Ranking Index in the Pareto Optimal Front Based Evolutionary Programming Technique for Best Selection of Several Optimal Multi-Objective Capacity Benefit Margins

2015 ◽  
Vol 785 ◽  
pp. 506-510
Author(s):  
Muhammad Murtadha Othman ◽  
Nurulazmi Abd Rahman ◽  
Ismail Musirin
Keyword(s):  
Electricity Market ◽  
Evolutionary Programming ◽  
Test System ◽  
Pareto Optimal ◽  
Programming Technique ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Transfer Capability ◽  
Loss Of Load Expectation ◽  
Ranking Index

The amount of power transfer between areas is purporting as imperative information required by the utility so that this will assist them towards an effective operation of electricity market performed in a deregulated power system. CBM is defined as the amount of the transfer capability reserved by the load-serving entities which will be used during the case of generation deficiency. This paper presents a new approach used to perform simultaneous determination of capacity benefit margin (CBM) for all areas by using the evolutionary programming (EP) technique. Ranking index in the Pareto optimal front cluster of total loss-of-load expectation (LOLE) and total LOLE difference will be used for selecting several best solutions of multi-objective CBMs. Eventually, performance of the proposed method is investigated thoroughly via a test system of modified IEEE-RTS79. Utilization of the proposed technique is superior in terms of offering flexibility to the utility in selecting several best solutions of multi-objective CBMs.

scholarly journals A Heuristic Ranking Approach on Capacity Benefit Margin Determination Using Pareto-Based Evolutionary Programming Technique

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Muhammad Murtadha Othman ◽  
Nurulazmi Abd Rahman ◽  
Ismail Musirin ◽  
Mahmud Fotuhi-Firuzabad ◽  
Abbas Rajabi-Ghahnavieh
Keyword(s):  
Evolutionary Programming ◽  
Available Transfer Capability ◽  
Interconnected System ◽  
Programming Technique ◽  
Numerical Studies ◽  
Transfer Capability ◽  
System Operator ◽  
Multiobjective Approach ◽  
Account System ◽  
Loss Of Load Expectation

This paper introduces a novel multiobjective approach for capacity benefit margin (CBM) assessment taking into account tie-line reliability of interconnected systems. CBM is the imperative information utilized as a reference by the load-serving entities (LSE) to estimate a certain margin of transfer capability so that a reliable access to generation through interconnected system could be attained. A new Pareto-based evolutionary programming (EP) technique is used to perform a simultaneous determination of CBM for all areas of the interconnected system. The selection of CBM at the Pareto optimal front is proposed to be performed by referring to a heuristic ranking index that takes into account system loss of load expectation (LOLE) in various conditions. Eventually, the power transfer based available transfer capability (ATC) is determined by considering the firm and nonfirm transfers of CBM. A comprehensive set of numerical studies are conducted on the modified IEEE-RTS79 and the performance of the proposed method is numerically investigated in detail. The main advantage of the proposed technique is in terms of flexibility offered to an independent system operator in selecting an appropriate solution of CBM simultaneously for all areas.

scholarly journals Multi-Objective Evolutionary Programming for Static VAR Compensator (SVC) in Power System Considering Contingencies (N-m)

2018 ◽  
Vol 9 (2) ◽  
pp. 880
Author(s):  
Nor Rul Hasma Abdullah ◽  
Mahaletchumi A P Morgan ◽  
Mahfuzah Mustafa ◽  
Rosdiyana Samad ◽  
Dwi Pebrianti
Keyword(s):  
Power System ◽  
Transmission Lines ◽  
Power Flow ◽  
Evolutionary Programming ◽  
Test System ◽  
Programming Technique ◽  
Multi Objective ◽  
Static Var Compensators ◽  
Current Transmission ◽  
Flexible Alternating Current Transmission

<span>Static VAR Compensators (SVCs) is a Flexible Alternating Current Transmission System (FACTS) device that can control the power flow in transmission lines by injecting capacitive or inductive current components at the midpoint of interconnection line or in load areas. This device is capable of minimizing the overall system losses and concurrently improves the voltage stability. A line index, namely <em>SVSI</em> becomes indicator for the placement of SVC and the parameters of SVCs are tuned by using the multi-objective evolutionary programming technique, effectively able to control the power. The algorithm was tested on IEEE-30 Bus Reliability Test System (RTS). Comparative studies were conducted based on the performance of SVC in terms of their location and sizing for installations in power system.</span>

scholarly journals Integration of Operability Framework and Pareto Optimal Front to Calculate Optimal Condition of Ethane Cracking Process

