Optimal hybrid PV/WT/FC sizing and distribution system reconfiguration using multi-objective artificial bee colony (MOABC) algorithm

In this paper, a novel multi-objective artificial bee colony algorithm is presented to solve the distribution system reconfiguration and hybrid (photo voltaic/wind turbine/fuel cell) energy system sizing. The purposes of the multi-objective optimization problem include the total power loss, the total electrical energy cost, and the total emission produced by hybrid energy system and the grid minimization, and the voltage stability index (VSI) of distribution system maximization. In the proposed algorithm, an external archive of non-dominated solutions is kept which is updated in each iteration. In addition, for preserving the diversity in the archive of Pareto solutions, the crowding distance operator is used. This algorithm is tested on 33 bus distribution systems and obtained non-dominated solutions are compared with the well-known NSGA-II and MOPSO methods. The solutions obtained by the MOABC algorithm have a good quality and a better diversity of the Pareto front compared with those of NSGA-II and MOPSO methods.     

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.     

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.     

Optimal operation management of fuel cell/wind/photovoltaic power sources connected to distribution networks

In this paper a new multiobjective modified honey bee mating optimization (MHBMO) algorithm is presented to investigate the distribution feeder reconfiguration (DFR) problem considering renewable energy sources (RESs) (photovoltaics, fuel cell and wind energy) connected to the distribution network. The objective functions of the problem to be minimized are the electrical active power losses, the voltage deviations, the total electrical energy costs and the total emissions of RESs and substations. During the optimization process, the proposed algorithm finds a set of non-dominated (Pareto) optimal solutions which are stored in an external memory called repository. Since the objective functions investigated are not the same, a fuzzy clustering algorithm is utilized to handle the size of the repository in the specified limits. Moreover, a fuzzy-based decision maker is adopted to select the ‘best’ compromised solution among the non-dominated optimal solutions of multiobjective optimization problem. In order to see the feasibility and effectiveness of the proposed algorithm, two standard distribution test systems are used as case studies.     

Energy,exergy, and exergoeconomics (3E) analysis and multi-objective optimization of a multi-generation energy system for day and night time power generation - Case study: Dezful city

The present study aimed to investigate a multi-generation energy system for the production of hydrogen, freshwater, electricity, cooling, heating, and hot water. Steam Rankine cycle (SRC), organic Rankine cycle (ORC), absorption chiller, Parabolic trough collectors (PTCs), geothermal well, proton exchange membrane (PEM) electrolyzer, and reverse osmosis (RO) desalination are the main subsystems of the cycle. The amount of exergy destruction is calculated for each component after modeling and thermodynamic analysis. The PTCs, absorption chiller, and PEM electrolyzer had the highest exergy destruction, respectively. According to meteorological data, the system was annually and hourly tested for Dezful City. For instance, it had a production capacity of 13.25 kg/day of hydrogen and 147.42 m³/day of freshwater on 17th September. Five design parameters are considered for multi-objective optimization after investigating objective functions, including cost rate and exergy efficiency. Using a Group method of data handling (GMDH), a mathematical relation is obtained between the input and output of the system. Next, a multi-objective optimization algorithm, a non-dominated sorting genetic algorithm (NSGA-II), was used to optimize the relations. A Pareto frontier with a set of optimal points is obtained after the optimization. In the Pareto frontier, the best point is selected by the decision criterion of TOPSIS. At the TOPSIS point, the exergy efficiency is 31.66%, and the total unit cost rate is 21.9 $/GJ.     

Modeling and multi-objective optimization of a stand-alone photovoltaic-wind turbine-hydrogen-battery hybrid energy system based on hysteresis band

Hybrid renewable energy system (HRES) can provide power without emission for off-grid areas. Due to intermittency of renewable energy, energy storage system (ESS) is essential for reliable power supply, while its cost is still relatively high. Appropriate power management strategy (PMS) can help to delay the degradation of energy storage devices and reduce the system cost. In this study, power management strategy and configuration optimization of the system are focused and the study includes three main contributions. First, mathematical models of the system, including photovoltaics (PVs), wind turbines (WTs), batteries, fuel cells (FCs), electrolyzers (ELZs), and hydrogen tanks are developed. The degradation of fuel cells and electrolyzers is considered in the modeling process. Second, power management strategy considering hysteresis band is employed to control energy flow to delay fuel cell and electrolyzer degradation. Third, a multi-objective optimization function including the system net annual value (NAV), loss of power supply probability (LPSP) and excess energy (E_excess) is established. Non-dominating Sorting Genetic Algorithm II (NSGA-II) is used to solve objective function. The results demonstrate that using hysteresis band help improve the system performance and reduce the cost. In addition, by setting the goal of excess energy, system reliability is well preserved with a LPSP as low as 0.92%. Compared with other optimization algorithms such as MOEA/D, NSGA-II has a smaller SI value of 422.10 and a larger DI value of 830.78, therefore the Pareto solution obtained by NSGA-II has a more uniform distribution and larger coverage.     

