000110639 001__ 110639
000110639 005__ 20220215124222.0
000110639 0247_ $$2doi$$a10.1016/j.ijepes.2017.10.037
000110639 0248_ $$2sideral$$a104138
000110639 037__ $$aART-2018-104138
000110639 041__ $$aeng
000110639 100__ $$aLujano-Rojas, J.M.
000110639 245__ $$aNovel probabilistic optimization model for lead-acid and vanadium redox flow batteries under real-time pricing programs
000110639 260__ $$c2018
000110639 5060_ $$aAccess copy available to the general public$$fUnrestricted
000110639 5203_ $$aThe integration of storage systems into smart grids is being widely analysed in order to increase the flexibility of the power system and its ability to accommodate a higher share of wind and solar power. The success of this process requires a comprehensive techno-economic study of the storage technology in contrast with electricity market behaviour. The focus of this work is on lead-acid and vanadium redox flow batteries. This paper presents a novel probabilistic optimization model for managing energy storage systems. The model is able to incorporate the forecasting error of electricity prices, offering with this a near-optimal control option. Using real data from the Spanish electricity market from the year 2016, the probability distribution of forecasting error is determined. The model determines electricity price uncertainty by means of Monte Carlo Simulation and includes it in the energy arbitrage problem, which is eventually solved by using an integer-coded genetic algorithm. In this way, the probability distribution of the revenue is determined with consideration of the complex behaviours of lead-acid and vanadium redox flow batteries as well as their associated operating devices such as power converters.
000110639 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000110639 590__ $$a4.418$$b2018
000110639 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b46 / 265 = 0.174$$c2018$$dQ1$$eT1
000110639 592__ $$a1.26$$b2018
000110639 593__ $$aEnergy Engineering and Power Technology$$c2018$$dQ1
000110639 593__ $$aElectrical and Electronic Engineering$$c2018$$dQ1
000110639 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/submittedVersion
000110639 700__ $$aZubi, G.
000110639 700__ $$0(orcid)0000-0002-1490-6423$$aDufo-López, R.$$uUniversidad de Zaragoza
000110639 700__ $$0(orcid)0000-0003-2813-1240$$aBernal-Agustín, J.L.$$uUniversidad de Zaragoza
000110639 700__ $$aCatalão, J.P.S.
000110639 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica
000110639 773__ $$g97 (2018), 72-84$$pInt. J. Electr. Power Energy Syst.$$tINTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS$$x0142-0615
000110639 8564_ $$s815223$$uhttps://zaguan.unizar.es/record/110639/files/texto_completo.pdf$$yPreprint
000110639 8564_ $$s1916936$$uhttps://zaguan.unizar.es/record/110639/files/texto_completo.jpg?subformat=icon$$xicon$$yPreprint
000110639 909CO $$ooai:zaguan.unizar.es:110639$$particulos$$pdriver
000110639 951__ $$a2022-02-15-10:18:46
000110639 980__ $$aARTICLE