Sine-Cosine Algorithm for OPF Analysis in Distribution Systems to Size Distributed Generators
This paper addresses the analysis the optimal power flow (OPF) problem in alternating current (AC) radial distribution networks by using a new metaheuristic optimization technique known as a sine-cosine algorithm (SCA). This combinatorial optimization approach allows for solving the nonlinear non-co...
- 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:20.500.12585/9183
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9183
- Palabra clave:
- Optimal power flow
Optimal sizing of distributed generation
Radial distribution networks
Sine-cosine algorithm
Soft computing optimization technique
Acoustic generators
Combinatorial optimization
Convex optimization
Distributed power generation
Electric impedance measurement
Electric load flow
Genetic algorithms
MATLAB
Particle size analysis
Particle swarm optimization (PSO)
Silicon compounds
Soft computing
Optimal power flows
Optimal sizing
Optimization techniques
Radial distribution networks
Sine-cosine algorithm
Electric load dispatching
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Sine-Cosine Algorithm for OPF Analysis in Distribution Systems to Size Distributed Generators |
| title |
Sine-Cosine Algorithm for OPF Analysis in Distribution Systems to Size Distributed Generators |
| spellingShingle |
Sine-Cosine Algorithm for OPF Analysis in Distribution Systems to Size Distributed Generators Optimal power flow Optimal sizing of distributed generation Radial distribution networks Sine-cosine algorithm Soft computing optimization technique Acoustic generators Combinatorial optimization Convex optimization Distributed power generation Electric impedance measurement Electric load flow Genetic algorithms MATLAB Particle size analysis Particle swarm optimization (PSO) Silicon compounds Soft computing Optimal power flows Optimal sizing Optimization techniques Radial distribution networks Sine-cosine algorithm Electric load dispatching |
| title_short |
Sine-Cosine Algorithm for OPF Analysis in Distribution Systems to Size Distributed Generators |
| title_full |
Sine-Cosine Algorithm for OPF Analysis in Distribution Systems to Size Distributed Generators |
| title_fullStr |
Sine-Cosine Algorithm for OPF Analysis in Distribution Systems to Size Distributed Generators |
| title_full_unstemmed |
Sine-Cosine Algorithm for OPF Analysis in Distribution Systems to Size Distributed Generators |
| title_sort |
Sine-Cosine Algorithm for OPF Analysis in Distribution Systems to Size Distributed Generators |
| dc.contributor.editor.none.fl_str_mv |
Figueroa-Garcia J.C. Duarte-Gonzalez M. Jaramillo-Isaza S. Orjuela-Canon A.D. Diaz-Gutierrez Y. |
| dc.subject.keywords.none.fl_str_mv |
Optimal power flow Optimal sizing of distributed generation Radial distribution networks Sine-cosine algorithm Soft computing optimization technique Acoustic generators Combinatorial optimization Convex optimization Distributed power generation Electric impedance measurement Electric load flow Genetic algorithms MATLAB Particle size analysis Particle swarm optimization (PSO) Silicon compounds Soft computing Optimal power flows Optimal sizing Optimization techniques Radial distribution networks Sine-cosine algorithm Electric load dispatching |
| topic |
Optimal power flow Optimal sizing of distributed generation Radial distribution networks Sine-cosine algorithm Soft computing optimization technique Acoustic generators Combinatorial optimization Convex optimization Distributed power generation Electric impedance measurement Electric load flow Genetic algorithms MATLAB Particle size analysis Particle swarm optimization (PSO) Silicon compounds Soft computing Optimal power flows Optimal sizing Optimization techniques Radial distribution networks Sine-cosine algorithm Electric load dispatching |
| description |
This paper addresses the analysis the optimal power flow (OPF) problem in alternating current (AC) radial distribution networks by using a new metaheuristic optimization technique known as a sine-cosine algorithm (SCA). This combinatorial optimization approach allows for solving the nonlinear non-convex optimization OPF problem by using a master-slave strategy. In the master stage, the soft computing SCA is used to define the power dispatch at each distributed generator (dimensioning problem). In the slave stage, it is used a conventional radial power flow formulated by incidence matrices is used for evaluating the total power losses (objective function evaluation). Two conventional highly used distribution feeders with 33 and 69 nodes are employed for validating the proposed master-slave approach. Simulation results are compared with different literature methods such as genetic algorithm, particle swarm optimization, and krill herd algorithm. All the simulations are performed in MATLAB programming environment, and their results show the effectiveness of the proposed approach in contrast to previously reported methods. © 2019, Springer Nature Switzerland AG. |
