A stochastic mixed-integer convex programming model for long-term distribution system expansion planning considering greenhouse gas emission mitigation

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorHome-Ortiz, Juan M.en_US
dc.contributor.authorMelgar-Dominguez, Ozy D.en_US
dc.contributor.authorPourakbari-Kasmaei, Mahdien_US
dc.contributor.authorMantovani, José Roberto Sanchesen_US
dc.contributor.departmentDepartment of Electrical Engineering and Automationen
dc.contributor.organizationSão Paulo State Universityen_US
dc.date.accessioned2019-02-25T08:52:07Z
dc.date.available2019-02-25T08:52:07Z
dc.date.embargoinfo:eu-repo/date/embargoEnd/2021-01-08en_US
dc.date.issued2019-06-01en_US
dc.description.abstractThis paper proposes a multistage convex distribution system planning model to find the best reinforcement plan over a specified horizon. This strategy determines planning actions such as reinforcement of existing substations, conductor replacement of overloaded feeders, and siting and sizing of renewable and dispatchable distributed generation units. Besides, the proposed approach aims at mitigating the greenhouse gas emissions of electric power distribution systems via a monetary form. Inherently, this problem is a non-convex optimization model that can be an obstacle to finding the optimal global solution. To remedy this issue, convex envelopes are used to recast the original problem into a mixed integer conic programming (MICP) model. The MICP model guarantees convergence to optimal global solution by using existing commercial solvers. Moreover, to address the prediction errors in wind output power and electricity demands, a two-stage stochastic MICP model is developed. To validate the proposed model, detail analysis is carried out over various case studies of a 34-node distribution system under different conditions, while to show its potential and effectiveness a 135-node system with two substations is used. Numerical results confirm the effectiveness of the proposed planning scheme in obtaining an economic investment plan at the presence of several planning alternatives and to promote an environmentally committed electric power distribution network.en
dc.description.versionPeer revieweden
dc.format.extent10
dc.format.extent86-95
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationHome-Ortiz, J M, Melgar-Dominguez, O D, Pourakbari-Kasmaei, M & Mantovani, J R S 2019, ' A stochastic mixed-integer convex programming model for long-term distribution system expansion planning considering greenhouse gas emission mitigation ', International Journal of Electrical Power and Energy Systems, vol. 108, pp. 86-95 . https://doi.org/10.1016/j.ijepes.2018.12.042en
dc.identifier.doi10.1016/j.ijepes.2018.12.042en_US
dc.identifier.issn0142-0615
dc.identifier.issn1879-3517
dc.identifier.otherPURE UUID: b5fb1ce4-2acb-4169-8b2a-927e1a5d2955en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/b5fb1ce4-2acb-4169-8b2a-927e1a5d2955en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85059578096&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/31653825/ELEC_Mahdi_stochastic_mixed_integer_convex_programming_model.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/36867
dc.identifier.urnURN:NBN:fi:aalto-201902252024
dc.language.isoenen
dc.publisherElsevier Limited
dc.relation.ispartofseriesInternational Journal of Electrical Power and Energy Systemsen
dc.relation.ispartofseriesVolume 108en
dc.rightsopenAccessen
dc.subject.keywordConic programmingen_US
dc.subject.keywordDistributed energyen_US
dc.subject.keywordMultistage distribution system expansion planningen_US
dc.subject.keywordRenewable energy sourcesen_US
dc.subject.keywordStochastic programmingen_US
dc.titleA stochastic mixed-integer convex programming model for long-term distribution system expansion planning considering greenhouse gas emission mitigationen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
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