Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm
This paper focuses on minimizing the annual operative costs in monopolar DC distribution networks with the inclusion of solar photovoltaic (PV) generators while considering a planning period of 20 years. This problem is formulated through a mixed-integer nonlinear programming (MINLP) model, in which...
- Autores:
-
Montoya, Oscar Danilo
Gil-González, Walter
Grisales-Noreña, Luis Fernando
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/11124
- Palabra clave:
- Monopolar DC networks
Solar PV generation
Generalized normal distribution optimizer
Master–slave optimization
Successive approximation power flow method
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
| id |
UTB2_8b727b287a90298e1fb66e0bb4360204 |
|---|---|
| oai_identifier_str |
oai:repositorio.utb.edu.co:20.500.12585/11124 |
| network_acronym_str |
UTB2 |
| network_name_str |
Repositorio Institucional UTB |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm |
| title |
Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm |
| spellingShingle |
Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm Monopolar DC networks Solar PV generation Generalized normal distribution optimizer Master–slave optimization Successive approximation power flow method |
| title_short |
Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm |
| title_full |
Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm |
| title_fullStr |
Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm |
| title_full_unstemmed |
Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm |
| title_sort |
Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm |
| dc.creator.fl_str_mv |
Montoya, Oscar Danilo Gil-González, Walter Grisales-Noreña, Luis Fernando |
| dc.contributor.author.none.fl_str_mv |
Montoya, Oscar Danilo Gil-González, Walter Grisales-Noreña, Luis Fernando |
| dc.subject.keywords.spa.fl_str_mv |
Monopolar DC networks Solar PV generation Generalized normal distribution optimizer Master–slave optimization Successive approximation power flow method |
| topic |
Monopolar DC networks Solar PV generation Generalized normal distribution optimizer Master–slave optimization Successive approximation power flow method |
| description |
This paper focuses on minimizing the annual operative costs in monopolar DC distribution networks with the inclusion of solar photovoltaic (PV) generators while considering a planning period of 20 years. This problem is formulated through a mixed-integer nonlinear programming (MINLP) model, in which binary variables define the nodes where the PV generators must be located, and continuous variables are related to the power flow solution and the optimal sizes of the PV sources. The implementation of a master–slave optimization approach is proposed in order to address the complexity of the MINLP formulation. In the master stage, the discrete-continuous generalized normal distribution optimizer (DCGNDO) is implemented to define the nodes for the PV sources along with their sizes. The slave stage corresponds to a specialized power flow approach for monopolar DC networks known as the successive approximation power flow method, which helps determine the total energy generation at the substation terminals and its expected operative costs in the planning period. Numerical results in the 33- and 69-bus grids demonstrate the effectiveness of the DCGNDO optimizer compared to the discrete-continuous versions of the Chu and Beasley genetic algorithm and the vortex search algorithm. |
