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Multi-objective optimization and off-design evaluation of organic rankine cycle (ORC) for low-grade waste heat recovery

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ENERGY 2020年 203卷 117809-117809页

作者： Wang, Lingbao Bu, Xianbiao Li, Huashan Chinese Acad Sci Guangzhou Inst Energy Convers Guangzhou 510640 Peoples R China CAS Key Lab Renewable Energy Guangzhou 510640 Peoples R China Guangdong Prov Key Lab New & Renewable Energy Res Guangzhou 510640 Peoples R China

Organic Rankine Cycle (ORC) is a technically feasible way for low temperature waste heat recovery. Multi-objective optimization of the ORC using R245fa is conducted considering both thermodynamic performance and economic factors simultaneously, by means of non-dominated sorting genetic algorithm-ii. The optimum operating parameters were achieved by sorting the Pareto-optimal solutions using the technique for order preference by similarity to ideal situation. A comprehensive analysis on the off-design performance of the optimized ORC system is examined. R-ls is defined as the ratio of latent heat to sensible heat of the input heat to investigate the relation between the distribution of the input heat and the system performance. The effects of hot water temperature and mass flow rate, cooling water temperature and mass flow rate, superheated degree, subcooling degree and working pump rotational speed, on thermal efficiency, exergy efficiency, net power output, investment cost per unit power and R-ls were investigated. The fitted correlations between R-ls and the system performance indexes were derived. The novelties of the present paper are the multi-objective optimization for the further off-design behavior analysis and the quantitative relation investigation between R-ls and system performance indexes. It is indicated that the direction of system optimization is to reduce R-ls. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Organic rankine cycle Multi-objective optimization non-dominated sorting genetic algorithm ii Off-design analysis Input heat

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Designing, optimizing and comparing distributed generation technologies as a substitute system for reducing life cycle costs, CO2 emissions, and power losses in residential buildings

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ENERGY 2022年 253卷 1页

作者： Sadeghi, Delnia Ahmadi, Seyed Ehsan Amiri, Nima Marzband, Mousa Abusorrah, Abdullah Rawa, Muhyaddin Rawa, Muhyaddin Northumbria Univ Elect Power & Control Syst Res Grp Ellison Pl Newcastle Upon Tyne NE1 8ST England King Abdulaziz Univ Ctr Res Excellence Renewable Energy & Power Syst Jeddah 21589 Saudi Arabia King Abdulaziz Univ Fac Engn K A CARE Energy Res & Innovat Ctr Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia

The optimization of distributed generation technologies and storage systems are essential for a reliable, cost-effective, and secure system due to the uncertainties of Renewable Energy Sources (RESs) and load demand. In this study, two algorithms, the Multi-Objective Particle Swarm Optimization (MOPSO) and the non-Dominant sorting genetic algorithm ii (NSGA-ii) were utilized to design five different case studies (CSs) (photovoltaic (PV)/wind turbine (WT)/battery/diesel generator (DG), PV/WT/battery/fuel cell (FC)/electrolyzer (EL)/hydrogen tank (HT), PV/WT/battery/grid-connected, PV/WT/battery/gridconnected with Demand Response Program (DRP), and PV/WT/battery/electric vehicle (EV)) to minimize life cycle cost (LCC), loss of power supply probability (LPSP), and CO2 emissions. In fact, different backups are provided for (PV/WT/battery), which is considered as the base system. Further, the uncertainties in RES and load were modeled by the Taguchi method, and Monte Carlo simulation (MCS) was used to model the uncertainties in EV to achieve accurate results. In addition, in CS4, a Demand Response Program (DRP) based on Time-of-Use (TOU) price is considered to study the effect on the number of specific components and other parameters. Finally, the simulation results verify that the NSGA-ii calculation provides accurate and reliable outcomes compared to the MOPSO method, and the PV/WT/ battery/EV combination is the most suitable option among the five designed scenarios. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： demand Response program Electric vehicle Taguchi method Monte Carlo simulation Multi-objective particle swarm optimization non-dominated sorting genetic algorithm ii

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A Two-Stage Multi-Objective Design Optimization Model for a 6 MW Direct-Drive Permanent Magnet Synchronous Generator

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ENERGIES 2024年第16期17卷 4147页

作者： Tian, De Wu, Xiaoxuan Meng, Huiwen Su, Yi North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China

