Standardi, Laura5; Edlund, Kristian6; Poulsen, Niels Kjølstad5; Jørgensen, John Bagterp7
1 Center for Energy Resources Engineering, Center, Technical University of Denmark2 Department of Informatics and Mathematical Modeling, Technical University of Denmark3 Scientific Computing, Department of Informatics and Mathematical Modeling, Technical University of Denmark4 Mathematical Statistics, Department of Informatics and Mathematical Modeling, Technical University of Denmark5 Department of Applied Mathematics and Computer Science, Technical University of Denmark6 Department of Chemical and Biochemical Engineering, Technical University of Denmark7 Copenhagen Center for Health Technology, Center, Technical University of Denmark
Future power systems will consist of a large number of decentralized power producers and a large number of controllable power consumers in addition to stochastic power producers such as wind turbines and solar power plants. Control of such large scale systems requires new control algorithms. In this paper, we formulate the control of such a system as an Economic Model Predictive Control (MPC) problem. When the power producers and controllable power consumers have linear dynamics, the Economic MPC may be expressed as a linear program and we apply Dantzig-Wolfe decomposition for solution of this linear program. The Dantzig-Wolfe decomposition algorithm for Economic MPC is tested on a simulated case study with a large number of power producers. The Dantzig-Wolfe algorithm is compared to a standard linear programming (LP) solver for the Economic MPC. Simulation results reveal that the Dantzig-Wolfe algorithm is faster than the standard LP solver and enables solution of larger problems.
10th European Workshop on Advanced Control and Diagnosis (acd 2012), 2012
Economic Model Predictive Control; Linear programming; Distributed Optimization; Power systems
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10th European Workshop on Advanced Control and Diagnosis, 2012