scholarly journals Optimal allocation of microgrid using a differential multi-agent multi-objective evolution algorithm

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Liheng Liu ◽  
Miaomiao Niu ◽  
Dongliang Zhang ◽  
Li Liu ◽  
Dietmar Frank
Keyword(s):  
Genetic Algorithm ◽  
Optimal Allocation ◽  
Environmental Benefits ◽  
Nsga Ii ◽  
Multi Objective ◽  
Calculation Results ◽  
Key Issues ◽  
Multi Agent ◽  
Evolution Algorithm ◽  
Simulation Results

Abstract The optimal configuration and allocation of a microgrid are one of the key issues to guarantee the economic and reliable working of a microgrid. This is a multi-objective optimisation problem within which the economic index and the load power shortage rate index should be considered when optimising the configuration. In this article, a differential multi-agent multi-objective evolutionary algorithm (DMAMOEA) was designed to optimise the capacity configuration of a microgrid system, which includes three kinds of equipment: wind turbine, photovoltaic equipment and battery. The final optimisation results were compared with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm. Simulation results showed the effectiveness of the algorithm. At the end of this article, the representative solutions in the calculation results are compared and explained and the environmental benefits are analysed, which show the effectiveness of the implementation of the microgrid system.

scholarly journals Optimal integration of wind energy with a shunt-FACTS controller for reductions in electrical power loss

2021 ◽  
Vol 23 (1) ◽  
pp. 41
Author(s):  
I Made Wartana ◽  
Ni Putu Agustini ◽  
Sasidharan Sreedharan
Keyword(s):  
Genetic Algorithm ◽  
Wind Energy ◽  
Power Loss ◽  
Optimal Allocation ◽  
Optimization Problems ◽  
Test System ◽  
Optimal Size ◽  
Voltage Profile ◽  
Nsga Ii ◽  
Multi Objective

The integration of distributed generators (DGs) with flexible alternating current transmission systems (FACTS) can improve the performance of the grid system. In this study, we determine the location and optimal size of one type of DG, based on wind energy, with a shunt-FACTS control device called a static var compensator (SVC). The voltage profile is increase and the power loss reduced due to an improvement in performance from the maximizing load bus system scenario. Newton-Raphson power flow with a wind turbine generator (WTG) and SVC are formulated as a multi-objective problem called MLB system and minimizing system power loss (Ploss) by satisfying various system constraints, namely the loading limits, generation limits, voltage limits, and the small-signal stability. A variant of the genetic algorithm, called the non-dominated sorting genetic algorithm II (NSGA-II), is used to solve these conflicting multi-objective optimization problems. Modifications to the IEEE 14-bus standard and practical test system integrated to the WTG and SVC in the PSAT software are used as a test system. The simulation results indicate that the optimal allocation of the WTG and SVC, determined using the proposed technique, results in improved system performance, since all the specified constraints are met.

scholarly journals Analisis Perbandingan Aggregat Of Function (AOF) dengan Non-Dominated Sorting Genetic Algorithm (NSGA-II) dalam Menentukan Optimasi Multi-Objective pada Penjadwalan Mesin Produksi Flow Shop

2018 ◽  
Vol 17 (2) ◽  
pp. 158
Author(s):  
Fifin Sonata ◽  
Dede Prabowo Wiguna
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Total Tardiness ◽  
Pareto Optimal ◽  
Nsga Ii ◽  
Trade Off ◽  
Multi Objective

Penjadwalan mesin produksi dalam dunia industri memiliki peranan penting sebagai bentuk pengambilan keputusan. Salah satu jenis sistem penjadwalan mesin produksi adalah sistem penjadwalan mesin produksi tipe flow shop. Dalam penjadwalan flow shop, terdapat sejumlah pekerjaan (job) yang tiap-tiap job memiliki urutan pekerjaan mesin yang sama. Optimasi penjadwalan mesin produksi flow shop berkaitan dengan penyusunan penjadwalan mesin yang mempertimbangkan 2 objek yaitu makespan dan total tardiness. Optimasi kedua permasalahan tersebut merupakan optimasi yang bertolak belakang sehingga diperlukan model yang mengintegrasikan permasalahan tersebut dengan optimasi multi-objective A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimazitaion : NSGA-II. Dalam penelitian ini akan dibandingkan 2 buah metode yaitu Aggregat Of Function (AOF) dengan NSGA-II agar dapat terlihat nilai solusinya. Penyelesaian penjadwalan mesin produksi flow shop dengan algoritma NSGA-II untuk membangun jadwal dengan meminimalkan makespan dan total tardiness.Tujuan yang ingin dicapai adalah mengetahui bahwa model yang dikembangkan akan memberikan solusi penjadwalan mesin produksi flow shop yang efisien berupa solusi pareto optimal yang dapat memberikan sekumpulan solusi alternatif bagi pengambil keputusan dalam membuat penjadwalan mesin produksi yang diharapkan. Solusi pareto optimal yang dihasilkan merupakan solusi optimasi multi-objective yang optimal dengan trade-off terhadap seluruh objek, sehingga seluruh solusi pareto optimal sama baiknya.

