scholarly journals Optimal Placement and Sizing of Distributed Generators Based on Multiobjective Particle Swarm Optimization

2021 ◽  
Vol 9 ◽  
Author(s):  
Deyu Yang ◽  
Junqing Jia ◽  
Wenli Wu ◽  
Wenchao Cai ◽  
Dong An ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Renewable Energy Sources ◽  
Particle Swarm ◽  
Power Grids ◽  
Optimal Placement ◽  
Stable Operation ◽  
Voltage Profile ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
The Impact

To solve the problems of environmental pollution and energy consumption, the development of renewable energy sources becomes the top priority of current energy transformation. Therefore, distributed power generation has received extensive attention from engineers and researchers. However, the output of distributed generation (DG) is generally random and intermittent, which will cause various degrees of impact on the safe and stable operation of power system when connected to different locations, different capacities, and different types of power grids. Thus, the impact of sizing, type, and location needs to be carefully considered when choosing the optimal DG connection scheme to ensure the overall operation safety, stability, reliability, and efficiency of power grid. This work proposes a distinctive objective function that comprehensively considers power loss, voltage profile, pollution emissions, and DG costs, which is then solved by the multiobjective particle swarm optimization (MOPSO). Finally, the effectiveness and feasibility of the proposed algorithm are verified based on the IEEE 33-bus and 69-bus distribution network.

scholarly journals Determining Optimal Size and Placement of Renewable DG Considering Variation of Load

2019 ◽  
Vol 8 (2S7) ◽  
pp. 310-315
Keyword(s):  
Particle Swarm Optimization ◽  
Distribution System ◽  
Renewable Energy Sources ◽  
Particle Swarm ◽  
Optimal Size ◽  
Voltage Profile ◽  
Swarm Optimization ◽  
Local Optima ◽  
Superior Result ◽  
Variable Weight

An advance algorithm to decide optimal size and place of renewable DG by applying adaptive particle swarm optimization (A-PSO) technique is reported in this work. A multiobjective function has been proposed to improve voltage profile, maximize economic benefit and reduction in active power losses. This work includes renewable energy sources based distributed generation (DG) technique like solar and biomass DGs. The time variations characteristics for solar DG and load have been considered in the system. Due to the problem of easily get trapped into local optima, particle Swarm optimization (PSO) may not be able to solve complex power system problems. Hence, APSO with variable weight function has been used in this work. The implementation of proposed technique has been presented for practical 94-bus portuguese radial distribution system. Simultaneous placement of both renewable DGs provides maximum benefits. The comparison between proposed and existing techniques have been presented which confirms that the suggested technique gives the superior result

scholarly journals A New Particle Swarm Optimization Based Strategy for the Economic Emission Dispatch Problem Including Wind Energy Sources

2021 ◽  
Vol 11 (5) ◽  
pp. 7585-7590
Author(s):  
G. A. Alshammari ◽  
F. A. Alshammari ◽  
T. Guesmi ◽  
B. M. Alshammari ◽  
A. S. Alshammari ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Power Grids ◽  
Chance Constraint ◽  
Power Balance ◽  
Swarm Optimization ◽  
Loading Effects ◽  
Emission Dispatch ◽  
The Impact ◽  
Economic Emission Dispatch

Power dispatch has become an important issue due to the high integration of Wind Power (WP) in power grids. Within this context, this paper presents a new Particle Swarm Optimization (PSO) based strategy for solving the stochastic Economic Emission Dispatch Problem (EEDP). This problem was solved considering several constraints such as power balance, generation limits, and Valve Point Loading Effects (VPLEs). The power balance constraint is described by a chance constraint to consider the impact of WP intermittency on the EEDP solution. In this study, the chance constraint represents the tolerance that the power balance constraint cannot meet. The suggested framework was successfully evaluated on a ten-unit system. The problem was solved for various threshold tolerances to study further the impact of WP penetration.

MULTIOBJECTIVE PARTICLE SWARM OPTIMIZATION BASED ON DIMENSIONAL UPDATE

2013 ◽  
Vol 22 (03) ◽  
pp. 1350015 ◽  
Author(s):  
HEMING XU ◽  
YINGLIN WANG ◽  
XIN XU
Keyword(s):  
Particle Swarm Optimization ◽  
Pareto Front ◽  
Particle Swarm ◽  
Experimental Results ◽  
Entire Region ◽  
Swarm Optimization ◽  
Nondominated Solutions ◽  
Limited Size ◽  
Multiobjective Particle Swarm Optimization ◽  
The Impact

For multiobjective particle swarm optimization (MOPSO), two particles may be incomparable, i. e., not dominated by each other. The personal best and the global best for the particle become less optimal, thus the convergence becomes slow. Even worse, an archive of a limited size can not cover the entire region dominated by the Pareto front, the uncovered region can contain unidentifiable nondominated solutions that are not optimal, and thus the precision the algorithm achieves encounters a plateau. Therefore we propose dimensional update, i. e., evaluating the particle's fitness after updating each variable of its position. Separate consideration of the impact of each variable decreases the occurrence of incomparable relations, thus improves the performance. Experimental results validate the efficiency of our algorithm.

