Optimal Location and Operation of PV Sources in DC Grids to Reduce Annual Operating Costs While Considering Variable Power Demand and Generation
Due to the need to include renewable energy resources in electrical grids as well as the development and high implementation of PV generation and DC grids worldwide, it is necessary to propose effective optimization methodologies that guarantee that PV generators are located and sized on the DC elec...
- Autores:
-
Grisales-Noreña, Luis Fernando
Montoya, Oscar Danilo
Ramos-Paja, Carlos Andres
- 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:20.500.12585/12419
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12419
- Palabra clave:
- Microgrid;
DC-DC Converter;
Electric Potential
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.title.spa.fl_str_mv |
Optimal Location and Operation of PV Sources in DC Grids to Reduce Annual Operating Costs While Considering Variable Power Demand and Generation |
title |
Optimal Location and Operation of PV Sources in DC Grids to Reduce Annual Operating Costs While Considering Variable Power Demand and Generation |
spellingShingle |
Optimal Location and Operation of PV Sources in DC Grids to Reduce Annual Operating Costs While Considering Variable Power Demand and Generation Microgrid; DC-DC Converter; Electric Potential LEMB |
title_short |
Optimal Location and Operation of PV Sources in DC Grids to Reduce Annual Operating Costs While Considering Variable Power Demand and Generation |
title_full |
Optimal Location and Operation of PV Sources in DC Grids to Reduce Annual Operating Costs While Considering Variable Power Demand and Generation |
title_fullStr |
Optimal Location and Operation of PV Sources in DC Grids to Reduce Annual Operating Costs While Considering Variable Power Demand and Generation |
title_full_unstemmed |
Optimal Location and Operation of PV Sources in DC Grids to Reduce Annual Operating Costs While Considering Variable Power Demand and Generation |
title_sort |
Optimal Location and Operation of PV Sources in DC Grids to Reduce Annual Operating Costs While Considering Variable Power Demand and Generation |
dc.creator.fl_str_mv |
Grisales-Noreña, Luis Fernando Montoya, Oscar Danilo Ramos-Paja, Carlos Andres |
dc.contributor.author.none.fl_str_mv |
Grisales-Noreña, Luis Fernando Montoya, Oscar Danilo Ramos-Paja, Carlos Andres |
dc.subject.keywords.spa.fl_str_mv |
Microgrid; DC-DC Converter; Electric Potential |
topic |
Microgrid; DC-DC Converter; Electric Potential LEMB |
dc.subject.armarc.none.fl_str_mv |
LEMB |
description |
Due to the need to include renewable energy resources in electrical grids as well as the development and high implementation of PV generation and DC grids worldwide, it is necessary to propose effective optimization methodologies that guarantee that PV generators are located and sized on the DC electrical network. This will reduce the operation costs and cover the investment and maintenance cost related to the new technologies (PV distributed generators), thus satisfying all technical and operative constraints of the distribution grid. It is important to propose solution methodologies that require short processing times, with the aim of exploring a large number of scenarios while planning energy projects that are to be presented in public and private contracts, as well as offering solutions to technical problems of electrical distribution companies within short periods of time. Based on these needs, this paper proposes the implementation of a Discrete–Continuous Parallel version of the Particle Swarm Optimization algorithm (DCPPSO) to solve the problem regarding the integration of photovoltaic (PV) distributed generators (DGs) in Direct Current (DC) grids, with the purpose of reducing the annual costs related to energy purchasing as well as the investment and maintenance cost associated with PV sources in a scenario of variable power demand and generation. In order to evaluate the effectiveness, repeatability, and robustness of the proposed methodology, four comparison methods were employed, i.e., a commercial software and three discrete–continuous methodologies, as well as two test systems of 33 and 69 buses. In analyzing the results obtained in terms of solution quality, it was possible to identify that the DCPPSO proposed obtained the best performance in relation to the comparison methods used, with excellent results in relation to the processing times and standard deviation. The main contribution of the proposed methodology is the implementation of a discrete–continuous codification with a parallel processing tool for the evaluation of the fitness function. The results obtained and the reports in the literature for alternating current networks demonstrate that the DCPPSO is the optimization methodology with the best performance in solving the problem of the optimal integration of PV sources in economic terms and for any kind of electrical system and size. © 2022 by the authors. |
