Optimal energy dispatch in a smart micro-grid system using economic model predictive control

The problem of energy dispatch in heterogeneous complex systems such as smart grids cannot be efficiently addressed using classical control or ad hoc methods. This article discusses the application of economic model predictive control to the management of a smart micro-grid system connected to an electrical power grid. The considered system is composed of several subsystems, namely, some photovoltaic panels, a wind generator, a hydroelectric generator, a diesel generator, and some storage devices (batteries). The batteries are charged with the energy from the photovoltaic panels, wind and hydroelectric generators, and they are discharged whenever the generators produce less energy than needed. The subsystems are interconnected via a DC Bus, from which load demands are satisfied. Modeling smart grids components is based on the generalized flow-based networked systems paradigm, and assuming energy generators to be stable, load demands and energy prices are known. This study shows that economic model predictive control is economically superior to a two-layer hierarchical model predictive control.

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Robust Economic Model Predictive Control Based on a Zonotope and Local Feedback Controller for Energy Dispatch in Smart-Grids Considering Demand Uncertainty

Energies ◽

10.3390/en13030696 ◽

2020 ◽

Vol 13 (3) ◽

pp. 696

Author(s):

Mohamadou Nassourou ◽

Joaquim Blesa ◽

Vicenç Puig

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Economic Model ◽

Smart Grids ◽

Demand Uncertainty ◽

Control Method ◽

Mimo Systems ◽

State Feedback Control ◽

Economic Model Predictive Control ◽

Micro Grid

Electrical smart grids are complex MIMO systems whose operation can be noticeably affected by the presence of uncertainties such as load demand uncertainty. In this paper, based on a restricted representation of the demand uncertainty, we propose a robust economic model predictive control method that guarantees an optimal energy dispatch in a smart micro-grid. Load demands are uncertain, but viewed as bounded. The proposed method first decomposes control inputs into dependent and independent components, and then tackles the effect of demand uncertainty by tightening the system constraints as the uncertainty propagates along the prediction horizon using interval arithmetic and local state feedback control law. The tightened constraints’ upper and lower limits are computed off-line. The proposed method guarantees stability through a periodic terminal state constraint. The method is faster and simpler compared to other approaches based on Closed-loop min–max techniques. The applicability of the proposed approach is demonstrated using a smart micro-grid that comprises a wind generator, some photovoltaic (PV) panels, a diesel generator, a hydroelectric generator and some storage devices linked via two DC-buses, from which load demands can be adequately satisfied.

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