scholarly journals Multi-Objective Day-Ahead Optimal Scheduling of Isolated Microgrid Considering Flexibility

2018 ◽  
Vol 53 ◽  
pp. 01024
Author(s):  
Liang Zhang ◽  
Bo Pang ◽  
Ruipeng Yi ◽  
Pengyu Gai ◽  
Chunqing Xin ◽  
...  
Keyword(s):  
Renewable Energy Sources ◽  
Operating Cost ◽  
Optimal Solution ◽  
Economic Losses ◽  
Optimal Operating ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Schedule Flexibility ◽  
Optimal Solution Set ◽  
Optimizing Model

In isolated microgrid, renewable energy sources including photovoltaic and wind power, have the nature of intermittence and variability. Making a reasonable day-ahead generation schedule could improve system ability to cope with uncertainty. Therefore, based on day-ahead generation schedule, flexibility insufficiency rate is proposed considering economy and flexibility. Aiming at the lowest flexibility insufficiency rate and optimal operating cost, a day-ahead generation schedule optimizing model of isolated microgrid is established. Under multi-objective particle swarm optimization, Pareto optimal solution set of the day-ahead generation schedule is found. Simulation results of an isolated microgrid show that, day-ahead generation schedule made with the proposed method can improve ability of power system to cope with uncertainty and reduce economic losses.

scholarly journals Multi-objective optimization of multi-microgrid power dispatch under uncertainties using interval optimization

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Shungen Luo ◽  
Xiuping Guo
Keyword(s):  
Optimization Problem ◽  
Operating Cost ◽  
Optimal Solution ◽  
Renewable Energies ◽  
Energy Utilization ◽  
Pollutant Emission ◽  
Interval Optimization ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimal Solution Set

<p style='text-indent:20px;'>The microgrid technology, which can dispatch power independently, is an effective way to increase the efficiency of energy utilization meanwhile develop and utilize the clean and renewable energy. However, the power generation of a single microgrid is unstable, because it is greatly affected by the external environment. Therefore, the development and application of the multi-microgrid system have gradually drawn various countries' attention. In order to minimize the operating cost and gaseous pollutant emission of the multi-microgrid system, which is composed of renewable energies and electric vehicles and so on, this paper builds a 24 hours day-ahead multi-objective complex constrained optimization model, using interval optimization to handle uncertainties of renewable energies. In view of the model characteristics, the metaheuristic strategies about initialization and repair of solution are designed. Furthermore, the fuzzy membership degree and Chebyshev function are used in parallel to decompose the multi-objective optimization problem, thus a multi-objective evolutionary algorithm based on hybrid decomposition (MOEA/HD) is constructed. Finally, the effectiveness of the metaheuristic strategies can be verified by analyzing the simulation results in this paper. Moreover, the results prove that the MOEA/HD is more efficient, which can get a higher-quality Pareto optimal solution set when compared to other algorithms.</p>

scholarly journals Adaptive Multi-Objective Particle Swarm Optimization Using Three-Stage Strategy

2021 ◽  
Author(s):  
Weimin Huang ◽  
Wei Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Strategy ◽  
Adaptive Optimization ◽  
Swarm Optimization ◽  
Nondominated Solutions ◽  
Multi Objective ◽  
Optimal Solution Set ◽  
Optimization Efficiency

Abstract It is one of the crucial problems in solving multi-objective problems (MOPs) that balance the convergence and diversity of the algorithm to obtain an outstanding Pareto optimal solution set. In order to elevate the performance further and improve the optimization efficiency of multi-objective particle swarm optimization (MOPSO), a novel adaptive MOPSO using a three-stage strategy (tssAMOPSO) is proposed in this paper, which can effectively balance the exploration and exploitation of the population and facilitate the convergence and diversity of MOPSO. Firstly, an adaptive flight parameter adjustment, formulated by the convergence contribution of nondominated solutions, can ameliorate the convergence and diversity of the algorithm enormously. Secondly, the population carries out the three-stage strategy of optimization in each iteration, namely adaptive optimization, decomposition, and Gaussian attenuation mutation. The three-stage strategy remarkably promotes the diversity and efficiency of the optimization process. Moreover, the convergence of three-stage optimization strategy is analyzed. Then, memory interval is equipped with particles to record the recent positions, which vastly improves the reliability of personal best selection. In the maintenance of external archive, the proposed fusion index can enhance the quality of nondominated solutions directly. Finally, comparative experiments are designed by a series of benchmark instances to verify the performance of tssAMOPSO. Experimental results show that the proposed algorithm achieves admirable performance compared with other contrast algorithms.

