scholarly journals Multi-objective optimization based optimal sizing & placement of multiple distributed generators for distribution network performance improvement

2021 ◽  
Author(s):  
Anilkumar Trikambhai Markana ◽  
Gargi Trivedi ◽  
Dr. Praghnesh Bhatt
Keyword(s):  
Load Balancing ◽  
Network Performance ◽  
Renewable Energy Sources ◽  
Distribution Network ◽  
Multi Objective Optimization ◽  
Optimal Sizing ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Voltage Deviation ◽  
Teaching Learning

Integration of Distributed Generations (DGs) into radial distribution network (RDN) is an emerging need to explore the benefits of renewable energy sources (RES). Increasing penetration of RES based DGs in RDN without proper planning leads to several operational problems such as excessive energy losses, poor voltage quality and load balancing. Hence, in this work, multi-objective optimization (MOO) problem is formulated by carefully chosen three conflicting objectives such as power loss minimization, enhancement of load balancing index (LBI) and aggregate voltage deviation index (AVDI). Teaching- Learning-Based-Optimization (TLBO) is used to optimize MOO problem considering placement of DGs at multiple locations in RDN satisfying the constraints on bus voltage magnitude, branch flows and DG size. Comprehensive simulation studies have been carried out to obtain optimal performance for 69- nodes RDN with the increasing penetration of DGs at multiple locations. It is shown that determination of optimal sizing of DGs at multiple locations in RDN with MOO results in lesser power losses, improved voltage profiles and better load balancing as compared to placement of single DG in RDN. Performance measures such as spacing and spread indicators are used for characterizing Pareto solutions for MOO problem. Such set of non-dominated solutions obtained from Pareto front during multi-objective TLBO gives proper guidelines to the utility operator about sizing and placement of DGs based on the assigned priorities to the objectives.

scholarly journals Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution Networks

2021 ◽  
Vol 11 (19) ◽  
pp. 8916
Author(s):  
Zhiwen Xu ◽  
Changsong Chen ◽  
Mingyang Dong ◽  
Jingyue Zhang ◽  
Dongtong Han ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Reactive Power ◽  
Renewable Energy Sources ◽  
Distribution Network ◽  
Operating Cost ◽  
Distribution Networks ◽  
Double Loop ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Ac Converters

By constructing a DC multi-microgrid system (MMGS) including renewable energy sources (RESs) and electric vehicles (EVs) to coordinate with the distribution network, the utilization rate of RESs can be effectively improved and carbon emissions can be reduced. To improve the economy of MMGS and reduce the network loss of the distribution network, a cooperative double-loop optimization strategy is proposed. The inner-loop economic dispatching reduces the daily operating cost of MMGS by optimizing the active power output of RESs, EVs, and DC/AC converters in MMGS. The outer-loop reactive power optimization reduces the network loss of the distribution network by optimizing the reactive power of the bidirectional DC/AC converters. The double-loop, which synergistically optimizes the economic cost and carbon emissions of MMGS, not only improves the economy of MMGS and operational effectiveness of the distribution network but also realizes the low-carbon emissions. The Across-time-and-space energy transmission (ATSET) of the EVs is considered, whose impact on economic dispatching is analyzed. Particle Swarm Optimization (PSO) is applied to iterative solutions. Finally, the rationality and feasibility of the cooperative multi-objective optimization model are proved by a revised IEEE 33-node system.

Efficient Computation of Probabilistic Dominance in Multi-objective Optimization

2021 ◽  
Vol 1 (4) ◽  
pp. 1-26
Author(s):  
Faramarz Khosravi ◽  
Alexander Rass ◽  
Jürgen Teich
Keyword(s):  
Optimization Problems ◽  
Statistical Tests ◽  
Optimization Techniques ◽  
Simultaneous Optimization ◽  
Sampled Data ◽  
Efficient Computation ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Comparison Operator

