A convex OPF approximation for selecting the best candidate nodes for optimal location of power sources on DC resistive networks
This paper proposes a convex approximation approach for solving the optimal power flow (OPF) problem in direct current (DC) networks with constant power loads by using a sequential quadratic programming approach. A linearization method based on the Taylor series is used for the convexification of th...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/8767
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/8767
- Palabra clave:
- Convex model
Direct current networks
Linear power flow approximation
Optimal power flow
Power loss reduction
Relaxation of binary variables
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
| id |
UTB2_861d0d84fea09be100886fa798831220 |
|---|---|
| oai_identifier_str |
oai:repositorio.utb.edu.co:20.500.12585/8767 |
| network_acronym_str |
UTB2 |
| network_name_str |
Repositorio Institucional UTB |
| repository_id_str |
|
| dc.title.none.fl_str_mv |
A convex OPF approximation for selecting the best candidate nodes for optimal location of power sources on DC resistive networks |
| title |
A convex OPF approximation for selecting the best candidate nodes for optimal location of power sources on DC resistive networks |
| spellingShingle |
A convex OPF approximation for selecting the best candidate nodes for optimal location of power sources on DC resistive networks Convex model Direct current networks Linear power flow approximation Optimal power flow Power loss reduction Relaxation of binary variables |
| title_short |
A convex OPF approximation for selecting the best candidate nodes for optimal location of power sources on DC resistive networks |
| title_full |
A convex OPF approximation for selecting the best candidate nodes for optimal location of power sources on DC resistive networks |
| title_fullStr |
A convex OPF approximation for selecting the best candidate nodes for optimal location of power sources on DC resistive networks |
| title_full_unstemmed |
A convex OPF approximation for selecting the best candidate nodes for optimal location of power sources on DC resistive networks |
| title_sort |
A convex OPF approximation for selecting the best candidate nodes for optimal location of power sources on DC resistive networks |
| dc.subject.keywords.none.fl_str_mv |
Convex model Direct current networks Linear power flow approximation Optimal power flow Power loss reduction Relaxation of binary variables |
| topic |
Convex model Direct current networks Linear power flow approximation Optimal power flow Power loss reduction Relaxation of binary variables |
| description |
This paper proposes a convex approximation approach for solving the optimal power flow (OPF) problem in direct current (DC) networks with constant power loads by using a sequential quadratic programming approach. A linearization method based on the Taylor series is used for the convexification of the power balance equations. For selecting the best candidate nodes for optimal location of distributed generators (DGs) on a DC network, a relaxation of the binary variables that represent the DGs location is proposed. This relaxation allows identifying the most important nodes for reducing power losses as well as the unimportant nodes. The optimal solution obtained by the proposed convex model is the best possible solution and serves for adjusting combinatorial optimization techniques for recovering the binary characteristics of the decision variables. The solution of the non-convex OPF model is achieved via GAMS software in conjunction with the CONOPT solver; in addition the sequential quadratic programming model is solved via quadprog from MATLAB for reducing the estimation errors in terms of calculation of the power losses. To compare the results of the proposed convex model, three metaheuristic approaches were employed using genetic algorithms, particle swarm optimization, continuous genetic algorithms, and black hole optimizers. © 2019 Karabuk University |
| publishDate |
2019 |
| dc.date.accessioned.none.fl_str_mv |
2019-11-06T19:05:21Z |
| dc.date.available.none.fl_str_mv |
2019-11-06T19:05:21Z |
| dc.date.issued.none.fl_str_mv |
2019 |
| dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.spa.none.fl_str_mv |
Artículo |
| dc.identifier.citation.none.fl_str_mv |
