This article deals with distributed control of microgrids composed of storages, generators, renewable energy sources, and critical and controllable loads. We consider a stochastic formulation of the optimal control problem associated with the microgrid that appropriately takes into account the unpredictable nature of the power generated by renewables. The resulting problem is a mixed-integer linear program and is NP-hard and nonconvex. Moreover, the peculiarity of the considered framework is that no central unit can be used to perform the optimization, but rather the units must cooperate with each other by means of neighboring communication. To solve the problem, we resort to a distributed methodology based on a primal decomposition approach. The resulting algorithm is able to compute high-quality feasible solutions to a two-stage stochastic optimization problem, for which we also provide a theoretical upper bound on the constraint violation. Finally, a Monte Carlo numerical computation on a scenario with a large number of devices shows the efficacy of the proposed distributed control approach. The numerical experiments are performed on realistic scenarios obtained from Generative Adversarial Networks (GANs) trained an open-source historical dataset of the EU.

A Distributed Mixed-Integer Framework to Stochastic Optimal Microgrid Control / Camisa, A; Notarstefano, G. - In: IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY. - ISSN 1063-6536. - ELETTRONICO. - 31:1(2023), pp. 9780652.208-9780652.220. [10.1109/TCST.2022.3174968]

A Distributed Mixed-Integer Framework to Stochastic Optimal Microgrid Control

Camisa, A
;
Notarstefano, G
2023

Abstract

This article deals with distributed control of microgrids composed of storages, generators, renewable energy sources, and critical and controllable loads. We consider a stochastic formulation of the optimal control problem associated with the microgrid that appropriately takes into account the unpredictable nature of the power generated by renewables. The resulting problem is a mixed-integer linear program and is NP-hard and nonconvex. Moreover, the peculiarity of the considered framework is that no central unit can be used to perform the optimization, but rather the units must cooperate with each other by means of neighboring communication. To solve the problem, we resort to a distributed methodology based on a primal decomposition approach. The resulting algorithm is able to compute high-quality feasible solutions to a two-stage stochastic optimization problem, for which we also provide a theoretical upper bound on the constraint violation. Finally, a Monte Carlo numerical computation on a scenario with a large number of devices shows the efficacy of the proposed distributed control approach. The numerical experiments are performed on realistic scenarios obtained from Generative Adversarial Networks (GANs) trained an open-source historical dataset of the EU.
2023
A Distributed Mixed-Integer Framework to Stochastic Optimal Microgrid Control / Camisa, A; Notarstefano, G. - In: IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY. - ISSN 1063-6536. - ELETTRONICO. - 31:1(2023), pp. 9780652.208-9780652.220. [10.1109/TCST.2022.3174968]
Camisa, A; Notarstefano, G
File in questo prodotto:
File Dimensione Formato  
A_Distributed_Mixed-Integer_Framework_to_Stochastic_Optimal_Microgrid_Control.pdf

accesso aperto

Tipo: Versione (PDF) editoriale
Licenza: Creative commons
Dimensione 1.53 MB
Formato Adobe PDF
1.53 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/896747
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 3
social impact