A New Approach for the Monte-Carlo Method to Locate and Size DGs in Distribution Systems
This paper proposes a new approach for a Parallel implementation of Monte-Carlo method aimed for optimal location and sizing of distributed generators in distribution networks. In this approach, a reduction of the solution space is performed, using heuristic strategies, to improve processing times,...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/8859
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/8859
- Palabra clave:
- Distributed generators
Monte-carlo method
Optimization techniques
Parallel processing
Distributed power generation
Electric load flow
Electric losses
Functions
Genetic algorithms
Location
MATLAB
Software testing
Distributed generators
Distribution systems
Mathematical formulation
Optimization strategy
Optimization techniques
Parallel implementations
Parallel Monte Carlo
Parallel processing
Monte Carlo methods
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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A New Approach for the Monte-Carlo Method to Locate and Size DGs in Distribution Systems |
| title |
A New Approach for the Monte-Carlo Method to Locate and Size DGs in Distribution Systems |
| spellingShingle |
A New Approach for the Monte-Carlo Method to Locate and Size DGs in Distribution Systems Distributed generators Monte-carlo method Optimization techniques Parallel processing Distributed power generation Electric load flow Electric losses Functions Genetic algorithms Location MATLAB Software testing Distributed generators Distribution systems Mathematical formulation Optimization strategy Optimization techniques Parallel implementations Parallel Monte Carlo Parallel processing Monte Carlo methods |
| title_short |
A New Approach for the Monte-Carlo Method to Locate and Size DGs in Distribution Systems |
| title_full |
A New Approach for the Monte-Carlo Method to Locate and Size DGs in Distribution Systems |
| title_fullStr |
A New Approach for the Monte-Carlo Method to Locate and Size DGs in Distribution Systems |
| title_full_unstemmed |
A New Approach for the Monte-Carlo Method to Locate and Size DGs in Distribution Systems |
| title_sort |
A New Approach for the Monte-Carlo Method to Locate and Size DGs in Distribution Systems |
| dc.subject.keywords.none.fl_str_mv |
Distributed generators Monte-carlo method Optimization techniques Parallel processing Distributed power generation Electric load flow Electric losses Functions Genetic algorithms Location MATLAB Software testing Distributed generators Distribution systems Mathematical formulation Optimization strategy Optimization techniques Parallel implementations Parallel Monte Carlo Parallel processing Monte Carlo methods |
| topic |
Distributed generators Monte-carlo method Optimization techniques Parallel processing Distributed power generation Electric load flow Electric losses Functions Genetic algorithms Location MATLAB Software testing Distributed generators Distribution systems Mathematical formulation Optimization strategy Optimization techniques Parallel implementations Parallel Monte Carlo Parallel processing Monte Carlo methods |
| description |
This paper proposes a new approach for a Parallel implementation of Monte-Carlo method aimed for optimal location and sizing of distributed generators in distribution networks. In this approach, a reduction of the solution space is performed, using heuristic strategies, to improve processing times, power losses and voltage profiles considering the location of distributed generators in electric distribution networks. The mathematical formulation of the problem considers a single-objective function, which is composed by weighting factors associated with active power losses and square voltage error minimization; moreover, classical power flow constraints and distributed generation capabilities are considered as restrictions. A master-slave optimization strategy is used to solve the problem: the master stage corresponds to the proposed parallel Monte-Carlo with space solution reduction, which performs the optimal location of the distributed generators; the slave strategy is in charge of solving the resulting optimal power problem. Classical 33-node and 69node test systems are used to validate the proposed approach via MATLAB/MATPOWER software. For comparison purposes, the loss sensitivity factor (LSF), genetic algorithm (GA) and classical parallel Monte-Carlo (PMC) solutions are also tested. The simulations confirm that the proposed reduction to the space solution for the PMC provides improved results in comparison with the existing approaches. © 2018 IEEE. |
| publishDate |
2018 |
| dc.date.issued.none.fl_str_mv |
2018 |
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2020-03-26T16:32:31Z |
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2020-03-26T16:32:31Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_c94f |
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Conferencia |
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publishedVersion |
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2018 IEEE 9th Power, Instrumentation and Measurement Meeting, EPIM 2018 |
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9781538678428 |
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https://hdl.handle.net/20.500.12585/8859 |
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10.1109/EPIM.2018.8756405 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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55791991200 56919564100 57205565936 22836502400 |
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2018 IEEE 9th Power, Instrumentation and Measurement Meeting, EPIM 2018 9781538678428 10.1109/EPIM.2018.8756405 Universidad Tecnológica de Bolívar Repositorio UTB 55791991200 56919564100 57205565936 22836502400 |
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https://hdl.handle.net/20.500.12585/8859 |
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eng |
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eng |
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14 November 2018 through 16 November 2018 |
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Atribución-NoComercial 4.0 Internacional |
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Recurso electrónico |
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application/pdf |
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Institute of Electrical and Electronics Engineers Inc. |
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Institute of Electrical and Electronics Engineers Inc. |
