Reference:
R. Carli,
G. Cavone,
T. Pippia,
B. De Schutter, and
M. Dotoli,
"A robust MPC energy scheduling strategy for multi-carrier
microgrids," Proceedings of the 16th IEEE International Conference
on Automation Science and Engineering (CASE 2020), Virtual
Conference, pp. 152-158, Aug. 2020.
Abstract:
We present a Robust Model Predictive Control (RMPC) approach for
multi-carrier microgrids, i.e., microgrids based on gas and
electricity. The microgrid that we consider includes thermal loads,
electrical loads, renewable energy sources, energy storage systems,
heat pumps, and combined heat and power plants. Moreover, the system
under control is affected by several external disturbances, e.g.,
uncertainty in renewable energy generation, electrical and thermal
demand. The goal of the controller is to minimize the overall
economical cost and the energy exchange with the main grid, while
guaranteeing comfort. Whereas several RMPC methods have been developed
for electrical or thermal microgrids, little or no attention has been
devoted to robust control of multi-carrier microgrids. Therefore, we
consider a novel RMPC algorithm that can improve the performance with
respect to classical deterministic Model Predictive Control (Det-MPC)
controllers in the context of multi-carrier microgrids. The RMPC
method relies on the box-uncertainty-set robust optimization, where
uncertain parameters are assumed to take their values from different
intervals independently. The RMPC approach is able to successfully
satisfy the constraints even in the presence of the mentioned
disturbances. Simulations of a realistic residential case study show
the benefits of the proposed approach with respect to Det-MPC
controllers.
Bibtex entry:
@inproceedings{CarCav:20-005,