The increasing penetration of Renewable Energy Source (RES) makes consumption flexibility one of the major requirements to maintain electric grid stability. A key role in effectively matching volatile RES production with load demand can be played by manufacturing enterprises, as they offer a few flexibility options. The aim of this work is from one side to develop a new control algorithm for increasing the flexibility and from the other side to identify and evaluate new flexibility options within manufacturing enterprises, with the aim to move towards the concept of Net-Zero-Energy Factories. A Model Predictive Control strategy, featured with a Mixed-Integer-Linear-Programming algorithm, has been implemented to optimally scheduling the production of a furniture industry. A sensitivity analysis on buffer stocks dimension is carried out in order to identify the optimal storage sizing (for material and energy) allowing to minimize the net yearly energy exchange with the grid. The studied approach allows to reduce by 27% the total energy exchange with the grid with respect to a baseline case study. Furtherdecrease of 50 % has been obtained with the introduction of a battery storage system.

Bartolucci, L., Cordiner, S., Mulone, V., Santarelli, M., Lombardi, P., Arendarski, B., et al. (2019). MPC-based Electric Energy Storage Sizing for a Net Zero Energy Factory. In 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019 : proceedings. New York : IEEE.

MPC-based Electric Energy Storage Sizing for a Net Zero Energy Factory

Bartolucci, L;Cordiner, S;Mulone, V;
2019-01-01

Abstract

The increasing penetration of Renewable Energy Source (RES) makes consumption flexibility one of the major requirements to maintain electric grid stability. A key role in effectively matching volatile RES production with load demand can be played by manufacturing enterprises, as they offer a few flexibility options. The aim of this work is from one side to develop a new control algorithm for increasing the flexibility and from the other side to identify and evaluate new flexibility options within manufacturing enterprises, with the aim to move towards the concept of Net-Zero-Energy Factories. A Model Predictive Control strategy, featured with a Mixed-Integer-Linear-Programming algorithm, has been implemented to optimally scheduling the production of a furniture industry. A sensitivity analysis on buffer stocks dimension is carried out in order to identify the optimal storage sizing (for material and energy) allowing to minimize the net yearly energy exchange with the grid. The studied approach allows to reduce by 27% the total energy exchange with the grid with respect to a baseline case study. Furtherdecrease of 50 % has been obtained with the introduction of a battery storage system.
IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019
2019
Rilevanza internazionale
2019
Settore ING-IND/08 - MACCHINE A FLUIDO
English
energy storage systems; Net-Zero-Energy factories; renewable energy sources; model predictive control
Intervento a convegno
Bartolucci, L., Cordiner, S., Mulone, V., Santarelli, M., Lombardi, P., Arendarski, B., et al. (2019). MPC-based Electric Energy Storage Sizing for a Net Zero Energy Factory. In 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019 : proceedings. New York : IEEE.
Bartolucci, L; Cordiner, S; Mulone, V; Santarelli, M; Lombardi, P; Arendarski, B; Komarnicki, P
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/231555
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