Optimization of active and reactive power in smart buildings using a distributed model predictive control

Maroufi, Seyede Masoome (2020) Optimization of active and reactive power in smart buildings using a distributed model predictive control. [Laurea magistrale], Università di Bologna, Corso di Studio in Ingegneria dell'energia elettrica [LM-DM270], Documento full-text non disponibile
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Growth in Distributed Energy Resources (DERs) and low-inertia renewable energy sources in smart grids require imperative Volt-VAR Control (VVC). Moreover, this growth combined with increasing deployment of information technologies in smart grids fuels communication uncertainties and reveals transient stability challenges for Distributed Network Operators (DNOs). Innovative approaches have been proposed to use the inherent thermal inertia of buildings to provide ancillary services to the grid to tackle the problems posed by the increasing trend of volatile DERs. Although numerous approaches harness traditional VVC devices to compensate for voltage violations, synthetic inertia and control of Energy Storage System (ESS) exist to improve transient stability with an increase of DERs. While ample strategies tackle these two problems separately, the ability of smart buildings to provide active and reactive power support simultaneously has not yet been exploited. This study explores the concurrent effects of modulating loads’ apparent power consumption on the grid’s frequency and voltage profile. A Distributed Model Predictive Control (DMPC) strategy for voltage and frequency control in the DN is employed by using smart buildings and sensitivity analysis without compromising customers’ climate control performance in smart buildings. The robustness of this strategy is validated on a modified IEEE 13 bus system modelled in MathWorks Simulink.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Maroufi, Seyede Masoome
Relatore della tesi
Correlatore della tesi
Corso di studio
Electrical Engineering
Ordinamento Cds
Parole chiave
distributed network,frequency control,model predictive control,smart buildingds,thermostatically controlled load,voltage control
Data di discussione della Tesi
21 Luglio 2020

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