publishDate |
2022 |
dc.date.issued.none.fl_str_mv |
2022 |
dc.date.accessioned.none.fl_str_mv |
2023-07-24T20:49:07Z |
dc.date.available.none.fl_str_mv |
2023-07-24T20:49:07Z |
dc.date.submitted.none.fl_str_mv |
2023 |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_b1a7d7d4d402bcce |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.hasversion.spa.fl_str_mv |
info:eu-repo/semantics/draft |
dc.type.spa.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
status_str |
draft |
dc.identifier.citation.spa.fl_str_mv |
Grisales-Noreña, L. F., Montoya, O. D., & Ramos-Paja, C. A. (2022). Optimal Location and Operation of PV Sources in DC Grids to Reduce Annual Operating Costs While Considering Variable Power Demand and Generation. Mathematics, 10(23), 4512. |
dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/12419 |
dc.identifier.doi.none.fl_str_mv |
10.3390/math10234512 |
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 |
Grisales-Noreña, L. F., Montoya, O. D., & Ramos-Paja, C. A. (2022). Optimal Location and Operation of PV Sources in DC Grids to Reduce Annual Operating Costs While Considering Variable Power Demand and Generation. Mathematics, 10(23), 4512. 10.3390/math10234512 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
url |
https://hdl.handle.net/20.500.12585/12419 |
dc.language.iso.spa.fl_str_mv |
eng |
language |
eng |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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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 |
17 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 |
Mathematics |
institution |
Universidad Tecnológica de Bolívar |
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Grisales-Noreña, Luis Fernando7c27cda4-5fe4-4686-8f72-b0442c58a5d1Montoya, Oscar Danilo8a59ede1-6a4a-4d2e-abdc-d0afb14d4480Ramos-Paja, Carlos Andres6c8f6752-cad7-4a04-9a85-1d54832135102023-07-24T20:49:07Z2023-07-24T20:49:07Z20222023Grisales-Noreña, L. F., Montoya, O. D., & Ramos-Paja, C. A. (2022). Optimal Location and Operation of PV Sources in DC Grids to Reduce Annual Operating Costs While Considering Variable Power Demand and Generation. Mathematics, 10(23), 4512.https://hdl.handle.net/20.500.12585/1241910.3390/math10234512Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarDue to the need to include renewable energy resources in electrical grids as well as the development and high implementation of PV generation and DC grids worldwide, it is necessary to propose effective optimization methodologies that guarantee that PV generators are located and sized on the DC electrical network. This will reduce the operation costs and cover the investment and maintenance cost related to the new technologies (PV distributed generators), thus satisfying all technical and operative constraints of the distribution grid. It is important to propose solution methodologies that require short processing times, with the aim of exploring a large number of scenarios while planning energy projects that are to be presented in public and private contracts, as well as offering solutions to technical problems of electrical distribution companies within short periods of time. Based on these needs, this paper proposes the implementation of a Discrete–Continuous Parallel version of the Particle Swarm Optimization algorithm (DCPPSO) to solve the problem regarding the integration of photovoltaic (PV) distributed generators (DGs) in Direct Current (DC) grids, with the purpose of reducing the annual costs related to energy purchasing as well as the investment and maintenance cost associated with PV sources in a scenario of variable power demand and generation. In order to evaluate the effectiveness, repeatability, and robustness of the proposed methodology, four comparison methods were employed, i.e., a commercial software and three discrete–continuous methodologies, as well as two test systems of 33 and 69 buses. In analyzing the results obtained in terms of solution quality, it was possible to identify that the DCPPSO proposed obtained the best performance in relation to the comparison methods used, with excellent results in relation to the processing times and standard deviation. The main contribution of the proposed methodology is the implementation of a discrete–continuous codification with a parallel processing tool for the evaluation of the fitness function. The results obtained and the reports in the literature for alternating current networks demonstrate that the DCPPSO is the optimization methodology with the best performance in solving the problem of the optimal