21 research outputs found
Performance of MV distributed energy power systems under model-predictive control and conventional power systems state estimators
© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, the performance of a middle voltage (MV) distributed power generation system under a Model Predictive Control (MPC) strategy is tested. The designed controller receives process information from an state estimation unit, composed of a Weighted Least-Squares strategy (WLS). The contribution is focused on performing the frequency and voltage regulation of the system, considering moreover the dynamic behavior of storage units and capacitors, and feeding back information to the control system considering the transmission delay. The results show that the closed loop based on the proposed strategy achieves the required performance and can be useful to be applied to more complex systems such as low-voltage (LV) generation systems.Peer ReviewedPostprint (author's final draft
Two-Stage Consensus-Based Distributed MPC for Interconnected Microgrids
In this paper, we propose a model predictive control based two-stage energy
management system that aims at increasing the renewable infeed in
interconnected microgrids (MGs). In particular, the proposed approach ensures
that each MG in the network benefits from power exchange. In the first stage,
the optimal islanded operational cost of each MG is obtained. In the second
stage, the power exchange is determined such that the operational cost of each
MG is below the optimal islanded cost from the first stage. In this stage, a
distributed augmented Lagrangian method is used to solve the optimisation
problem and determine the power flow of the network without requiring a central
entity. This algorithm has faster convergence and same information exchange at
each iteration as the dual decomposition algorithm. The properties of the
algorithm are illustrated in a numerical case study
A Distributed Mixed-Integer Framework to Stochastic Optimal Microgrid Control
This paper deals with distributed control of microgrids composed of storages,
generators, renewable energy sources, critical and controllable loads. We
consider a stochastic formulation of the optimal control problem associated to
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 trained an
open-source historical dataset of the EU