Load distribution is a key technology in hot strip rolling process, which directly influences strip product quality. A multi-objective load distribution model, which takes into account the rolling force margin balance...Load distribution is a key technology in hot strip rolling process, which directly influences strip product quality. A multi-objective load distribution model, which takes into account the rolling force margin balance, roll wear ratio and strip shape control, is presented. To avoid the selection of weight coefficients encountered in single objective optimization, a multi-objective differential evolutionary algorithm, called MaximinDE, is proposed to solve this model. The experimental results based on practical production data indicate that MaximinDE can obtain a good pareto-optimal solution set, which consists of a series of alternative solutions to load distribution. Decision-makers can select a trade-off solution from the pareto-optimal solution set based on their experience or the importance of ob- iectives. In comparison with the empirical load distribution solution, the trade-off solution can achieve a better per- formance, which demonstrates the effectiveness of the multi-objective load distribution optimization. Moreover, the conflicting relationship among different objectives can be also found, which is another advantage of multi-objective load distribution optimization.展开更多
In order to compare two advanced multi-objective evolutionary algorithms,a multi-objective water distribution problem is formulated in this paper.The multi-objective optimization has received more attention in the wat...In order to compare two advanced multi-objective evolutionary algorithms,a multi-objective water distribution problem is formulated in this paper.The multi-objective optimization has received more attention in the water distribution system design.On the one hand the cost of water distribution system including capital,operational,and maintenance cost is mostly concerned issue by the utilities all the time;on the other hand improving the performance of water distribution systems is of equivalent importance,which is often conflicting with the previous goal.Many performance metrics of water networks are developed in recent years,including total or maximum pressure deficit,resilience,inequity,probabilistic robustness,and risk measure.In this paper,a new resilience metric based on the energy analysis of water distribution systems is proposed.Two optimization objectives are comprised of capital cost and the new resilience index.A heuristic algorithm,speedconstrained multi-objective particle swarm optimization( SMPSO) extended on the basis of the multi-objective particle swarm algorithm,is introduced to compare with another state-of-the-art heuristic algorithm,NSGA-II.The solutions are evaluated by two metrics,namely spread and hypervolume.To illustrate the capability of SMPSO to efficiently identify good designs,two benchmark problems( two-loop network and Hanoi network) are employed.From several aspects the results demonstrate that SMPSO is a competitive and potential tool to tackle with the optimization problem of complex systems.展开更多
The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worke...The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.展开更多
In the framework of vigorous promotion of low-carbon power system growth as well as economic globalization,multi-resource penetration in active distribution networks has been advancing fiercely.In particular,distribut...In the framework of vigorous promotion of low-carbon power system growth as well as economic globalization,multi-resource penetration in active distribution networks has been advancing fiercely.In particular,distributed generation(DG)based on renewable energy is critical for active distribution network operation enhancement.To comprehensively analyze the accessing impact of DG in distribution networks from various parts,this paper establishes an optimal DG location and sizing planning model based on active power losses,voltage profile,pollution emissions,and the economics of DG costs as well as meteorological conditions.Subsequently,multiobjective particle swarm optimization(MOPSO)is applied to obtain the optimal Pareto front.Besides,for the sake of avoiding the influence of the subjective setting of the weight coefficient,the decisionmethod based on amodified ideal point is applied to execute a Pareto front decision.Finally,simulation tests based on IEEE33 and IEEE69 nodes are designed.The experimental results show thatMOPSO can achieve wider and more uniformPareto front distribution.In the IEEE33 node test system,power loss,and voltage deviation decreased by 52.23%,and 38.89%,respectively,while taking the economy into account.In the IEEE69 test system,the three indexes decreased by 19.67%,and 58.96%,respectively.展开更多
Estimation of distribution algorithms are a class of evolutionary optimization algorithms based on probability distribution model. In this article, a Pareto-based multi-objective estimation of distribution algorithm w...Estimation of distribution algorithms are a class of evolutionary optimization algorithms based on probability distribution model. In this article, a Pareto-based multi-objective estimation of distribution algorithm with multivariate T-copulas is proposed. The algorithm employs Pareto-based approach and multivariate T-copulas to construct probability distribution model. To estimate joint distribution of the selected solutions, the correlation matrix of T-copula is firstly estimated by estimating Kendall’s tau and using the relationship of Kendall’s tau and correlation matrix. After the correlation matrix is estimated, the degree of freedom of T-copula is estimated by using the maximum likelihood method. Afterwards, the Monte Carte simulation is used to generate new individuals. An archive with maximum capacity is used to maintain the non-dominated solutions. The Pareto optimal solutions are selected from the archive on the basis of the diversity of the solutions, and the crowding-distance measure is used for the diversity measurement. The archive gets updated with the inclusion of the non-dominated solutions from the combined population and current archive, and the archive which exceeds the maximum capacity is cut using the diversity consideration. The proposed algorithm is applied to some well-known benchmark. The relative experimental results show that the algorithm has better performance and is effective.展开更多
