@article {3701,
	title = {Distributed model predictive control for energy management in a network of microgrids using the dual decomposition method},
	journal = {Optimal Control Applications and Methods},
	year = {2020},
	chapter = {1-17},
	abstract = {<p>Summary This paper deals with the application of model predictive control (MPC) to optimize power flows in a network of interconnected microgrids (MGs). More specifically, a distributed MPC (DMPC) approach is used to compute for each MG how much active power should be exchanged with other MGs and with the outer power grid. Due to the presence of coupled variables, the DMPC approach must be used in a suitable way to guarantee the feasibility of the consensus procedure among the MGs. For this purpose, we adopt a tailored dual decomposition method that allows us to reach a feasible solution while guaranteeing the privacy of single MGs (ie, without having to share private information like the amount of generated energy or locally consumed energy). Simulation results demonstrate the features of the proposed cooperative control strategy and the obtained benefits with respect to other classical centralized control methods.</p>
},
	keywords = {distributed systems, dual decomposition, microgrids, model predictive control},
	doi = {10.1002/oca.2504},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/oca.2504},
	author = {M. Razzanelli and E. Crisostomi and L. Pallottino and G. Pannocchia}
}
@conference {1864,
	title = {DCL: a real time portable distributed control telelaboratory},
	booktitle = {IEEE Mediterranean Conference on Automation and Control},
	year = {2010},
	month = {Jun},
	pages = {{\textendash}},
	keywords = {Robotics},
	author = {A. Balestrino and A. Caiti and V. Calabr{\`o} and E. Crisostomi}
}
