Dynamic economic dispatch of power systems by multi-pass dynamic programming
The purpose of this research is to improve area Automatic Generation Control (AGC) by using the Multi-Pass Dynamic Programming developed in this research. The AGC of power systems coordinates the Load Frequency Control problem and the Economic Dispatch problem together to form a dynamic optimal con...
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ndltd-VTETD-oai-vtechworks.lib.vt.edu-10919-641392020-12-22T05:44:57Z Dynamic economic dispatch of power systems by multi-pass dynamic programming Chen, Nanming Electrical Engineering LD5655.V855 1977.C44 Power resources The purpose of this research is to improve area Automatic Generation Control (AGC) by using the Multi-Pass Dynamic Programming developed in this research. The AGC of power systems coordinates the Load Frequency Control problem and the Economic Dispatch problem together to form a dynamic optimal control problem. A power system was partitioned into the Electric Power Network subsystem and the Mechanical Power Control subsystem. Earlier work has solved the Electric Network subsystem control problem, used analytical methods to find optimal trajectories and controls for the Mechanical Power Control subsystem, but only for the limited case of two generators. This research develops the multi-pass dynamic programming, checks convergence, derives the conditions for singular solutions and provides optimal control sequences and optimal trajectories for cases involving several generators. Parameter sensitivity is also studied here. Finally, some consideration is given to the comparison of multi-pass dynamic programming and conventional dynamic programming. Master of Science 2015-11-13T20:44:50Z 2015-11-13T20:44:50Z 1977 Thesis Text http://hdl.handle.net/10919/64139 en_US OCLC# 21732540 In Copyright http://rightsstatements.org/vocab/InC/1.0/ 93 leaves application/pdf application/pdf Virginia Polytechnic Institute and State University |
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LD5655.V855 1977.C44 Power resources Chen, Nanming Dynamic economic dispatch of power systems by multi-pass dynamic programming |
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The purpose of this research is to improve area Automatic Generation Control (AGC) by using the Multi-Pass Dynamic Programming developed in this research.
The AGC of power systems coordinates the Load Frequency Control problem and the Economic Dispatch problem together to form a dynamic optimal control problem. A power system was partitioned into the Electric Power Network subsystem and the Mechanical Power Control subsystem. Earlier work has solved the Electric Network subsystem control problem, used analytical methods to find optimal trajectories and controls for the Mechanical Power Control subsystem, but only for the limited case of two generators.
This research develops the multi-pass dynamic programming, checks convergence, derives the conditions for singular solutions and provides optimal control sequences and optimal trajectories for cases involving several generators. Parameter sensitivity is also studied here.
Finally, some consideration is given to the comparison of multi-pass dynamic programming and conventional dynamic programming. === Master of Science |
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Electrical Engineering |
author_facet |
Electrical Engineering Chen, Nanming |
author |
Chen, Nanming |
author_sort |
Chen, Nanming |
title |
Dynamic economic dispatch of power systems by multi-pass dynamic programming |
title_short |
Dynamic economic dispatch of power systems by multi-pass dynamic programming |
title_full |
Dynamic economic dispatch of power systems by multi-pass dynamic programming |
title_fullStr |
Dynamic economic dispatch of power systems by multi-pass dynamic programming |
title_full_unstemmed |
Dynamic economic dispatch of power systems by multi-pass dynamic programming |
title_sort |
dynamic economic dispatch of power systems by multi-pass dynamic programming |
publisher |
Virginia Polytechnic Institute and State University |
publishDate |
2015 |
url |
http://hdl.handle.net/10919/64139 |
work_keys_str_mv |
AT chennanming dynamiceconomicdispatchofpowersystemsbymultipassdynamicprogramming |
_version_ |
1719371399653490688 |