Engineering Science and Technology, an International Journal |
| dc.identifier.issn.none.fl_str_mv |
2215-0986 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/8767 |
| dc.identifier.doi.none.fl_str_mv |
10.1016/j.jestch.2019.06.010 |
| dc.identifier.instname.none.fl_str_mv |
Universidad Tecnológica de Bolívar |
| dc.identifier.reponame.none.fl_str_mv |
Repositorio UTB |
| identifier_str_mv |
Engineering Science and Technology, an International Journal 2215-0986 10.1016/j.jestch.2019.06.010 Universidad Tecnológica de Bolívar Repositorio UTB |
| url |
https://hdl.handle.net/20.500.12585/8767 |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.uri.none.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| dc.rights.accessRights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.cc.none.fl_str_mv |
Atribución-NoComercial 4.0 Internacional |
| rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ Atribución-NoComercial 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.medium.none.fl_str_mv |
Recurso electrónico |
| dc.format.mimetype.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Elsevier B.V. |
| publisher.none.fl_str_mv |
Elsevier B.V. |
| dc.source.none.fl_str_mv |
https://www2.scopus.com/inward/record.uri?eid=2-s2.0-85068989753&doi=10.1016%2fj.jestch.2019.06.010&partnerID=40&md5=e49682c8ba7c24b0aae90ebd13b55237 Scopus 56919564100 |
| institution |
Universidad Tecnológica de Bolívar |
| bitstream.url.fl_str_mv |
https://repositorio.utb.edu.co/bitstream/20.500.12585/8767/1/DOI10_1016j_jestch_2019_06_010.pdf https://repositorio.utb.edu.co/bitstream/20.500.12585/8767/4/DOI10_1016j_jestch_2019_06_010.pdf.txt https://repositorio.utb.edu.co/bitstream/20.500.12585/8767/5/DOI10_1016j_jestch_2019_06_010.pdf.jpg |
| bitstream.checksum.fl_str_mv |
1f6aa3c05551bb5ee3e59edea1248a9e 8f5a410731e063d67ec5454cd5e35ebc 5af337685b9880adf098c63b88c3a886 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional UTB |
| repository.mail.fl_str_mv |
repositorioutb@utb.edu.co |
| _version_ |
1814021690939670528 |
| spelling |
2019-11-06T19:05:21Z2019-11-06T19:05:21Z2019Engineering Science and Technology, an International Journal2215-0986https://hdl.handle.net/20.500.12585/876710.1016/j.jestch.2019.06.010Universidad Tecnológica de BolívarRepositorio UTBThis paper proposes a convex approximation approach for solving the optimal power flow (OPF) problem in direct current (DC) networks with constant power loads by using a sequential quadratic programming approach. A linearization method based on the Taylor series is used for the convexification of the power balance equations. For selecting the best candidate nodes for optimal location of distributed generators (DGs) on a DC network, a relaxation of the binary variables that represent the DGs location is proposed. This relaxation allows identifying the most important nodes for reducing power losses as well as the unimportant nodes. The optimal solution obtained by the proposed convex model is the best possible solution and serves for adjusting combinatorial optimization techniques for recovering the binary characteristics of the decision variables. The solution of the non-convex OPF model is achieved via GAMS software in conjunction with the CONOPT solver; in addition the sequential quadratic programming model is solved via quadprog from MATLAB for reducing the estimation errors in terms of calculation of the power losses. To compare the results of the proposed convex model, three metaheuristic approaches were employed using genetic algorithms, particle swarm optimization, continuous genetic algorithms, and black hole optimizers. © 2019 Karabuk UniversityUniversidad Tecnológica de Pereira, UTP: C2018P020, Departamento Administrativo de Ciencia, Tecnología e Innovación, COLCIENCIAS, Department of Science, Information Technology and Innovation, Queensland Government, DSITIRecurso electrónicoapplication/pdfengElsevier B.V.http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2https://www2.scopus.com/inward/record.uri?eid=2-s2.0-85068989753&doi=10.1016%2fj.jestch.2019.06.010&partnerID=40&md5=e49682c8ba7c24b0aae90ebd13b55237Scopus 56919564100A convex OPF approximation for selecting the best candidate nodes for optimal location of power sources on DC resistive networksinfo:eu-repo/semantics/articleArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Convex modelDirect current networksLinear power flow