| publishDate |
2022 |
| dc.date.accessioned.none.fl_str_mv |
2022-10-03T16:03:26Z |
| dc.date.available.none.fl_str_mv |
2022-10-03T16:03:26Z |
| dc.date.issued.none.fl_str_mv |
2022-08-05 |
| dc.date.submitted.none.fl_str_mv |
2022-09-28 |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.hasversion.spa.fl_str_mv |
info:eu-repo/semantics/restrictedAccess |
| dc.type.spa.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.identifier.citation.spa.fl_str_mv |
Montoya, O.D.; GilGonzález, W.; Grisales-Noreña, L.F. Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm. Algorithms 2022, 15, 277. https://doi.org/10.3390/a15080277 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/11124 |
| dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.3390/a15080277 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Tecnológica de Bolívar |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Montoya, O.D.; GilGonzález, W.; Grisales-Noreña, L.F. Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm. Algorithms 2022, 15, 277. https://doi.org/10.3390/a15080277 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/11124 https://doi.org/10.3390/a15080277 |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.uri.*.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.cc.*.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
| rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.none.fl_str_mv |
16 Páginas |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.place.spa.fl_str_mv |
Cartagena de Indias |
| dc.source.spa.fl_str_mv |
Algorithms Vol. 15 N° 8 ( 2022) |
| institution |
Universidad Tecnológica de Bolívar |
| bitstream.url.fl_str_mv |
https://repositorio.utb.edu.co/bitstream/20.500.12585/11124/1/algorithms-15-00277.pdf https://repositorio.utb.edu.co/bitstream/20.500.12585/11124/2/license_rdf https://repositorio.utb.edu.co/bitstream/20.500.12585/11124/3/license.txt https://repositorio.utb.edu.co/bitstream/20.500.12585/11124/4/algorithms-15-00277.pdf.txt https://repositorio.utb.edu.co/bitstream/20.500.12585/11124/5/algorithms-15-00277.pdf.jpg |
| bitstream.checksum.fl_str_mv |
c64cac609577704372f90abad10e1c11 4460e5956bc1d1639be9ae6146a50347 e20ad307a1c5f3f25af9304a7a7c86b6 07c0155031790dd130dcec43296cacfd da61b6e76e54c09ed7df93718eda17d2 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional UTB |
| repository.mail.fl_str_mv |
repositorioutb@utb.edu.co |
| _version_ |
1814021583632596992 |
| spelling |
Montoya, Oscar Danilo9fa8a75a-58fa-436d-a6e2-d80f718a4ea8Gil-González, Walter72191491-1c75-451d-a5c5-f7f45373ecd0Grisales-Noreña, Luis Fernando7c27cda4-5fe4-4686-8f72-b0442c58a5d12022-10-03T16:03:26Z2022-10-03T16:03:26Z2022-08-052022-09-28Montoya, O.D.; GilGonzález, W.; Grisales-Noreña, L.F. Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm. Algorithms 2022, 15, 277. https://doi.org/10.3390/a15080277https://hdl.handle.net/20.500.12585/11124https://doi.org/10.3390/a15080277Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThis paper focuses on minimizing the annual operative costs in monopolar DC distribution networks with the inclusion of solar photovoltaic (PV) generators while considering a planning period of 20 years. This problem is formulated through a mixed-integer nonlinear programming (MINLP) model, in which binary variables define the nodes where the PV generators must be located, and continuous variables are related to the power flow solution and the optimal sizes of the PV sources. The implementation of a master–slave optimization approach is proposed in order to address the complexity of the MINLP formulation. In the master stage, the discrete-continuous generalized normal distribution optimizer (DCGNDO) is implemented to define the nodes for the PV sources along with their sizes. The slave stage corresponds to a specialized power flow approach for monopolar DC networks known as the successive approximation power flow method, which helps determine the total energy generation at the substation terminals and its expected operative costs in the planning period. Numerical results in the 33- and 69-bus grids demonstrate the effectiveness of the DCGNDO optimizer compared to the discrete-continuous versions of the Chu and Beasley genetic algorithm and the vortex search algorithm.16 Páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Algorithms Vol. 15 N° 8 ( 2022)Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithminfo:eu-repo/semantics/articleinfo:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/resource_type/c_2df8fbb1Monopolar DC networksSolar PV generationGeneralized normal distribution optimizerMaster–slave optimizationSuccessive approximation power flow methodCartagena de IndiasInvestigadoresLi, J.; Liu, F.; Wang, Z.; Low, S.H.; Mei, S. Optimal Power Flow in Stand-Alone DC Microgrids. IEEE Trans. Power Syst. 2018, 33, 5496–5506.Garces, A. On the Convergence of Newton's Method in Power Flow Studies for DC Microgrids. IEEE Trans. Power Syst. 2018, 33, 5770–5777Strzelecki, R.M.; Benysek, G. (Eds.) Power Electronics in Smart Electrical Energy Networks; Springer: London, UK, 2008.Junior, M.E.T.S.; Freitas, L.C.G. Power Electronics for Modern Sustainable Power Systems: Distributed Generation, Microgrids and Smart Grids—A Review. Sustainability 2022, 14, 3597.Lee, J.O.; Kim, Y.S.; Jeon, J.H. Generic power flow algorithm for bipolar DC microgrids based on Newton–Raphson method. Int. J. Electr. Power Energy Syst. 2022, 142, 108357Lamb, W.F.; Grubb, M.; Diluiso, F.; Minx, J.C. Countries with sustained greenhouse gas emissions reductions: An analysis of trends and progress by sector. Clim. Policy 2021, 22, 1–17Lima, M.; Mendes, L.; Mothé, G.; Linhares, F.; de Castro, M.; da Silva, M.; Sthel, M. Renewable energy in reducing greenhouse gas emissions: Reaching the goals of the Paris agreement in Brazil. Environ. Dev. 2020, 33, 100504.López, A.R.; Krumm, A.; Schattenhofer, L.; Burandt, T.; Montoya, F.C.; Oberländer, N.; Oei, P.Y. Solar PV generation in Colombia— A qualitative and quantitative approach to analyze the potential of solar energy market. Renew. Energy 2020, 148, 1266–1279.Cuervo, F.I.; Arredondo-Orozco, C.A.; Marenco-Maldonado, G.C. Photovoltaic power purchase agreement valuation under real options approach. Renew. Energy Focus 2021, 36, 