Table 1 Comparison of (I-P)-PDN controller with commonly used controllers.
From: Enhanced load frequency regulation in microgrids with renewable energy sources and electric vehicles
Ref | Areas | Controller | Optimization Algorithm | ∆F1 | ∆F2 | ∆F3 | Ts |
|---|---|---|---|---|---|---|---|
OSH /USH | OSH/USH | OSH/USH | |||||
Multi | ITPFOD | Satin Bowerbird Optimizer (SBO) | -/- | -/- | -/- | - | |
Multi | Virtual Synch Gen (VSG)-based VIC via HVDC link | Particle Swarm Optimization (PSO) | -/- | -/- | -/- | - | |
Multi | - | Modified Artificial Flora Optimization (MAFO) | -/- | -/- | -/- | - | |
Multi | PID | Enhanced Whale Optimization Algorithm (EWOA) | -/- | -/- | -/- | - | |
01 & Multi | Cascaded Fuzzy-CFFOPI-FOPID | Imperialist Competitive Algorithm (ICA) | 0.02110/0.0425 | 0.0063/0.0147 | 0.0019/0.0112 | Ts1:8.4 Ts2:12.02 Ts3:12.15 | |
Multi | FOPID-TID(LFC), FOPID (SMES) | Manta Ray Foraging Optimization (MRFO) | 0.0135/- | 0.0078/- | -/- | Ts1:18 Ts2:23 | |
03 | Type-2 Fuzzy Controller | Discrete Water Cycle Algorithm (d-WCA) | -/- | -/- | -/- | - | |
03 | T2F-CPIF | Crow Search Algorithm (CSA) | 0.002/- 0.024 | -/- | 0.001/- | Ts1:3.86 Ts3:3.26 | |
03 | PI | Teaching Learning-Based Optimization (TLBO) with LMI | 0.6235/- | 0.4264/- | 0.3572/- | - | |
03 | MPC | JAYA Algorithm | -/- | -/- | -/- | - | |
01 | LFC controller Unknown Input Observer (UIO) | RL-DDPG | -/- | -/- | -/- | - | |
01 | Nonlinear-PI | Dandelion Optimizer (DO) | 0.007860/0.000083 | -/- | -/- | 6.610 | |
01 | PD–PI | Gorilla Troops Optimization (GTO) | 0.00569/0.00527 | -/- | -/- | 1.599 | |
01-IµG | PI and PID | Artificial Rabbits Optimization (ARO) | 0.013/-0.115 | -/- | -/- | Ts1:2.96 | |
MµG | PIλDND2N2 | Grey Wolf Optimizer (GWO) | -/- |  | -/- | - | |
01 | FO-Multistage PD/(1 + PI) | Least Squares Method + Minimum Variance | 0.06/-0.001 | 0.062/- | -/- | Ts1:6.8 Ts2:6.2 | |
01 | H ∞ and LQG | Kalman Filter + Optimal Weighting | - | -/- | -/- | - | |
01 | PI–PD | Salp Swarm Optimization (SSO) | 0.0011/0.0011 | -/- | -/- | Ts1:3.5459 | |
02 | FOPIDN-(1 + PIDN) | GWO | 0.00000/0.05450 | 0.00000/0.00953 | -/- | Ts1:14.39 Ts2:19.69 | |
02 | (FO-Fuzzy PSS) | Advanced Sine Cosine Algorithm (a-SCA) | 0.0042/-0.0084 | 0.0062/-0.0262 | -/- | Ts1: 8.620 Ts2:7.812 | |
02 | (Fuzzy-T2-PSS) | Modified Crow Search Algorithm (M-CSA) | 0.0724/0.0912 | 0.0342/0.0012 | -/- | Ts1:3.8206 Ts2: 4.201 | |
02 | FO-T2FC | Quassi-Oppositional Path Finder Algorithm (QO-PFA) | 0.0382/-0.0676 | 0.0426/-0.0462 | -/- | Ts1:5.68 Ts2:6.48 | |
02 | Intelligent-FOI | Grey Wolf Optimizer (GWO) | -/- | -/- | -/- | - | |
02 | TD-TI | Quantum Chaos Game Optimizer (QCGO) | 52.43/60.01 | 99.36/86.4 | -/- | - | |
02 | FOPID + TID | Artificial Ecosystem Optimization (AEO) | -/0.0217 | 0.0002/0.0072 | -/- | Ts1:67 Ts2:91 | |
02 | Self-tuned AGC | Improved Sine Cosine Algorithm (i-SCA) | -/- | -/- | -/- | - | |
02 | Adaptive-MPC | MPC algorithm | -/- | -/- | -/- | - | |
02 | FOPIDA-FOIDN | Hybrid AGTO-EO (HGTOEO) | -/- | -/- | -/- | - | |
Proposed | 02 | (I-P)-PDN | Black-winged kite Algorithm | 0.0000227/ -0.00128 | -0.00000917/ -0.00120 | -/- | Ts1: 0.75 Ts2: 0.98 Ts3Ptie:1.189 |
0.000418/ 0.00000431 | 0.000378/ -0.00037 | -/- |
