PSO Optimized Nonlinear Control of DC Microgrid with Intelligent Energy Management System

Abstract

To meet the United Nations climate stabilization goals by 2050, Clean Energy is playing a vital role to minimize global warming. This study proposes a Clean Energy DC Microgrid with a 2-level control structure. A high level controller consisting of Artificial Neural Network (ANN) based Energy Management System (EMS) with maximum power point tracking (MPPT) schemes and a local level Quick Reaching Law based Global Terminal Sliding Mode Controller (QRL-GTSMC). The Microgrid consists of a Fuel Cell (FC), Electrolyzer, Hydrogen Tank, Battery Energy Storage System (BESS), Photovoltaic (PV) array and a Wind Energy Conversion System (WECS). The inclusion of multiple Distributed Energy Resources (DER) increases the complexity of the system but also provides more flexibility. The proposed configuration provides a Hydrogen cycle with production, storage and utilization. The ANN EMS is trained by data accumulated through a State Logic Controller (SLC). The local level control laws are synthesized using QRL-GTSMC, and the resultant parametric variables are tuned by Particle Swarm Optimization (PSO) algorithm using Integral of Absolute Error (IAE) as the minimization performance index. The system's overall stability, response and performance is tested by introducing uncertainties in the form of changing weather conditions. The results are compared with conventional control technique without a reaching law. The proposed system has been evaluated using the MATLAB/Simulink environment. © 2023 IEEE.