Renewable energy source deployment and fossil fuel use reduction are the main strategies put in action in order to meet "20- 20-20" European commitments. Such strategies have led to the concept of distributed generation systems, i.e. microgrids, for their capability to allow for high penetration of renewables in the land as well as reducing carbon footprint. A proper management strategy is key to improve the microgrid performances and reliability, which depend on generators/load interaction. In a previous paper, a Model Predictive Control logic has been applied to a model of a domestic microgrid composed of PV panels, a Fuel Cell and a battery pack. Results showed theoretical advantages in terms of energy savings and system downsizing potential. A physical control is presented in this paper, by means of two level controllers, and two strategies are presented (SOC and FC follow), giving different results in terms of system design and energy conversion performance.

Domestic microgrid energy management: model predictive control strategies toward experimental validation

2015

Abstract

Renewable energy source deployment and fossil fuel use reduction are the main strategies put in action in order to meet "20- 20-20" European commitments. Such strategies have led to the concept of distributed generation systems, i.e. microgrids, for their capability to allow for high penetration of renewables in the land as well as reducing carbon footprint. A proper management strategy is key to improve the microgrid performances and reliability, which depend on generators/load interaction. In a previous paper, a Model Predictive Control logic has been applied to a model of a domestic microgrid composed of PV panels, a Fuel Cell and a battery pack. Results showed theoretical advantages in terms of energy savings and system downsizing potential. A physical control is presented in this paper, by means of two level controllers, and two strategies are presented (SOC and FC follow), giving different results in terms of system design and energy conversion performance.
2015
Istituto Motori - IM - Sede Napoli
Distributed Generation
Microgrids
Fuel Cells
Model Predictive Control
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/296618
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