Optimization,modeling and accurate prediction of thermal conductivity and dynamic viscosity of stabilized ethylene glycol and water mixture Al2O3 nanofluids by NSGA-II using ANN

In this study, multi-objective optimization of nanofluid aluminum oxide in a mixture of water and ethylene glycol (40:60) is studied. In order to reduce viscosity and increase thermal conductivity of nanofluids, NSGA-II algorithm is used to alter the temperature and volume fraction of nanoparticles. Neural network modeling of experimental data is used to obtain the values of viscosity and thermal conductivity on temperature and volume fraction of nanoparticles. In order to evaluate the optimization objective functions, neural network optimization is connected to NSGA-II algorithm and at any time assessment of the fitness function, the neural network model is called. Finally, Pareto Front and the corresponding optimum points are provided and introduced. Optimal results showed that the optimum viscosity and thermal conductivity occurs at maximum temperature.     

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.     

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.     

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.     

Genetic fuzzy rule extraction for optimised sizing and control of hybrid renewable energy hydrogen systems

A major challenge related to the design of a hybrid renewable energy hydrogen system is which energy sources to include and at what capacity, for regionally different potentials of renewable energy and hydrogen demand. In addition, once the plant is in operation, control variables need to be optimised. The problem resorts to an area of multiple criteria decision making referred to as multi-objective optimisation. The results obtained from these type of algorithms include not only one optimal solution, but a set of optimal solutions (Pareto front) thereby offering a system designer several options. This set of solutions can be hard to interpret and a method is needed to automatically extract useful design and control strategies from this information. A methodology that is quite successful in deriving human interpretable rules from this type of information is genetic fuzzy systems. In this work a k-means clustering algorithm is used to generate membership functions and a fuzzy rule-base is trained by means of a genetic algorithm. The genetic fuzzy system obtained is reduced by determining the minimum number of rules followed by a membership function reduction process. The reduced genetic fuzzy system is deemed more interpretable. Geographic weather data from three different sites are used to generate data to be used in the genetic fuzzy method. Results show that the technique provides valuable information that can be used for the design of such hybrid renewable energy hydrogen production systems.     

Performance assessment and optimization of an integrated solid oxide fuel cell-gas turbine cogeneration system

The combined solid oxide fuel cells and gas turbine (SOFC/GT) system is known to be a potential alternative for distributed power generation. In this paper, a novel SOFC/GT based cogeneration system, which integrated a transcritical carbon dioxide cycle (TRCC) with a LNG cold energy utilization system is proposed. A mathematical (zero-dimensional) model is developed to analyze the co-generation system performance from the perspective of thermodynamic (energy and exergy) and economic costs. The main parameters of the system are chosen to analyze their effects on thermodynamic performance. The results show that the current system can achieve 64.40% thermal efficiency and 62.13% exergy efficiency under given conditions, and can further improve efficiency through parameter optimization. Finally, the multi-objective optimization program using NSGA-II (Non-dominated Sorting Genetic Algorithm II) is used to obtain the optimal value of the system design parameters. In the multi-objective analysis, the thermodynamic efficiency and economic cost of the system are considered as objective functions. The optimization results show that the final optimized design selected from the Pareto front can achieve 63.08% thermal efficiency and 61.10% exergy efficiency, respectively.     

