposed method for reducing the total cost of the system.     

Adaptive particle swarm optimization approach for static and dynamic economic load dispatch

This paper presents a novel heuristic optimization approach to constrained economic load dispatch (ELD) problems using the adaptive–variable population – PSO technique. The proposed methodology easily takes care of different constraints like transmission losses, dynamic operation constraints (ramp rate limits) and prohibited operating zones and also accounts for non-smoothness of cost functions arising due to the use of multiple fuels. Simulations were performed over various systems with different numbers of generating units, and comparisons are performed with other existing relevant approaches. The findings affirmed the robustness, fast convergence and proficiency of the proposed methodology over other existing techniques.     

Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system

The aim of this study is to introduce a comprehensive comparison of various energy management strategies of fuel cell/supercapacitor/battery storage systems. These strategies are utilized to manage the energy demand response of hybrid systems, in an optimal way, under highly fluctuating load condition. Two novel strategies based on salp swarm algorithm (SSA) and mine-blast optimization are proposed. The outcomes of these strategies are compared with commonly used strategies like fuzzy logic control, classical proportional integral control, the state machine, equivalent fuel consumption minimization, maximization, external energy maximization, and equivalent consumption minimization. Hydrogen fuel economy and overall efficiency are used for the comparison of these different strategies. Results demonstrate that the proposed SSA management strategy performed best compared with all other used strategies in terms of hydrogen fuel economy and overall efficiency. The minimum consumed hydrogen and maximum efficiency are found 19.4 gm and 85.61%, respectively.     

Joint economic and emission dispatch in energy markets: A multiobjective mathematical programming approach

Economic load dispatch is the method of determining the most efficient, low-cost and reliable operation of a power system by dispatching the available electricity generation resources to supply the load on the system. Environmental concerns that arise due to the operation of fossil fuel fired electric generators, change the classical problem into multiobjective emission/economic dispatch (MEED) which is formulated as a constrained nonlinear multiobjective mathematical programming (MMP). The proposed MEED formulation includes emission minimization objective, AC load flow constraints and security constraints of the power system which usually are found simultaneously in real-world power systems. The proposed model has a more accurate evaluation of transmission losses obtained from the load flow equations. The MMP approach based on ?-constraint algorithm has been proposed for generating Pareto-optimal solutions of power systems MEED problem. Moreover, fuzzy decision making process is employed to extract one of the Pareto-optimal solutions as the best compromise nondominated solution. The proposed approach is simulated on the IEEE 30-bus six-generator test system and obtained results have been comprehensively compared with some of the most recently published research in the area (from the both aspects of precision and execution tome) which confirms the potential and effectiveness of the proposed approach.     

Application of the invasive weed optimization algorithm to economic dispatch problems

In this paper the invasive weed optimization algorithm has been applied to a variety of economic dispatch (ED) problems. The ED problem is concerned with minimizing the fuel cost by optimally loading the electrical generators which are committed to supply a given demand. Some involve prohibited operating zones, transmission losses and valve point loading. In general, they are non-linear non-convex optimization problems which cannot be directly solved by conventional methods. In this work the invasive weed algorithm, a meta-heuristic method inspired by the proliferation of weeds, has been applied to four numerical examples and has resulted in promising solutions compared to published results.     

A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem

Economic dispatch (ED) plays an important role in power system operation. ED problem is a non-smooth and non-convex problem when valve-point effects of generation units are taken into account. This paper presents an efficient hybrid evolutionary approach for solving the ED problem considering the valve-point effect. The proposed algorithm combines a fuzzy adaptive particle swarm optimization (FAPSO) algorithm with Nelder–Mead (NM) simplex search called FAPSO-NM. In the resulting hybrid algorithm, the NM algorithm is used as a local search algorithm around the global solution found by FAPSO at each iteration. Therefore, the proposed approach improves the performance of the FAPSO algorithm significantly. The algorithm is tested on two typical systems consisting of 13 and 40 thermal units whose incremental fuel cost functions take into account the valve-point loading effects.     

