Multi-objective locating and sizing optimization of distributed generation

2013 ◽  
Author(s):  
Wei Dai
Keyword(s):  
Distributed Generation ◽  
Sizing Optimization ◽  
Multi Objective

scholarly journals Multi-Objective Sizing Optimization of Hybrid Renewable Energy Microgrid in a Stand-Alone Marine Context

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 174
Author(s):  
Wenqiang Zhu ◽  
Jiang Guo ◽  
Guo Zhao
Keyword(s):  
Renewable Energy ◽  
Optimization Problem ◽  
System Optimization ◽  
Economic Benefits ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Energy Management Strategy ◽  
Sizing Optimization ◽  
Multi Objective ◽  
Hybrid Renewable Energy

Islands are the main platforms for exploration and utilization of marine resources. In this paper, an island hybrid renewable energy microgrid devoted to a stand-alone marine application is established. The specific microgrid is composed of wind turbines, tidal current turbines, and battery storage systems considering the climate resources and precious land resources. A multi-objective sizing optimization method is proposed comprehensively considering the economy, reliability and energy utilization indexes. Three optimization objectives are presented: minimizing the Loss of Power Supply Probability, the Cost of Energy and the Dump Energy Probability. An improved multi-objective grey wolf optimizer based on Halton sequence and social motivation strategy (HSMGWO) is proposed to solve the proposed sizing optimization problem. MATLAB software is utilized to program and simulate the optimization problem of the hybrid energy system. Optimization results confirm that the proposed method and improved algorithm are feasible to optimally size the system, and the energy management strategy effectively matches the requirements of system operation. The proposed HSMGWO shows better convergence and coverage than standard multi-objective grey wolf optimizer (MOGWO) and multi-objective particle swarm optimization (MOPSO) in solving multi-objective sizing problems. Furthermore, the annual operation of the system is simulated, the power generation and economic benefits of each component are analyzed, as well as the sensitivity.

scholarly journals Capacity-Based Optimization Using Whale Optimization Technique of a Power Distribution Network

2020 ◽  
Author(s):  
Bawoke Simachew ◽  
baseem khan ◽  
Josep M Guerrero ◽  
Sanjeevikumar *Padmanaban ◽  
Om Prakash Mahela ◽  
...  
Keyword(s):  
Distributed Generation ◽  
Power Distribution ◽  
Optimization Technique ◽  
Distribution Network ◽  
Optimal Size ◽  
Voltage Profile ◽  
Power Distribution Network ◽  
Multi Objective ◽  
Whale Optimization ◽  
Profile Management

In the power distribution network, real power loss and voltage profile management are critical issues. By providing active and reactive power support, both of these issues can be managed. This paper utilized the Meta heuristic-based method for the optimal size and placement of distributed generation (DG) and capacitor (QG) sources for loss reduction by incorporating network current carrying capacity constraint in the optimization problem. The overall problem is optimized using an upgraded method of the fitness assignment and solution chasing based on the aggregate approach called Multi-objective Whale Optimization Algorithm (MWOA). Wind and solar photovoltaic sources are utilized as the distributed generation with their probabilistic outputs. The developed method is tested using two feeders of practical Bahir Dar Distribution Network, Ethiopia. The results of loss minimization and voltage profile management with MWOA are compared with multi-objective particle swam optimization (MPSO) with an equal number of iteration to show the superiority of the developed method.

Novel hybrid fuzzy-Bees algorithm for optimal feeder multi-objective reconfiguration by considering multiple-distributed generation

Energy ◽  
2014 ◽  
Vol 71 ◽  
pp. 507-515 ◽  
Author(s):  
Hajar Bagheri Tolabi ◽  
Mohd Hasan Ali ◽  
Shahrin Bin Md Ayob ◽  
M. Rizwan
Keyword(s):  
Distributed Generation ◽  
Bees Algorithm ◽  
Multi Objective

Plant propagation algorithm for integration of distributed generation in power systems

2018 ◽  
Vol 37 (1) ◽  
pp. 401-417
Author(s):  
Shreya Mahajan ◽  
Shelly Vadhera
Keyword(s):  
Power System ◽  
Objective Function ◽  
Distributed Generation ◽  
Weighting Factor ◽  
Voltage Profile ◽  
Plant Propagation ◽  
Content Type ◽  
Multi Objective ◽  
Propagation Algorithm ◽  
Optimisation Techniques

Purpose The purpose of this study/paper is to integrate distributed generation optimally in power system using plant propagation algorithm. Distributed generation is a growing concept in the field of electricity generation. It mainly comprises small generation units installed at calculated points of a power system network. The challenge of optimal allocation and sizing of DG is of utmost importance. Design/methodology/approach Plant propagation algorithm and particle swarm optimisation techniques have been implemented where a weighting factor-based multi-objective function is minimised. The objective is to cut down real losses and to improve the voltage profile of the system. Findings The results obtained using plant propagation algorithm technique for IEEE 33-bus systems are compared to those attained using particle swarm optimisation technique. The paper deals with the optimisation of weighting factor-based objective function, which counterpoises the losses and improves the voltage profile of the system and, therefore, helps to deliver the best outcomes. Originality/value This paper fulfils an identified need to study the multi-objective optimisation techniques for integration of distributed generation in the concerned power system network. The paper proposes a novel plant-propagation-algorithm-based technique in appropriate allocation and sizing of distributed generation unit.

scholarly journals Multi-objective Optimization of Accommodation Capacity for Distributed Generation Based on Mixed Strategy Nash Equilibrium, Considering Distribution Network Flexibility

2019 ◽  
Vol 9 (20) ◽  
pp. 4395 ◽  
Author(s):  
Weisheng Liu ◽  
Jian Wu ◽  
Fei Wang ◽  
Yixin Huang ◽  
Qiongdan Dai ◽  
...  
Keyword(s):  
Nash Equilibrium ◽  
Distributed Generation ◽  
Mixed Strategy ◽  
Distribution Network ◽  
Distribution Networks ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Mixed Strategy Nash Equilibrium ◽  
Network Flexibility ◽  
Strategy Nash Equilibrium

The increasing penetration of distributed generation (DG) brings about great fluctuation and uncertainty in distribution networks. In order to improve the ability of distribution networks to cope with disturbances caused by uncertainties and to evaluate the maximum accommodation capacity of DG, a multi-objective programming method for evaluation of the accommodation capacity of distribution networks for DG is proposed, considering the flexibility of distribution networks in this paper. Firstly, a multi-objective optimization model for determining the maximum accommodation of DG by considering the flexibility of distribution networks is constructed, aiming at maximizing the daily energy consumption, minimizing the voltage amplitude deviation, and maximizing the line capacity margin. Secondly, the comprehensive learning particle swarm optimization (CLPSO) algorithm is used to solve the multi-objective optimization model. Then, the mixed strategy Nash equilibrium is introduced to obtain the frontier solution with the optimal joint equilibrium value in the Pareto solution set. Finally, the effectiveness of the proposed method is demonstrated with an actual distribution network in China. The simulation results show that the proposed planning method can effectively find the Pareto optimal solution set by considering multiple objectives, and can obtain the optimal equilibrium solution for DG accommodation capacity and distribution network flexibility.

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