RT Journal Article
T1 Probabilistic Load-Flow Analysis of Biomass-Fuelled Gas Engines with Electrical Vehicles in Distribution Systems
A1 Ruiz Rodríguez, Francisco Javier
A1 Hernández, Jesús C.
A1 Jurado, Francisco
AB Feeding biomass-fueled gas engines (BFGEs) with olive tree pruning residues offersnew opportunities to decrease fossil fuel use in road vehicles and electricity generation. BFGEs,coupled to radial distribution systems (RDSs), provide renewable energy and power that can feedelectric vehicle (EV) charging stations. However, the combined impact of BFGEs and EVs on RDSsmust be assessed to assure the technical constraint fulfilment. Because of the stochastic natureof source/load, it was decided that a probabilistic approach was the most viable option for thisassessment. Consequently, this research developed an analytical technique to evaluate the technicalconstraint fulfilment in RDSs with this combined interaction. The proposed analytical technique(PAT) involved the calculation of cumulants and the linearization of load-flow equations, along withthe application of the cumulant method, and Cornish-Fisher expansion. The uncertainties relatedto biomass stock and its heating value (HV) were important factors that were assessed for the firsttime. Application of the PAT in a Spanish RDS with BFGEs and EVs confirmed the feasibility ofthe proposal and its additional benefits. Specifically, BFGEs were found to clearly contribute to thevoltage constraint fulfilment. The computational cost of the PAT was lower than that associated withMonte-Carlo simulations (MCSs).
PB MDPI
SN 1996-1073
YR 2017
FD 2017-10
LK http://hdl.handle.net/10272/16087
UL http://hdl.handle.net/10272/16087
LA eng
NO Francisco J. Ruiz-Rodríguez, Jesús C. Hernández, Francisco Jurado. Probabilistic Load-Flow Analysis of Biomass-Fuelled Gas Engines with Electrical Vehicles in Distribution Systems. Energies 2017, 10, 1536. ISSN: 1996-1073 DOI: 10.3390/en10101536
NO This work is supported by the Spanish National Plan for Scientific, Technical and Innovation Research (Grant No. ENE2013-46205).
DS Repositorio Institucional de la Universidad de Huelva
RD 31 may 2026