A Model Predictive Control Approach to Microgrid Operation Optimization

TLDR

Microgrids are subsystems of the distribution grid that combine generation, storage, and controllable loads into a single controllable system, operating either connected to or isolated from the utility grid. The study applies model predictive control to efficiently optimize microgrid operations while meeting time‑varying requests and operational constraints. The authors formulate the problem as a mixed‑integer linear program, solve it efficiently with commercial solvers, and validate the approach on a microgrid in Athens, Greece, through a case study. Experimental results demonstrate the feasibility and effectiveness of the proposed MPC‑based optimization approach.

Abstract

Microgrids are subsystems of the distribution grid, which comprises generation capacities, storage devices, and controllable loads, operating as a single controllable system either connected or isolated from the utility grid. In this paper, we present a study on applying a model predictive control approach to the problem of efficiently optimizing microgrid operations while satisfying a time-varying request and operation constraints. The overall problem is formulated using mixed-integer linear programming (MILP), which can be solved in an efficient way by using commercial solvers without resorting to complex heuristics or decompositions techniques. Then, the MILP formulation leads to significant improvements in solution quality and computational burden. A case study of a microgrid is employed to assess the performance of the online optimization-based control strategy and the simulation results are discussed. The method is applied to an experimental microgrid located in Athens, Greece. The experimental results show the feasibility and the effectiveness of the proposed approach.