Enhanced Virtual Inertia Control for Microgrids with High-Penetration Renewables Based on Whale Optimization

Abstract

High penetration of renewable energy sources into isolated microgrids (µGs) is considered a critical challenge, as µGs’ operation at low inertia results in frequency stability problems. To solve this challenge, virtual inertia control based on an energy storage system is applied to enhance the inertia and damping properties of the µG. On the other hand, utilization of a phase-locked loop (PLL) is indispensable for measuring system frequency; however, its dynamics, such as measurement delay and noise generation, cause extra deterioration of frequency stability. In this paper, to improve µG frequency stability and minimize the impact of PLL dynamics, a new optimal frequency control technique is proposed. A whale optimization algorithm is used to enhance the virtual inertia control loop by optimizing the parameters of the virtual inertia controller with consideration of PLL dynamics and the uncertainties of system inertia. The proposed controller has been validated through comparisons with an optimized virtual inertia PI controller which is tuned utilizing MATLAB internal model control methodology and with H∞-based virtual inertia control. The results show the effectiveness of the proposed controller against different operating conditions and system disturbances and uncertainties.

A New Scheme of Harris Hawk Optimizer With Memory Saving Strategy (HHO-MSS) for Controlling Parameters of Power System Stabilizer and Virtual Inertia in Renewable Microgrid Power System

“…It can be calculated manually with a mathematical approach. However, the approach has been developed to optimal and adaptive approaches, such as Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) [16], [17], Proportional-Integral-Derivative (PID) [18], [19], etc. These approaches are very practical.…”

“…Similar to the PVES model, the simplified WTES model based on low order equation is also adequate to represent the dynamic behavior. Thus, the WTES can be defined as in Equation (18).…”

A Hybrid Controlling Parameters of Power System Stabilizer and Virtual Inertia Using Harris Hawk Optimizer in Interconnected Renewable Power Systems

“…In this paper, by constituting the dynamic equation of each component, the state-space model for interconnected renewable power systems is given by Equation ( 17) until Equation (21).…”

“…Thus, the Virtual Inertia (VI) concept appeared as a viable solution [18]. VI scheme has been implemented in islanded, grid-connected, and interconnected power systems with low to high levels of RES [19]- [21]. VI can imitate the damping and inertia properties in RES generators by regulating the Energy Storage System (ESS) and inverter working behavior with the proper controlling scheme.…”

“…The next group is intelligent algorithms based on learning algorithms, like Machine Learning, Deep Learning, Reinforcement Learning, etc [14], [26], [35]. Besides that, the most popular group is intelligent optimizers based on metaheuristic algorithms, such as the Whale Optimizer Algorithm (WOA) [21], Mayfly Optimizer Algorithm (MOA) [10], Manta Ray Foraging Algorithm (MRFA) [36], Equilibrium Optimizer Algorithm (EOA) [37], Arithmetic Optimizer Algorithm (AOA) [8], Moth Flame Optimizer (MFO) [38], Rat Swarm Optimizer (RSO) [30], Harris Hawk Optimizer (HHO) [39], etc. Intelligent optimizers based on metaheuristic algorithms offer convenience and flexibility in exploring and exploiting the best results.…”