approximationOptimal power flowPower loss reductionRelaxation of binary variablesMontoya, O.D.Abdi, H., Beigvand, S.D., Scala, M.L., A review of optimal power flow studies applied to smart grids and microgrids (2017) Renewable Sustainable Energy Rev., 71, pp. 742-766Ahmed, H.M.A., Eltantawy, A.B., Salama, M.M.A., A planning approach for the network configuration of ac-dc hybrid distribution systems (2018) IEEE Trans. Smart Grid, 9 (3), pp. 2203-2213Alam, M.S., Miah, M.D., Hammoudeh, S., Tiwari, A.K., The nexus between access to electricity and labour productivity in developing countries (2018) Energy Policy, 122, pp. 715-726Fletcher, J.R.E., Fernando, T., Iu, H.H., Reynolds, M., Fani, S., Spatial optimization for the planning of sparse power distribution networks (2018) IEEE Trans. Power Syst., 33 (6), pp. 6686-6695Fortenbacher, P., Demiray, T., Linear/quadratic programming-based optimal power flow using linear power flow and absolute loss approximations (2019) Int. J. Electr. Power Energy Syst., 107, pp. 680-689Garces, A., A linear three-phase load flow for power distribution systems (2016) IEEE Trans. Power Syst., 31 (1), pp. 827-828Garces, A., Uniqueness of the power flow solutions in low voltage direct current grids (2017) Electr. Power Syst. Res., 151, pp. 149-153Garces, A., On the convergence of newton's method in power flow studies for DC microgrids (2018) IEEE Trans. Power Syst., 33 (5), pp. 5770-5777Garces, A., Montoya, D., Torres, R., Optimal power flow in multiterminal HVDC systems considering DC/DC converters (2016) 2016 IEEE 25th International Symposium on Industrial Electronics (ISIE), pp. 1212-1217Gavriluta, C., Candela, I., Citro, C., Luna, A., Rodriguez, P., Design considerations for primary control in multi-terminal VSC-HVDC grids (2015) Electr. Power Syst. Res., 122, pp. 33-41Gil-González, W., Montoya, O.D., Holguín, E., Garces, A., Grisales-Noreña, L.F., Economic dispatch of energy storage systems in dc microgrids employing a semidefinite programming model (2019) J. Energy Storage, 21, pp. 1-8Grisales-Noreña, L.F., Gonzalez-Montoya, D., Ramos-Paja, C.A., Optimal sizing and location of distributed generators based on PBIL and PSO techniques (2018) Energies, 11 (1018), pp. 1-27Hou, K., Xu, X., Jia, H., Yu, X., Jiang, T., Zhang, K., Shu, B., A reliability assessment approach for integrated transportation and electrical power systems incorporating electric vehicles (2018) IEEE Trans. Smart Grid, 9 (1), pp. 88-100Kaur, S., Kumbhar, G., Sharma, J., A MINLP technique for optimal placement of multiple DG units in distribution systems (2014) Int. J. Electr. Power Energy Syst. 63 (Supplement, C), pp. 609-617Keane, A., Ochoa, L.F., Borges, C.L.T., Ault, G.W., Alarcon-Rodriguez, A.D., Currie, R.A.F., Pilo, F., Harrison, G.P., State-of-the-art techniques and challenges ahead for distributed generation planning and optimization (2013) IEEE Trans. Power Syst., 28 (2), pp. 1493-1502Li, J., Liu, F., Wang, Z., Low, S., Mei, S., Optimal power flow in stand-alone DC microgrids (2018) IEEE Trans. Power Syst., , 1–1Li, R., Wang, W., Xia, M., Cooperative planning of active distribution system with renewable energy sources and energy storage systems (2018) IEEE Access, 6, pp. 5916-5926Lotfi, H., Khodaei, A., AC versus DC microgrid planning (2017) IEEE Trans. Smart Grid, 8 (1), pp. 296-304Mao, Y., Zhang, J., Letaief, K.B., Grid energy consumption and QoS tradeoff in hybrid energy supply wireless networks (2016) IEEE Trans. Wireless Commun., 15 (5), pp. 3573-3586Masrur, M.A., Toward ground vehicle electrification in the u.s. army: an overview of recent activities (2016) IEEE Electrific. Mag., 4 (1), pp. 33-45Masteri, K., Venkatesh, B., Freitas, W., A feeder investment model for distribution system planning including battery energy storage (2018) Fall Canadian J. Elect. Comput. Eng., 41 (4), pp. 162-171Mateo Domingo, C., Gomez San Roman, T., Sanchez-Miralles, A., Peco Gonzalez, J.P., Candela Martinez, A., A reference network model for large-scale distribution planning with automatic street map generation (2011) IEEE Trans. Power Syst., 26 (1), pp. 190-197Mehar, S., Zeadally, S., Rémy, G., Senouci, S.M., Sustainable transportation management system for a fleet of electric vehicles (2015) IEEE Trans. Intell. Transp. Syst., 16 (3), pp. 1401-1414Mohamed, S., Shaaban, M.F., Ismail, M., Serpedin, E., Qaraqe, K.A., An efficient planning