This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved pr...This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved proximal policy optimization(IPPO)method to make real-time decisions for the DHHBFSP.A multi-objective Markov decision process is modeled for the DHHBFSP,where the reward function is represented by a vector with dynamic weights instead of the common objectiverelated scalar value.A factory agent(FA)is formulated for each factory to select unscheduled jobs and is trained by the proposed IPPO to improve the decision quality.Multiple FAs work asynchronously to allocate jobs that arrive randomly at the shop.A two-stage training strategy is introduced in the IPPO,which learns from both single-and dual-policy data for better data utilization.The proposed IPPO is tested on randomly generated instances and compared with variants of the basic proximal policy optimization(PPO),dispatch rules,multi-objective metaheuristics,and multi-agent reinforcement learning methods.Extensive experimental results suggest that the proposed strategies offer significant improvements to the basic PPO,and the proposed IPPO outperforms the state-of-the-art scheduling methods in both convergence and solution quality.展开更多
The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency...The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency. A multi-objective model was presented for the material distribution routing problem in mixed manufacturing systems, and it was solved by a hybrid multi-objective evolutionary algorithm (HMOEA). The characteristics of the HMOEA are as follows: 1) A route pool is employed to preserve the best routes for the population initiation; 2) A specialized best?worst route crossover (BWRC) mode is designed to perform the crossover operators for selecting the best route from Chromosomes 1 to exchange with the worst one in Chromosomes 2, so that the better genes are inherited to the offspring; 3) A route swap mode is used to perform the mutation for improving the convergence speed and preserving the better gene; 4) Local heuristics search methods are applied in this algorithm. Computational study of a practical case shows that the proposed algorithm can decrease the total travel distance by 51.66%, enhance the average vehicle load rate by 37.85%, cut down 15 routes and reduce a deliver vehicle. The convergence speed of HMOEA is faster than that of famous NSGA-II.展开更多
This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously a...This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.展开更多
In this paper, a distributed algorithm is proposed to solve a kind of multi-objective optimization problem based on the alternating direction method of multipliers. Compared with the centralized algorithms, this algor...In this paper, a distributed algorithm is proposed to solve a kind of multi-objective optimization problem based on the alternating direction method of multipliers. Compared with the centralized algorithms, this algorithm does not need a central node. Therefore, it has the characteristics of low communication burden and high privacy. In addition, numerical experiments are provided to validate the effectiveness of the proposed algorithm.展开更多
The empirical Bayes test problem is considered for scale parameter of twoparameter exponential distribution under type-II censored data.By using wavelets estimation method,the EB test function is constructed,of which ...The empirical Bayes test problem is considered for scale parameter of twoparameter exponential distribution under type-II censored data.By using wavelets estimation method,the EB test function is constructed,of which the asymptotic optimality and convergence rates are obtained.Finally,an example concerning the main result is given.展开更多
Due to development of distribution systems and increase in electricity demand,the use of capacitor banks increases.From the other point of view,nonlinear loads generate and inject considerable harmonic currents into p...Due to development of distribution systems and increase in electricity demand,the use of capacitor banks increases.From the other point of view,nonlinear loads generate and inject considerable harmonic currents into power system.Under this condition if capacitor banks are not properly selected and placed in the power system,they could amplify and propagate these harmonics and deteriorate power quality to unacceptable levels.With attention of disadvantages of passive filters,such as occurring resonance,nowadays the usage of this type of harmonic compensator is restricted.On the other side,one of parallel multi-function compensating devices which are recently used in distribution system to mitigate voltage sag and harmonic distortion,performs power factor correction,and improves the overall power quality as active power conditioner(APC).Therefore,the utilization of APC in harmonic distorted system can affect and change the optimal location and size of shunt capacitor bank under harmonic distortion condition.This paper presents an optimization algorithm for improvement of power quality using simultaneous optimal placement and sizing of APC and shunt capacitor banks in radial distribution networks in the presence of voltage and current harmonics.The algorithm is based on particle swarm optimization(PSO).The objective function includes the cost of power losses,energy losses and those of the capacitor banks and APCs.展开更多
