Comparison between dynamic programming and genetic algorithm for hydro unit economic load dispatch
The hydro unit economic load dispatch (ELD) is of great importance in energy conservation and emission reduction. Dynamic programming (DP) and genetic algorithm (GA) are two representative algorithms for solving ELD problems. The goal of this study was to examine the performance of DP and GA while t...
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doaj-c87b1597e553497a80b945f622b2e3182020-11-24T23:12:50ZengElsevierWater Science and Engineering1674-23702014-10-017442043210.3882/j.issn.1674-2370.2014.04.007Comparison between dynamic programming and genetic algorithm for hydro unit economic load dispatchBin Xu0Ping-an Zhong1Yun-fa Zhao2Yu-zuo Zhu3Gao-qi Zhang4College of Hydrology and Water Resources, Hohai University, Nanjing 210098, P.R. ChinaCollege of Hydrology and Water Resources, Hohai University, Nanjing 210098, P.R. ChinaChina Three Gorges Corporation, Beijing 100038, P.R. ChinaDatang Yantan Hydropower Corporation, Nanning 530022, P.R. ChinaYellow River Engineering Consulting Co., Ltd., Zhengzhou 450003, P.R. ChinaThe hydro unit economic load dispatch (ELD) is of great importance in energy conservation and emission reduction. Dynamic programming (DP) and genetic algorithm (GA) are two representative algorithms for solving ELD problems. The goal of this study was to examine the performance of DP and GA while they were applied to ELD. We established numerical experiments to conduct performance comparisons between DP and GA with two given schemes. The schemes included comparing the CPU time of the algorithms when they had the same solution quality, and comparing the solution quality when they had the same CPU time. The numerical experiments were applied to the Three Gorges Reservoir in China, which is equipped with 26 hydro generation units. We found the relation between the performance of algorithms and the number of units through experiments. Results show that GA is adept at searching for optimal solutions in low-dimensional cases. In some cases, such as with a number of units of less than 10, GA's performance is superior to that of a coarse-grid DP. However, GA loses its superiority in high-dimensional cases. DP is powerful in obtaining stable and high-quality solutions. Its performance can be maintained even while searching over a large solution space. Nevertheless, due to its exhaustive enumerating nature, it costs excess time in low-dimensional cases.http://www.sciencedirect.com/science/article/pii/S1674237015302994hydro uniteconomic load dispatchdynamic programminggenetic algorithmnumerical experiment |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Bin Xu Ping-an Zhong Yun-fa Zhao Yu-zuo Zhu Gao-qi Zhang |
spellingShingle |
Bin Xu Ping-an Zhong Yun-fa Zhao Yu-zuo Zhu Gao-qi Zhang Comparison between dynamic programming and genetic algorithm for hydro unit economic load dispatch Water Science and Engineering hydro unit economic load dispatch dynamic programming genetic algorithm numerical experiment |
author_facet |
Bin Xu Ping-an Zhong Yun-fa Zhao Yu-zuo Zhu Gao-qi Zhang |
author_sort |
Bin Xu |
title |
Comparison between dynamic programming and genetic algorithm for hydro unit economic load dispatch |
title_short |
Comparison between dynamic programming and genetic algorithm for hydro unit economic load dispatch |
title_full |
Comparison between dynamic programming and genetic algorithm for hydro unit economic load dispatch |
title_fullStr |
Comparison between dynamic programming and genetic algorithm for hydro unit economic load dispatch |
title_full_unstemmed |
Comparison between dynamic programming and genetic algorithm for hydro unit economic load dispatch |
title_sort |
comparison between dynamic programming and genetic algorithm for hydro unit economic load dispatch |
publisher |
Elsevier |
series |
Water Science and Engineering |
issn |
1674-2370 |
publishDate |
2014-10-01 |
description |
The hydro unit economic load dispatch (ELD) is of great importance in energy conservation and emission reduction. Dynamic programming (DP) and genetic algorithm (GA) are two representative algorithms for solving ELD problems. The goal of this study was to examine the performance of DP and GA while they were applied to ELD. We established numerical experiments to conduct performance comparisons between DP and GA with two given schemes. The schemes included comparing the CPU time of the algorithms when they had the same solution quality, and comparing the solution quality when they had the same CPU time. The numerical experiments were applied to the Three Gorges Reservoir in China, which is equipped with 26 hydro generation units. We found the relation between the performance of algorithms and the number of units through experiments. Results show that GA is adept at searching for optimal solutions in low-dimensional cases. In some cases, such as with a number of units of less than 10, GA's performance is superior to that of a coarse-grid DP. However, GA loses its superiority in high-dimensional cases. DP is powerful in obtaining stable and high-quality solutions. Its performance can be maintained even while searching over a large solution space. Nevertheless, due to its exhaustive enumerating nature, it costs excess time in low-dimensional cases. |
topic |
hydro unit economic load dispatch dynamic programming genetic algorithm numerical experiment |
url |
http://www.sciencedirect.com/science/article/pii/S1674237015302994 |
work_keys_str_mv |
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