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2020-03-26T16:32:31Z2020-03-26T16:32:31Z20182018 IEEE 9th Power, Instrumentation and Measurement Meeting, EPIM 20189781538678428https://hdl.handle.net/20.500.12585/885910.1109/EPIM.2018.8756405Universidad Tecnológica de BolívarRepositorio UTB55791991200569195641005720556593622836502400This paper proposes a new approach for a Parallel implementation of Monte-Carlo method aimed for optimal location and sizing of distributed generators in distribution networks. In this approach, a reduction of the solution space is performed, using heuristic strategies, to improve processing times, power losses and voltage profiles considering the location of distributed generators in electric distribution networks. The mathematical formulation of the problem considers a single-objective function, which is composed by weighting factors associated with active power losses and square voltage error minimization; moreover, classical power flow constraints and distributed generation capabilities are considered as restrictions. A master-slave optimization strategy is used to solve the problem: the master stage corresponds to the proposed parallel Monte-Carlo with space solution reduction, which performs the optimal location of the distributed generators; the slave strategy is in charge of solving the resulting optimal power problem. Classical 33-node and 69node test systems are used to validate the proposed approach via MATLAB/MATPOWER software. For comparison purposes, the loss sensitivity factor (LSF), genetic algorithm (GA) and classical parallel Monte-Carlo (PMC) solutions are also tested. The simulations confirm that the proposed reduction to the space solution for the PMC provides improved results in comparison with the existing approaches. © 2018 IEEE.Universidad Nacional de Colombia, UN Departamento Administrativo de Ciencia, Tecnología e Innovación, COLCIENCIAS Department of Science, Information Technology and Innovation, Queensland Government, DSITI UNAL-ITM-39823/P17211FINANCIAL SUPPORT This work was supported by the Administrative Department of Science, Technology and Innovation of Colombia (COLCIENCIAS) through the National Scholarship Program, calling contest 727-2015, and the Universidad Nacional de Colombia and the Instituto Tecnológico Metropolitano under the project UNAL-ITM-39823/P17211.Recurso electrónicoapplication/pdfengInstitute of Electrical and Electronics Engineers Inc.http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85069767265&doi=10.1109%2fEPIM.2018.8756405&partnerID=40&md5=6511d3a81f3a361e5c21b94f7ba6d99a9th IEEE Power, Instrumentation and Measurement Meeting, EPIM 2018A New Approach for the Monte-Carlo Method to Locate and Size DGs in Distribution Systemsinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionConferenciahttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fDistributed generatorsMonte-carlo methodOptimization techniquesParallel processingDistributed power generationElectric load flowElectric lossesFunctionsGenetic algorithmsLocationMATLABSoftware testingDistributed generatorsDistribution systemsMathematical formulationOptimization strategyOptimization techniquesParallel implementationsParallel Monte CarloParallel processingMonte Carlo methods14 November 2018 through 16 November 2018Grisales-Noreña L.F.Montoya O.D.González-Montoya D.Ramos-Paja C.A.Grisales, L.F., Cuestas, B.J.R., Jaramillo, F.E., Ubicación y dimensionamiento de generación distribuida: Una revisíon (2017) Ciencia e Ingeniera Neogranadina, 27, pp. 157-176. , 12Grisales, L.F., Grajales, A., Montoya, O.D., Hincapie, R.A., Granada, M., Castro, C.A., Optimal location, sizing and operation of energy storage in distribution systems using multi-objective approach (2017) IEEE Latin America Transactions, 15 (6), pp. 1084-1090. , JuneGupta, P., Pandit, M., Kothari, D.P., A review on optimal sizing and siting of distributed generation system: Integrating distributed generation into the grid (2014) 2014 6th IEEE Power India International Conference (PIICON), pp. 1-6. , DecTheo, W.L., Lim, J.S., Ho, W.S., Hashim, H., Lee, C.T., Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods (2017) Renewable Sustainable Energy Rev., 67, pp. 531-573Vlachogiannis, J.G., Hatziargyriou, N.D., Lee, K.Y., Ant colony system-based algorithm for constrained load flow problem (2005) IEEE Trans. Power Syst., 20 (3), pp. 1241-1249. , AugRezaee Jordehi, A., Allocation of distributed generation units in electric power systems: A review (2016) Renewable Sustainable Energy Rev., 56, pp. 893-905. , aprPrakash, P., Khatod, D.K., Optimal sizing and siting techniques for distributed generation in distribution systems: A review (2016) Renewable Sustainable Energy Rev., 57, pp. 111-130. , mayAli, E., Abd Elazim, S., Abdelaziz, A., Ant Lion Optimization Algorithm for optimal location and sizing of renewable distributed generations (2017) Renewable Energy, 101, pp. 1311-1324. , febMoradi, M., Abedini, M., A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems (2012) Int. J. Electr. Power Energy Syst., 34 (1), pp. 66-74. , janMartinez, J.A., Guerra, G., A parallel Monte Carlo method for optimum allocation of distributed generation (2014) IEEE Trans. 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October, augMohamed Imran, A., Kowsalya, M., Optimal size and siting of multiple distributed generators in distribution system using bacterial foraging optimization (2014) Swarm Evol. Comput., 15, pp. 58-65. , aprChakravorty, M., Das, D., Voltage stability analysis of radial distribution networks (2001) Int. J. Electr. Power Energy Syst., 23 (2), pp. 129-135Zidan, A., Gabbar, H., DG mix and energy storage units for optimal planning of self-sufficient micro energy grids (2016) Energies, 9 (12), p. 616. , augHarrison, G.P., Piccolo, A., Siano, P., Wallace, A.R., Hybrid GA and OPF evaluation of network capacity for distributed generation connections (2008) Electr. Power Syst. Res., 78 (3), pp. 392-398Georgilakis, P.S., Hatziargyriou, N.D., Optimal distributed generation placement in power distribution networks: Models, methods, and future research (2013) IEEE Trans. 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Manage., 47 (18-19), pp. 3288-3306. , novhttp://purl.org/coar/resource_type/c_c94fTHUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/8859/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/8859oai:repositorio.utb.edu.co:20.500.12585/88592021-02-02 13:40:17.759Repositorio Institucional UTBrepositorioutb@utb.edu.co |