| publishDate |
2019 |
| dc.date.issued.none.fl_str_mv |
2019 |
| dc.date.accessioned.none.fl_str_mv |
2020-03-26T16:33:09Z |
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2020-03-26T16:33:09Z |
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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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info:eu-repo/semantics/conferenceObject |
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info:eu-repo/semantics/publishedVersion |
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Conferencia |
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Communications in Computer and Information Science; Vol. 1052, pp. 28-39 |
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9783030310189 |
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18650929 |
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https://hdl.handle.net/20.500.12585/9183 |
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10.1007/978-3-030-31019-6_3 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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57212008879 56919564100 57208126635 55791991200 57191493648 |
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Communications in Computer and Information Science; Vol. 1052, pp. 28-39 9783030310189 18650929 10.1007/978-3-030-31019-6_3 Universidad Tecnológica de Bolívar Repositorio UTB 57212008879 56919564100 57208126635 55791991200 57191493648 |
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https://hdl.handle.net/20.500.12585/9183 |
| dc.language.iso.none.fl_str_mv |
eng |
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eng |
| dc.relation.conferencedate.none.fl_str_mv |
16 October 2019 through 18 October 2019 |
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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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Springer |
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Springer |
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6th Workshop on Engineering Applications, WEA 2019 |
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Figueroa-Garcia J.C.Duarte-Gonzalez M.Jaramillo-Isaza S.Orjuela-Canon A.D.Diaz-Gutierrez Y.Manrique M.L.Montoya O.D.Garrido Arévalo, Víctor ManuelGrisales-Noreña L.F.Gil-González W.2020-03-26T16:33:09Z2020-03-26T16:33:09Z2019Communications in Computer and Information Science; Vol. 1052, pp. 28-39978303031018918650929https://hdl.handle.net/20.500.12585/918310.1007/978-3-030-31019-6_3Universidad Tecnológica de BolívarRepositorio UTB5721200887956919564100572081266355579199120057191493648This paper addresses the analysis the optimal power flow (OPF) problem in alternating current (AC) radial distribution networks by using a new metaheuristic optimization technique known as a sine-cosine algorithm (SCA). This combinatorial optimization approach allows for solving the nonlinear non-convex optimization OPF problem by using a master-slave strategy. In the master stage, the soft computing SCA is used to define the power dispatch at each distributed generator (dimensioning problem). In the slave stage, it is used a conventional radial power flow formulated by incidence matrices is used for evaluating the total power losses (objective function evaluation). Two conventional highly used distribution feeders with 33 and 69 nodes are employed for validating the proposed master-slave approach. Simulation results are compared with different literature methods such as genetic algorithm, particle swarm optimization, and krill herd algorithm. All the simulations are performed in MATLAB programming environment, and their results show the effectiveness of the proposed approach in contrast to previously reported methods. © 2019, Springer Nature Switzerland AG.Recurso electrónicoapplication/pdfengSpringerhttp://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-s2.0-85075672718&doi=10.1007%2f978-3-030-31019-6_3&partnerID=40&md5=7b578464309de0f72fbd68cbf2cc00836th Workshop on Engineering Applications, WEA 2019Sine-Cosine Algorithm for OPF Analysis in Distribution Systems to Size Distributed Generatorsinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionConferenciahttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fOptimal power flowOptimal sizing of distributed generationRadial distribution networksSine-cosine algorithmSoft computing optimization techniqueAcoustic generatorsCombinatorial optimizationConvex optimizationDistributed power generationElectric impedance measurementElectric load flowGenetic algorithmsMATLABParticle size analysisParticle swarm optimization (PSO)Silicon compoundsSoft computingOptimal power flowsOptimal sizingOptimization techniquesRadial distribution networksSine-cosine algorithmElectric load dispatching16 October 2019 through 18 October 2019Keane, A., State-of-the-art techniques and challenges