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PEV storage in multi-bus scheduling problems

I. Momber, G. Morales-España, A. Ramos, T. Gómez

Modeling electricity storage to address challenges and opportunities of its applications for smart grids requires inter-temporal equalities to keep track of energy content over time. Prevalently, these constraints present crucial modeling elements as to what extent energy storage applications can enhance future electric power systems? sustainability, reliability, and efficiency. This paper presents a novel and improvedmixed-integer linear problem (MILP) formulation for energy storage of plug-in (hybrid) electric vehicles (PEVs) for reserves in power system models. It is based on insights from the field of System Dynamics, in which complex interactions between different elements are studied by means of feedback loops as well as stocks, flows and co-flows. Generalized to a multi-bus system, this formulation includes improvements in the energy balance and surpasses shortcomings in the way existing literature deals with reserve constraints. Tested on the IEEE 14-bus system with realistic PEV mobility patterns, the deterministic results show changes in the scheduling of the units, often referred to as unit commitment (UC).


Palabras clave: Direct load control, mixed-integer linear programming (MILP), multi-bus unit-commitment (UC), plug-in electric vehicles (PEVs), reserves.


IEEE Transactions on Smart Grid. Volumen: 5 Numero: 2 Páginas: 1079-1087

Índice de impacto JCR y cuartil Scopus: JCR impact factor: 4.252 (2014); 10.486 (2018).

Referencia DOI: DOI icon 10.1109/TSG.2013.2290594    

Publicado en papel: Marzo 2014. Publicado on-line: Febrero 2014.



Cita:
I. Momber, G. Morales-España, A. Ramos, T. Gómez. PEV storage in multi-bus scheduling problems. IEEE Transactions on Smart Grid. vol. 5, no. 2, pp. 1079-1087, Marzo 2014. [Online: Febrero 2014]


    Líneas de investigación:
  • *Smart grids
  • *Programación de la Operación a Corto Plazo, Elaboración de Ofertas y Análisis de Reservas de Operación
  • *Modelado, Simulación y Optimización

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