2020 ◽  
pp. 105-113
Author(s):  
M. Farsi
Keyword(s):  
Pareto Front ◽  
Optimal Solution ◽  
Optimization Procedure ◽  
Operating Conditions ◽  
Base Case ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Cracking Process

The main aim of this research is to present an optimization procedure based on the integration of operability framework and multi-objective optimization concepts to find the single optimal solution of processes. In this regard, the Desired Pareto Index is defined as the ratio of desired Pareto front to the Pareto optimal front as a quantitative criterion to analyze the performance of chemical processes. The Desired Pareto Front is defined as a part of the Pareto front that all outputs are improved compared to the conventional operating condition. To prove the efficiency of proposed optimization method, the operating conditions of ethane cracking process is optimized as a base case. The ethylene and methane production rates are selected as the objectives in the formulated multi-objective optimization problem. Based on the simulation results, applying the obtained operating conditions by the proposed optimization procedure on the ethane cracking process improve ethylene production by about 3% compared to the conventional condition.  

scholarly journals Integrated monte carlo-evolutionary programming technique for distributed generation studies in distribution system

2019 ◽  
Vol 8 (3) ◽  
pp. 978-984
Author(s):  
Nur Ainna Shakinah Abas ◽  
Ismail Musirin ◽  
Shahrizal Jelani ◽  
Mohd Helmi Mansor ◽  
Naeem M. S. Honnoon ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Distributed Generation ◽  
Distribution System ◽  
Optimization Technique ◽  
Evolutionary Programming ◽  
Test System ◽  
Voltage Profile ◽  
Power Losses ◽  
Programming Technique ◽  
Distributed Generations

This paper presents the optimal multiple distributed generations (MDGs) installation for improving the voltage profile and minimizing power losses of distribution system using the integrated monte-carlo evolutionary programming (EP). EP was used as the optimization technique while monte carlo simulation is used to find the random number of locations of MDGs. This involved the testing of the proposed technique on IEEE 69-bus distribution test system. It is found that the proposed approach successfully solved the MDGs installation problem by reducing the power losses and improving the minimum voltage of the distribution system.

scholarly journals A Pareto Optimal Solution for Environmental/Economic Power Dispatch using Multi-objective Genetic Algorithm

2019 ◽  
Vol 11 (4) ◽  
Author(s):  
Jawad Talaq
Keyword(s):  
Genetic Algorithm ◽  
Environmental Economic ◽  
Economic Power ◽  
Pareto Optimal ◽  
Fuel Cost ◽  
Trade Off ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Power Dispatch ◽  
Economic Power Dispatch

The aim of this paper is to apply genetic algorithm (GA) to the solution of the environmental economic power dispatch problem. The environmental economic power dispatch is a multi-objective optimization problem. Fuel cost is considered as one of the objectives. The other objective is emissions such as SO2 or NOx or a combination of both. A trade-off relation between fuel cost and emissions can be formed through a pareto optimal front. Valve point opening and prohibited operating zones add non-smoothness and non-convexities to the objective functions. Evolutionary algorithms can efficiently solve such non-smooth and non-convex problems. Solutions need to be diversified and distributed among the whole range of the pareto optimal front. This allows operators to trade-off between fuel cost and emissions in feasible optimal regions. Applying genetic algorithm with diversity enhancement proves its effectiveness. Application of the algorithm on three and six unit systems is demonstrated

Pareto-optimal front for multi-objective optimization of the suspension of a full-vehicle model in the frequency domain

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Giovani Gaiardo Fossati ◽  
Letícia Fleck Fadel Miguel ◽  
Walter Jesus Paucar Casas
Keyword(s):  
Frequency Domain ◽  
Computational Time ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Vehicle Model ◽  
Content Type ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Suspension Systems ◽  
Road Profile

