Multi-objective approach in thermoenvironomic optimization of a benchmark cogeneration system

Multi-objective optimization for designing of a benchmark cogeneration system known as CGAM cogeneration system has been performed. In optimization approach, the exergetic, economic and environmental aspects have been considered, simultaneously. The thermodynamic modeling has been implemented comprehensively while economic analysis conducted in accordance with the total revenue requirement (TRR) method. The results for the single objective thermoeconomic optimization have been compared with the previous studies in optimization of CGAM problem. In multi-objective optimization of the CGAM problem, the three objective functions including the exergetic efficiency, total levelized cost rate of the system product and the cost rate of environmental impact have been considered. The environmental impact objective function has been defined and expressed in cost terms. This objective has been integrated with the thermoeconomic objective to form a new unique objective function known as a thermoenvironomic objective function. The thermoenvironomic objective has been minimized while the exergetic objective has been maximized. One of the most suitable optimization techniques developed using a particular class of search algorithms known as multi-objective evolutionary algorithms (MOEAs) has been considered here. This approach which is developed based on the genetic algorithm has been applied to find the set of Pareto optimal solutions with respect to the aforementioned objective functions. An example of decision-making has been presented and a final optimal solution has been introduced. The sensitivity of the solutions to the interest rate and the fuel cost has been studied.     

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.     

Single and Multi-Objective Optimization of a Cogeneration System Using Hybrid Algorithms

Current design and operation of energy systems must consider the efficient utilization of energy resources, reduced environmental harms, and sustainable development. Many techniques for energy systems analysis and optimization have thus been developed worldwide. To evaluate different methodologies, the benchmark CGAM problem was proposed, which consisted of the optimization of a cogeneration system with explicit physical, thermodynamic, and economic models. The original CGAM problem was formulated as a single objective optimization problem, where the objective function was the sum of the purchased equipment, maintenance and operation, and fuel consumption costs. However, in real-life applications, costs must be analyzed individually: for example, one might increase equipment costs but save in fuel consumption for the entire system life. In this paper, single- and multi-objective hybrid optimizations of the CGAM system are performed. A hybrid optimization algorithm combines the strengths of deterministic and heuristic methods. Usually, it employs a heuristic method to locate a region where the global extreme point lies, and then switches to a deterministic method to get to the exact point faster. The objective functions are the fuel consumption cost rate and the total capital investment. Thus, a Pareto front is obtained for all non-dominated solutions, from which the final decision can be made considering appropriate scenarios.     

Design and optimization of a non-TEMA type tubular recuperative heat exchanger used in a regenerative gas turbine cycle

A special non-TEMA type tubular recuperative heat exchanger used as a regenerator of a gas turbine cycle is considered for multi-criteria optimization. It is assumed that the recuperator is designed for an existing gas turbine cycle to be retrofitted. Three scenarios for optimization of the proposed system have been considered. In one scenario, the objective is minimizing the cost of recuperator; while in another scenario maximizing the cycle exergetic efficiency is considered. In third scenario, both objectives are optimized simultaneously in a multi-objective optimization approach. Geometric specification of the recuperator including tubes length, tubes outside/inside diameters, tube pitch in the tube bundle, inside shell diameter, outer and inner tube limits of the tube bundle and the total number of disc and doughnut baffles are considered as decision variables. Combination of these objectives and decision variables with suitable engineering and physical constraints (including NO_x and CO emission limitations) makes a set of MINLP optimization problem. 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. It is shown that the multi-objective optimization scenario can be considered as a generalized optimization approach in which balances between economical viewpoints of both heat exchanger manufacturer and end user of recuperator.     

New rules for the exergo-economic optimization methodology

New rules for the exergo-economic optimization methodology for multiproduct systems are presented. The rules include the loss of exergy unit cost due to irreversibilities when it is used in a subsystem. A comparison between the methodology using the new rules and with a different set of rules is presented here for the case of a cogeneration plant similar to the CGAM problem but using a combined cycle more detailed than CGAM. We found an exergy unit value lower than that found by Valero’s methodology for a waste stream.     

Exergoeconomic optimization of a cogeneration plant

This paper illustrates, with a specifie example, the methodology presented in a previous paper for the exergoeconomic optimization of complex, multicomponent-multiproduct systems. The application selected is a cogeneration plant (the CGAM problem) to which methodologies developed by different authors have been applied previously. The CGAM problem involves a regenerative gas-turbine system and a heat-recovery steam generator producing 30 MW (net power) and 14 kg/sec of saturated steam at 2 MPa.     