The design optimization of a direct-drive permanent magnet synchronous generator (DDPMSG) is of great significance for wind turbines because of its unique advantages. This paper proposes a two-stage model to realize multi-objective design optimization for a 6 MW DDPMSG. In the first stage, a surrogate optimized response surface model based on an improved sparrow search algorithm (ISSA) was established for modeling the cogging torque and generator efficiency. In the second-stage model, a multi-objective optimization model is proposed to optimize the cogging torque and generator efficiency of the DDPMSG. Finally, the proposed two-stage model was used for a 6 MW DDPMSG design optimization, and the simulation results demonstrated the superiority and rationality of the proposed model. In the first-stage model, the proposed surrogate model based on the ISSA had a better modeling accuracy and lower errors. Compared with traditional response surface models and correlation analysis models, the proposed optimized surrogate model reduced errors in the cogging torque by 34.63% and 42.97%, respectively, while the errors in the efficiency models were reduced by 12.92% and 60.78%, respectively, which indicates the superiority of the first-stage model. In the second stage, compared with the single-objective optimization model, the multi-objective optimization model achieved a trade-off optimization between the cogging torque and the efficiency. Compared with the cogging torque optimization model, the proposed model optimized the efficiency by 101.41%. Compared with the efficiency optimization model, the proposed model reduced the cogging torque by 16.67%. These results verified the superiority and rationality of the second-stage model.

关键词： permanent magnet synchronous generators multi-objective design optimization improved sparrow search algorithm non-dominated sorting genetic algorithm ii

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Optimal design of thermal control system for space optical remote sensor based on NSGA-ii and opto-mechanical-thermal integration analysis

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CASE STUDIES IN THERMAL ENGINEERING 2023年 43卷

作者： Yuan, Zhipeng Chen, Liheng Han, Hasiaoqier Ren, Limin Liu, Shuai Wang, Renxin Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing 10049 Peoples R China Univ Chinese Acad Sci Ctr Mat Sci & Optoelect Engn Beijing 100049 Peoples R China

The thermal control system (TCS) is a key technology for ensuring good imaging quality of a space optical remote sensor (SORS) in orbit, and its research is of practical significance. In this paper, a multidisciplinary optimization method was proposed for the TCS of a SORS. A particle-swarm -optimized back-propagation neural network was used as the surrogate model to reduce the computational cost of the opto-mechanical-thermal integrated simulation model. Subsequently, the mean and variance of the modulation transfer function (MTF) of 12 fields of view of the SORS at the Nyquist frequency were considered as objective functions, multi-objective optimization of the design parameters of the TCS was performed using the non-dominated sorting genetic algo-rithm ii (NSGA-ii), and the Pareto front composed of a series of Pareto solutions was obtained. A compromise solution was selected as the optimal TCS design. The results showed that, upon comparison with the initial design, the optimal design scheme increased the mean of the MTFs of the SORS by 34.4%, reduced the variance by 31.3%, and significantly improved the compre-hensive optical performance of the SORS.

关键词： Space optical remote sensor Thermal control system Opto-mechanical-thermal integration Surrogate model Multi-objective optimization (MOO) non-dominated sorting genetic algorithm ii

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Balancing Cost and Economic Efficiency: Consider the multi-purpose optimization of green energy market's function in Green energy interactive infrastructure

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ENERGY 2024年 309卷

作者： Zhang, Ce Dong, Huijie Xu, Yuanlu Nick, Lucky Univ Portsmouth Sch Business Portsmouth PO1 2UP England Baotou Rura Commercia Bank Co Ltd Baotou Peoples R China Guangzhou Energy Storage Grp Co LTD Guangzhou 510062 Guangdong Peoples R China Univ Western Sydney Sch Econ & Finance Penrith Australia

The increasing reliance on electric micro-mobility vehicles (EMVs) in the delivery industry necessitates efficient battery-swapping infrastructure. This study presents a framework that predicts battery-swapping needs for EMVs using a simulation model that considers the entire travel chain of activities. We propose a multi-objective optimization model to strategically determine battery-swapping station locations, balancing construction and transit costs. Utilizing the non-dominated sorting genetic algorithm ii (NSGA-ii), we identified 35 nondominated solutions within the Pareto front. For Nanjing City, our model indicated that the construction of an optimal network could be achieved with costs ranging from 2.85 to 4.94 million Yuan, corresponding to battery-swapping trip costs between 9700 and 197,000 Yuan. The simulation predicted a daily battery-swapping demand of 675 instances, with peak hours at 11:00 a.m. (301 swaps) and 5:00 p.m. (198 swaps). Sensitivity analysis showed that reducing the charging period from 3 h to 1 h could decrease construction costs by 1.55 million Yuan on average, while maintaining a consistent battery-swapping travel cost of around 23,000 Yuan. Additionally, a 5 % increase in battery-swapping penetration rate led to an average increase of 480,000 Yuan in construction costs and 847 Yuan in travel costs. This study integrates demand forecasting and infrastructure optimization, providing actionable insights for planning and managing battery-swapping stations.