Water Distribution Systems (WDSs) design and operation are usually done on a case-by-case basis. Numerous models have been proposed in the literature to solve specific problems in this field. The implementation of the...Water Distribution Systems (WDSs) design and operation are usually done on a case-by-case basis. Numerous models have been proposed in the literature to solve specific problems in this field. The implementation of these models to any real-world WDS optimization problem is left to the discretion of designers who lack the necessary tools that will guide them in the decision-making process for a given WDS design project. Practitioners are not always very familiar with optimization applied to water network design. This results in a quasi-exclusive use of engineering judgment when dealing with this issue. In order to support a decision process in this field, the present article suggests a step-by-step approach to solve the multi-objective design problem by using both engineering and optimization. A genetic algorithm is proposed as the optimization tool and the targeted objectives are: 1) to minimize the total cost (capital and operation), 2) to minimize the residence time of the water within the system and 3) to maximize a network reliability metric. The results of the case study show that preliminary analysis can significantly reduce decision variables and computational burden. Therefore, the approach will help network design practitioners to reduce optimization problems to a more manageable size.展开更多
Recently,with the increasing demand of the electric vehicle(EV)in transportation,the power grid faces critical challenges in meeting the extra power demand.Companies are focusing on expanding EV charging infrastructur...Recently,with the increasing demand of the electric vehicle(EV)in transportation,the power grid faces critical challenges in meeting the extra power demand.Companies are focusing on expanding EV charging infrastructure to meet customer requirements.Ensuring power supply security,reliability,and economics for EV charging stations remains a challenge,despite efforts to align photovoltaic(PV)and battery energy storage system(BESS)based designs with distribution system requirements.A criteria weight ranking mechanism has been designed to accept charging requests for EVs depending on the criteria weights specified by the EV owner.This paper uses a multi-objective remora optimization algorithm(MOROA)to determine the optimal location of two electric vehicle charging stations(EVCS)in the distribution system,and capacity of PV&BESS units in two EVCS for optimizing three conflicting objective functions,such as(1)minimizing total power loss;(2)minimizing annual substation power cost,and annual capital,operation&maintenance cost of the PV and BESS,and(3)minimizing emission from upstream grid.Moreover,the EVs are also scheduled optimally at each charging station.The effectiveness of these methodologies has been demonstrated through four case studies using IEEE 33 bus radial distribution system(RDS).Furthermore,the smart EV charge scheduling reduces the overall load burden on the grid network and the benefit of EVCS operators and EV owners.展开更多
This paper deals with the optimal placement of distributed generation(DG) units in distribution systems via an enhanced multi-objective particle swarm optimization(EMOPSO) algorithm. To pursue a better simulation of t...This paper deals with the optimal placement of distributed generation(DG) units in distribution systems via an enhanced multi-objective particle swarm optimization(EMOPSO) algorithm. To pursue a better simulation of the reality and provide the designer with diverse alternative options, a multi-objective optimization model with technical and operational constraints is constructed to minimize the total power loss and the voltage fluctuation of the power system simultaneously. To enhance the convergence of MOPSO, special techniques including a dynamic inertia weight and acceleration coefficients have been integrated as well as a mutation operator. Besides, to promote the diversity of Pareto-optimal solutions, an improved non-dominated crowding distance sorting technique has been introduced and applied to the selection of particles for the next iteration. After verifying its effectiveness and competitiveness with a set of well-known benchmark functions, the EMOPSO algorithm is employed to achieve the optimal placement of DG units in the IEEE 33-bus system. Simulation results indicate that the EMOPSO algorithm enables the identification of a set of Pareto-optimal solutions with good tradeoff between power loss and voltage stability. Compared with other representative methods, the present results reveal the advantages of optimizing capacities and locations of DG units simultaneously, and exemplify the validity of the EMOPSO algorithm applied for optimally placing DG units.展开更多
Various kinds of new engineering technologies have been studied to realize the low-carbon and sustainable power supply systems all over the world.In actual implementation of these technologies,mostly,there are multipl...Various kinds of new engineering technologies have been studied to realize the low-carbon and sustainable power supply systems all over the world.In actual implementation of these technologies,mostly,there are multiple objectives with trade off relationships among each other,and also various constraints in the achievement of these objectives.Therefore,it should be essential to solve multiobjective optimization problems effectively in the applications of these new technologies in power systems.This paper proposes an improved method to realize multiobjective optimization for critical challenges in advanced power systems.To realize that,in an optimal dispersed generation installation problem,that is,one of effective measures for low-carbon power systems,various optimization methods and their combination methods are evaluated and a hybrid method for evolutionary algorithms was developed.The method can provide improved results compared with other state-of-the-art multi-objective optimization methods.展开更多