Evolutionary algorithms for multi-objective energetic and economic optimization in thermal system design

Thermoeconomic analyses in thermal system design are always focused on the economic objective. However, knowledge of only the economic minimum may not be sufficient in the decision making process, since solutions with a higher thermodynamic efficiency, in spite of small increases in total costs, may result in much more interesting designs due to changes in energy market prices or in energy policies. This paper suggests how to perform a multi-objective optimization in order to find solutions that simultaneously satisfy exergetic and 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 an evolutionary algorithm, that features a new diversity preserving mechanism using as a test case the well-known CGAM problem.     

基于PSO算法的柴油机性能多目标优化

以玉柴YC6K420LN-C31型柴油机为研究对象,基于RBF(radial basis function)神经网络算法建立发动机数据模型,采用PSO(particle swarm optimization)算法进行基于模型的多目标优化研究.研究表明:RBF神经网络建立的NOx、总碳氢化合物(THC)、CO和燃油消耗率(brake specific fuel consumption,BSFC)数据模型的决定系数R2分别为0.93、0.98、0.96和0.95,模型的预测准确度均大于90％,拟合优度和预测能力满足多目标优化的需求;采用PSO算法对发动机进行多 目标优化,将适应度目标NOx、THC、CO和BSFC的权重最终均设置为0.25,生成控制图谱并进行台架验证,在推进特性工况下总排放量和油耗相比于原机平均降低了 22.9％与5.3％.     

A practical multi-objective PSO algorithm for optimal operation management of distribution network with regard to fuel cell power plants

In this paper a novel Multi-objective fuzzy self adaptive hybrid particle swarm optimization (MFSAHPSO) evolutionary algorithm to solve the Multi-objective optimal operation management (MOOM) is presented. The purposes of the MOOM problem are to decrease the total electrical energy losses, the total electrical energy cost and the total pollutant emission produced by fuel cells and substation bus. Conventional algorithms used to solve the multi-objective optimization problems convert the multiple objectives into a single objective, using a vector of the user-predefined weights. In this conversion several deficiencies can be detected. For instance, the optimal solution of the algorithms depends greatly on the values of the weights and also some of the information may be lost in the conversion process and so this strategy is not expected to provide a robust solution. This paper presents a new MFSAHPSO algorithm for the MOOM problem. The proposed algorithm maintains a finite-sized repository of non-dominated solutions which gets iteratively updated in the presence of new solutions. Since the objective functions are not the same, a fuzzy clustering technique is used to control the size of the repository, within the limits. The proposed algorithm is tested on a distribution test feeder and the results demonstrate the capabilities of the proposed approach, to generate true and well-distributed Pareto-optimal non-dominated solutions of the MOOM problem.     

Effectiveness of local air pollution and GHG taxes: The case of Chilean industrial sources

In 2017, environmental taxes began to be applied to CO₂, PM, NOx and SO₂ emissions in Chile to reduce the negative environmental effects of fossil fuels burned in industrial and thermoelectric sources with a thermal power greater than or equal to 50 MW. In this context, the present study generates an economic optimization model to simulate how different tax scenarios would modify the behavior of regulated industrial sources considering the alternatives they have to minimize their costs (tax payment, fuel change and/or installation of abatement technologies). The main results show that, under the current tax scenario, CO₂, PM and SO₂ emissions would decrease by 11%, 48% and 49% respectively, while NO_X emissions would increase by 5%. By extending the tax to all industrial sources regardless of their thermal power, CO₂, PM and SO₂ emissions would decrease respectively by 14%, 98% and 66%, while NO_X emissions would increase by 7.1%. Finally, it is determined that modifying the tax rate of a single pollutant while maintaining the rest of the constant rates generates a low impact on the other pollutants emissions.     

Probabilistic multiobjective wind-thermal economic emission dispatch based on point estimated method

In this paper, wind power generators are being incorporated in the multiobjective economic emission dispatch problem which minimizes wind-thermal electrical energy cost and emissions produced by fossil-fueled power plants, simultaneously. Large integration of wind energy sources necessitates an efficient model to cope with uncertainty arising from random wind variation. Hence, a multiobjective stochastic search algorithm based on 2m point estimated method is implemented to analyze the probabilistic wind-thermal economic emission dispatch problem considering both overestimation and underestimation of available wind power. 2m point estimated method handles the system uncertainties and renders the probability density function of desired variables efficiently. Moreover, a new population-based optimization algorithm called modified teaching-learning algorithm is proposed to determine the set of non-dominated optimal solutions. During the simulation, the set of non-dominated solutions are kept in an external memory (repository). Also, a fuzzy-based clustering technique is implemented to control the size of the repository. In order to select the best compromise solution from the repository, a niching mechanism is utilized such that the population will move toward a smaller search space in the Pareto-optimal front. In order to show the efficiency and feasibility of the proposed framework, three different test systems are represented as case studies.     