OPTIMAL PREDICTION AND DESIGN OF SURFACE ROUGHNESS FOR CNC TURNING OF AL7075-T6 BY USING THE TAGUCHI HYBRID QPSO ALGORITHM

2016 ◽  
Vol 40 (5) ◽  
pp. 883-895 ◽  
Author(s):  
Wen-Jong Chen ◽  
Chuan-Kuei Huang ◽  
Qi-Zheng Yang ◽  
Yin-Liang Yang
Keyword(s):  
Surface Roughness ◽  
Particle Swarm Optimization ◽  
Predictive Models ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Cutting Parameters ◽  
Optimization Methods ◽  
Swarm Optimization ◽  
Cnc Turning ◽  
Multi Objective

This paper combines the Taguchi-based response surface methodology (RSM) with a multi-objective hybrid quantum-behaved particle swarm optimization (MOHQPSO) to predict the optimal surface roughness of Al7075-T6 workpiece through a CNC turning machining. First, the Taguchi orthogonal array L27 (36) was applied to determine the crucial cutting parameters: feed rate, tool relief angle, and cutting depth. Subsequently, the RSM was used to construct the predictive models of surface roughness (Ra, Rmax, and Rz). Finally, the MOHQPSO with mutation was used to determine the optimal roughness and cutting conditions. The results show that, compared with the non-optimization, Taguchi and classical multi-objective particle swarm optimization methods (MOPSO), the roughness Ra using MOHQPSO along the Pareto optimal solution are improved by 68.24, 59.31 and 33.80%, respectively. This reveals that the predictive models established can improve the machining quality in CNC turning of Al7075-T6.

scholarly journals A Multi-Objective Particle Swarm Optimization for Trajectory Planning of Fruit Picking Manipulator

Agronomy ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2286
Author(s):  
Xiaoman Cao ◽  
Hansheng Yan ◽  
Zhengyan Huang ◽  
Si Ai ◽  
Yongjun Xu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Evaluation Method ◽  
Technical Problem ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Advantages And Disadvantages ◽  
Optimal Evaluation

Stable, efficient and lossless fruit picking has always been a difficult problem, perplexing the development of fruit automatic picking technology. In order to effectively solve this technical problem, this paper establishes a multi-objective trajectory model of the manipulator and proposes an improved multi-objective particle swarm optimization algorithm (represented as GMOPSO). The algorithm combines the methods of mutation operator, annealing factor and feedback mechanism to improve the diversity of the population on the basis of meeting the stable motion, avoiding the local optimal solution and accelerating the convergence speed. By adopting the average optimal evaluation method, the robot arm motion trajectory has been testified to constructively fulfill the picking standards of stability, efficiency and lossless. The performance of the algorithm is verified by ZDT1~ZDT3 benchmark functions, and its competitive advantages and disadvantages with other multi-objective evolutionary algorithms are further elaborated. In this paper, the algorithm is simulated and verified by practical experiments with the optimization objectives of time, energy consumption and pulsation. The simulation results show that the solution set of the algorithm is close to the real Pareto frontier. The optimal solution obtained by the average optimal evaluation method is as follows: the time is 34.20 s, the energy consumption is 61.89 °/S2 and the pulsation is 72.18 °/S3. The actual test results show that the trajectory can effectively complete fruit picking, the average picking time is 25.5 s, and the success rate is 96.67%. The experimental results show that the trajectory of the manipulator obtained by GMOPSO algorithm can make the manipulator run smoothly and facilitates efficient, stable and nondestructive picking.

Multi-objective optimization of an industrial slurry phase ethylene polymerization reactor

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Amit K. Thakur ◽  
Santosh K. Gupta ◽  
Rahul Kumar ◽  
Nilanjana Banerjee ◽  
Pranava Chaudhari
Keyword(s):  
Ethylene Polymerization ◽  
Operating Cost ◽  
Operating Conditions ◽  
Multi Objective Optimization ◽  
Reactor Model ◽  
Optimal Operating ◽  
Nsga Ii ◽  
Multi Objective ◽  
Maximum Productivity ◽  
Slurry Phase

Abstract Slurry polymerization processes using Zeigler–Natta catalysts are most widely used for the production of polyethylene due to their several advantages over other processes. Optimal operating conditions are required to obtain the maximum productivity of the polymer at minimal cost while ensuring operational safety in the slurry phase ethylene polymerization reactors. The main focus of this multi-objective optimization study is to obtain the optimal operating conditions corresponding to the maximization of productivity and yield at a minimal operating cost. The tuned reactor model has been optimized. The single objective optimization (SOO) and multi-objective optimization (MOO) problems are solved using non-dominating sorting genetic algorithm-II (NSGA-II). A complete range of Pareto optimal solutions are obtained to obtain the maximum productivity and polymer yield at different input costs.