96–107Moradi, M.H.; Abedini, M. A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. Int. J. Electr. Power Energy Syst. 2012, 34, 66–74.Paz-Rodríguez, A.; Castro-Ordoñez, J.F.; Montoya, O.D.; Giral-Ramírez, D.A. Optimal integration of photovoltaic sources in distribution networks for daily energy losses minimization using the vortex search algorithm. Appl. Sci. 2021, 11, 4418.Selim, A.; Kamel, S.; Alghamdi, A.S.; Jurado, F. Optimal placement of DGs in distribution system using an improved harris hawks optimizer based on single-and multi-objective approaches. IEEE Access 2020, 8, 52815–52829Mohanty, B.; Tripathy, S. A teaching learning based optimization technique for optimal location and size of DG in distribution network. J. Electr. Syst. Inf. Technol. 2016, 3, 33–44Ayodele, T.; Ogunjuyigbe, A.; Akinola, O. Optimal location, sizing, and appropriate technology selection of distributed generators for minimizing power loss using genetic algorithm. J. Renew. Energy 2015, 2015, 832917.Raharjo, J.; Adam, K.B.; Priharti, W.; Zein, H.; Hasudungan, J.; Suhartono, E. Optimization of Placement and Sizing on Distributed Generation Using Technique of Smalling Area. In Proceedings of the 2021 IEEE Electrical Power and Energy Conference (EPEC), Toronto, ON, Canada, 22–31 October 2021; pp. 475–479Grisales-Noreña, L.F.; Gonzalez Montoya, D.; Ramos-Paja, C.A. Optimal sizing and location of distributed generators based on PBIL and PSO techniques. Energies 2018, 11, 1018Kaur, S.; Kumbhar, G.; Sharma, J. A MINLP technique for optimal placement of multiple DG units in distribution systems. Int. J. Electr. Power Energy Syst. 2014, 63, 609–617Montoya, O.D.; Grisales-Noreña, L.F.; Gil-González, W.; Alcalá, G.; Hernandez-Escobedo, Q. Optimal location and sizing of PV sources in DC networks for minimizing greenhouse emissions in diesel generators. Symmetry 2020, 12, 322Gil-González, W.; Montoya, O.D.; Grisales-Noreña, L.F.; Perea-Moreno, A.J.; Hernandez-Escobedo, Q. Optimal placement and sizing of wind generators in AC grids considering reactive power capability and wind speed curves. Sustainability 2020, 12, 2983Radosavljevi´c, J.; Arsi´c, N.; Milovanovi´c, M.; Ktena, A. Optimal placement and sizing of renewable distributed generation using hybrid metaheuristic algorithm. J. Mod. Power Syst. Clean Energy 2020, 8, 499–510.Gil-González, W.; Garces, A.; Montoya, O.D.; Hernández, J.C. A mixed-integer convex model for the optimal placement and sizing of distributed generators in power distribution networks. Appl. Sci. 2021, 11, 627Cortés-Caicedo, B.; Molina-Martin, F.; Grisales-Noreña, L.F.; Montoya, O.D.; Hernández, J.C. Optimal Design of PV Systems in Electrical Distribution Networks by Minimizing the Annual Equivalent Operative Costs through the Discrete-Continuous Vortex Search Algorithm. Sensors 2022, 22, 851Hlaili, M.; Mechergui, H. Comparison of Different MPPT Algorithms with a Proposed One Using a Power Estimator for Grid Connected PV Systems. Int. J. Photoenergy 2016, 2016, 1728398Montoya, O.D.; Grisales-Noreña, L.F.; Ramos-Paja, C.A. Optimal Allocation and Sizing of PV Generation Units in Distribution Networks via the Generalized Normal Distribution Optimization Approach. Computers 2022, 11, 53.Farivar, M.; Low, S.H. Branch Flow Model: Relaxations and Convexification—Part I. IEEE Trans. Power Syst. 2013, 28, 2554–2564.Crainic, T.G.; Toulouse, M. Parallel Meta-heuristics. In International Series in Operations Research & Management Science; Springer: Berlin/Heidelberg, Germany, 2010; pp. 497–541Zhang, Y.; Jin, Z.; Mirjalili, S. Generalized normal distribution optimization and its applications in parameter extraction of photovoltaic models. Energy Convers. Manag. 