A decomposition/aggregation method for solving electrical power dispatch problems

This paper presents a new approach to solving the Economic Dispatch (ED) Problem for a large number of generators using a decomposition / aggregation method. A program has been developed to demonstrate the algorithm using the MATLAB programming language. A 5-bus test system and the IEEE 26-bus test...

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Main Authors: Mansor M.H., Irving M.R., Taylor G.A.
Other Authors: 56372667100
Format: Conference paper
Published: 2023
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spelling my.uniten.dspace-295462023-12-28T14:30:28Z A decomposition/aggregation method for solving electrical power dispatch problems Mansor M.H. Irving M.R. Taylor G.A. 56372667100 56217310700 55568974100 Aggregation Decompositon Economic Dispatch (ED) Evolutionary Programming (EP) Agglomeration Computer programming Evolutionary algorithms Optimization Scheduling Aggregation methods Decomposition scheme Decompositon Economic Dispatch Economic dispatch problems Electrical power Generating unit Load flow Optimisations Real power loss Small area Test systems Problem oriented languages This paper presents a new approach to solving the Economic Dispatch (ED) Problem for a large number of generators using a decomposition / aggregation method. A program has been developed to demonstrate the algorithm using the MATLAB programming language. A 5-bus test system and the IEEE 26-bus test system are used as demonstration systems. Each test system is decomposed into small areas and each area has been solved for Economic Dispatch (locally) using an Evolutionary Programming (EP) technique. It was ensured that each area contains at least one generating unit and one supplied load. The EP will minimise the objective funtion for each area, minimising the local operating cost including the effects of real power losses in each area. The optimisation problem for each area can be regarded as a sub-problem of the decomposition scheme. Subsequently, the solutions from the areas are combined (aggregated) to solve the overall system problem. The results obtained using the decomposition / aggregation method are compared with the results found when the ED Problem was solved using a centralised EP approach and the base-case results found from solving a (non-optimal) load flow. It was found that applying the aggregation method is a prospective approach for solving economic dispatch problems with a large numbers of generators in a power system. � 2012 IEEE. Final 2023-12-28T06:30:28Z 2023-12-28T06:30:28Z 2012 Conference paper 10.1109/UPEC.2012.6398675 2-s2.0-84872858437 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872858437&doi=10.1109%2fUPEC.2012.6398675&partnerID=40&md5=cc2e31ca23fafcae4fe0d4a18628990e https://irepository.uniten.edu.my/handle/123456789/29546 6398675 Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Aggregation
Decompositon
Economic Dispatch (ED)
Evolutionary Programming (EP)
Agglomeration
Computer programming
Evolutionary algorithms
Optimization
Scheduling
Aggregation methods
Decomposition scheme
Decompositon
Economic Dispatch
Economic dispatch problems
Electrical power
Generating unit
Load flow
Optimisations
Real power loss
Small area
Test systems
Problem oriented languages
spellingShingle Aggregation
Decompositon
Economic Dispatch (ED)
Evolutionary Programming (EP)
Agglomeration
Computer programming
Evolutionary algorithms
Optimization
Scheduling
Aggregation methods
Decomposition scheme
Decompositon
Economic Dispatch
Economic dispatch problems
Electrical power
Generating unit
Load flow
Optimisations
Real power loss
Small area
Test systems
Problem oriented languages
Mansor M.H.
Irving M.R.
Taylor G.A.
A decomposition/aggregation method for solving electrical power dispatch problems
description This paper presents a new approach to solving the Economic Dispatch (ED) Problem for a large number of generators using a decomposition / aggregation method. A program has been developed to demonstrate the algorithm using the MATLAB programming language. A 5-bus test system and the IEEE 26-bus test system are used as demonstration systems. Each test system is decomposed into small areas and each area has been solved for Economic Dispatch (locally) using an Evolutionary Programming (EP) technique. It was ensured that each area contains at least one generating unit and one supplied load. The EP will minimise the objective funtion for each area, minimising the local operating cost including the effects of real power losses in each area. The optimisation problem for each area can be regarded as a sub-problem of the decomposition scheme. Subsequently, the solutions from the areas are combined (aggregated) to solve the overall system problem. The results obtained using the decomposition / aggregation method are compared with the results found when the ED Problem was solved using a centralised EP approach and the base-case results found from solving a (non-optimal) load flow. It was found that applying the aggregation method is a prospective approach for solving economic dispatch problems with a large numbers of generators in a power system. � 2012 IEEE.
author2 56372667100
author_facet 56372667100
Mansor M.H.
Irving M.R.
Taylor G.A.
format Conference paper
author Mansor M.H.
Irving M.R.
Taylor G.A.
author_sort Mansor M.H.
title A decomposition/aggregation method for solving electrical power dispatch problems
title_short A decomposition/aggregation method for solving electrical power dispatch problems
title_full A decomposition/aggregation method for solving electrical power dispatch problems
title_fullStr A decomposition/aggregation method for solving electrical power dispatch problems
title_full_unstemmed A decomposition/aggregation method for solving electrical power dispatch problems
title_sort decomposition/aggregation method for solving electrical power dispatch problems
publishDate 2023
_version_ 1806424109366640640
score 13.214268