Maximum power point tracking of a proton exchange membrane fuel cell system using PSO-PID controller

Fuel cells output power depends on the operating conditions, including cell temperature, oxygen partial pressure, hydrogen partial pressure, and membrane water content. In each particular condition, there is only one unique operating point for a fuel cell system with the maximum output. Thus, a maximum power point tracking (MPPT) controller is needed to increase the efficiency of the fuel cell systems. In this paper an efficient method based on the particle swarm optimization (PSO) and PID controller (PSO-PID) is proposed for MPPT of the proton exchange membrane (PEM) fuel cells. The closed loop system includes the PEM fuel cell, boost converter, battery and PSO-PID controller. PSO-PID controller adjusts the operating point of the PEM fuel cell to the maximum power by tuning of the boost converter duty cycle. To demonstrate the performance of the proposed algorithm, simulation results are compared with perturb and observe (P&O) and sliding mode (SM) algorithms under different operating conditions. PSO algorithm with fast convergence, high accuracy and very low power fluctuations tracks the maximum power point of the fuel cell system.     

Application of ELD and load forecast in optimal operation of industrial boiler plants equipped with thermal stores

Optimal operation of industrial boiler plants with objects of high energy efficiency and low fuel cost is still well worth investigating when energy problem becomes a world’s concern, for there are a great number of boiler plants serving industries. The optimization of operation is a measure that is less expensive and easier to carry out than many other measures. Economic load dispatch (ELD) is an effective approach to optimal operation of industrial boiler plants. In the paper a newly developed method referred to as the method of minimum-departure model (MDM) is used in the ELD for boiler plants. It is more convenient for carrying out ELD when boiler plants are equipped with thermal energy stores that usually adopt the working mode of optimal segmentation of a daily load curve. In the case of industrial boiler plants, ELD needs a prerequisite, viz., the accurate load forecast, which is performed using artificial neural networks in this paper. A computer program for the optimal operation was completed and applied to an example, which results the minimum daily fuel cost of the whole boiler plant.     

Effective methodology based on neural network optimizer for extracting model parameters of PEM fuel cells

I/V polarization curves of proton‐exchange membrane fuel cells (PEMFCs) are used to characterize the performance of single cells and stacks. Numerous semi‐empirical models are presented to predict such polarization curves by determining the unknown parameters of mathematical model of the PEMFCs stack. In this paper, a novel optimization approach, namely neural network algorithm (NNA) is applied for an estimation of the unknown PEMFC model parameters. The NNA is employed to minimize adopted objective function, which is formulated as the sum of squared deviations (SSD) between the actual data and estimated voltage points subjects to set of inequality constraints are satisfied. Three commercial types of PEMFCs stack namely Ballard Mark V, BCS‐500 W, and Nedstack PS6 are numerically simulated to show the effectiveness of the proposed NNA‐based tool for parameter identification. The minimum values of SSD are 0.8536 V ² for Ballard Mark V, 0.011698 V ² for BCS‐500 W stack, and 2.14487 V ² for Nedstack PS6, respectively. The obtained results of the NNA are compared with other optimizers recently published in the literature such as flower pollination algorithm, slap swarm optimizer, grey wolf algorithm, grasshopper optimization algorithm, and shark smell algorithm under the same conditions. The comparisons and other performance tests indicate the robustness and the competition of the adopted NNA‐based method for producing accurate I/V polarization curves under different operating scenarios.     

Thermochemical production of liquid fuels from biomass: Thermo-economic modeling,process design and process integration analysis

A detailed thermo-economic model combining thermodynamics with economic analysis and considering different technological alternatives for the thermochemical production of liquid fuels from lignocellulosic biomass is presented. Energetic and economic models for the production of Fischer–Tropsch fuel (FT), methanol (MeOH) and dimethyl ether (DME) by means of biomass drying with steam or flue gas, directly or indirectly heated fluidized bed or entrained flow gasification, hot or cold gas cleaning, fuel synthesis and upgrading are reviewed and developed. The process is integrated and the optimal utility system is computed. The competitiveness of the different process options is compared systematically with regard to energetic, economic and environmental considerations. At several examples, it is highlighted that process integration is a key element that allows for considerably increasing the performance by optimal utility integration and energy conversion. The performance computations of some exemplary technology scenarios of integrated plants yield overall energy efficiencies of 59.8% (crude FT-fuel), 52.5% (MeOH) and 53.5% (DME), and production costs of 89, 128 and 113 € MWh^?1 on fuel basis. The applied process design approach allows to evaluate the economic competitiveness compared to fossil fuels, to study the influence of the biomass and electricity price and to project for different plant capacities. Process integration reveals in particular potential energy savings and waste heat valorization. Based on this work, the most promising options for the polygeneration of fuel, power and heat will be determined in a future thermo-economic optimization.     