Multi-objective optimization of biogas systems producing hydrogen and electricity with solid oxide fuel cells

The design of solid oxide fuel cells (SOFC) using biogas for distributed power generation is a promising alternative to reduce greenhouse gas emissions in the energy and waste management sectors. Furthermore, the high efficiency of SOFCs in conjunction with the possibility to produce hydrogen may be a financially attractive option for biogas plants. However, the influence of design variables in the optimization of revenues and efficiency has seldom been studied for these novel cogeneration systems. Thus, in order to fulfill this knowledge gap, a multi-objective optimization problem using the NSGA-II algorithm is proposed to evaluate optimal solutions for systems producing hydrogen and electricity from biogas. Moreover, a mixed-integer linear optimization routine is used to ensure an efficient heat recovery system with minimal number of heat exchanger units. The results indicate that hydrogen production with a fuel cell downstream is able to achieve high exergy efficiencies (65–66%) and a drastic improvement in net present value (1346%) compared with sole power generation. Despite the additional equipment, the investment costs are estimated to be quite similar (12% increase) to conventional steam reforming systems and the levelized cost of hydrogen is very competitive (2.27 USD/kgH₂).     

火电站多目标负荷调度及其算法的研究

对传统意义下负荷调度模型进行修正,同时考虑最小化燃料费用和污染排放量,提出了火电站多目标负荷调度模型;并将强度Pareto进化算法(SPEA2)与并行遗传算法(PGA)相结合对其求解.结果表明:该算法求得的Pareto最优解分布均匀、收敛速度快、寻优能力强,决策者可根据不同的侧重点在Pareto解集中选择最终的满意解.应用该算法对某电厂进行多目标负荷调度,验证了其可行性和有效性.     

Bilateral based robust load frequency control

Load frequency control (LFC) has been one of the major subjects in electric power system design/operation and is becoming much more significant today in accordance with increasing size and the changing structure and complexity of interconnected power systems. In practice, power systems use simple proportional-integral (PI) controllers for frequency regulation and load tracking. However, since the PI controller parameters are usually tuned based on classical or trial and error approaches, they are incapable of obtaining good dynamical performance for a wide range of operating conditions and various load changes scenarios in a restructured power system.This paper addresses a new decentralized robust LFC design in a deregulated power system under a bilateral based policy scheme. In each control area, the effect of bilateral contracts is taken into account as a set of new input signals in a modified traditional dynamical model. The LFC problem is formulated as a multi-objective control problem via a mixed H₂/H_∞ control technique. In order to design a robust PI controller, the control problem is reduced to a static output feedback control synthesis, and then, it is solved using a developed iterative linear matrix inequalities algorithm to get a robust performance index close to a specified optimal one. The proposed method is applied to a 3 control area power system with possible contract scenarios and a wide range of load changes. The results of the proposed multi-objective PI controllers are compared with H₂/H_∞ dynamic controllers.     

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.     

Power and cogeneration technology environomic performance typification in the context of CO2 abatement part II: Combined heat and power cogeneration

This is the second of a series of two articles, dealing with a new approach of environomic (thermodynamic, economic and environmental) performance ‘Typification’ and optimization of power generation technologies. This part treats specifically of combined heat and power (CHP) cogeneration technologies in the context of CO₂ abatement and provides a methodology for a flexible and fast project based CHP system design evaluation. One of the aspect of the approach is the post-optimization integration of the operating and capital costs, in order to effectively deal with the uncertainty of the project specific design and operation conditions (fuel, electricity and heat selling prices, project financial conditions such as investment amortization periods, annual operating hours, etc). In addition the approach also allows to efficiently evaluate the influence of the external cost such as the CO₂ tax level under a tax scheme or the CO₂ permit price in the emission trading market.     