Optimal Placement and Sizing of Distributed Generation in Distribution System Using Modified Particle Swarm Optimization Algorithm

2018 ◽  
pp. 485-507 ◽  
Author(s):  
Mahesh Kumar ◽  
Perumal Nallagownden ◽  
Irraivan Elamvazuthi ◽  
Pandian Vasant
Keyword(s):  
Particle Swarm Optimization ◽  
Distributed Generation ◽  
Distribution System ◽  
Reactive Power ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimal Placement ◽  
Voltage Profile ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization

The electricity demand, fossil fuel depletion and environment issues increase the interest of power engineers to integrate small power generations i.e. called distributed generation (DGs) in the distribution system. The DG in distribution system has many positive effects such as it reduces the system power losses, improves the voltage profile and strengthen the voltage stability etc. The placement and sizing of DG play a major role in optimizing these parameters. Therefore, this chapter proposes a modified Particle Swarm Optimization (PSO) algorithm for finding the optimal placement and sizing of distributed generation in the radial distribution system. Two types of DGs such as an active power and reactive power DGs are tested on standard IEEE 33 radial bus system. Moreover, it can be realized that proposed method gives very effective results when both of active and reactive power DGs are integrated into the distribution system.

scholarly journals Application of a Particle Swarm Optimization for Improving Voltage Profile with Distributed Generation: A Case Study of 33/0.415KV Abuja Airport Injection Substation

2019 ◽  
Vol 4 (3) ◽  
pp. 100-106
Author(s):  
Stephen Oodo ◽  
Felix Sanjo Owolabi
Keyword(s):  
Particle Swarm Optimization ◽  
Distributed Generation ◽  
Power Distribution ◽  
Distribution Network ◽  
Mathematical Formulation ◽  
Particle Swarm ◽  
Voltage Profile ◽  
Swarm Optimization ◽  
System A ◽  
The Impact

The important of electric power distribution is to have centralized plants distributing electricity through Distributed generation (DG) which reduces the cost of maintenance on transmission and distribution station and also improve voltage profile. This research paper present the application of generation based on Biogas power renewable energy source to the Distribution network and how it stabilizes the network by normalizing the fluctuating voltage profile at the distribution end of power system. A Particle Swarm Optimization (PSO) model was performed and evaluation of the impact of the DG by stimulating the developed model in the system. A mathematical formulation and optimization algorithm was performed using the MATLAB/Simulink program. The results obtained were correction of the faulty buses voltages and stable power supply which is 29.4% better than the conventional one. The result shows the implementation of the optimisation technique has improved the energy efficiency of the distribution network.

scholarly journals Knowledge-guided multiobjective particle swarm optimization with fusion learning strategies

2021 ◽  
Author(s):  
Wei Li ◽  
Xiang Meng ◽  
Ying Huang ◽  
Soroosh Mahmoodi
Keyword(s):  
Particle Swarm Optimization ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Particle Swarm ◽  
Population Diversity ◽  
Selection Strategy ◽  
Rapid Loss ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization

AbstractMultiobjective particle swarm optimization (MOPSO) algorithm faces the difficulty of prematurity and insufficient diversity due to the selection of inappropriate leaders and inefficient evolution strategies. Therefore, to circumvent the rapid loss of population diversity and premature convergence in MOPSO, this paper proposes a knowledge-guided multiobjective particle swarm optimization using fusion learning strategies (KGMOPSO), in which an improved leadership selection strategy based on knowledge utilization is presented to select the appropriate global leader for improving the convergence ability of the algorithm. Furthermore, the similarity between different individuals is dynamically measured to detect the diversity of the current population, and a diversity-enhanced learning strategy is proposed to prevent the rapid loss of population diversity. Additionally, a maximum and minimum crowding distance strategy is employed to obtain excellent nondominated solutions. The proposed KGMOPSO algorithm is evaluated by comparisons with the existing state-of-the-art multiobjective optimization algorithms on the ZDT and DTLZ test instances. Experimental results illustrate that KGMOPSO is superior to other multiobjective algorithms with regard to solution quality and diversity maintenance.

scholarly journals Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Ya-zhong Luo ◽  
Li-ni Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Mission Design ◽  
Trajectory Design ◽  
Time Transfer ◽  
Pareto Solution ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Asteroid Mission

A new preliminary trajectory design method for asteroid rendezvous mission using multiobjective optimization techniques is proposed. This method can overcome the disadvantages of the widely employed Pork-Chop method. The multiobjective integrated launch window and multi-impulse transfer trajectory design model is formulated, which employes minimum-fuel cost and minimum-time transfer as two objective functions. The multiobjective particle swarm optimization (MOPSO) is employed to locate the Pareto solution. The optimization results of two different asteroid mission designs show that the proposed approach can effectively and efficiently demonstrate the relations among the mission characteristic parameters such as launch time, transfer time, propellant cost, and number of maneuvers, which will provide very useful reference for practical asteroid mission design. Compared with the PCP method, the proposed approach is demonstrated to be able to provide much more easily used results, obtain better propellant-optimal solutions, and have much better efficiency. The MOPSO shows a very competitive performance with respect to the NSGA-II and the SPEA-II; besides a proposed boundary constraint optimization strategy is testified to be able to improve its performance.

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