Kriging based Surrogate Modeling for Fractional Order Control of Microgrids

TL;DR

This paper presents a kriging-based surrogate modeling approach to efficiently tune fractional order controllers for microgrids with renewable and storage systems, improving optimization efficiency and system performance.

Contribution

It introduces a novel application of kriging surrogate models for fractional order controller tuning in microgrids, reducing computational cost compared to traditional methods.

Findings

Kriging surrogate modeling reduces optimization time.

FO controllers outperform IO controllers in simulations.

The method is applicable to other power system optimization problems.

Abstract

This paper investigates the use of fractional order (FO) controllers for a microgrid. The microgrid employs various autonomous generation systems like wind turbine generator (WTG), solar photovoltaic (PV), diesel energy generator (DEG) and fuel-cells (FC). Other storage devices like the battery energy storage system (BESS) and the flywheel energy storage system (FESS) are also present in the power network. An FO control strategy is employed and the FO-PID controller parameters are tuned with a global optimization algorithm to meet system performance specifications. A kriging based surrogate modeling technique is employed to alleviate the issue of expensive objective function evaluation for the optimization based controller tuning. Numerical simulations are reported to prove the validity of the proposed methods. The results for both the FO and the integer order (IO) controllers are compared with standard evolutionary optimization techniques and the relative merits and demerits of the kriging based surrogate modeling are discussed. This kind of optimization technique is not only limited to this specific case of microgrid control but also can be ported to other computationally expensive power system optimization problems.