Power and cogeneration technology environomic performance typification in the context of CO2 abatement part I: Power generation

In this series of two articles, the concepts and approaches of environomic (thermodynamic, economic and environmental) performance ‘Typification’ of power generation technologies (Part I) and of combined heat and power (CHP) cogeneration technologies (Part II) in the context of CO₂ abatement are introduced. A methodology is then proposed for a flexible and fast project based power or CHP cogeneration system design evaluation though post-optimization integration of the operating and capital costs. This allows to effectively deal with the uncertainty of the project specific design and operation conditions (fuel, electricity and heat selling prices, project financial conditions such as investment amortization periods, annual operating hours, etc). Furthermore, the uncertainties linked to the external cost such as the CO₂ tax level under a tax scheme or the CO₂ permit price in the emission trading market can be assessed.     

Thermal–economic–environmental analysis and multi-objective optimization of an internal-reforming solid oxide fuel cell–gas turbine hybrid system

In this article, an internal-reforming solid oxide fuel cell–gas turbine (IRSOFC–GT) hybrid system is modeled and analyzed from thermal (energy and exergy), economic, and environmental points of view. The model is validated using available data in the literature. Utilizing the genetic algorithm optimization technique, multi-objective optimization of modeled system is carried out and the optimal values of system design parameters are obtained. In the multi-objective optimization procedure, the exergy efficiency and the total cost rate of the system (including the capital and maintenance costs, operational cost (fuel cost), and social cost of air pollution for CO, NO_x, and CO₂) are considered as objective functions. A sensitivity analysis is also performed in order to study the effect of variations of the fuel unit cost on the Pareto optimal solutions and their corresponding design parameters. The optimization results indicate that the final optimum design chosen from the Pareto front results in exergy efficiency of 65.60% while it leads to total cost of 3.28 million US$ year⁻¹. It is also demonstrated that the payback time of the chosen design is 6.14 years.     

A nonlinear regression based multi-objective optimization of parameters based on experimental data from an IC engine fueled with biodiesel blends

This study reports the results of an experimental investigation of the performance of an IC engine fueled with a Karanja biodiesel blends, followed by multi-objective optimization with respect to engine emissions and fuel economy, in order to determine the optimum biodiesel blend and injection timings complying with Bharat Stage II emission norms. Nonlinear regression has been used to regress the experimentally obtained data to predict the brake thermal efficiency, NO_x, HC and smoke emissions based on injection timing, blend ratio and power output. To acquire the data, experimental studies have been conducted on a single cylinder, constant speed (1500 rpm), direct injection diesel engine under variable load conditions and injection timings for neat diesel and various Karanja biodiesel blends (5%, 10%, 15%, 20%, 50% and 100%). Retarding the injection timing for neat Karanja biodiesel resulted in an improved efficiency and lower HC emissions. A tradeoff relationship between the NO_x and smoke emissions is observed, which makes it difficult to determine the optimum blend ratio. The functional relationship developed between the correlating variables using nonlinear regression is able to predict the performance and emission characteristics with a correlation coefficient (R) in the range of 0.95-0.99 and very low root mean square errors. The outputs obtained using these functions are used to evaluate the multi-objective function of optimization process in the 0-20% blend range. The overall optimum is found to be 13% biodiesel-diesel blend with an injection timing of 24°bTDC, when equal weightage is given to emissions and efficiency.     

Exergoenvironmental analysis and optimization of a cogeneration plant system using Multimodal Genetic Algorithm (MGA)

In the present work, a combined heat and power plant for cogeneration purposes that produces 50 MW of electricity and 33.3 kg/s of saturated steam at 13 bar is optimized using genetic algorithm. The design parameters of the plant considered are compressor pressure ratio (r_AC), compressor isentropic efficiency (η_comp), gas turbine isentropic efficiency (η_GT), combustion chamber inlet temperature (T₃), and turbine inlet temperature (TIT). In addition, to optimally find the optimum design parameters, an exergoeconomic approach is employed. A new objective function, representing total cost rate of the system product including cost rate of each equipment (sum of the operating cost, related to the fuel consumption) and cost rate of environmental impact (NO_x and CO) is considered. Finally, the optimal values of decision variables are obtained by minimizing the objective function using evolutionary genetic algorithm. Moreover, the influence of changes in the demanded power on various design parameters are parametrically studied for 50, 60, 70 MW of net power output. The results show that for a specific unit cost of fuel, the values of design parameters increase, as the required, with net power output increases. Also, the variations of the optimal decision variables versus unit cost of fuel reveal that by increasing the fuel cost, the pressure ratio, r_AC, compressor isentropic efficiency, η_AC, turbine isentropic efficiency, η_GT, and turbine inlet temperature (TIT) increase.