Based on the large-scale penetration of electric vehicles(EV)into the building cluster,a multi-objective optimal strategy considering the coordinated dispatch of EV is proposed,for improving the safe and economical op...Based on the large-scale penetration of electric vehicles(EV)into the building cluster,a multi-objective optimal strategy considering the coordinated dispatch of EV is proposed,for improving the safe and economical operation problems of distribution network.The system power loss and node voltage excursion can be effectively reduced,by taking measures of time-of-use(TOU)price mechanism bonded with the reactive compensation of energy storage devices.Firstly,the coordinate charging/discharging load model for EV has been established,to obtain a narrowed gap between load peak and valley.Next,a multi-objective optimization model of the distribution grid is also defined,and the active power loss and node voltage fluctuation are chosen to be the objective function.For improving the efficiency of optimization process,an advanced genetic algorithm associated with elite preservation policy is used.Finally,reactive compensation capacity supplied by capacitor banks is dynamically determined according to the varying building loads.The proposed strategy is demonstrated on the IEEE 33-node test case,and the simulation results show that the power supply pressure can be obviously relieved by introducing the coordinated charging/discharging behavior of EV;in the meantime,via reasonable planning of the compensation capacitor,the remarkably lower active power loss and voltage excursion can be realized,ensuring the safe and economical operation of the distribution system.展开更多
Distributed generation(DG)allocation in the distribution network is generally a multi-objective optimization problem.The maximum benefits of DG injection in the distribution system highly depend on the selection of an...Distributed generation(DG)allocation in the distribution network is generally a multi-objective optimization problem.The maximum benefits of DG injection in the distribution system highly depend on the selection of an appropriate number of DGs and their capacity along with the best location.In this paper,the improved decomposition based evolutionary algorithm(I-DBEA)is used for the selection of optimal number,capacity and site of DG in order to minimize real power losses and voltage deviation,and to maximize the voltage stability index.The proposed I-DBEA technique has the ability to incorporate non-linear,nonconvex and mixed-integer variable problems and it is independent of local extrema trappings.In order to validate the effectiveness of the proposed technique,IEEE 33-bus,69-bus,and 119-bus standard radial distribution networks are considered.Furthermore,the choice of optimal number of DGs in the distribution system is also investigated.The simulation results of the proposed method are compared with the existing methods.The comparison shows that the proposed method has the ability to get the multi-objective optimization of different conflicting objective functions with global optimal values along with the smallest size of DG.展开更多
Ranked set sample is applicable whenever ranking of a set of sample units can be done easily by a judgement method of the study variable or of the auxiliary variable. This paper considers ranked set sample based on th...Ranked set sample is applicable whenever ranking of a set of sample units can be done easily by a judgement method of the study variable or of the auxiliary variable. This paper considers ranked set sample based on the auxiliary variable X which is correlated with the study variable Y, where （X, Y） follows Morgenstern type bivariate exponential distribution. The authors discuss the optional allocation for unbiased estimators of the correlation coefficient p of the random variables X and Y when the auxiliary variable X is used for ranking the sample units and the study variable Y is measured for estimating the correlation coefficient. This paper first gives a class of unbiased estimators of p when the mean 0 of the study variable Y is known and obtains an essentially complete subclass of this class. Further, the optimal allocation of the unbiased estimators is found in this subclass and is proved to be Bayes, admissible, and minimax. Finally, the unbiased estimator of p under the optimal allocation in the case of known θ is reformed for estimating p in the case of unknown θ, and the reformed estimator is shown to be strongly consistent.展开更多
Bipolar direct current(DC)distribution networks can effectively improve the connection flexibility for renewable generations and loads.In practice,concerns regarding the potential voltage unbalance issue of the distri...Bipolar direct current(DC)distribution networks can effectively improve the connection flexibility for renewable generations and loads.In practice,concerns regarding the potential voltage unbalance issue of the distribution networks and the frequency of switching still remain.This paper proposes a day-ahead polarity switching strategy to reduce voltage unbalance by optimally switching the polarity of renewable generations and loads while minimizing the switching times simultaneously in the range of a full day.First,a multi-objective optimization model is constructed to minimize the weighted sum of voltage unbalance factors and the sum of number of switching actions in the day based on the power flow model.Second,a two-step solution strategy is proposed to solve the optimization model.Finally,the proposed strategy is validated using 11-node and 34-node distribution networks as case studies,and a switching and stabilizing device is designed to enable unified switching of renewable generations and loads.Numerical results demonstrate that the proposed strategy can effectively reduce the switching times without affecting the improvement of voltage balance.展开更多
