Enhancing multi-area microgrid stability with virtual damping and energy storage inertia techniques using hybrid lyrebird - Pattern search optimized PI-(1+DD) controller

Renewable Energy Sources (RES) are crucial for modern power systems, providing clean, cost-effective electricity, but these systems lack inertia, which can negatively impact microgrid stability and synchronization. This paper proposes a photovoltaic-based virtual inertia virtual damping system combined with an energy storage system (ESS) to enhance the dynamic performance of multi-area microgrids. The Hybrid Lyrebird-Pattern Search (hLOA-PS) optimization technique is utilized to tune the PI-(1 + DD) controller parameters, with other algorithms like Grey Wolf Optimization (hGWO-PS), Artificial Bee Colony (hABC-PS), and Osprey pattern search optimization (hOOA-PS). Convergence tests and Wilcoxon's rank-signed non-parametric statistical test indicate that hLOA-PS outperforms the other algorithms. The proposed photovoltaic virtual inertia virtual damping energy storage virtual inertia model demonstrates superior dynamic performance in terms of rise time, settling time, overshoot, and undershoot compared to conventional, energy storage virtual inertia and photovoltaic virtual inertia energy storage virtual inertia system. Specifically, the photovoltaic virtual inertia virtual damping energy storage virtual inertia model achieved reductions in rise time by 18 %, 5 %, 4 %, and 7 %, 10 % for ΔF₁ and ΔF₂ compared to conventional, energy storage virtual inertia, and photovoltaic virtual inertia energy storage virtual inertia system. The high penetration rate of RES, which reduces system inertia, is also compensated by the proposed model, achieving reductions of 75 %, 55 % for ∆F₁, 74 %, 55 % for ∆F₂, and 92 %, 66 % for ∆P₁₂ in maximum frequency drop & reductions of 84 %, 30 % for ∆F₁, 76 %, 46 % for ∆F₂, and 92 %, 66 % for ∆P₁₂ in maximum frequency overshoot.