Optimal Frequency Control Management of Grid Integration PV/Wind/FC/Storage Battery Based Smart Grid Using Multi Objective Particle Swarm Optimization MOPSO and Model Predictive Control (MPC)

Abstract

This article forecasts the performance of smart-grid electrical transmission systems and integrated battery/FC/Wind/PV storage system renewable power sources in the context of unpredictable solar and wind power supplies. The research provided a hybrid renewable energy sources smart grid power system electrical frequency control solution using adaptive control techniques and model predictive control (MPC) based on the Multi-Objective Practical Swarm Optimization Algorithm MOPSO. To solve the problems of parameter tuning in Load Frequency Control, the suggested adaptive control approach is utilized to accomplish on-line adjustment of the Load Frequency Control parameters. During the electrical grid's integration, the system under investigation is a hybrid Wind/PV/FC/Battery smart grid with variable demand load. To achieve optimal outcomes, all of the controller settings for various units in power grids are determined by means of a customized objective function and a particle swarm optimization method rather than a regular objective function with fluctuating restrictions. To suppress the consumption and generation balance, MPCs were designed for each of the Storage Battery, Wind Turbine Generation, and the model Photovoltaic Generation. In addition, demand response (real-time pricing) was used in this scheme to reduce the load frequency by adjusting the controlled loads. The suggested control strategy is evaluated in the Simulink /MATLAB environment in order to analyse the suggested approach's working in the power system, as well as its effectiveness, reliability, robustness, and stability. The simulation findings show that the proposed control method generally converges to an optimal operating point that minimises total user disutility, restores normal frequency and planned tie-line power flows, and maintains transmission line thermal restrictions. The simulation results further indicate that the convergence holds even when the control algorithm uses inaccurate system parameters. Finally, numerical simulations are used to illustrate the proposed algorithm's robustness, optimality, and effectiveness. In compared to previous methodologies, the system frequency recovers effectively and efficiently in the event of a power demand disturbance, as demonstrated. A sensitivity test is also performed to assess the suggested technique's effectiveness.

Improving the Frequency Response of Hybrid Microgrid under Renewable Sources’ Uncertainties Using a Robust LFC-Based African Vulture Optimization Algorithm

“…In order to obtain the optimized parameters of these conventional controllers, various optimization techniques have been applied such as Genetic Algorithm (GA) [5], Ant Colony Optimization [6], Bacterial Foraging Optimization Algorithm (BFOA) [7], Differential Evolution (DE) [8], particle swarm optimization (PSO) [9], and grasshopper optimization (GOA) [10]. A model predictive control (MPC) optimized using the Multi-Purpose Practical Swarm Optimization (MOPSO) algorithm and adaptive control techniques was introduced as a frequency control mechanism for a renewable energy-powered smart grid [11]. In [12], the Firefly algorithm has been used in a two-zone hybrid system that incorporates electric vehicles (EVs) as controllable using an integrated controller, but it does not cover the development of a PID controller or even a PI controller.…”

“…Compute the vulture fitness value; • Select R(i) using Equation ( 12) for all vultures; • Use Equation (11) to compute the position of the best vulture;…”