Approach to Control of Hybrid Renewable Power System on the Basis of AE-Method Using Genetic Algorithm

Author(s):  
V. Ten ◽  
B. Matkarimov ◽  
N. Isembergenov
Author(s):  
Haidar Rahman ◽  
Ridwan Budi Prasetyo

In this research, the problem to find an evaluator to determine a location to build the standalone power system can be seen as problem which can be solved with Kernels Regression where, it will receive 2 inputs such as time and wind speed in order to predict the future wind speed. Afterward the obtained predicted wind speed will be converted into potential electrical energy with maximum and minimum energy and we will be using the Genetic Algorithm (GA) to solve the Economic Dispatch (EDC) to see the operational cost when dispatch into the grid. The data was taken from Baron Techno-Park and PLTH Pantai Baru, and will only be using data from the month of September - December since it is the rainy season. Therefore, since significant parameters such as energy per currency will show that operational cost of Baron Techno-Park have the least operational cost then PLTH Pantai Baru, hence the creation of renewable power plants in Baron Techno-Park are suitable and will have a good operational cost justification. Keywords: Economic Dispatch, Genetic Algorithm, Kernels Regression Standalone Power Plant.  


2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Hamza Yapıcı ◽  
Nurettin Çetinkaya

The power loss in electrical power systems is an important issue. Many techniques are used to reduce active power losses in a power system where the controlling of reactive power is one of the methods for decreasing the losses in any power system. In this paper, an improved particle swarm optimization algorithm using eagle strategy (ESPSO) is proposed for solving reactive power optimization problem to minimize the power losses. All simulations and numerical analysis have been performed on IEEE 30-bus power system, IEEE 118-bus power system, and a real power distribution subsystem. Moreover, the proposed method is tested on some benchmark functions. Results obtained in this study are compared with commonly used algorithms: particle swarm optimization (PSO) algorithm, genetic algorithm (GA), artificial bee colony (ABC) algorithm, firefly algorithm (FA), differential evolution (DE), and hybrid genetic algorithm with particle swarm optimization (hGAPSO). Results obtained in all simulations and analysis show that the proposed method is superior and more effective compared to the other methods.


Energy ◽  
2021 ◽  
pp. 123022
Author(s):  
Renato Haddad Simões Machado ◽  
Erik Eduardo Rego ◽  
Miguel Edgar Morales Udaeta ◽  
Viviane Tavares Nascimento
Keyword(s):  

2020 ◽  
Author(s):  
Hongtao Wang ◽  
Honglian Zhou ◽  
Qian Cao ◽  
Mengke Liao ◽  
Bin Wang ◽  
...  

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