000131168 001__ 131168
000131168 005__ 20240206154529.0
000131168 0247_ $$2doi$$a10.1049/iet-rpg.2017.0777
000131168 0248_ $$2sideral$$a107079
000131168 037__ $$aART-2018-107079
000131168 041__ $$aeng
000131168 100__ $$0(orcid)0000-0002-5801-0602$$aLujano-Rojas, J.M.$$uUniversidad de Zaragoza
000131168 245__ $$aProbabilistic methodology for estimating the optimal photovoltaic capacity in distribution systems to avoid power flow reversals
000131168 260__ $$c2018
000131168 5060_ $$aAccess copy available to the general public$$fUnrestricted
000131168 5203_ $$aThe large-scale integration of photovoltaic generation (PVG) on distribution systems (DSs) preserving their technical constraints related to voltage fluctuations and active power (AP) flow is a challenging problem. Solar resources are accompanied by uncertainty regarding their estimation and intrinsically variable nature. This study presents a new probabilistic methodology based on quasi-static time-series analysis combined with the golden section search algorithm to integrate low and high levels of PVG into DSs to prevent AP flow in reverse direction. Based on the analysis of two illustrative case studies, it was concluded that the successful integration of PVG is not only related to the photovoltaic-cell manufacturing prices and conversion efficiency but also with the manufacturing prices of power electronic devices required for reactive power control.
000131168 536__ $$9info:eu-repo/grantAgreement/EC/FP7/309048/EU/Smart and Sustainable Insular Electricity Grids Under Large-Scale Renewable Integration/SINGULAR$$9info:eu-repo/grantAgreement/ES/MINECO/ENE2013-48517-C2-1-R
000131168 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000131168 590__ $$a3.605$$b2018
000131168 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b64 / 265 = 0.242$$c2018$$dQ1$$eT1
000131168 591__ $$aGREEN & SUSTAINABLE SCIENCE & TECHNOLOGY$$b14 / 35 = 0.4$$c2018$$dQ2$$eT2
000131168 591__ $$aENERGY & FUELS$$b36 / 103 = 0.35$$c2018$$dQ2$$eT2
000131168 592__ $$a1.041$$b2018
000131168 593__ $$aRenewable Energy, Sustainability and the Environment$$c2018$$dQ2
000131168 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000131168 700__ $$0(orcid)0000-0002-1490-6423$$aDufo-López, R.$$uUniversidad de Zaragoza
000131168 700__ $$0(orcid)0000-0003-2813-1240$$aBernal-Agustín, J.L.$$uUniversidad de Zaragoza
000131168 700__ $$0(orcid)0000-0002-4770-0069$$aDomínguez-Navarro, J.A.$$uUniversidad de Zaragoza
000131168 700__ $$aCatalaõ, J.P.S.
000131168 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica
000131168 773__ $$g12, 9 (2018), 1045-1064$$pIET Renewable Power Generation$$tIET Renewable Power Generation$$x1752-1416
000131168 8564_ $$s586451$$uhttps://zaguan.unizar.es/record/131168/files/texto_completo.pdf$$yPostprint
000131168 8564_ $$s2614807$$uhttps://zaguan.unizar.es/record/131168/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000131168 909CO $$ooai:zaguan.unizar.es:131168$$particulos$$pdriver
000131168 951__ $$a2024-02-06-14:46:08
000131168 980__ $$aARTICLE