Distributed energy systems are considered as a promising technology for sustainable development and have become a popular research topic in the areas of building energy systems. This work presents a case study of opti...Distributed energy systems are considered as a promising technology for sustainable development and have become a popular research topic in the areas of building energy systems. This work presents a case study of optimizing an integrated distributed energy system consisting of combined heat and power(CHP), photovoltaics(PV), and electric and/or thermal energy storage for a hospital and large hotel buildings located in Texas and California. First, simulation models for all subsystems, which are developed individually, are integrated together according to a control strategy designed to satisfy both the electric and thermal energy requirements of a building. Subsequently, a multi-objective particle swarm optimization(MOPSO) is employed to obtain an optimal design of each subsystem. The objectives of the optimization are to minimize the simple payback period(PBP) and maximize the reduction of carbon dioxide emissions(RCDE). Finally, the energy performance for the selected building types and locations are analyzed after the optimization. Results indicate that the proposed optimization method could be applied to determine an optimal design of distributed energy systems, which reaches a trade-off between the economic and environmental performance for different buildings. With the presented distributed energy system, a peak shaving in electricity of about 300 kW and a reduction in boiler fuel consumption of 610 kW could be attained for the hospital building located in California for a winter day. For the summer and transition seasons, electricity peak shaving of 800 kW and 600 kW could be achieved, respectively.展开更多
基金Item Sponsored by National Natural Science Foundation of China(50974039)
文摘Load distribution is a key technology in hot strip rolling process, which directly influences strip product quality. A multi-objective load distribution model, which takes into account the rolling force margin balance, roll wear ratio and strip shape control, is presented. To avoid the selection of weight coefficients encountered in single objective optimization, a multi-objective differential evolutionary algorithm, called MaximinDE, is proposed to solve this model. The experimental results based on practical production data indicate that MaximinDE can obtain a good pareto-optimal solution set, which consists of a series of alternative solutions to load distribution. Decision-makers can select a trade-off solution from the pareto-optimal solution set based on their experience or the importance of ob- iectives. In comparison with the empirical load distribution solution, the trade-off solution can achieve a better per- formance, which demonstrates the effectiveness of the multi-objective load distribution optimization. Moreover, the conflicting relationship among different objectives can be also found, which is another advantage of multi-objective load distribution optimization.
基金Sponsored by the Project of Application Technology Research and Development Plan in Heilongjiang Province(Grant No.GA13C302)
文摘In order to compare two advanced multi-objective evolutionary algorithms,a multi-objective water distribution problem is formulated in this paper.The multi-objective optimization has received more attention in the water distribution system design.On the one hand the cost of water distribution system including capital,operational,and maintenance cost is mostly concerned issue by the utilities all the time;on the other hand improving the performance of water distribution systems is of equivalent importance,which is often conflicting with the previous goal.Many performance metrics of water networks are developed in recent years,including total or maximum pressure deficit,resilience,inequity,probabilistic robustness,and risk measure.In this paper,a new resilience metric based on the energy analysis of water distribution systems is proposed.Two optimization objectives are comprised of capital cost and the new resilience index.A heuristic algorithm,speedconstrained multi-objective particle swarm optimization( SMPSO) extended on the basis of the multi-objective particle swarm algorithm,is introduced to compare with another state-of-the-art heuristic algorithm,NSGA-II.The solutions are evaluated by two metrics,namely spread and hypervolume.To illustrate the capability of SMPSO to efficiently identify good designs,two benchmark problems( two-loop network and Hanoi network) are employed.From several aspects the results demonstrate that SMPSO is a competitive and potential tool to tackle with the optimization problem of complex systems.
基金supported by the Natural Science Foundation of Anhui Province(Grant Number 2208085MG181)the Science Research Project of Higher Education Institutions in Anhui Province,Philosophy and Social Sciences(Grant Number 2023AH051063)the Open Fund of Key Laboratory of Anhui Higher Education Institutes(Grant Number CS2021-ZD01).
文摘The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.
基金The authors gratefully acknowledge the support of the Enhancement Strategy of Multi-Type Energy Integration of Active Distribution Network(YNKJXM20220113).
文摘In the framework of vigorous promotion of low-carbon power system growth as well as economic globalization,multi-resource penetration in active distribution networks has been advancing fiercely.In particular,distributed generation(DG)based on renewable energy is critical for active distribution network operation enhancement.To comprehensively analyze the accessing impact of DG in distribution networks from various parts,this paper establishes an optimal DG location and sizing planning model based on active power losses,voltage profile,pollution emissions,and the economics of DG costs as well as meteorological conditions.Subsequently,multiobjective particle swarm optimization(MOPSO)is applied to obtain the optimal Pareto front.Besides,for the sake of avoiding the influence of the subjective setting of the weight coefficient,the decisionmethod based on amodified ideal point is applied to execute a Pareto front decision.Finally,simulation tests based on IEEE33 and IEEE69 nodes are designed.The experimental results show thatMOPSO can achieve wider and more uniformPareto front distribution.In the IEEE33 node test system,power loss,and voltage deviation decreased by 52.23%,and 38.89%,respectively,while taking the economy into account.In the IEEE69 test system,the three indexes decreased by 19.67%,and 58.96%,respectively.