integration of PV sources in economic terms and for any kind of electrical system and size. © 2022 by the authors.17 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_abf2MathematicsOptimal Location and Operation of PV Sources in DC Grids to Reduce Annual Operating Costs While Considering Variable Power Demand and Generationinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Microgrid;DC-DC Converter;Electric PotentialLEMBCartagena de IndiasMonteiro, V., Oliveira, C., Coelho, S., Afonso, J.L. Hybrid AC/DC Electrical Power Grids in Active Buildings: A Power Electronics Perspective (2022) Green Energy and Technology, pp. 71-97. Cited 3 times. www.springer.com/series/8059 doi: 10.1007/978-3-030-79742-3_4Ghiasi, M. Detailed study, multi-objective optimization, and design of an AC-DC smart microgrid with hybrid renewable energy resources (2019) Energy, 169, pp. 496-507. Cited 108 times. www.elsevier.com/inca/publications/store/4/8/3/ doi: 10.1016/j.energy.2018.12.083Ghiasi, M., Dehghani, M., Niknam, T., Baghaee, H.R., Padmanaban, S., Gharehpetian, G.B., Aliev, H. Resiliency/Cost-Based Optimal Design of Distribution Network to Maintain Power System Stability against Physical Attacks: A Practical Study Case (2021) IEEE Access, 9, art. no. 9380135, pp. 43862-43875. Cited 26 times. http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 doi: 10.1109/ACCESS.2021.3066419Ahmad, R., Mohamed, A.A.A., Rezk, H., Al‐dhaifallah, M. DC Energy Hubs for Integration of Community DERs, EVs, and Subway Systems (Open Access) (2022) Sustainability (Switzerland), 14 (3), art. no. 1558. Cited 4 times. https://www.mdpi.com/2071-1050/14/3/1558/pdf doi: 10.3390/su14031558Rezaeeian, S., Bayat, N., Rabiee, A., Nikkhah, S., Soroudi, A. Optimal Scheduling of Reconfigurable Microgrids in Both Grid-Connected and Isolated Modes Considering the Uncertainty of DERs (2022) Energies, 15 (15), art. no. 5369. Cited 2 times. http://www.mdpi.com/journal/energies/ doi: 10.3390/en15155369Elsayed, A.T., Mohamed, A.A., Mohammed, O.A. DC microgrids and distribution systems: An overview (Open Access) (2015) Electric Power Systems Research, 119, pp. 407-417. Cited 414 times. doi: 10.1016/j.epsr.2014.10.017Mohseni, S., Brent, A.C. Quantifying the effects of forecast uncertainty on the role of different battery technologies in grid-connected solar photovoltaic/wind/micro-hydro micro-grids: An optimal planning study (2022) Journal of Energy Storage, 51, art. no. 104412. Cited 7 times. http://www.journals.elsevier.com/journal-of-energy-storage/ doi: 10.1016/j.est.2022.104412Ghiasi, M., Olamaei, J. Optimal capacitor placement to minimizing cost and power loss in Tehran metro power distribution system using ETAP (A case study) (2016) Complexity, 21, pp. 483-493. Cited 24 times. https://www.hindawi.com/journals/complexity/ doi: 10.1002/cplx.21828Fathi, M., Ghiasi, M. Optimal dg placement to find optimal voltage profile considering minimum dg investment cost in smart neighborhood (2019) Smart Cities, 2 (2), pp. 328-344. Cited 24 times. https://www.mdpi.com/2624-6511/2/2/20 doi: 10.3390/smartcities2020020Wang, P., Wang, W., Xu, D. Optimal sizing of distributed generations in DC microgrids with comprehensive consideration of system operation modes and operation targets (2018) IEEE Access, 6, pp. 31129-31140. Cited 52 times. http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 doi: 10.1109/ACCESS.2018.2842119Grisales-Noreña, L.F., Montoya, O.D., Marín-García, E.-J., Ramos-Paja, C.A., Perea-Moreno, A.-J. Integration of PV Distributed Generators into Electrical Networks for Investment and Energy Purchase Costs Reduction by Using a Discrete–Continuous Parallel PSO (2022) Energies, 15 (20), art. no. 7465. Cited 3 times. http://www.mdpi.com/journal/energies/ doi: 10.3390/en15207465Ghiasi, M., Niknam, T., Dehghani, M., Siano, P., Alhelou, H.H., Al-Hinai, A. Optimal multi-operation energy management in smart microgrids in the presence of ress based on multi-objective improved de algorithm: Cost-emission based optimization (Open Access) (2021) Applied Sciences (Switzerland), 11 (8), art. no. 3661. Cited 36 times. https://www.mdpi.com/2076-3417/11/8/3661/pdf doi: 10.3390/app11083661Goli, A., Ala, A., Hajiaghaei-Keshteli, M. Efficient multi-objective meta-heuristic algorithms for energy-aware non-permutation flow-shop scheduling problem (2023) Expert Systems with Applications, Part B 213, art. no. 119077. Cited 14 times. https://www.journals.elsevier.com/expert-systems-with-applications doi: 10.1016/j.eswa.2022.119077Tirkolaee, E.B., Mahdavi, I., Esfahani, M.M.S., Weber, G.