ahead for distributed generation planning and optimization (2013) IEEE Trans. Power Syst., 28 (2), pp. 1493-1502Montoya, O.D., Garces, A., Castro, C.A., Optimal conductor size selection in radial distribution networks using a mixed-integer non-linear programming formulation (2018) IEEE Lat. Am. Trans., 16 (8), pp. 2213-2220Zeng, B., Zhang, J., Yang, X., Wang, J., Dong, J., Zhang, Y., Integrated planning for transition to low-carbon distribution system with renewable energy generation and demand response (2014) IEEE Trans. Power Syst., 29 (3), pp. 1153-1165Li, R., Wang, W., Xia, M., Cooperative planning of active distribution system with renewable energy sources and energy storage systems (2018) IEEE Access, 6, pp. 5916-5926Montoya, O.D., Grajales, A., Garces, A., Castro, C.A., Distribution systems operation considering energy storage devices and distributed generation (2017) IEEE Lat. Am. Trans., 15 (5), pp. 890-900Bai, X., Qu, L., Qiao, W., Robust AC optimal power flow for power networks with wind power generation (2016) IEEE Trans. Power Syst., 31 (5), pp. 4163-4164Gabash, A., Li, P., Active-reactive optimal power flow in distribution networks with embedded generation and battery storage (2012) IEEE Trans. Power Syst., 27 (4), pp. 2026-2035Wang, Y., Zhang, N., Li, H., Yang, J., Kang, C., Linear three-phase power flow for unbalanced active distribution networks with PV nodes (2017) CSEE J. Power Energy Syst., 3 (3), pp. 321-324Grisales-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. 1-27Teng, J.-H., A modified gauss–seidel algorithm of three-phase power flow analysis in distribution networks (2002) Int. J. Electr. Power Energy Syst., 24 (2), pp. 97-102Zamzam, A.S., Sidiropoulos, N.D., Dall’Anese, E., Beyond relaxation and Newton– Raphson: Solving AC OPF for multi-phase systems with renewables (2018) IEEE Trans. Smart Grid, 9 (5), pp. 3966-3975Garces, A., A linear three-phase load flow for power distribution systems (2016) IEEE Trans. Power Syst., 31 (1), pp. 827-828Lisboa, A., Guedes, L., Vieira, D., Saldanha, R., A fast power flow method for radial networks with linear storage and no matrix inversions (2014) Int. J. Electr. Power Energy Syst., 63, pp. 901-907Sultana, S., Roy, P.K., Krill herd algorithm for optimal location of distributed generator in radial distribution system (2016) Appl. Soft Comput., 40, pp. 391-404Attia, A.-F., Sehiemy, R.A.E., Hasanien, H.M., Optimal power flow solution in power systems using a novel Sine-Cosine algorithm (2018) Int. J. Electr. Power Energy Syst., 99, pp. 331-343Moradi, 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. 66-74Huang, S., Wu, Q., Wang, J., Zhao, H., A sufficient condition on convex relaxation of AC optimal power flow in distribution networks (2017) IEEE Trans. Power Syst., 32 (2), pp. 1359-1368Venzke, A., Halilbasic, L., Markovic, U., Hug, G., Chatzivasileiadis, S., Convex relaxations of chance constrained AC optimal power flow (2018) IEEE Trans. Power Syst., 33 (3), pp. 2829-2841Miao, Z., Fan, L., Aghamolki, H.G., Zeng, B., Least squares estimation based SDP cuts for SOCP relaxation of AC OPF (2018) IEEE Trans. Autom. Control, 63 (1), pp. 241-248Oliveira, E.J., Oliveira, L.W., Pereira, J., Honório, L.M., Silva, I.C., Marcato, A., An optimal power flow based on safety barrier interior point method (2015) Int. J. Electr. Power Energy Syst., 64, pp. 977-985Yang, J., He, L., Fu, S., An improved PSO-based charging strategy of electric vehicles in electrical distribution grid (2014) Appl. Energy, 128, pp. 82-92Todorovski, M., Rajicic, D., An initialization procedure in solving optimal power flow by genetic algorithm (2006) IEEE Trans. Power Syst., 21 (2), pp. 480-487Abido, M.A., Optimal power flow using tabu search algorithm (2002) Electr. Power Compon. Syst., 30 (5), pp. 469-483Kılıc, U., Ayan, K., Optimizing power flow of AC–DC power systems using artificial bee colony algorithm (2013) Int. J. Electr. Power Energy Syst., 53, pp. 592-602Balachennaiah, P., Suryakalavathi, M., Nagendra, P., Firefly algorithm based solution to minimize the real power loss in a power system (2018) Ain Shams Eng. J., 9 (1), pp. 89-100Montoya, O.D., Garrido, V.M., Gil-González, W., Grisales-Noreña, L.F., Power flow analysis in DC grids: Two alternative numerical methods (2019) IEEE Trans. Circuits Syst. II, 1Garces, A., Uniqueness of the power flow solutions in low voltage direct current grids (2017) Electr. Power Syst. Res., 151, pp. 149-153Injeti, S.K., Kumar, N.P., A novel approach to identify optimal access point and capacity of multiple DGs in a small, medium and large scale radial distribution systems (2013) Int. J. Electr. Power Energy Syst., 45 (1), pp. 142-151http://purl.org/coar/resource_type/c_c94fTHUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/9183/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/9183oai:repositorio.utb.edu.co:20.500.12585/91832023-05-26 10:08:57.612Repositorio Institucional UTBrepositorioutb@utb.edu.co |