文摘Estimation of distribution algorithms are a class of evolutionary optimization algorithms based on probability distribution model. In this article, a Pareto-based multi-objective estimation of distribution algorithm with multivariate T-copulas is proposed. The algorithm employs Pareto-based approach and multivariate T-copulas to construct probability distribution model. To estimate joint distribution of the selected solutions, the correlation matrix of T-copula is firstly estimated by estimating Kendall’s tau and using the relationship of Kendall’s tau and correlation matrix. After the correlation matrix is estimated, the degree of freedom of T-copula is estimated by using the maximum likelihood method. Afterwards, the Monte Carte simulation is used to generate new individuals. An archive with maximum capacity is used to maintain the non-dominated solutions. The Pareto optimal solutions are selected from the archive on the basis of the diversity of the solutions, and the crowding-distance measure is used for the diversity measurement. The archive gets updated with the inclusion of the non-dominated solutions from the combined population and current archive, and the archive which exceeds the maximum capacity is cut using the diversity consideration. The proposed algorithm is applied to some well-known benchmark. The relative experimental results show that the algorithm has better performance and is effective.
基金partially supported by the National Key Research and Development Program of the Ministry of Science and Technology of China(2022YFE0114200)the National Natural Science Foundation of China(U20A6004).
文摘This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved proximal policy optimization(IPPO)method to make real-time decisions for the DHHBFSP.A multi-objective Markov decision process is modeled for the DHHBFSP,where the reward function is represented by a vector with dynamic weights instead of the common objectiverelated scalar value.A factory agent(FA)is formulated for each factory to select unscheduled jobs and is trained by the proposed IPPO to improve the decision quality.Multiple FAs work asynchronously to allocate jobs that arrive randomly at the shop.A two-stage training strategy is introduced in the IPPO,which learns from both single-and dual-policy data for better data utilization.The proposed IPPO is tested on randomly generated instances and compared with variants of the basic proximal policy optimization(PPO),dispatch rules,multi-objective metaheuristics,and multi-agent reinforcement learning methods.Extensive experimental results suggest that the proposed strategies offer significant improvements to the basic PPO,and the proposed IPPO outperforms the state-of-the-art scheduling methods in both convergence and solution quality.
基金Project(50775089)supported by the National Natural Science Foundation of ChinaProject(2007AA04Z190,2009AA043301)supported by the National High Technology Research and Development Program of ChinaProject(2005CB724100)supported by the National Basic Research Program of China
文摘The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency. A multi-objective model was presented for the material distribution routing problem in mixed manufacturing systems, and it was solved by a hybrid multi-objective evolutionary algorithm (HMOEA). The characteristics of the HMOEA are as follows: 1) A route pool is employed to preserve the best routes for the population initiation; 2) A specialized best?worst route crossover (BWRC) mode is designed to perform the crossover operators for selecting the best route from Chromosomes 1 to exchange with the worst one in Chromosomes 2, so that the better genes are inherited to the offspring; 3) A route swap mode is used to perform the mutation for improving the convergence speed and preserving the better gene; 4) Local heuristics search methods are applied in this algorithm. Computational study of a practical case shows that the proposed algorithm can decrease the total travel distance by 51.66%, enhance the average vehicle load rate by 37.85%, cut down 15 routes and reduce a deliver vehicle. The convergence speed of HMOEA is faster than that of famous NSGA-II.
文摘This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.
文摘In this paper, a distributed algorithm is proposed to solve a kind of multi-objective optimization problem based on the alternating direction method of multipliers. Compared with the centralized algorithms, this algorithm does not need a central node. Therefore, it has the characteristics of low communication burden and high privacy. In addition, numerical experiments are provided to validate the effectiveness of the proposed algorithm.
基金Supported by the NNSF of China（70471057）Supported by the Natural Science Foundation of the Education Department of Shannxi Province（03JK065）
文摘The empirical Bayes test problem is considered for scale parameter of twoparameter exponential distribution under type-II censored data.By using wavelets estimation method,the EB test function is constructed,of which the asymptotic optimality and convergence rates are obtained.Finally,an example concerning the main result is given.
文摘Due to development of distribution systems and increase in electricity demand,the use of capacitor banks increases.From the other point of view,nonlinear loads generate and inject considerable harmonic currents into power system.Under this condition if capacitor banks are not properly selected and placed in the power system,they could amplify and propagate these harmonics and deteriorate power quality to unacceptable levels.With attention of disadvantages of passive filters,such as occurring resonance,nowadays the usage of this type of harmonic compensator is restricted.On the other side,one of parallel multi-function compensating devices which are recently used in distribution system to mitigate voltage sag and harmonic distortion,performs power factor correction,and improves the overall power quality as active power conditioner(APC).Therefore,the utilization of APC in harmonic distorted system can affect and change the optimal location and size of shunt capacitor bank under harmonic distortion condition.This paper presents an optimization algorithm for improvement of power quality using simultaneous optimal placement and sizing of APC and shunt capacitor banks in radial distribution networks in the presence of voltage and current harmonics.The algorithm is based on particle swarm optimization(PSO).The objective function includes the cost of power losses,energy losses and those of the capacitor banks and APCs.