关键词： Electric micro-mobility vehicles Battery-swapping infrastructure non-dominated sorting genetic algorithm ii

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An improved solution methodology for the urban transit routing problem

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COMPUTERS & OPERATIONS RESEARCH 2024年 163卷

作者： Huesselmann, G. van Vuuren, J. H. Andersen, S. J. Stellenbosch Univ Dept Ind Engn Private Bag 11 ZA-7602 Matieland South Africa Stellenbosch Univ Dept Civil Engn Private Bag 11 ZA-7602 Matieland South Africa

An improved solution methodology is proposed in this paper for the urban transit routing problem (UTRP). This methodology includes a procedure for the generation of improved initial solutions as well as improved metaheuristic search approaches, involving the use of hyperheuristics to manage search operators in both trajectory-based and population-based metaheuristics. The UTRP variant considered in this paper is that of deciding upon efficient bus transit routes. The design criteria embedded in our UTRP model are the simultaneous minimisation of the expected average passenger travel time and minimisation of the system operator's cost (measuring the latter as the sum total of all route lengths in the system). The model takes as input an origin-destination demand matrix for a pre-specified set of bus stops, along with an underlying road network structure, and returns as output a set of bus route trade-off solutions. The decision maker can then select one of these route sets subjectively, based on the desired degree of trade-off between the aforementioned transit system design criteria. This bi-objective minimisation problem is solved approximately in three distinct stages - a solution initialisation stage, an intermediate analysis stage, and an iterative metaheuristic search stage during which high-quality trade-off solutions are sought. A novel procedure is introduced for the solution initialisation stage, aimed at effectively generating high-quality initial feasible solutions. Two metaheuristics are implemented to solve instances of the problem, namely a dominance-based multi-objective simulated annealing algorithm and an improved non-dominated sorting genetic algorithm, each equipped with a hyperheuristic capable of managing the perturbation operators employed. Various novel operators are proposed for these metaheuristics, of which the most noteworthy take into account the demand of passengers.

关键词： Transit network design problem Urban transit routing problem Dominance-based multi-objective simulated annealing non-dominated sorting genetic algorithm ii Metaheuristic Hyperheuristic

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A Study of the Multi-Objective Neighboring Only Quadratic Minimum Spanning Tree Problem in the Context of Uncertainty

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APPLIED SCIENCES-BASEL 2024年第19期14卷 8941页

作者： Pal, Debosree Sharma, Haresh Kumar Prentkovskis, Olegas Chakraborty, Falguni Maskeliunaite, Lijana Boinchee Binapani Balika Vidyalaya Pandua 712135 India Indian Inst Technol Madras Dept Math Chennai 600036 India Birla Inst Management Technol Area Operat & Decis Sci Greater Noida 201306 India Vilnius Gediminas Tech Univ Dept Mobile Machinery & Railway Transport LT-10105 Vilnius Lithuania Dr BC Roy Engn Coll Dept Masters Comp Applicat Durgapur 713206 India

The pursuit of studying the quadratic minimum spanning tree (QMST) problem has captivated numerous academics because of its distinctive characteristic of taking into account the cost of interaction between pairs of edges. A QMST refers to the minimum spanning tree, which is a graph that is both acyclic and minimally connected. It also includes the interaction cost between a pair of edges in the minimum spanning tree. These interaction costs can occur between any pair of edges, whether they are adjacent or non-adjacent. In the QMST problem, our objective is to minimize both the cost of the edges and the cost of interactions. This eventually classifies the task as NP-hard. The interaction costs, sometimes referred to as quadratic costs, inherently exhibit a contradictory relationship with linear edge costs when solving a multi-objective problem that aims to minimize both linear and quadratic costs simultaneously. This study addresses the bi-objective adjacent only quadratic minimum spanning tree problem (AQMSTP) by incorporating the uncertain nature of the linear and quadratic costs associated with the problem. The focus is on the interaction costs between adjacent edges. Consequently, we have introduced a multi-objective problem called the uncertain adjacent only quadratic minimum spanning tree problem (mUAQMSTP) and formulated it using the uncertain chance-constrained programming technique. Afterwards, two MOEAs-non-dominated sorting genetic algorithm ii (NSGAii) and duplicate elimination non-dominated sorting evolutionary algorithm (DENSEA)-and the traditional multi-objective solution approach, the global criterion method, are employed to solve the deterministic transformation of the model. Finally, we provide a suitable numerical illustration to substantiate our suggested framework.