2020, 224, 113301Abdel-Basset, M.; Mohamed, R.; Abouhawwash, M.; Chang, V.; Askar, S. A Local Search-Based Generalized Normal Distribution Algorithm for Permutation Flow Shop Scheduling. Appl. Sci. 2021, 11, 4837Xu, J.; Zhang, J. Exploration-exploitation tradeoffs in metaheuristics: Survey and analysis. In Proceedings of the 33rd Chinese Control Conference, Nanjing, China, 28–30 July 2014Garces, A. Uniqueness of the power flow solutions in low voltage direct current grids. Electr. Power Syst. Res. 2017, 151, 149–153.Sahin, O.; Akay, B. Comparisons of metaheuristic algorithms and fitness functions on software test data generation. Appl. Soft Comput. 2016, 49, 1202–1214Hanh, L.T.M.; Binh, N.T.; Tung, K.T. A Novel Fitness function of metaheuristic algorithms for test data generation for simulink models based on mutation analysis. J. Syst. Softw. 2016, 120, 17–30.http://purl.org/coar/resource_type/c_2df8fbb1ORIGINALalgorithms-15-00277.pdfalgorithms-15-00277.pdfapplication/pdf487364https://repositorio.utb.edu.co/bitstream/20.500.12585/11124/1/algorithms-15-00277.pdfc64cac609577704372f90abad10e1c11MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.utb.edu.co/bitstream/20.500.12585/11124/2/license_rdf4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83182https://repositorio.utb.edu.co/bitstream/20.500.12585/11124/3/license.txte20ad307a1c5f3f25af9304a7a7c86b6MD53TEXTalgorithms-15-00277.pdf.txtalgorithms-15-00277.pdf.txtExtracted texttext/plain52637https://repositorio.utb.edu.co/bitstream/20.500.12585/11124/4/algorithms-15-00277.pdf.txt07c0155031790dd130dcec43296cacfdMD54THUMBNAILalgorithms-15-00277.pdf.jpgalgorithms-15-00277.pdf.jpgGenerated Thumbnailimage/jpeg7901https://repositorio.utb.edu.co/bitstream/20.500.12585/11124/5/algorithms-15-00277.pdf.jpgda61b6e76e54c09ed7df93718eda17d2MD5520.500.12585/11124oai:repositorio.utb.edu.co:20.500.12585/111242022-10-04 00:18:21.463Repositorio Institucional UTBrepositorioutb@utb.edu.coQXV0b3Jpem8gKGF1dG9yaXphbW9zKSBhIGxhIEJpYmxpb3RlY2EgZGUgbGEgSW5zdGl0dWNpw7NuIHBhcmEgcXVlIGluY2x1eWEgdW5hIGNvcGlhLCBpbmRleGUgeSBkaXZ1bGd1ZSBlbiBlbCBSZXBvc2l0b3JpbyBJbnN0aXR1Y2lvbmFsLCBsYSBvYnJhIG1lbmNpb25hZGEgY29uIGVsIGZpbiBkZSBmYWNpbGl0YXIgbG9zIHByb2Nlc29zIGRlIHZpc2liaWxpZGFkIGUgaW1wYWN0byBkZSBsYSBtaXNtYSwgY29uZm9ybWUgYSBsb3MgZGVyZWNob3MgcGF0cmltb25pYWxlcyBxdWUgbWUobm9zKSBjb3JyZXNwb25kZShuKSB5IHF1ZSBpbmNsdXllbjogbGEgcmVwcm9kdWNjacOzbiwgY29tdW5pY2FjacOzbiBww7pibGljYSwgZGlzdHJpYnVjacOzbiBhbCBww7pibGljbywgdHJhbnNmb3JtYWNpw7NuLCBkZSBjb25mb3JtaWRhZCBjb24gbGEgbm9ybWF0aXZpZGFkIHZpZ2VudGUgc29icmUgZGVyZWNob3MgZGUgYXV0b3IgeSBkZXJlY2hvcyBjb25leG9zIHJlZmVyaWRvcyBlbiBhcnQuIDIsIDEyLCAzMCAobW9kaWZpY2FkbyBwb3IgZWwgYXJ0IDUgZGUgbGEgbGV5IDE1MjAvMjAxMiksIHkgNzIgZGUgbGEgbGV5IDIzIGRlIGRlIDE5ODIsIExleSA0NCBkZSAxOTkzLCBhcnQuIDQgeSAxMSBEZWNpc2nDs24gQW5kaW5hIDM1MSBkZSAxOTkzIGFydC4gMTEsIERlY3JldG8gNDYwIGRlIDE5OTUsIENpcmN1bGFyIE5vIDA2LzIwMDIgZGUgbGEgRGlyZWNjacOzbiBOYWNpb25hbCBkZSBEZXJlY2hvcyBkZSBhdXRvciwgYXJ0LiAxNSBMZXkgMTUyMCBkZSAyMDEyLCBsYSBMZXkgMTkxNSBkZSAyMDE4IHkgZGVtw6FzIG5vcm1hcyBzb2JyZSBsYSBtYXRlcmlhLgoKQWwgcmVzcGVjdG8gY29tbyBBdXRvcihlcykgbWFuaWZlc3RhbW9zIGNvbm9jZXIgcXVlOgoKLSBMYSBhdXRvcml6YWNpw7NuIGVzIGRlIGNhcsOhY3RlciBubyBleGNsdXNpdmEgeSBsaW1pdGFkYSwgZXN0byBpbXBsaWNhIHF1ZSBsYSBsaWNlbmNpYSB0aWVuZSB1bmEgdmlnZW5jaWEsIHF1ZSBubyBlcyBwZXJwZXR1YSB5IHF1ZSBlbCBhdXRvciBwdWVkZSBwdWJsaWNhciBvIGRpZnVuZGlyIHN1IG9icmEgZW4gY3VhbHF1aWVyIG90cm8gbWVkaW8sIGFzw60gY29tbyBsbGV2YXIgYSBjYWJvIGN1YWxxdWllciB0aXBvIGRlIGFjY2nDs24gc29icmUgZWwgZG9jdW1lbnRvLgoKLSBMYSBhdXRvcml6YWNpw7NuIHRlbmRyw6EgdW5hIHZpZ2VuY2lhIGRlIGNpbmNvIGHDsW9zIGEgcGFydGlyIGRlbCBtb21lbnRvIGRlIGxhIGluY2x1c2nDs24gZGUgbGEgb2JyYSBlbiBlbCByZXBvc2l0b3JpbywgcHJvcnJvZ2FibGUgaW5kZWZpbmlkYW1lbnRlIHBvciBlbCB0aWVtcG8gZGUgZHVyYWNpw7NuIGRlIGxvcyBkZXJlY2hvcyBwYXRyaW1vbmlhbGVzIGRlbCBhdXRvciB5IHBvZHLDoSBkYXJzZSBwb3IgdGVybWluYWRhIHVuYSB2ZXogZWwgYXV0b3IgbG8gbWFuaWZpZXN0ZSBwb3IgZXNjcml0byBhIGxhIGluc3RpdHVjacOzbiwgY29uIGxhIHNhbHZlZGFkIGRlIHF1ZSBsYSBvYnJhIGVzIGRpZnVuZGlkYSBnbG9iYWxtZW50ZSB5IGNvc2VjaGFkYSBwb3IgZGlmZXJlbnRlcyBidXNjYWRvcmVzIHkvbyByZXBvc2l0b3Jpb3MgZW4gSW50ZXJuZXQgbG8gcXVlIG5vIGdhcmFudGl6YSBxdWUgbGEgb2JyYSBwdWVkYSBzZXIgcmV0aXJhZGEgZGUgbWFuZXJhIGlubWVkaWF0YSBkZSBvdHJvcyBzaXN0ZW1hcyBkZSBpbmZvcm1hY2nDs24gZW4gbG9zIHF1ZSBzZSBoYXlhIGluZGV4YWRvLCBkaWZlcmVudGVzIGFsIHJlcG9zaXRvcmlvIGluc3RpdHVjaW9uYWwgZGUgbGEgSW5zdGl0dWNpw7NuLCBkZSBtYW5lcmEgcXVlIGVsIGF1dG9yKHJlcykgdGVuZHLDoW4gcXVlIHNvbGljaXRhciBsYSByZXRpcmFkYSBkZSBzdSBvYnJhIGRpcmVjdGFtZW50ZSBhIG90cm9zIHNpc3RlbWFzIGRlIGluZm9ybWFjacOzbiBkaXN0aW50b3MgYWwgZGUgbGEgSW5zdGl0dWNpw7NuIHNpIGRlc2VhIHF1ZSBzdSBvYnJhIHNlYSByZXRpcmFkYSBkZSBpbm1lZGlhdG8uCgotIExhIGF1dG9yaXphY2nDs24gZGUgcHVibGljYWNpw7NuIGNvbXByZW5kZSBlbCBmb3JtYXRvIG9yaWdpbmFsIGRlIGxhIG9icmEgeSB0b2RvcyBsb3MgZGVtw6FzIHF1ZSBzZSByZXF1aWVyYSBwYXJhIHN1IHB1YmxpY2FjacOzbiBlbiBlbCByZXBvc2l0b3Jpby4gSWd1YWxtZW50ZSwgbGEgYXV0b3JpemFjacOzbiBwZXJtaXRlIGEgbGEgaW5zdGl0dWNpw7NuIGVsIGNhbWJpbyBkZSBzb3BvcnRlIGRlIGxhIG9icmEgY29uIGZpbmVzIGRlIHByZXNlcnZhY2nDs24gKGltcHJlc28sIGVsZWN0csOzbmljbywgZGlnaXRhbCwgSW50ZXJuZXQsIGludHJhbmV0LCBvIGN1YWxxdWllciBvdHJvIGZvcm1hdG8gY29ub2NpZG8gbyBwb3IgY29ub2NlcikuCgotIExhIGF1dG9yaXphY2nDs24gZXMgZ3JhdHVpdGEgeSBzZSByZW51bmNpYSBhIHJlY2liaXIgY3VhbHF1aWVyIHJlbXVuZXJhY2nDs24gcG9yIGxvcyB1c29zIGRlIGxhIG9icmEsIGRlIGFjdWVyZG8gY29uIGxhIGxpY2VuY2lhIGVzdGFibGVjaWRhIGVuIGVzdGEgYXV0b3JpemFjacOzbi4KCi0gQWwgZmlybWFyIGVzdGEgYXV0b3JpemFjacOzbiwgc2UgbWFuaWZpZXN0YSBxdWUgbGEgb2JyYSBlcyBvcmlnaW5hbCB5IG5vIGV4aXN0ZSBlbiBlbGxhIG5pbmd1bmEgdmlvbGFjacOzbiBhIGxvcyBkZXJlY2hvcyBkZSBhdXRvciBkZSB0ZXJjZXJvcy4gRW4gY2FzbyBkZSBxdWUgZWwgdHJhYmFqbyBoYXlhIHNpZG8gZmluYW5jaWFkbyBwb3IgdGVyY2Vyb3MgZWwgbyBsb3MgYXV0b3JlcyBhc3VtZW4gbGEgcmVzcG9uc2FiaWxpZGFkIGRlbCBjdW1wbGltaWVudG8gZGUgbG9zIGFjdWVyZG9zIGVzdGFibGVjaWRvcyBzb2JyZSBsb3MgZGVyZWNob3MgcGF0cmltb25pYWxlcyBkZSBsYSBvYnJhIGNvbiBkaWNobyB0ZXJjZXJvLgoKLSBGcmVudGUgYSBjdWFscXVpZXIgcmVjbGFtYWNpw7NuIHBvciB0ZXJjZXJvcywgZWwgbyBsb3MgYXV0b3JlcyBzZXLDoW4gcmVzcG9uc2FibGVzLCBlbiBuaW5nw7puIGNhc28gbGEgcmVzcG9uc2FiaWxpZGFkIHNlcsOhIGFzdW1pZGEgcG9yIGxhIGluc3RpdHVjacOzbi4KCi0gQ29uIGxhIGF1dG9yaXphY2nDs24sIGxhIGluc3RpdHVjacOzbiBwdWVkZSBkaWZ1bmRpciBsYSBvYnJhIGVuIMOtbmRpY2VzLCBidXNjYWRvcmVzIHkgb3Ryb3Mgc2lzdGVtYXMgZGUgaW5mb3JtYWNpw7NuIHF1ZSBmYXZvcmV6Y2FuIHN1IHZpc2liaWxpZGFkCgo= |