Real-world problems typically require the simultaneous optimization of multiple, often conflicting objectives. Many of these multi-objective optimization problems are characterized by wide ranges of uncertainties in their decision variables or objective functions. To cope with such uncertainties, stochastic and robust optimization techniques are widely studied aiming to distinguish candidate solutions with uncertain objectives specified by confidence intervals, probability distributions, sampled data, or uncertainty sets. In this scope, this article first introduces a novel empirical approach for the comparison of candidate solutions with uncertain objectives that can follow arbitrary distributions. The comparison is performed through accurate and efficient calculations of the probability that one solution dominates the other in terms of each uncertain objective. Second, such an operator can be flexibly used and combined with many existing multi-objective optimization frameworks and techniques by just substituting their standard comparison operator, thus easily enabling the Pareto front optimization of problems with multiple uncertain objectives. Third, a new benchmark for evaluating uncertainty-aware optimization techniques is introduced by incorporating different types of uncertainties into a well-known benchmark for multi-objective optimization problems. Fourth, the new comparison operator and benchmark suite are integrated into an existing multi-objective optimization framework that features a selection of multi-objective optimization problems and algorithms. Fifth, the efficiency in terms of performance and execution time of the proposed comparison operator is evaluated on the introduced uncertainty benchmark. Finally, statistical tests are applied giving evidence of the superiority of the new comparison operator in terms of \epsilon -dominance and attainment surfaces in comparison to previously proposed approaches.

Stochastic multi-objective optimization of photovoltaics integrated three-phase distribution network based on dynamic scenarios

2018 ◽  
Vol 231 ◽  
pp. 985-996 ◽  
Author(s):  
Jian Xu ◽  
Jing Wang ◽  
Siyang Liao ◽  
Yuanzhang Sun ◽  
Deping Ke ◽  
...  
Keyword(s):  
Phase Distribution ◽  
Distribution Network ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Three Phase

Dynamic Template Generation

2021 ◽  
pp. 1-37
Keyword(s):  
Optimization Problems ◽  
Real Life ◽  
Dynamic Test ◽  
Test Paper ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Life Problems ◽  
Conflicting Objectives ◽  
Complex Optimization ◽  
Dynamic Template

A test blueprint/test template, also known as the table of specifications, represents the structure of a test. It has been highly recommended in assessment textbook to carry out the preparation of a test with a test blueprint. This chapter focuses on modeling a dynamic test paper template using multi-objective optimization algorithm and makes use of the template in dynamic generation of examination test paper. Multi-objective optimization-based models are realistic models for many complex optimization problems. Modeling a dynamic test paper template, similar to many real-life problems, includes solving multiple conflicting objectives satisfying the template specifications.

scholarly journals Single- and Multi-Objective Optimization of a Dual-Chamber Microbial Fuel Cell Operating in Continuous-Flow Mode at Steady State

Processes ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 839
Author(s):  
Ibrahim M. Abu-Reesh
Keyword(s):  
Flow Rate ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Dual Chamber ◽  
Worst Case ◽  
Multi Objective ◽  
Model Based ◽  
Conflicting Objectives

Microbial fuel cells (MFCs) are a promising technology for bioenergy generation and wastewater treatment. Various parameters affect the performance of dual-chamber MFCs, such as substrate flow rate and concentration. Performance can be assessed by power density ( PD ), current density ( CD ) production, or substrate removal efficiency ( SRE ). In this study, a mathematical model-based optimization was used to optimize the performance of an MFC using single- and multi-objective optimization (MOO) methods. Matlab’s fmincon and fminimax functions were used to solve the nonlinear constrained equations for the single- and multi-objective optimization, respectively. The fminimax method minimizes the worst-case of the two conflicting objective functions. The single-objective optimization revealed that the maximum PD ,   CD , and SRE were 2.04 W/m2, 11.08 A/m2, and 73.6%, respectively. The substrate concentration and flow rate significantly impacted the performance of the MFC. Pareto-optimal solutions were generated using the weighted sum method for maximizing the two conflicting objectives of PD and CD in addition to PD and SRE   simultaneously. The fminimax method for maximizing PD and CD showed that the compromise solution was to operate the MFC at maximum PD conditions. The model-based optimization proved to be a fast and low-cost optimization method for MFCs and it provided a better understanding of the factors affecting an MFC’s performance. The MOO provided Pareto-optimal solutions with multiple choices for practical applications depending on the purpose of using the MFCs.