A mathematical programming framework for energy planning in services’ sector buildings under uncertainty in load demand: The case of a hospital in Athens

The aim of this paper is to provide an integrated modeling and optimization framework for energy planning in large consumers of the services’ sector based on mathematical programming. The power demand is vaguely known and the underlying uncertainty is modeled using elements from fuzzy set theory. The defined fuzzy programming model is subsequently transformed to an equivalent multi-objective problem, where the minimization of cost and the maximization of demand satisfaction are the objective functions. The Pareto optimal solutions of this problem are obtained using a novel version of the ε-constraint method and represent the possibly optimal solutions of the original problem under uncertainty. In the present case, in order to select the most preferred Pareto optimal solution, the minimax regret criterion is properly used to indicate the preferred configuration of the system (i.e. the size of the installed units) given the load uncertainty. Furthermore, the paper proposes a model reduction technique that can be used in similar cases and further examines its effect in the final results. The above methodology is applied to the energy rehabilitation of a hospital in the Athens area. The technologies under consideration include a combined heat and power unit for providing power and heat, an absorption unit and/or a compression unit for providing cooling load. The obtained results demonstrate that, increasing the degree of demand satisfaction, the total annual cost increases almost linearly. Although data compression allows obtaining realistic results, the size of the proposed units might be slightly changed.     

Intelligent optimization of renewable resource mixes incorporating the effect of fuel risk,fuel cost and CO2 emission

Power system planning is a capital intensive investment-decision problem. The majority of the conventional planning conducted since the last half a century has been based on the least cost approach, keeping in view the optimization of cost and reliability of power supply. Recently, renewable energy sources have found a niche in power system planning owing to concerns arising from fast depletion of fossil fuels, fuel price volatility as well as global climatic changes. Thus, power system planning is under-going a paradigm shift to incorporate such recent technologies. This paper assesses the impact of renewable sources using the portfolio theory to incorporate the effects of fuel price volatility as well as CO₂ emissions. An optimization framework using a robust multi-objective evolutionary algorithm, namely NSGA-II, is developed to obtain Pareto optimal solutions. The performance of the proposed approach is assessed and illustrated using the Indian power system considering real-time design practices. The case study for Indian power system validates the efficacy of the proposed methodology as developing countries are also increasing the investment in green energy to increase awareness about clean energy technologies.     

Multi-objective self-adaptive algorithm for highly constrained problems: Novel method and applications

As a substantive input to resolve the industrial systems and challenging optimization problems, which are multi-objective in nature, the authors introduce an emerging systematic multi-objective optimization methodology for large-scale and highly-constrained industrial production systems. The methodology uses a simulation-based optimization framework built on a novel multi-objective evolutionary algorithm that exhibits several specific innovative features to maintain genetic diversity within the population of solutions and to drive the search towards the Pareto-optimal set/front. This novel algorithm was validated using standard test functions and the results demonstrate undoubtedly that the proposed algorithm computes accurately the Pareto-optimal set for optimization problems of at least two-objective functions. Next, the algorithm was applied on a base case cogeneration optimization problem with three-objective functions named the modified CGAM problem. The modified problem includes concentrations and tax rates of pollutant emissions (i.e. CO₂ and NO_x). The multi-objective optimization of such a problem consists of simultaneously maximizing the exergetic efficiency of the cogeneration plant, minimizing the total cost rate (including pollutant tax rate), and minimizing the specific rate of pollutant emissions. A fuel-to-air equivalence ratio ranging from 0.5 to 1.0, and pollutant tax rates of 0.15 $/kg CO₂, and 7.50 $/kg NO_x were used to compute the surfaces of the Pareto fronts. The results found for the modified CGAM problem clearly demonstrate the applicability of the proposed algorithm for optimization problems of more than two-objective functions with multiple constraints. The results strengthen the fact that there is no single optimal solution but rather a set of optimal solutions that present the best trade-off alternatives from which a decision-maker can select the appropriate final decision. Also, the study emphasizes the key role of both economic and environmental issues in the optimization problem of energy systems.     

Determination of optimal operating conditions for a polymer electrolyte membrane fuel cell stack: optimal operating condition based on multiple criteria

A methodology for optimal control of the polymer electrolyte membrane fuel cell (PEMFC) with multiple criteria is presented here. In this regard, thermoelectric objectives and thermoeconomic objective are considered, simultaneously. The proposed fuel cell is a 1200 W Ballard PEMFC namely Nexa? power module. The net power density and exergetic efficiency of the PEMFC are maximized, and the unit cost of the generated power is minimized in a multi‐objective optimization procedure using the NSGA‐II (non‐dominated sorting genetic algorithm). Operating temperature and pressure, air stoichiometric coefficient at the cathode and the current density are considered as controlling parameters in order to acquire optimal performance of the PEMFC. A set of optimal solution namely the Pareto frontier is obtained, and a final optimal solution is selected from available solutions located on the Pareto frontier using the fuzzy decision‐making process based on the Bellman–Zadeh approach. Results are compared with corresponding results obtained previously in single objective optimization scenarios. It has been shown that the optimal operating condition obtained based on the multiple criteria approach has least deviation from the ideal features of the fuel cell in comparison to the corresponding optimal solution obtained in conventional single‐objective optimization approaches. Copyright © 2013 John Wiley & Sons, Ltd.     