PurposeThis study aims to propose a complete and powerful methodology that allows the optimization of the passive suspension system of vehicles, which simultaneously takes comfort and safety into account and provides a set of optimal solutions through a Pareto-optimal front, in a low computational time.Design/methodology/approachUnlike papers that consider simple vehicle models (quarter vehicle model or half car model) and/or simplified road profiles (harmonic excitation, for example) and/or perform a single-objective optimization and/or execute the dynamic analysis in the time domain, this paper presents an effective and fast methodology for the multi-objective optimization of the suspension system of a full-car model (including the driver seat) traveling on an irregular road profile, whose dynamic response is determined in the frequency domain, considerably reducing computational time.FindingsThe results showed that there was a reduction of 28% in the driver seat vertical acceleration weighted root mean square (RMS) value of the proposed model, which is directly related to comfort, and, simultaneously, an improvement or constancy concerning safety, with low computational cost. Hence, the proposed methodology can be indicated as a successful tool for the optimal design of the suspension systems, considering, simultaneously, comfort and safety.Originality/valueDespite the extensive literature on optimizing vehicle passive suspension systems, papers combining multi-objective optimization presenting a Pareto-optimal front as a set of optimal results, a full-vehicle model (including the driver seat), an irregular road profile and the determination of the dynamic response in the frequency domain are not found.

Multi Objective Optimization of Electro Discharge Machining of Resin Bonded Silicon Carbide

2011 ◽  
Vol 110-116 ◽  
pp. 1556-1560
Author(s):  
R. Venkataraman
Keyword(s):  
Silicon Carbide ◽  
Gas Turbines ◽  
Operating Conditions ◽  
Machining Process ◽  
Pareto Optimal ◽  
Electrode Wear ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Electro Discharge Machining ◽  
Pulse On Time

This work is aimed at optimizing the various parameters of the electro discharge machining process in order to Maximize material removal rate (MRR) and Minimize electrode wear rate (EWR) for machining silicon or resin bonded silicon carbide, which is widely used in various applications like high-temperature gas turbines, bearings, seals and linings of industrial furnaces. The five parameters being optimized are intensity supplied by the generator of the EDM machine, open voltage, pulse on time, duty cycle and pressure of flushing fluid. The polynomial models for MRR and EWR proposed by Luis, Puertas and Villa [1] in terms of the five input parameters was used for formation of the objective function. Optimization was carried out using the multi objective genetic algorithm, which is a heuristic search technique that mimics natural selection. A Pareto-optimal front was obtained using this technique, and the points lying on this front represent the set of optimal solutions for the optimization problem. The resultant Pareto– optimal front can be used to select the appropriate operating conditions depending on the specific MRR, EWR or combination requirements.

Multi-objective optimization of the GFRP injection molding process parameters by using GA-ELM, MOFA and GRA-TOPSIS

2021 ◽  
Author(s):  
Xin Liu ◽  
Xiying Fan ◽  
Yonghuan Guo ◽  
Yanli Cao ◽  
Chunxiao Li
Keyword(s):  
Injection Molding ◽  
Extreme Learning Machine ◽  
Prediction Models ◽  
Volume Shrinkage ◽  
Injection Molding Process ◽  
Pareto Optimal ◽  
Molding Process ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Learning Machine

Due to the influence of injection molding process, warpage and volume shrinkage are two common quality defects for products manufactured by the glass fiber-reinforced plastic (GFRP) injection molding. In order to minimize the two defects, the extreme learning machine optimized by genetic algorithm (GA-ELM), multi-objective firefly algorithm (MOFA) and a multi-objective decision-making method called GRA-TOPSIS are implemented in this study. All experiments based on Latin hypercubic sampling (LHS) are conducted by Moldflow software to obtain results of warpage and volume shrinkage. The prediction accuracy of defect prediction models based on the extreme learning machine (ELM) and GA-ELM algorithm is compared. The results show that GA-ELM models can better predict defect values. Finally, MOFA is utilized to find the Pareto optimal front, and the GRA-TOPSIS method is used to find the optimum solution from the Pareto optimal front. According to the results of the simulation verification, the warpage and volume shrinkage are effectively reduced by 12.25% and 6.11% compared with those before optimization, respectively, which indicates the effectiveness and reliability of the optimization method.

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