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Abstract
In this thesis, we consider control strategies for large and distributed energy systems that are important for the implementation of smart grid technologies. An electrical grid has to ensure reliability and avoid longterm interruptions in the power supply. Moreover, the share of Renewable Energy Sources (RESs) in the smart grids is increasing. These energy sources bring uncertainty to the production due to their fluctuations. Hence,smart grids need suitable control systems that are able to continuously balance power production and consumption. We apply the Economic Model Predictive Control (EMPC) strategy to optimise the economic performances of the energy systems and to balance the power production and consumption. In the case of largescale energy systems, the electrical grid connects a high number of power units. Because of this, the related control problem involves a high number of variables and constraints and its solution requires high computational times. Energy systems have a hierarchical control framework and the controllers have to work in the timescale required by their hierarchy level. Dedicated optimisation techniques efficiently solve the control problem and reduce computational time. We implement the DantzigWolfe decomposition technique to efficiently solve the EMPC problem.
The contributions of this thesis are primarily on:
Largescale energy systemSmartgrids connect a high number of energy units. In such a largescale scenario the energy units are independent and dynamically decoupled. The mathematical model of the largescale energy system embodies the decoupled dynamics of each power units. Moreover,all units of the grid contribute to the overall power production.
Economic Model Predictive Control (EMPC)
This control strategy is an extension of the Model Predictive Control (MPC)strategy. Energy systems often involve stochastic variables due to the share of fluctuating Renewable Energy Sources (RESs). Moreover, the related control problems are multi variables and they are hard, or impossible, to split into singleinputsingleoutput control systems. MPC strategy can handle multi variables control problems and it can embody stochastic variables. The Economic MPC (EMPC) policy optimises the economic performances of the process. In this work, we apply the EMPC to energy systems and it computes the control trajectory for each energy unit. This control policy minimises production costs and ensures that the power production satisfies the customers’ demand. The EMPC designs a linear control problem that has a blockangular constraints matrix and it has two sets of constraints. The independent dynamics of the energy units define the decoupling constraints sited on the diagonal. The coupling constraints represent the common goal of all power units in the energy system and this is to satisfy the customers’ demand. The DantzigWolfe optimisation technique applies to this structure of the constraints matrix in the view of fastening the control algorithm and increase its applicability.
DantzigWolfe decompositionThe DantzigWolfe decomposition solves the EMPC problem through a distributed optimisation technique. The EMPC problem via DantzigWolfe decomposition algorithm computes the optimal input trajectory for each energy unit and reduces the computation times. Moreover, such a control algorithm applies to largescale energy systems and the number of energy units does not affect the performances of the controller. In this thesis, we also investigate suboptimal solutions of the EMPC problem via modified versions of the DantzigWolfe decomposition algorithms. The feasibility of the suboptimal solutions suffices for stability. The goal of these modified DantzigWolfe decomposition algorithms is to reduce computation time in the solution of the EMPC problem.
Original language  English 

Place of Publication  Kgs. Lyngby 

Publisher  Technical University of Denmark 
Number of pages  179 
Publication status  Published  2015 
Series  DTU Compute PHD2014 

Number  356 
ISSN  09093192 
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Dive into the research topics of 'Economic Model Predictive Control for LargeScale and Distributed Energy Systems'. Together they form a unique fingerprint.Projects
 1 Finished

Economic MPC for Large and Distributed Energy Systems
Standardi, L. (PhD Student), Jørgensen, J. B. (Main Supervisor), Morales González, J. M. (Examiner), Rossiter, J. A. (Examiner), Larsen, L. F. S. (Examiner) & Poulsen, N. K. (Supervisor)
Eksternt finansieret virksomhed
01/11/2011 → 04/03/2015
Project: PhD