Multi-Objective Pricing Optimization for a High-Speed Rail Network Under Competition

2019 ◽  
Vol 2673 (7) ◽  
pp. 215-226 ◽  
Author(s):  
Huizhuo Cao ◽  
Xuemei Li ◽  
Vikrant Vaze ◽  
Xueyan Li
Keyword(s):  
High Speed ◽  
Programming Model ◽  
Pareto Frontier ◽  
Multiple Objectives ◽  
Multi Objective Optimization ◽  
High Speed Rail ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Single Objective

Multi-objective pricing of high-speed rail (HSR) passenger fares becomes a challenge when the HSR operator needs to deal with multiple conflicting objectives. Although many studies have tackled the challenge of calculating the optimal fares over railway networks, none of them focused on characterizing the trade-offs between multiple objectives under multi-modal competition. We formulate the multi-objective HSR fare optimization problem over a linear network by introducing the epsilon-constraint method within a bi-level programming model and develop an iterative algorithm to solve this model. This is the first HSR pricing study to use an epsilon-constraint methodology. We obtain two single-objective solutions and four multi-objective solutions and compare them on a variety of metrics. We also derive the Pareto frontier between the objectives of profit and passenger welfare to enable the operator to choose the best trade-off. Our results based on computational experiments with Beijing–Shanghai regional network provide several new insights. First, we find that small changes in fares can lead to a significant improvement in passenger welfare with no reduction in profitability under multi-objective optimization. Second, multi-objective optimization solutions show considerable improvements over the single-objective optimization solutions. Third, Pareto frontier enables decision-makers to make more informed decisions about choosing the best trade-offs. Overall, the explicit modeling of multiple objectives leads to better pricing solutions, which have the potential to guide pricing decisions for the HSR operators.

A Thompson Sampling Efficient Multi-Objective Optimization Algorithm (TSEMO) for Lithium-Ion Battery Liquid-Cooled Thermal Management System: Study of Hydrodynamic, Thermodynamic, and Structural Performance

2020 ◽  
Vol 18 (2) ◽  
Author(s):  
A. Garg ◽  
Cheng Liu ◽  
A. K. Jishnu ◽  
Liang Gao ◽  
My Loan Le Phung ◽  
...  
Keyword(s):  
Thermal Management ◽  
Optimization Algorithm ◽  
Cooling System ◽  
Hydrodynamic Performance ◽  
Design Parameters ◽  
Inlet Temperature ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Thompson Sampling ◽  
Conflicting Objectives

Abstract The efficient design of battery thermal management systems (BTMSs) plays an important role in enhancing the performance, life, and safety of electric vehicles (EVs). This paper aims at designing and optimizing cold plate-based liquid cooling BTMS. Pitch sizes of channels, inlet velocity, and inlet temperature of the outermost channel are considered as design parameters. Evaluating the influence and optimization of design parameters by repeated computational fluid dynamics calculations is time consuming. To tackle this, the effect of design parameters is studied by using surrogate modeling. Optimized design variables should ensure a perfect balance between certain conflicting goals, namely, cooling efficiency, BTMS power consumption (parasitic power), and size of the battery. Therefore, the optimization problem is decoupled into hydrodynamic performance, thermodynamic performance, and mechanical structure performance. The optimal design involving multiple conflicting objectives in BTMS is solved by adopting the Thompson sampling efficient multi-objective optimization algorithm. The results obtained are as follows. The optimized average battery temperature after optimization decreased from 319.86 K to 319.2759 K by 0.18%. The standard deviation of battery temperature decreased from 5.3347 K to 5.2618 K by 1.37%. The system pressure drop decreased from 7.3211 Pa to 3.3838 Pa by 53.78%. The performance of the optimized battery cooling system has been significantly improved.

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