A Reduced Dantzig-Wolfe Decomposition for a Suboptimal Linear MPC

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• 作者：Laura Standardi ^{laus@dtu.dk" class="auth_mail" title="E-mail the corresponding author} ; Niels Kjø ; lstad Poulsen ^{nkpo@dtu.dk" class="auth_mail" title="E-mail the corresponding author} ; John Bagterp Jø ; rgensen ^{jbjo@dtu.dk" class="auth_mail" title="E-mail the corresponding author}
• 刊名：IFAC-PapersOnLine
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：47
• 期：3
• 页码：2207-2212
• 全文大小：340 K

文摘

Linear Model Predictive Control (MPC) is an efficient control technique that repeatedly solves online constrained linear programs. In this work we propose an economic linear MPC strategy for operation of energy systems consisting of multiple and independent power units. These systems cooperate to meet the supply of power demand by minimizing production costs. The control problem can be formulated as a linear program with block-angular structure. To speed-up the solution of the optimization control problem, we propose a reduced Dantzig-Wolfe decomposition. This decomposition algorithm computes a suboptimal solution to the economic linear MPC control problem and guarantees feasibility and stability. Finally, six scenarios are performed to show the decrease in computation time in comparison with the classic Dantzig-Wolfe algorithm.