Using Artificial Intelligence for Microgrid Operation and Control in Presence of Sustainable Power Systems

Abstract

Nowadays, and with the growth use of renewable energy sources (RESs), an intelligent control scheme is requisite to ensure a stable power generation system especially in isolated and small areas. In the classical grid, the load frequency control loop (LFC) is widely used to cope with sudden and random load variation. However, with the large share of RESs and due to their stochastic behavior, a robust controller is needed to suppress frequency deviation and handle system frequency at scheduled value. In this scope, this paper aims to propose the use of first time a recently population-based optimization algorithm named Sine Cosine Algorithm (SCA) as artificial intelligence strategy to design an optimal LFC controller that allow the integration of large amount of RESs in the microgrid (MG). A hybrid microgrid including diesel engine, wind farm and photovoltaic generator was investigated. The SCA algorithm was employed to optimize the PI controller parameters of the LFC loop. To promote the use of RESs and minimize the use of fuel, a Hybrid Energy Storage System (HESS) combined Redox Flow Batteries (RFB) and Superconducting Magnetic Energy Storage (SMES) was considered. The HESS was used to support the LFC loop and reduce frequency fluctuation in presence of RESs. To prove the validity of the proposed strategy, various scenarios have been simulated. In addition, a comparative study between the proposed SCA algorithm and some metaheurisctic algorithms such PSO and GA have been performed. Furthermore, robustness analysis have been carried out with different rate of RESs penetration. Finally, the obtained results demonstrate the effectiveness and superiority of the developed strategy for dynamic microgird control.