A Distributed Mixed-Integer Framework to Stochastic Optimal Microgrid Control

TL;DR

This paper presents a distributed control framework for microgrids with renewable sources, storages, and loads, addressing stochastic uncertainties without central coordination, using a primal decomposition approach.

Contribution

It introduces a novel distributed algorithm for solving a complex stochastic mixed-integer optimization problem in microgrid control, with theoretical bounds and practical validation.

Findings

Algorithm computes high-quality feasible solutions.

The approach effectively manages stochastic renewable generation.

Numerical results demonstrate the method's efficacy on realistic scenarios.

Abstract

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.