Comprehensive modelling for approaching the Kyoto targets on a local scale

This study aimed to evaluate the effectiveness of the MARKAL comprehensive model in the development of coherent medium-term strategies and sound climate protection policies at local level. The local case study (Val d’Agri, Basilicata region, Italy) discusses the possible role of local communities in the achievement of the national objectives derived by the Kyoto Protocol, investigating the traditional sectors responsible for air pollution and providing a full picture of the main energy and material flows. A scenario by scenario analysis was performed to analyse the response of the modelled system to the introduction of an exogenous constraint on carbon dioxide (CO₂) emissions. The main effects are presented with reference to fuel mix, technology choice, real market prices and reduced costs of competing options. The comparison of the solutions obtained for the different scenarios is useful to point out the effects of the CO₂ constraint on the total system cost and on the emission levels of other atmospheric pollutants. A further multiobjective optimisation was performed to analyse the effects of combined environmental constraints (CO₂ and particulate) on the overall system cost as well as in terms of marginal costs.     

The European power plant infrastructure—Presentation of the Chalmers energy infrastructure database with applications

This paper presents a newly established database of the European power plant infrastructure (power plants, fuel infrastructure, fuel resources and CO₂ storage options) for the EU25 member states (MS) and applies the database in a general discussion of the European power plant and natural gas infrastructure as well as in a simple simulation analysis of British and German power generation up to the year 2050 with respect to phase-out of existing generation capacity, fuel mix and fuel dependency. The results are discussed with respect to age structure of the current production plants, CO₂ emissions, natural gas dependency and CO₂ capture and storage (CCS) under stringent CO₂ emission constraints.     

A new technique for solving the multi-objective optimization problem using hybrid approach

Energy efficiency, which consists of using less energy or improving the level of service to energy consumers, refers to an effective way to provide overall energy. But its increasing pressure on the energy sector to control greenhouse gases and to reduce CO₂ emissions forced the power system operators to consider the emission problem as a consequential matter besides the economic problems. The economic power dispatch problem has, therefore, become a multi-objective optimization problem. Fuel cost, pollutant emissions, and system loss should be minimized simultaneously while satisfying certain system constraints. To achieve a good design with different solutions in a multi-objective optimization problem, fuel cost and pollutant emissions are converted into single optimization problem by introducing penalty factor. Now the power dispatch is formulated into a bi-objective optimization problem, two objectives with two algorithms, firefly algorithm for optimization the fuel cost, pollutant emissions and the real genetic algorithm for minimization of the transmission losses. In this paper the new approach (firefly algorithm-real genetic algorithm, FFA-RGA) has been applied to the standard IEEE 30-bus 6-generator. The effectiveness of the proposed approach is demonstrated by comparing its performance with other evolutionary multi-objective optimization algorithms. Simulation results show the validity and feasibility of the proposed method.     

改进的人工鱼群算法在电力系统经济负荷分配中的应用

经济负荷分配是提高电力系统经济运行、减少发电成本的关键问题,针对现行优化电力系统经济负荷分配方法存在的不足,在传统人工鱼群算法基础上,引入了动态变量(可变感知距离、变尺度移动步长和随机移动的概率)和t分布概率算子,提出了一种改进的人工鱼群算法对机组有功出力进行合理分配,并利用该方法对某地3机系统进行多时段仿真。结果表明,该算法不仅优化了多台机组负荷的最优分配,且能更快更准地获取全局最优解,具有较高的使用价值。     

Optimal parameter identification of PEMFC stacks using Adaptive Sparrow Search Algorithm

In this paper, a new optimization algorithm called Adaptive Sparrow Search Algorithm (ASSA) is proposed for optimal model parameters identification of the proton exchange membrane fuel cell (PEMFC) stacks. The proposed ASSA is utilized for minimizing the sum of squared error (SSE) between the empirical stack voltage and the calculated stack voltage by optimal selection of the mentioned parameters in the PEMFC stack. The method is then performed to three case studies including Ballard Mark V, Horizon H-12, and NedStack PS6 under different operating conditions and give 0.82, 5.14, and 0.097 of SEE which is the least value for all three case studies. The results of the algorithm are compared with some reported works in the literature including CGOA, GRA, and basic SSA to show the method prominence. The final results indicated that the proposed ASSA has the best efficiency toward the others.     