A multi-objective evolutionary algorithm based on niche selection in solving irregular Pareto fronts

2021 ◽  
pp. 1-21
Author(s):  
Xin Li ◽  
Xiaoli Li ◽  
Kang Wang
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Environmental Selection ◽  
Optimal Solution Set ◽  
Pareto Fronts

The key characteristic of multi-objective evolutionary algorithm is that it can find a good approximate multi-objective optimal solution set when solving multi-objective optimization problems(MOPs). However, most multi-objective evolutionary algorithms perform well on regular multi-objective optimization problems, but their performance on irregular fronts deteriorates. In order to remedy this issue, this paper studies the existing algorithms and proposes a multi-objective evolutionary based on niche selection to deal with irregular Pareto fronts. In this paper, the crowding degree is calculated by the niche method in the process of selecting parents when the non-dominated solutions converge to the first front, which improves the the quality of offspring solutions and which is beneficial to local search. In addition, niche selection is adopted into the process of environmental selection through considering the number and the location of the individuals in its niche radius, which improve the diversity of population. Finally, experimental results on 23 benchmark problems including MaF and IMOP show that the proposed algorithm exhibits better performance than the compared MOEAs.

Flexible Synthesis of Heat Exchanger Network with Particle Swarm Optimization Algorithm

2011 ◽  
Vol 214 ◽  
pp. 569-572 ◽  
Author(s):  
Xio Ling Zhang ◽  
Hong Chao Yin ◽  
Zhao Yi Huo
Keyword(s):  
Particle Swarm Optimization ◽  
Heat Exchanger ◽  
Operating Cost ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Nonlinear Program ◽  
Heat Exchanger Network ◽  
Swarm Optimization

In this paper, the flexible synthesis problem for heat exchanger network(HEN) is formulated to a mixed integer nonlinear program(MINLP) model. The objection function of the model consists of two components: First, a candidate HEN structure has to satisfy flexible criterion during input span. Second, a minimized annual cost consisting of investment cost and operating cost is investigated. The solution strategy based on particle swarm optimization(PSO) algorithm is proposed to obtain the optimal solution of the presented model. Finally, a four streams example is investigated to show the advantage of the whole proposed optimization approach.

An Improved Adaptive Evolutionary Algorithm for Multi-Objective Optimization

2013 ◽  
Vol 303-306 ◽  
pp. 1494-1500
Author(s):  
Jian Wei Wang ◽  
Jian Ming Zhang
Keyword(s):  
Evolutionary Algorithm ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Relative Fitness ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Fitness Value ◽  
Crossover Probability ◽  
Optimal Solution Set ◽  
Adaptive Evolutionary Algorithms

Aiming at effectively overcoming the disadvantages of traditional evolutionary algorithm which converge slowly and easily run into local extremism, an improved adaptive evolutionary algorithms is proposed. Firstly, in order to choose the optimal objective fitness value from the population in every generation, the absolute and relative fitness are defined. Secondly, fuzzy technique is adopted to adjust the weights of objective functions, crossover probability, mutation probability, crossover positions and mutation positions during the iterative process. Finally, three classical test functions are given to illustrate the validity of improved adaptive evolutionary algorithm, simulation results show that the diversity and practicability of the optimal solution set are better by using the proposed method than other multi-objective optimization methods.

Multi-Objective Operation Optimization of a Micro-Grid Using Modified Honey Bee Mating Optimization Algorithm

2014 ◽  
Vol 494-495 ◽  
pp. 1593-1597 ◽  
Author(s):  
Li Zhen Wu ◽  
Xiao Hong Hao
Keyword(s):  
Honey Bee ◽  
Renewable Energy Sources ◽  
Operating Cost ◽  
Pollutant Emissions ◽  
Superior Performance ◽  
Emissions Reduction ◽  
Nonlinear Constraint ◽  
Multi Objective ◽  
Micro Grid ◽  
Honey Bee Mating Optimization

Recently, it becomes the head of concern for the Micro-grid to derive an optimal operational planning with regard to energy costs minimization, pollutant emissions reduction and better utilization of renewable energy sources (RESs), which accompanied by a Wind Turbine/Fuel Cell/Photovoltaic and Battery hybrid power source to level the power mismatch or to store the surplus of energy when its needed. In this paper, a new method based on multi-objective Modified Honey Bee Mating Optimization (MHBMO) algorithm is proposed and implemented to dispatch the generations in a typical micro-grid considering economy and emission as competitive objectives. The problem is formulated as a nonlinear constraint multi-objective optimization problem to minimize the total operating cost and the net emission simultaneously. The proposed algorithm is tested on a typical MG and its superior performance is compared to those from other evolutionary algorithms such as GA (Genetic Algorithm) and the original Honey Bee Mating Optimization (HBMO).

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