Dynamic economic dispatch in restructured power systems considering transmission costs using genetic algorithm

2004 ◽  
Author(s):  
S.H. Hosseini ◽  
M. Kheradmandi
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Economic Dispatch ◽  
Dynamic Economic Dispatch

scholarly journals Some Properties for Adjustable Entropy Function Method and Its Application to Power Systems Dynamic Economic Dispatch with Wind Integrated

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yaming Ren
Keyword(s):  
Power Systems ◽  
Function Method ◽  
Economic Dispatch ◽  
Entropy Function ◽  
Prediction Errors ◽  
Mathematical Methods ◽  
Spinning Reserve ◽  
Aggregate Function ◽  
Adjustable Entropy Function ◽  
Dynamic Economic Dispatch

With the continuous development of the world economy, the development and utilization of environmentally friendly and renewable energy have become the trend in many countries. In this paper, we study the dynamic economic dispatch with wind integrated. Firstly, we take advantage of the positive and negative spinning reserve to deal with wind power output prediction errors in order to establish a dynamic economic dispatch model of wind integrated. The existence of a min function makes the dynamic economic dispatch model nondifferentiable, which results in the inability to directly use the traditional mathematical methods based on gradient information to solve the model. Inspired by the aggregate function, we can easily transform the nondifferentiable model into a smooth model when parameter p tends to infinity. However, the aggregate function will cause data overflow when p tends to infinity. Then, for solving this problem, we take advantage of the adjustable entropy function method to replace of aggregate function method. In addition, we further discuss the adjustable entropy function method and point out that the solution generated by the adjustable entropy function method can effectively approximate the solution of the original problem without parameter p tending to infinity. Finally, simulation experiments are given, and the simulation results prove the effectiveness and correctness of the adjustable entropy function method.

scholarly journals Dynamic economic dispatch: a comparative study for differential evolution, particle swarm optimization, evolutionary programming, genetic algorithm, and simulated annealing

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Jagat Kishore Pattanaik ◽  
Mousumi Basu ◽  
Deba Prasad Dash
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Comparative Study ◽  
Differential Evolution ◽  
Economic Dispatch ◽  
Particle Swarm ◽  
Evolutionary Programming ◽  
Swarm Optimization ◽  
Dynamic Economic Dispatch

AbstractThis paper presents a comparative study for five artificial intelligent (AI) techniques to the dynamic economic dispatch problem: differential evolution, particle swarm optimization, evolutionary programming, genetic algorithm, and simulated annealing. Here, the optimal hourly generation schedule is determined. Dynamic economic dispatch determines the optimal scheduling of online generator outputs with predicted load demands over a certain period of time taking into consideration the ramp rate limits of the generators. The AI techniques for dynamic economic dispatch are evaluated against a ten-unit system with nonsmooth fuel cost function as a common testbed and the results are compared against each other.

scholarly journals Solving Bid-Based Dynamic Economic Dispatch in Competitive Electricity Market Using Improved Simulated Annealing Algorithm

2020 ◽  
Vol 7 (4) ◽  
pp. 621-630
Author(s):  
Riyadh Bouddou ◽  
Farid Benhamida ◽  
Ismail Ziane ◽  
Amine Zeggai ◽  
Moussa Belgacem
Keyword(s):  
Simulated Annealing ◽  
Power Systems ◽  
Electricity Markets ◽  
Electricity Market ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Economic Dispatch ◽  
Unit System ◽  
Annealing Algorithm ◽  
Dynamic Economic Dispatch

Electricity markets are open after the deregulation of power systems due to competition. An optimization problem based on dynamic economic dispatch has recently come up in the new context of deregulated power systems known as bid-based dynamic economic dispatch (BBDED). It is one of the major operations and control functions in the electricity markets used to determine the optimal operations of market participants with scheduled load demands during a specified period. BBDED involves power generation companies (GENCOs) and customers to submit energy and price bids to the independent system operator (ISO) in a day-ahead market. The ISO clears the market with the objective of social profit maximization. In this paper, a BBDED problem is solved using an improved simulated annealing algorithm (ISA), including system constraints with different periods under bidding strategies. The proposed ISA technique is implemented in MATLAB and applied on a 3-unit system, a 6-unit system, and a 40-unit large-scale system. The proposed ISA is evaluated by comparison with relevant methods available in the literature, to demonstrate and confirm its potential in terms of convergence, robustness, and effectiveness for solving the BBDED 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.

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