scholarly journals A Novel Real-Coded Genetic Algorithm for Dynamic Economic Dispatch Integrating Plug-In Electric Vehicles

2021 ◽  
Vol 9 ◽  
Author(s):  
Wenqiang Yang ◽  
Zhanlei Peng ◽  
Wei Feng ◽  
Muhammad Ilyas Menhas
Keyword(s):  
Genetic Algorithm ◽  
Electric Vehicles ◽  
Economic Dispatch ◽  
Optimal Level ◽  
Discharge Management ◽  
Real Coded Genetic Algorithm ◽  
High Efficient ◽  
Dynamic Economic Dispatch ◽  
Handling Method

Massive popularity of plug-in electric vehicles (PEVs) may bring considerable opportunities and challenges to the power grid. The scenario is highly dependent on whether PEVs can be effectively managed. Dynamic economic dispatch with PEVs (DED with PEVs) determines the optimal level of online units and PEVs, to minimize the fuel cost and grid fluctuations. Considering valve-point effects and transmission losses is a complex constrained optimization problem with non-smooth, non-linear, and non-convex characteristics. High efficient DED method provides a powerful tool in both power system scheduling and PEVs charging coordination. In this study, firstly, PEVs are integrated into the DED problem, which can carry out orderly charge and discharge management to improve the quality of the grid. To tackle this, a novel real-coded genetic algorithm (RCGA), namely, dimension-by-dimension mutation based on feature intervals (GADMFI), is proposed to enhance the exploitation and exploration of conventional RCGAs. Thirdly, a simple and efficient constraint handling method is proposed for an infeasible solution for DED. Finally, the proposed method is compared with the current literature on six cases with three scenarios, including only thermal units, units with disorderly PEVs, and units with orderly PEVs. The proposed GADMFI shows outstanding advantages on solving the DED with/without PEVs problem, obtaining the effect of cutting peaks and filling valleys on the DED with orderly PEVs problem.

scholarly journals A Parallel Mix Self-Adaptive Genetic Algorithm for Solving the Dynamic Economic Dispatch Problem

2020 ◽  
Author(s):  
Egidio De Carvalho Ribeiro Júnior ◽  
Omar Andres Carmona Cortes ◽  
Osvaldo Ronald Saavedra
Keyword(s):  
Genetic Algorithm ◽  
Economic Dispatch ◽  
Adaptive Genetic Algorithm ◽  
Random Algorithm ◽  
Dynamic Economic Dispatch ◽  
Crossover And Mutation ◽  
Adaptive Code ◽  
Better Than ◽  
Self Adaptive

The purpose of this paper is to propose a parallel genetic algorithm that has adaptive and self-adaptive characteristics at the same time for solving the Dynamic Economic Dispatch (DED) problem that is a challenging problem to solve. The algorithm selects the proper operators (using adaptive features) and probabilities (using the self-adaptive code) that produce the most fittable individuals. Regarding operations, the choice is made between four different types of crossover: simple, arithmetical, non-uniform arithmetical, and linear. Concerning mutation, we used four types of mutations (uniform, non-uniform, creep, and enhanced apso). The choice is made scholastically, which is uniform at the beginning of the algorithm, being adapted as the AG  executes. The crossover and mutation probabilities are coded into the genes, transforming this part of the algorithm into self-adaptive. The multicore version was coded using OpenMP. An ANOVA test, along with a Tukey test, proved that the mixed self-adaptive algorithm works better than both: a random algorithm, which chooses operators randomly, and a combination of operators set previously in the DED optimization. Regarding the performance of the parallel approach, results have shown that a speedup of up to 3.19 can be reached with no loss in the quality of solutions.

scholarly journals Real coded genetic algorithm based transmission system loss estimation in dynamic economic dispatch problem

2018 ◽  
Vol 57 (4) ◽  
pp. 3535-3547 ◽  
Author(s):  
C.H. Ram Jethmalani ◽  
Sishaj P. Simon ◽  
K. Sundareswaran ◽  
P. Srinivasa Rao Nayak ◽  
Narayana Prasad Padhy
Keyword(s):  
Genetic Algorithm ◽  
Economic Dispatch ◽  
Transmission System ◽  
Loss Estimation ◽  
Real Coded Genetic Algorithm ◽  
Economic Dispatch Problem ◽  
Dynamic Economic Dispatch

scholarly journals Cluster-Based Optimization of an Evacuation Process Using a Parallel Bi-Objective Real-Coded Genetic Algorithm

2020 ◽  
Vol 20 (3) ◽  
pp. 45-63
Author(s):  
Andranik S. Akopov ◽  
Levon A. Beklaryan ◽  
Armen L. Beklaryan
Keyword(s):  
Genetic Algorithm ◽  
Individual Characteristics ◽  
Nsga Ii ◽  
Human Agent ◽  
Performance Metric ◽  
Novel Approach ◽  
Distributed Processes ◽  
Real Coded Genetic Algorithm ◽  
Decision Making System

AbstractThis work presents a novel approach to the design of a decision-making system for the cluster-based optimization of an evacuation process using a Parallel bi-objective Real-Coded Genetic Algorithm (P-RCGA). The algorithm is based on the dynamic interaction of distributed processes with individual characteristics that exchange the best potential decisions among themselves through a global population. Such an approach allows the HyperVolume performance metric (HV metric) as reflected in the quality of the subset of the Pareto optimal solutions to be improved. The results of P-RCGA were compared with other well-known multi-objective genetic algorithms (e.g., -MOEA, NSGA-II, SPEA2). Moreover, P-RCGA was aggregated with the developed simulation of the behavior of human agent-rescuers in emergency through the objective functions to optimize the main parameters of the evacuation process.

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