An industrial microgrid (IMG) consists in a microgrid involving manufacturer plants which are usually equipped with distributed generation facilities, industrial electric vehicles, energy storage systems, etc. In this paper, the problem of IMG efficient operation in presence of plug-in electric vehicles is addressed. To this purpose, schedule of the different device operations of IMGs has to be optimally computed, minimizing the operation cost while guaranteeing electrical network stability and production constraints. Such a problem is formulated in a receding horizon framework involving dynamic optimal power flow equations. Uncertainty affecting plug-in electric vehicles is handled by means of a chance constraint approach. The obtained nonconvex problem is then approximately solved by exploiting suitable convex relaxation techniques. Numerical simulations have been performed showing computational feasibility and robustness of the proposed approach against increased penetration of electric vehicles.

Casini, M., Zanvettor, G.G., Kovjanic, M., Vicino, A. (2019). Optimal energy management and control of an industrial microgrid with plug-in electric vehicles. IEEE ACCESS, 7, 101729-101740 [10.1109/ACCESS.2019.2930274].

Optimal energy management and control of an industrial microgrid with plug-in electric vehicles

Casini, Marco
;
Zanvettor, Giovanni Gino;Vicino, Antonio
2019-01-01

Abstract

An industrial microgrid (IMG) consists in a microgrid involving manufacturer plants which are usually equipped with distributed generation facilities, industrial electric vehicles, energy storage systems, etc. In this paper, the problem of IMG efficient operation in presence of plug-in electric vehicles is addressed. To this purpose, schedule of the different device operations of IMGs has to be optimally computed, minimizing the operation cost while guaranteeing electrical network stability and production constraints. Such a problem is formulated in a receding horizon framework involving dynamic optimal power flow equations. Uncertainty affecting plug-in electric vehicles is handled by means of a chance constraint approach. The obtained nonconvex problem is then approximately solved by exploiting suitable convex relaxation techniques. Numerical simulations have been performed showing computational feasibility and robustness of the proposed approach against increased penetration of electric vehicles.
Casini, M., Zanvettor, G.G., Kovjanic, M., Vicino, A. (2019). Optimal energy management and control of an industrial microgrid with plug-in electric vehicles. IEEE ACCESS, 7, 101729-101740 [10.1109/ACCESS.2019.2930274].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1078328