关键词： adjacent only quadratic minimum spanning tree fuzzy chance-constrained programming global criterion method non-dominated sorting genetic algorithm ii duplicate elimination non-dominated sorting evolutionary algorithm

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A Bi-objective green tugboat scheduling problem with the tidal port time windows

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TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 2022年 110卷

作者： Zhong, Huiling Zhang, Yugang Gu, Yimiao South China Univ Technol Dept Elect Business Guangzhou Peoples R China

Tugboat assistance is essential when large ships are berthing and unberthing, and requires ports to produce efficient tugboat schedules considering various constraints. Few studies optimize tugboat scheduling from multiple vectors simultaneously, however. This paper thus constructs a bi-objective, mixed-integer linear programming, green tugboat scheduling model in order to minimize the maximum completion time and total fuel consumption, which improves port service levels, decreases tugboat company operating expenditures and thus environmental pollution emissions. The proposed model also considers the time window characteristics of the tidal port. We use the non-dominated sorting genetic algorithm ii (NSGA-ii) framework integrating the characteristics of tugboat scheduling to solve the model. Finally, we make a case study about Guangzhou Port to validate the model and the algorithm via a comparison of results solved by NSGA-ii and CPLEX solver. The Pareto fronts obtained show the trade-off relationship between the two objectives, and provide a basis for port tugboat scheduling plans.

关键词： Green tugboat scheduling Time windows Multi-objective optimization non-dominated sorting genetic algorithm ii

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A multi-objective framework for enhancing the reliability and minimizing the cost of PMU deployment in power systems

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SCIENTIA IRANICA 2016年第6期23卷 2917-2927页

作者： Aminifar, F. Safdarian, A. Fotuhi-Firuzabad, M. Shahidehpour, M. Univ Tehran Sch Elect & Comp Engn Coll Engn Tehran 14395515 Iran Sharif Univ Technol Dept Elect Engn Tehran 111559363 Iran IIT Robert W Galvin Ctr Chicago IL 60616 USA

Wide-Area Monitoring, Protection, And Control (WAMPAC) is a key factor in the implementation of smart transmission grids. WAMPAC has a crucial role in detection and prevention of widespread events with an ultimate goal of improving the electricity service reliability. Several mathematical techniques were proposed for the Optimal Phasor Measurement Units (PMU) Placement (OPP) problem which represents the first step toward the development of WAMPAC. These techniques consider either the solution to the network observability or realization of specific PMU applications. This paper proposes a multi-objective framework for OPP which emphasizes the reliability of power systems. The objective functions minimize the investment cost and maximize the system reliability. Since the objectives are in conflict, the non-dominated sorting genetic algorithm ii approach is adopted as an intelligent state sampling tool to find non-dominated solutions (Pareto front). The final placement scheme among Pareto points is chosen by a fuzzy decision making approach. (C) 2016 Sharif University of Technology. All rights reserved.

关键词： Multi-objective optimization non-dominated sorting genetic algorithm ii Phasor measurement unit Optimal PMU placement

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Solving fuzzy robust facility layout problem equipped with cranes using MPS algorithm and modified NSGA-ii

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 210卷

作者： Esmikhani, Soroush Kazemipoor, Hamed Sobhani, Farzad Movahedi Molana, Seyyed Mohammad Hadji Islamic Azad Univ Sci & Res Branch Tehran Iran Islamic Azad Univ Cent Tehran Branch Tehran Iran

Facility layout problems are defined as the placement of facilities in a plant region. In this paper a facility layout problem was clarified by the facility dimensions and the materials flow between facilities are uncertain as fuzzy random variables and the plant region was equipped with the wall mounted jib cranes and the small gate cranes and there were some forbidden areas in the plant region where the placement of facilities were forbidden. The objective functions of the problems have been contemplated as minimizing materials handling cost of the operators and the cranes and for the first time maximizing cranes usability of wall mounted jib cranes and small gate cranes by calculating their covered area on the facilities. To solve the problem a multi-objective populationbased on simulated annealing algorithm (MPS) and a Modified non-dominated sorting genetic algorithm ii (MNSGA-ii) was proposed then six case studies were solved by them. The results reveal that run time of the MNSGA-ii for large size problems are better than the MPS algorithm and the MPS algorithm finds better solutions with materials handling cost of the operators and the cranes and the MNSGA-ii finds better solutions with cranes usability.

关键词： Population-based simulated annealing non-dominated sorting genetic algorithm ii Small gantry crane Wall mounted jib crane Dynamic facility layout problem Forbidden areas

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