A genetic algorithm for solving the unit commitment problem of a hydro-thermal power system

1999 ◽  
Vol 14 (4) ◽  
pp. 1460-1468 ◽  
Author(s):  
A. Rudolf ◽  
R. Bayrleithner
Keyword(s):  
Genetic Algorithm ◽  
Power System ◽  
Unit Commitment ◽  
Thermal Power ◽  
Unit Commitment Problem ◽  
Thermal Power System ◽  
Commitment Problem

scholarly journals Optimization of Unit Commitment Problem Using Genetic Algorithm

2021 ◽  
Vol 10 (3) ◽  
pp. 21-37
Author(s):  
Aniket Agarwal ◽  
Kirti Pal
Keyword(s):  
Genetic Algorithm ◽  
Unit Commitment ◽  
Thermal Power ◽  
Test System ◽  
Optimal Location ◽  
Variable Load ◽  
Fuel Cost ◽  
Unit Commitment Problem ◽  
Solar Thermal Power ◽  
Commitment Problem

The main objective of the paper is to minimize the use of conventional generators and optimize the fuel cost. To minimize the use of conventional generators, solar thermal power plant (STPP) is proposed in this paper. An approach for optimal location of STPP is also proposed in this paper. To minimize the fuel cost, firstly unit commitment (UC) is applied in conventional generators. Then genetic algorithm (GA) is used to optimize the fuel cost of committed generators. The suggested method is tested on an IEEE 14 bus test system for 24 hr. schedule with variable load. The effectiveness of the proposed methodology is illustrated in three cases. Case 1 is used to identify the STPP location to reduce the fuel cost of conventional generator. In Case 2, unit-commitment is applied to save considerable fuel input and cost. In order to optimize the committed fuel cost, a genetic algorithm is applied in Case 3.

scholarly journals A meliorated Harris Hawks optimizer for combinatorial unit commitment problem with photovoltaic applications

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ayani Nandi ◽  
Vikram Kumar Kamboj
Keyword(s):  
Power Generation ◽  
Power System ◽  
Electric Power ◽  
Unit Commitment ◽  
Renewable Energy Sources ◽  
Electric Power System ◽  
Unit Commitment Problem ◽  
Spinning Reserve ◽  
Commitment Problem ◽  
Sine Cosine Algorithm

AbstractConventional unit commitment problem (UCP) consists of thermal generating units and its participation schedule, which is a stimulating and significant responsibility of assigning produced electricity among the committed generating units matter to frequent limitations over a scheduled period view to achieve the least price of power generation. However, modern power system consists of various integrated power generating units including nuclear, thermal, hydro, solar and wind. The scheduling of these generating units in optimal condition is a tedious task and involves lot of uncertainty constraints due to time carrying weather conditions. This difficulties come to be too difficult by growing the scope of electrical power sector day by day, so that UCP has connection with problem in the field of optimization, it has both continuous and binary variables which is the furthermost exciting problem that needs to be solved. In the proposed research, a newly created optimizer, i.e., Harris Hawks optimizer (HHO), has been hybridized with sine–cosine algorithm (SCA) using memetic algorithm approach and named as meliorated Harris Hawks optimizer and it is applied to solve the photovoltaic constrained UCP of electric power system. In this research paper, sine–cosine Algorithm is used for provision of power generation (generating units which contribute in electric power generation for upload) and economic load dispatch (ELD) is completed by Harris Hawks optimizer. The feasibility and efficacy of operation of the hybrid algorithm are verified for small, medium power systems and large system considering renewable energy sources in summer and winter, and the percentage of cost saving for power generation is found. The results for 4 generating units, 5 generating units, 6 generating units, 7 generating units, 10 generating units, 19 generating units, 20 generating units, 40 generating units and 60 generating units are evaluated. The 10 generating units are evaluated with 5% and 10% spinning reserve. The efficacy of the offered optimizer has been verified for several standard benchmark problem including unit commitment problem, and it has been observed that the suggested optimizer is too effective to solve continuous, discrete and nonlinear optimization problems.

scholarly journals Advanced Method for Unit Commitment Problem Using Genetic Algorithm and Mathematical Programming

1999 ◽  
Vol 119 (3) ◽  
pp. 333-343 ◽  
Author(s):  
Toru Takata ◽  
Junichi Takahashi ◽  
Hiroomi Yokoi ◽  
Hiroshi Nakano ◽  
Mari Aoyagi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Programming ◽  
Unit Commitment ◽  
Unit Commitment Problem ◽  
Advanced Method ◽  
Commitment Problem

Enhanced Genetic Algorithm with Guarantee of Feasibility for the Unit Commitment Problem

2008 ◽  
pp. 291-302 ◽  
Author(s):  
Guillaume Sandou ◽  
Stéphane Font ◽  
Sihem Tebbani ◽  
Arnaud Hiret ◽  
Christian Mondon
Keyword(s):  
Genetic Algorithm ◽  
Unit Commitment ◽  
Unit Commitment Problem ◽  
Commitment Problem

SOLVING THE UNIT COMMITMENT PROBLEM USING AN ADAPTIVE IMMUNE GENETIC ALGORITHM

2008 ◽  
Author(s):  
Anant Oonsivilai ◽  
Boonruang Marungsri ◽  
Nader Barsoum ◽  
Sermsak Uatrongjit ◽  
Pandian Vasant
Keyword(s):  
Genetic Algorithm ◽  
Unit Commitment ◽  
Unit Commitment Problem ◽  
Immune Genetic Algorithm ◽  
Adaptive Immune ◽  
Commitment Problem
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