文摘Water Distribution Systems (WDSs) design and operation are usually done on a case-by-case basis. Numerous models have been proposed in the literature to solve specific problems in this field. The implementation of these models to any real-world WDS optimization problem is left to the discretion of designers who lack the necessary tools that will guide them in the decision-making process for a given WDS design project. Practitioners are not always very familiar with optimization applied to water network design. This results in a quasi-exclusive use of engineering judgment when dealing with this issue. In order to support a decision process in this field, the present article suggests a step-by-step approach to solve the multi-objective design problem by using both engineering and optimization. A genetic algorithm is proposed as the optimization tool and the targeted objectives are: 1) to minimize the total cost (capital and operation), 2) to minimize the residence time of the water within the system and 3) to maximize a network reliability metric. The results of the case study show that preliminary analysis can significantly reduce decision variables and computational burden. Therefore, the approach will help network design practitioners to reduce optimization problems to a more manageable size.
文摘Recently,with the increasing demand of the electric vehicle(EV)in transportation,the power grid faces critical challenges in meeting the extra power demand.Companies are focusing on expanding EV charging infrastructure to meet customer requirements.Ensuring power supply security,reliability,and economics for EV charging stations remains a challenge,despite efforts to align photovoltaic(PV)and battery energy storage system(BESS)based designs with distribution system requirements.A criteria weight ranking mechanism has been designed to accept charging requests for EVs depending on the criteria weights specified by the EV owner.This paper uses a multi-objective remora optimization algorithm(MOROA)to determine the optimal location of two electric vehicle charging stations(EVCS)in the distribution system,and capacity of PV&BESS units in two EVCS for optimizing three conflicting objective functions,such as(1)minimizing total power loss;(2)minimizing annual substation power cost,and annual capital,operation&maintenance cost of the PV and BESS,and(3)minimizing emission from upstream grid.Moreover,the EVs are also scheduled optimally at each charging station.The effectiveness of these methodologies has been demonstrated through four case studies using IEEE 33 bus radial distribution system(RDS).Furthermore,the smart EV charge scheduling reduces the overall load burden on the grid network and the benefit of EVCS operators and EV owners.
基金Project supported by the Science&Technology Innovation Team of Outstanding Youth of Hubei Provincial Universities(No.T201319)the Scientific Research Foundation for Talents of China Three Gorges University(No.0620130076)
文摘This paper deals with the optimal placement of distributed generation(DG) units in distribution systems via an enhanced multi-objective particle swarm optimization(EMOPSO) algorithm. To pursue a better simulation of the reality and provide the designer with diverse alternative options, a multi-objective optimization model with technical and operational constraints is constructed to minimize the total power loss and the voltage fluctuation of the power system simultaneously. To enhance the convergence of MOPSO, special techniques including a dynamic inertia weight and acceleration coefficients have been integrated as well as a mutation operator. Besides, to promote the diversity of Pareto-optimal solutions, an improved non-dominated crowding distance sorting technique has been introduced and applied to the selection of particles for the next iteration. After verifying its effectiveness and competitiveness with a set of well-known benchmark functions, the EMOPSO algorithm is employed to achieve the optimal placement of DG units in the IEEE 33-bus system. Simulation results indicate that the EMOPSO algorithm enables the identification of a set of Pareto-optimal solutions with good tradeoff between power loss and voltage stability. Compared with other representative methods, the present results reveal the advantages of optimizing capacities and locations of DG units simultaneously, and exemplify the validity of the EMOPSO algorithm applied for optimally placing DG units.
文摘Various kinds of new engineering technologies have been studied to realize the low-carbon and sustainable power supply systems all over the world.In actual implementation of these technologies,mostly,there are multiple objectives with trade off relationships among each other,and also various constraints in the achievement of these objectives.Therefore,it should be essential to solve multiobjective optimization problems effectively in the applications of these new technologies in power systems.This paper proposes an improved method to realize multiobjective optimization for critical challenges in advanced power systems.To realize that,in an optimal dispersed generation installation problem,that is,one of effective measures for low-carbon power systems,various optimization methods and their combination methods are evaluated and a hybrid method for evolutionary algorithms was developed.The method can provide improved results compared with other state-of-the-art multi-objective optimization methods.
基金supported by Natural Science Foundation of Hunan Province(2017JJ5044).