Ignition and combustion of a gaseous fuel pocket array in an oxidizing environment

ABSTRACT

Ignition and combustion of an infinite linear array of gaseous fuel pockets in a stagnant oxidizing environment under the microgravity condition is studied by a numerical approach. The combustion process is considered isobaric and the fluid motion is induced by density gradients due to the heat and mass transfer processes. A simple finite chemical reaction mechanism and the ideal gas equation of state are considered. The thermophysical properties, except density, are assumed constant. The Finite Volume Method is used with a hybrid non-staggered grid in a generalized system of coordinates. The SIMPLEC algorithm solves the modified pressure–velocity coupling. The Damköhler number effects on flame dynamics and on the fuel consumption are analyzed. Three stages in the burning processes: the induction time, the flame propagation and the diffusive burning are identified. The merging processes of the fuel pockets and of the flames are depicted.     

Identification of boundary conditions for non-Fourier heat conduction problems by differential transformation DRBEM and improved cuckoo search algorithm

Abstract

The differential transformation method is combined with the dual reciprocity boundary element method to solve the non-Fourier heat conduction problems in functionally gradient materials. The cuckoo search algorithm is improved by the Broyden–Fletcher–Goldfarb–Shanno algorithm to identify the boundary conditions for the heat conduction problems. The polynomial function related to coordinate and time is proposed to approximate the unknown boundary conditions. Numerical examples discuss the influences of measurement point numbers and measurement errors on inverse solutions. Numerical results demonstrate the effectiveness and accuracy of the proposed method.     

Impact of climate policy uncertainty on the adoption of electricity generating technologies

This paper presents a real options model where multiple options are evaluated simultaneously so that the effect of the individual options on each other is accounted for. We apply this model to the electricity sector, where we analyze three typical technologies based on fossil fuel, fossil fuel with carbon capture and renewable energy, respectively. In this way, we can analyze the transition from CO₂-intensive to CO₂-neutral electricity production in the face of rising and uncertain CO₂ prices. In addition, such a modelling approach enables us to estimate precisely the expected value of (perfect) information, i.e. the willingness of investors and producers to pay for information about the correct CO₂ price path. As can be expected, the expected value of information rises with increasing CO₂ price uncertainty. In addition, the larger the price uncertainty, the larger are the cumulative CO₂ emissions over the coming century. The reason for this is that the transition to less CO₂-intensive technologies is increasingly postponed with rising CO₂ price uncertainty. By testing different price processes (geometric Brownian motion versus jump processes with different jump frequencies), we can also make useful recommendations concerning the importance of policy predictability. We find that it is better to have climate change policies that are stable over a certain length of time and change abruptly than less abrupt but more frequently changing policies. Less frequent fluctuations reduce the expected value of information and result in smaller cumulative CO₂ emissions.     

Interactive DE for solving combined security environmental economic dispatch considering FACTS technology

This paper presents an efficient interactive differential evolution (IDE) to solve the multi-objective security environmental/economic dispatch (SEED) problem considering multi shunt flexible AC transmission system (FACTS) devices. Two sub problems are proposed.The first one is related to the active power planning to minimize the combined total fuel cost and emissions, while the second is a reactive power planning (RPP) using multi shunt FACTS device based static VAR compensator (SVC) installed at specified buses to make fine corrections to the voltage deviation, voltage phase profiles and reactive power violation. The migration operation inspired from biogeography-based optimization (BBO) algorithm is newly introduced in the proposed approach, thereby effectively exploring and exploiting promising regions in a space search by creating dynamically new efficient partitions. This new mechanism based migration between individuals from different subsystems makes the initial partitions to react more by changing experiences. To validate the robustness of the proposed approach, the proposed algorithm is tested on the Algerian 59-bus electrical network and on a large system, 40 generating units considering valve-point loading effect. Comparison of the results with recent global optimization methods show the superiority of the proposed IDE approach and confirm its potential for solving practical optimal power flow in terms of solution quality and convergence characteristics.