Cost functions for generation dispatching in microgrids for non-interconnected zones in Colombia

dc.creatorHoyos Velandia, Cristian
dc.creatorRamírez Hurtado, Lina
dc.creatorQuintero Restrepo, Jaime
dc.creatorMoreno Chuquen., Ricardo
dc.creatorGonzález Longatt, Francisco
dc.date.accessioned2023-05-05T20:34:37Z
dc.date.accessioned2023-06-06T14:29:26Z
dc.date.available2023-05-05T20:34:37Z
dc.date.available2023-06-06T14:29:26Z
dc.date.created2023-05-05T20:34:37Z
dc.date.issued2022-03-22
dc.identifier19961073
dc.identifierhttps://hdl.handle.net/10614/14703
dc.identifierUniversidad Autónoma de Occidente
dc.identifierRepositorio Educativo Digital UAO
dc.identifierhttps://red.uao.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/6649416
dc.description.abstractGeneration dispatching is a challenge in islanded microgrids due to the operational and economic restrictions in isolated zones. Furthermore, the impact of usual operational network changes in topology, load demand, and generation availability may become significant considering the grid size. This research paper presents a detailed multiple cost function modeling methodology of an optimal power flow algorithm applied to a non-interconnected zone in Colombia. The optimal power flow (OPF) formulation includes cost functions related to renewable resources as presented in the isolated zone and a complete model of the charging and discharging of batteries. Additionally, the flexibility of the proposal is tested using three different network topologies with a characteristic daily load curve from the zone. The main contribution of this paper lies in the implementation of an optimal power flow including cost functions of renewable sources for isolated microgrids. A test case for a non-interconnected zone in Colombia is performed for various operation cases.
dc.languageeng
dc.publisherMDPI
dc.publisherBasel, Suiza
dc.relation14
dc.relation7
dc.relation1
dc.relation15
dc.relationHoyos Velandia, C., Ramírez Hurtado, L., Quintero Restrepo, J., Moreno Chuquen, R., González Longatt F. (2022). Cost Functions for Generation Dispatching in Microgrids forNon-Interconnected Zones in Colombia. Energies, 15(7), pp. 1-14
dc.relationEnergies
dc.relationHirsch, A.; Parag, Y.; Guerrero, J. Microgrids: A review of technologies, key drivers, and outstanding issues. Renew. Sustain. Energy Rev. 2018, 90, 402–411
dc.relationJung, J.; Villaran, M. Optimal planning and design of hybrid renewable energy systems for microgrids. Renew. Sustain. Energy Rev. 2017, 75, 180–191.
dc.relationXiaoping, L.; Ming, D.; Jianghong, H.; Pingping, H.; Yali, P. Dynamic economic dispatch for microgrids including battery energy storage. In Proceedings of the 2nd International Symposium on Power Electronics for Distributed Generation Systems, Hefei, China, 16–18 June 2010; pp. 914–917.
dc.relationBalducci, P.; Mongird, K.; Wu, D.; Wang, D.; Fotedar, V.; Dahowski, R. An evaluation of the economic and resilience benefits of a microgrid in Northampton, Massachusetts. Energies 2020, 13, 4802.
dc.relationPozo, D.; Contreras, J.; Sauma, E.E. Unit commitment with ideal and generic energy storage units. IEEE Trans. Power Syst. 2014, 29, 2974–2984.
dc.relationMontoya, O.D.; Gil-González, W.; Grisales-Noreña, L.; Orozco-Henao, C.; Serra, F. Economic dispatch of BESS and renewable generators in DC microgrids using voltage-dependent load models. Energies 2019, 12, 4494
dc.relationLee, G.H.; Park, J.Y.; Ham, S.J.; Kim, Y.J. Comparative study on optimization solvers for implementation of a two-stage economic dispatch strategy in a microgrid energy management System. Energies 2020, 13, 1096.
dc.relationModiri-Delshad, M.; Koohi-Kamali, S.; Taslimi, E.; Kaboli, S.H.A.; Rahim, N.A. Economic dispatch in a microgrid through an iterated-based algorithm. In Proceedings of the 2013 IEEE Conference on Clean Energy and Technology (CEAT), Langkawi, Malaysia, 18–20 November 2013
dc.relationElkelawy, M.; Bastawissi, H.A.E.; Esmaeil, K.K.; Radwan, A.M.; Panchal, H.; Sadasivuni, K.K.; Ponnamma, D.; Walvekar, R. Experimental studies on the biodiesel production parameters optimization of sunflower and soybean oil mixture and DI engine combustion, performance, and emission analysis fueled with diesel/biodiesel blends. Fuel 2019, 255, 115791.
dc.relationCastillo, A.; Gayme, D.F. Profit maximizing storage allocation in power grids. In Proceedings of the 52nd IEEE Conference on Decision and Control, Firenze, Italy, 10–13 December 2013; pp. 429–435.
dc.relationPfenninger, S.; Staffell, I. Long-term patterns of european PV output using 30 years of validated hourly reanalysis and satellite data. Energy 2016, 114, 1251–1265.
dc.relationHatziargyriou, N. Microgrids: Architectures and Control; John Wiley & Sons: Hoboken, NJ, USA, 2014. 39. Dimeas, A.L.; Hatziargyriou, N.D. Operation of a multiagent system for microgrid control. IEEE Trans. Power Syst. 2005, 20, 1447–1455. [C
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rightsDerechos reservados - MDPI, 2022
dc.titleCost functions for generation dispatching in microgrids for non-interconnected zones in Colombia
dc.typeArtículo de revista


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