文摘Based on the large-scale penetration of electric vehicles(EV)into the building cluster,a multi-objective optimal strategy considering the coordinated dispatch of EV is proposed,for improving the safe and economical operation problems of distribution network.The system power loss and node voltage excursion can be effectively reduced,by taking measures of time-of-use(TOU)price mechanism bonded with the reactive compensation of energy storage devices.Firstly,the coordinate charging/discharging load model for EV has been established,to obtain a narrowed gap between load peak and valley.Next,a multi-objective optimization model of the distribution grid is also defined,and the active power loss and node voltage fluctuation are chosen to be the objective function.For improving the efficiency of optimization process,an advanced genetic algorithm associated with elite preservation policy is used.Finally,reactive compensation capacity supplied by capacitor banks is dynamically determined according to the varying building loads.The proposed strategy is demonstrated on the IEEE 33-node test case,and the simulation results show that the power supply pressure can be obviously relieved by introducing the coordinated charging/discharging behavior of EV;in the meantime,via reasonable planning of the compensation capacitor,the remarkably lower active power loss and voltage excursion can be realized,ensuring the safe and economical operation of the distribution system.
文摘Distributed generation(DG)allocation in the distribution network is generally a multi-objective optimization problem.The maximum benefits of DG injection in the distribution system highly depend on the selection of an appropriate number of DGs and their capacity along with the best location.In this paper,the improved decomposition based evolutionary algorithm(I-DBEA)is used for the selection of optimal number,capacity and site of DG in order to minimize real power losses and voltage deviation,and to maximize the voltage stability index.The proposed I-DBEA technique has the ability to incorporate non-linear,nonconvex and mixed-integer variable problems and it is independent of local extrema trappings.In order to validate the effectiveness of the proposed technique,IEEE 33-bus,69-bus,and 119-bus standard radial distribution networks are considered.Furthermore,the choice of optimal number of DGs in the distribution system is also investigated.The simulation results of the proposed method are compared with the existing methods.The comparison shows that the proposed method has the ability to get the multi-objective optimization of different conflicting objective functions with global optimal values along with the smallest size of DG.
基金supported by the National Natural Science Foundation of China under Grant Nos.10571070 and 11001097
文摘Ranked set sample is applicable whenever ranking of a set of sample units can be done easily by a judgement method of the study variable or of the auxiliary variable. This paper considers ranked set sample based on the auxiliary variable X which is correlated with the study variable Y, where （X, Y） follows Morgenstern type bivariate exponential distribution. The authors discuss the optional allocation for unbiased estimators of the correlation coefficient p of the random variables X and Y when the auxiliary variable X is used for ranking the sample units and the study variable Y is measured for estimating the correlation coefficient. This paper first gives a class of unbiased estimators of p when the mean 0 of the study variable Y is known and obtains an essentially complete subclass of this class. Further, the optimal allocation of the unbiased estimators is found in this subclass and is proved to be Bayes, admissible, and minimax. Finally, the unbiased estimator of p under the optimal allocation in the case of known θ is reformed for estimating p in the case of unknown θ, and the reformed estimator is shown to be strongly consistent.
基金supported by Fundamental Research Funds for the Central Universities(No.2022CDJXY-007)。
文摘Bipolar direct current(DC)distribution networks can effectively improve the connection flexibility for renewable generations and loads.In practice,concerns regarding the potential voltage unbalance issue of the distribution networks and the frequency of switching still remain.This paper proposes a day-ahead polarity switching strategy to reduce voltage unbalance by optimally switching the polarity of renewable generations and loads while minimizing the switching times simultaneously in the range of a full day.First,a multi-objective optimization model is constructed to minimize the weighted sum of voltage unbalance factors and the sum of number of switching actions in the day based on the power flow model.Second,a two-step solution strategy is proposed to solve the optimization model.Finally,the proposed strategy is validated using 11-node and 34-node distribution networks as case studies,and a switching and stabilizing device is designed to enable unified switching of renewable generations and loads.Numerical results demonstrate that the proposed strategy can effectively reduce the switching times without affecting the improvement of voltage balance.
文摘Distributed energy systems are considered as a promising technology for sustainable development and have become a popular research topic in the areas of building energy systems. This work presents a case study of optimizing an integrated distributed energy system consisting of combined heat and power(CHP), photovoltaics(PV), and electric and/or thermal energy storage for a hospital and large hotel buildings located in Texas and California. First, simulation models for all subsystems, which are developed individually, are integrated together according to a control strategy designed to satisfy both the electric and thermal energy requirements of a building. Subsequently, a multi-objective particle swarm optimization(MOPSO) is employed to obtain an optimal design of each subsystem. The objectives of the optimization are to minimize the simple payback period(PBP) and maximize the reduction of carbon dioxide emissions(RCDE). Finally, the energy performance for the selected building types and locations are analyzed after the optimization. Results indicate that the proposed optimization method could be applied to determine an optimal design of distributed energy systems, which reaches a trade-off between the economic and environmental performance for different buildings. With the presented distributed energy system, a peak shaving in electricity of about 300 kW and a reduction in boiler fuel consumption of 610 kW could be attained for the hospital building located in California for a winter day. For the summer and transition seasons, electricity peak shaving of 800 kW and 600 kW could be achieved, respectively.