algorithm for hybrid remote microgrids (2019) IEEE Trans. Sustain. Energy, 10 (1), pp. 257-267Montoya, O.D., Numerical approximation of the maximum power consumption in DC-MGs With CPLs via an SDP model (2019) IEEE Trans. Circuits Syst. II, 66 (4), pp. 642-646Montoya, O.D., On linear analysis of the power flow equations for DC and AC grids with CPLs (2019) IEEE Trans. Circuits Syst., 2, pp. 1-5Montoya, O.D., Garrido, V.M., Gil-González, W., Grisales-Noreña, L., Power flow analysis in dc grids: two alternative numerical methods (2019) IEEE Trans. Circuits Syst., 2, pp. 1-5Montoya, O.D., Gil-González, W., Garces, A., Optimal Power Flow on DC Microgrids: A Quadratic Convex Approximation (2019) IEEE Trans. Circuits Syst. II, 66 (6), pp. 1018-1022Montoya, O.D., Gil-González, W., Grisales-Noreña, L.F., Optimal power dispatch of DGs in DC power grids: a hybrid gauss-seidel-genetic-algorithm methodology for solving the OPF problem (2018) WSEAS Transactions on Power Systems, 13 (33), pp. 335-346Montoya, O.D., Gil-González, W., Garces, A., Sequential quadratic programming models for solving the OPF problem in DC grids (2019) Electr. Power Syst. Res., 169, pp. 18-23Montoya, O.D., Grisales-Noreña, L.F., González-Montoya, D., Ramos-Paja, C., Garces, A., Linear power flow formulation for low-voltage DC power grids (2018) Electr. Power Syst. Res., 163, pp. 375-381Nasir, M., Iqbal, S., Khan, H.A., Optimal planning and design of low-voltage low-power solar DC microgrids (2018) IEEE Trans. Power Syst., 33 (3), pp. 2919-2928Nasir, M., Khan, H.A., Zaffar, N.A., Vasquez, J.C., Guerrero, J.M., Scalable solar dc micrigrids: on the path to revolutionizing the electrification architecture of developing communities (2018) IEEE Electrific. Mag., 6 (4), pp. 63-72Parhizi, S., Lotfi, H., Khodaei, A., Bahramirad, S., State of the art in research on microgrids: a review (2015) IEEE Access, 3, pp. 890-925Raghavan, S.S., Khaligh, A., Electrification potential factor: energy-based value proposition analysis of plug-in hybrid electric vehicles (2012) IEEE Trans. Veh. Technol., 61 (3), pp. 1052-1059Simpson-Porco, J.W., Dorfler, F., Bullo, F., Networks, A.O.R., Briefs of constant-power devices (2015) IEEE Trans. Circuits Syst. II Express, 62 (8), pp. 811-815Steinbuks, J., Assessing the accuracy of electricity production forecasts in developing countries (2019) Int. J. ForecastingSudabattula, S.K., Kowsalya, M., Optimal allocation of solar based distributed generators in distribution system using Bat algorithm (2016) Perspect. Sci., 8, pp. 270-272. , recent Trends in Engineering and Material SciencesSultana, S., Roy, P.K., Krill herd algorithm for optimal location of distributed generator in radial distribution system (2016) Appl. Soft Comput., 40, pp. 391-404Velasquez, O.S., Montoya, O.D., Garrido, V.M., Grisales-Noreña, L.F., Optimal power flow in direct-current power grids via black hole optimization (2019) Adv. Electr. Electron. Eng., 17 (1), pp. 24-32Wang, P., Zhang, L., Xu, D., Optimal Sizing of Distributed Generations in DC Microgrids with Lifespan Estimated Model of Batteries (2018) 2018 21st International Conference on Electrical Machines and Systems (ICEMS), pp. 2045-2049Washburn, C., Pablo-Romero, M., Measures to promote renewable energies for electricity generation in Latin American countries (2019) Energy Policy, 128, pp. 212-222Zheng, G., Huang, Q., Energy optimization study of rural deep well two-stage water supply pumping station (2016) IEEE Trans. Control Syst. Technol., 24 (4), pp. 1308-1316http://purl.org/coar/resource_type/c_6501ORIGINALDOI10_1016j_jestch_2019_06_010.pdfapplication/pdf667739https://repositorio.utb.edu.co/bitstream/20.500.12585/8767/1/DOI10_1016j_jestch_2019_06_010.pdf1f6aa3c05551bb5ee3e59edea1248a9eMD51TEXTDOI10_1016j_jestch_2019_06_010.pdf.txtDOI10_1016j_jestch_2019_06_010.pdf.txtExtracted texttext/plain42067https://repositorio.utb.edu.co/bitstream/20.500.12585/8767/4/DOI10_1016j_jestch_2019_06_010.pdf.txt8f5a410731e063d67ec5454cd5e35ebcMD54THUMBNAILDOI10_1016j_jestch_2019_06_010.pdf.jpgDOI10_1016j_jestch_2019_06_010.pdf.jpgGenerated Thumbnailimage/jpeg114129https://repositorio.utb.edu.co/bitstream/20.500.12585/8767/5/DOI10_1016j_jestch_2019_06_010.pdf.jpg5af337685b9880adf098c63b88c3a886MD5520.500.12585/8767oai:repositorio.utb.edu.co:20.500.12585/87672020-10-23 04:49:27.806Repositorio Institucional UTBrepositorioutb@utb.edu.co |