Multi-objective optimization of HVAC system with an evolutionary computation algorithm

A data-mining approach for the optimization of a HVAC (heating, ventilation, and air conditioning) system is presented. A predictive model of the HVAC system is derived by data-mining algorithms, using a dataset collected from an experiment conducted at a research facility. To minimize the energy while maintaining the corresponding IAQ (indoor air quality) within a user-defined range, a multi-objective optimization model is developed. The solutions of this model are set points of the control system derived with an evolutionary computation algorithm. The controllable input variables — supply air temperature and supply air duct static pressure set points — are generated to reduce the energy use. The results produced by the evolutionary computation algorithm show that the control strategy saves energy by optimizing operations of an HVAC system.     

基于改进微分进化算法的分布式电源优化配置

为研究分布式电源接入对配网的影响,建立了包含发电收益最优、有功耗损最低和电压稳定度的多目标优化模型,并运用改进的微分进化算法对接入配网的分布式电源进行整体寻优,该算法将自适应策略融入到传统微分进化算法中,并结合Pareto占优概念将所有代非占优解存储到外部精英存档中。通过对IEEE-33节点的标准配电网算例分析,表明分布式电源的合理配置可使配网电压水平提升、减小有功网损、提升电压稳定指标,且所提模型和算法合理、可行。     

Multi-objective exergy-based optimization of a continuous photobioreactor applied to produce hydrogen using a novel combination of soft computing techniques

This work was aimed at proposing a flexible and reliable framework based on combination of three soft computing techniques, i.e., artificial neural network, genetic algorithm, and fuzzy systems for multi-objective exergetic optimization of continuous photobiohydrogen production process from syngas by Rhodospirillum rubrum bacterium. To this end, artificial neural network (ANN) coupled with fuzzy clustering method (FCM) to model exergetic outputs on the basis of input variables. The outputs of modeling system were then fed into a novel optimization approach developed by hybridizing additive linear interdependent fuzzy multi-objective optimization (ALIFMO) and the elitist non-dominated sorting genetic algorithm (NSGA-II). The optimization was carried out to minimize the normalized exergy destruction and maximize the rational and process exergetic efficiencies, simultaneously. The solutions of the proposed approach were also compared with conventional fuzzy multi-objective optimization procedure with independent objectives. Overall, the modeling system predicted the exergetic parameters of photobioreactor with a coefficient of determination higher than 0.90. Furthermore, the optimization approach suggested syngas flow rate of 13.35 mL/min and agitation speed of 383.34 rpm as the best operational condition by considering the preferences of process exergy efficiency, rational exergy efficiency, and normalized exergy destruction, respectively. This condition could yield the normalized exergy destruction of 1.56, process exergetic efficiency of 21.66%, and rational exergetic efficiency of 85.65%. The obtained results showed the superiority of the proposed approach over the conventional fuzzy method in optimizing the complex biofuel production systems.     

A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources

Electrical generators of renewable electricity resources are quiet, clean and reliable. Optimal placement of renewable electricity generators (REGs) results in reduction of objective functions like losses, costs of electrical generation and voltage deviation. Because of recent technology developments of photovoltaic units, wind turbine and fuel cell units, only these generators are considered in this paper. This work presents a multiobjective optimization algorithm for the siting and sizing of renewable electricity generators. The objectives consist of minimization of costs, emission and losses of distributed system and optimization of voltage profile. This multiobjective optimization is solved by the Improved honey bee mating optimization (HBMO) algorithm. In the proposed algorithm, an external repository is considered to save non-dominated (Pareto) solutions found during the search process. Since the objective functions are not the same, a fuzzy clustering technique is used to control the size of the repository within the limits. This algorithm is executed on a typical 70-bus test system. Results of the case study show the proper siting and sizing of REGs are important to improve the voltage profile, reduce costs, emission and losses of distribution system. The main feature of the algorithm refers to its accuracy and calculation speed.