scholarly journals Economic Environmental Dispatch of Wind Integrated Thermal Power System

2020 ◽  
Vol 8 (6) ◽  
pp. 5186-5192
Keyword(s):  
Genetic Algorithm ◽  
Power Plant ◽  
Thermal Power ◽  
Nsga Ii ◽  
Strength Pareto Evolutionary Algorithm ◽  
Experimental Constraint ◽  
Thermal Power System ◽  
Power Plant Operation ◽  
Simulation Results ◽  
The Given

In electric power plant operation, Economic Environmental Dispatch (EED) of a thermal-wind is a significant chore to involve allocation of production amongst the running units so the price, NOx extraction status and SO2 extraction status are enhanced concurrently whilst gratifying each and every experimental constraint. This is an exceedingly controlled multiobjective optimizing issue concerning contradictory objectives having Primary and Secondary constraints. For the given work, a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is recommended for taking care of EED issue. In simulation results that are obtained by applying the two test systems on the proposed scheme have been evaluated against Strength Pareto Evolutionary Algorithm 2 (SPEA 2).

scholarly journals Multi-agent collaborative search: an agent-based memetic multi-objective optimization algorithm applied to space trajectory design

2011 ◽  
Vol 225 (11) ◽  
pp. 1211-1227 ◽  
Author(s):  
M Vasile ◽  
F Zuiani
Keyword(s):  
Selection Criterion ◽  
Search Space ◽  
Trajectory Design ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Agent Based ◽  
Multi Objective ◽  
Collaborative Search ◽  
Multi Agent ◽  
Space Trajectory Design

This article presents an algorithm for multi-objective optimization that blends together a number of heuristics. A population of agents combines heuristics that aim at exploring the search space both globally and in a neighbourhood of each agent. These heuristics are complemented with a combination of a local and global archive. The novel agent-based algorithm is tested at first on a set of standard problems and then on three specific problems in space trajectory design. Its performance is compared against a number of state-of-the-art multi-objective optimization algorithms that use the Pareto dominance as selection criterion: non-dominated sorting genetic algorithm (NSGA-II), Pareto archived evolution strategy (PAES), multiple objective particle swarm optimization (MOPSO), and multiple trajectory search (MTS). The results demonstrate that the agent-based search can identify parts of the Pareto set that the other algorithms were not able to capture. Furthermore, convergence is statistically better although the variance of the results is in some cases higher.

Kinematic Multi-Objective Optimization of Circular-Toothed Gerotor Pumps by Genetic Algorithm

2017 ◽  
Author(s):  
Andrew J. Robison ◽  
Andrea Vacca
Keyword(s):  
Genetic Algorithm ◽  
Design Method ◽  
Third Party ◽  
Low Flow ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Generation Algorithm ◽  
Multi Objective ◽  
Pareto Curve

A gerotor gear generation algorithm has been developed that evaluates key performance objective functions to be minimized or maximized, and then an optimization algorithm is applied to determine the best design. Because of their popularity, circular-toothed gerotors are the focus of this study, and future work can extend this procedure to other gear forms. Parametric equations defining the circular-toothed gear set have been derived and implemented. Two objective functions were used in this kinematic optimization: maximize the ratio of displacement to pump radius, which is a measure of compactness, and minimize the kinematic flow ripple, which can have a negative effect on system dynamics and could be a major source of noise. Designs were constrained to ensure drivability, so the need for additional synchronization gearing is eliminated. The NSGA-II genetic algorithm was then applied to the gear generation algorithm in modeFRONTIER, a commercial software that integrates multi-objective optimization with third-party engineering software. A clear Pareto front was identified, and a multi-criteria decision-making genetic algorithm was used to select three optimal designs with varying priorities of compactness vs low flow variation. In addition, three pumps used in industry were scaled and evaluated with the gear generation algorithm for comparison. The scaled industry pumps were all close to the Pareto curve, but the optimized designs offer a slight kinematic advantage, which demonstrates the usefulness of the proposed gerotor design method.