-W. A robust green location-allocation-inventory problem to design an urban waste management system under uncertainty (2020) Waste Management, 102, pp. 340-350. Cited 127 times. www.elsevier.com/locate/wasman doi: 10.1016/j.wasman.2019.10.038Shirazi, H., Ghiasi, M., Dehghani, M., Niknam, T., Garpachi, M.G., Ramezani, A. Cost-Emission Control Based Physical-Resilience Oriented Strategy for Optimal Allocation of Distributed Generation in Smart Microgrid (Open Access) (2021) 2021 7th International Conference on Control, Instrumentation and Automation, ICCIA 2021, art. no. 9403561. Cited 9 times. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9403527 ISBN: 978-073812405-6 doi: 10.1109/ICCIA52082.2021.9403561Ishaq, S., Khan, I., Rahman, S., Hussain, T., Iqbal, A., Elavarasan, R.M. A review on recent developments in control and optimization of micro grids (Open Access) (2022) Energy Reports, 8, pp. 4085-4103. Cited 32 times. http://www.journals.elsevier.com/energy-reports/ doi: 10.1016/j.egyr.2022.01.080Anitha, D., Premkumar, K. DC microgrid: A review on issues and control (2022) Smart Grids and Green Energy Systems, pp. 207-229. https://www.wiley.com/en-us/Smart+Grids+and+Green+Energy+Systems-p-9781119872047 ISBN: 978-111987206-1; 978-111987204-7Kaushik, E., Prakash, V., Mahela, O.P., Khan, B., El-Shahat, A., Abdelaziz, A.Y. Comprehensive Overview of Power System Flexibility during the Scenario of High Penetration of Renewable Energy in Utility Grid (2022) Energies, 15 (2), art. no. 516. Cited 17 times. https://www.mdpi.com/1996-1073/15/2/516/pdf doi: 10.3390/en15020516Montoya, O.D., Gil-González, W., Grisales-Noreña, L.F. Relaxed convex model for optimal location and sizing of DGs in DC grids using sequential quadratic programming and random hyperplane approaches (2020) International Journal of Electrical Power and Energy Systems, 115, art. no. 105442. Cited 23 times. https://www.journals.elsevier.com/international-journal-of-electrical-power-and-energy-systems doi: 10.1016/j.ijepes.2019.105442Duman, S. A modified moth swarm algorithm based on an arithmetic crossover for constrained optimization and optimal power flow problems (2018) IEEE Access, 6, art. no. 8395276, pp. 45394-45416. Cited 32 times. http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 doi: 10.1109/ACCESS.2018.2849599Ma, X., Liu, S., Liu, H., Zhao, S. The Selection of Optimal Structure for Stand-Alone Micro-Grid Based on Modeling and Optimization of Distributed Generators (Open Access) (2022) IEEE Access, 10, pp. 40642-40660. Cited 14 times. http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 doi: 10.1109/ACCESS.2022.3164514Cortés-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 (2022) Sensors, 22 (3), art. no. 851. Cited 18 times. https://www.mdpi.com/1424-8220/22/3/851/pdf doi: 10.3390/s22030851Khezri, R., Mahmoudi, A., Haque, M.H. Two-Stage Optimal Sizing of Standalone Hybrid Electricity Systems with Time-of-Use Incentive Demand Response (Open Access) (2020) ECCE 2020 - IEEE Energy Conversion Congress and Exposition, art. no. 9236381, pp. 2759-2765. Cited 9 times. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9235288 ISBN: 978-172815826-6 doi: 10.1109/ECCE44975.2020.9236381Grisales-Noreña, L.F., Montoya-Giraldo, O.D., Gil-González, W. Optimal Integration of Distributed Generators into DC Microgrids Using a Hybrid Methodology: Genetic and Vortex Search Algorithms (Open Access) (2022) Arabian Journal for Science and Engineering, 47 (11), pp. 14657-14672. Cited 4 times. https://www.springer.com/journal/13369 doi: 10.1007/s13369-022-06866-7Montoya, O.D., Grisales-Noreña, L.F., Perea-Moreno, A.-J. Optimal investments in PV sources for grid-connected distribution networks: An application of the discrete–continuous genetic algorithm (Open Access) (2021) Sustainability (Switzerland), 13 (24), art. no. 13633. Cited 16 times. https://www.mdpi.com/2071-1050/13/24/13633/pdf doi: 10.3390/su132413633Montoya, O.D., Gil-González, W., Grisales-Noreña, L.F. Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm (Open Access) (2022) Algorithms, 15 (8), art. no. 277. Cited 7 times. www.mdpi.com/journal/algorithms/ doi: 10.3390/a15080277Grisales-Noreña, L.F., Montoya, O.D., Ramos-Paja, C.A. An energy management system for optimal operation of BSS in DC distributed generation environments based on a parallel PSO algorithm (Open Access) (2020) Journal of Energy Storage, 29, art. no. 101488. Cited 58 times. http://www.journals.elsevier.com/journal-of-energy-storage/ doi: 10.1016/j.est.2020.101488Kaur, S., Kumbhar, G., Sharma, J. A MINLP technique for optimal placement of multiple DG units in distribution systems (2014) International Journal of Electrical Power and Energy Systems, 63, pp. 609-617. Cited 194 times. doi: 10.1016/j.ijepes.2014.06.023Kendrick, D., Krishnan, R. A comparison of structured modeling and GAMS (Open Access) (1989) Computer Science in Economics and Management, 2 (1), pp. 17-36. 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