Abstract
| Original language | English |
|---|---|
| Journal | IEEE Transactions on Smart Grid |
| Volume | 7 |
| Issue number | 3 |
| Pages (from-to) | 1675-1682 |
| ISSN | 1949-3053 |
| DOIs | |
| Publication status | Published - 2016 |
Keywords
- Smart grid
- Model predictive control
- Douglas-Rachford splitting
Cite this
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Distributed Model Predictive Control for Smart Energy Systems. / Halvgaard, Rasmus Fogtmann; Vandenberghe, Lieven; Poulsen, Niels Kjølstad; Madsen, Henrik; Jørgensen, John Bagterp.
In: IEEE Transactions on Smart Grid, Vol. 7, No. 3, 2016, p. 1675-1682.Research output: Contribution to journal › Journal article › Research › peer-review
TY - JOUR
T1 - Distributed Model Predictive Control for Smart Energy Systems
AU - Halvgaard, Rasmus Fogtmann
AU - Vandenberghe, Lieven
AU - Poulsen, Niels Kjølstad
AU - Madsen, Henrik
AU - Jørgensen, John Bagterp
PY - 2016
Y1 - 2016
N2 - Integration of a large number of flexible consumers in a smart grid requires a scalable power balancing strategy. We formulate the control problem as an optimization problem to be solved repeatedly by the aggregator in a model predictive control framework. To solve the large-scale control problem in real-time requires decomposition methods. We propose a decomposition method based on Douglas–Rachford splitting to solve this large-scale control problem. The method decomposes the problem into smaller subproblems that can be solved in parallel, e.g., locally by each unit connected to an aggregator. The total power consumption is controlled through a negotiation procedure between all cooperating units and an aggregator that coordinates the overall objective. For large-scale systems, this method is faster than solving the original problem and can be distributed to include an arbitrary number of units. We show how different aggregator objectives are implemented and provide simulations of the controller including the computational performance.
AB - Integration of a large number of flexible consumers in a smart grid requires a scalable power balancing strategy. We formulate the control problem as an optimization problem to be solved repeatedly by the aggregator in a model predictive control framework. To solve the large-scale control problem in real-time requires decomposition methods. We propose a decomposition method based on Douglas–Rachford splitting to solve this large-scale control problem. The method decomposes the problem into smaller subproblems that can be solved in parallel, e.g., locally by each unit connected to an aggregator. The total power consumption is controlled through a negotiation procedure between all cooperating units and an aggregator that coordinates the overall objective. For large-scale systems, this method is faster than solving the original problem and can be distributed to include an arbitrary number of units. We show how different aggregator objectives are implemented and provide simulations of the controller including the computational performance.
KW - Smart grid
KW - Model predictive control
KW - Douglas-Rachford splitting
U2 - 10.1109/TSG.2016.2526077
DO - 10.1109/TSG.2016.2526077
M3 - Journal article
VL - 7
SP - 1675
EP - 1682
JO - I E E E Transactions on Smart Grid
JF - I E E E Transactions on Smart Grid
SN - 1949-3053
IS - 3
ER -