Solving Facility Location Problems with a Tol for Rapid Development of Multi-Objective Evolutionary Algorithms (MOEAs)

2007 ◽  
pp. 642-660
Author(s):  
A. L. Medaglia
Keyword(s):  
Genetic Algorithm ◽  
Facility Location ◽  
Rapid Development ◽  
Service Level ◽  
International Markets ◽  
Nsga Ii ◽  
Multi Objective ◽  
Location Models ◽  
Cost Cutting ◽  
Facility Location Models

The low price of coffee in the international markets has forced the Federación Nacional de Cafeteros de Colombia (FNCC) to look for cost-cutting opportunities. An alternative that has been considered is the reduction of the operating infrastructure by closing some of the FNCC-owned depots. This new proposal of the coffee supplier network is supported by (uncapacitated and capacitated) facility location models that minimize operating costs while maximizing service level (coverage). These bi-objective optimization models are solved by means of NSGA II, a multi-objective evolutionary algorithm (MOEA). From a computational perspective, this chapter presents the multi-objective Java Genetic Algorithm (MO-JGA) framework, a new tool for the rapid development of MOEAs built on top of the Java Genetic Algorithm (JGA). We illustrate MO-JGA by implementing NSGA II-based solutions for the bi-objective location models.

An improved procedure to implement NSGA-III in coordinate waste management for urban agglomeration

2019 ◽  
Vol 37 (11) ◽  
pp. 1161-1169 ◽  
Author(s):  
Lili Qu ◽  
Zhihao Zhou ◽  
Tianzhu Zhang ◽  
Huanan Zhang ◽  
Lei Shi
Keyword(s):  
Genetic Algorithm ◽  
Economic Benefits ◽  
Urban Agglomeration ◽  
Scrap Tire ◽  
Large Cities ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Calculation Results ◽  
Improved Performance ◽  
Multiple Recycling

With the growth of urbanization in countries globally, large cities have often formed clusters of urban agglomerations in metropolitan areas. The coordinated management of regional solid waste produced by such urban agglomeration poses a typical high-dimensional, multi-objective optimization issue. This paper aims to introduce a procedure to implement the third-generation genetic algorithm (NSGA-III), an established multi-objective genetic algorithm based on non-dominated sorting mechanisms, for the purpose of evaluating environmental and economic benefits simultaneously while seeking the optimal solutions for coordinated management among multiple recycling centres. In this study, two series of scenarios were abstracted from scrap tire recycling, representing linear calculation and nonlinear calculation cases separately. Several improvements were made to the originally published NSGA-III procedure that solve the problem of non-convergence for hypervolumes of the output. Through comparisons of calculation results, an improved procedure is suggested and shown to have improved performance.

scholarly journals Motion Planning Using a Memetic Evolution Algorithm for Swarm Robots

10.5772/45669 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 19 ◽  
Author(s):  
Chien-Chou Lin ◽  
Kun-Cheng Chen ◽  
Wei-Ju Chuang
Keyword(s):  
Genetic Algorithm ◽  
Formation Control ◽  
Memetic Algorithm ◽  
Initial Population ◽  
Local Motion ◽  
Computation Efficiency ◽  
Swarm Robots ◽  
Evolution Algorithm ◽  
Simulation Results ◽  
Path Planner

A hierarchical memetic algorithm (MA) is proposed for the path planning and formation control of swarm robots. The proposed algorithm consists of a global path planner (GPP) and a local motion planner (LMP). The GPP plans a trajectory within the Voronoi diagram (VD) of the free space. An MA with a non-random initial population plans a series of configurations along the path given by the former stage. The MA locally adjusts the robot positions to search for better fitness along the gradient direction of the distance between the swarm robots and the intermediate goals (IGs). Once the optimal configuration is obtained, the best chromosomes are reserved as the initial population for the next generation. Since the proposed MA has a non-random initial population and local searching, it is more efficient and the planned path is faster compared to a traditional genetic algorithm (GA). The simulation results show that the proposed algorithm works well in terms of path smoothness and computation efficiency.

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