Grey Wolf Optimized Load Frequency Control for Renewable Energy Integrated Power System
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- 4 minutes ago
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📘 Concept Overview
The adopted concept is based on a published research paper that investigates:
Integration of renewable energy sources (RES) with conventional thermal power plants
Two-area interconnected system
Advanced controller design for load frequency control
🔌 Two-Area Power System Structure
The complete system consists of two interconnected areas:
🟦 Area 1
☀️ PV-based renewable source
⚙️ Thermal plant with:
Governor
Turbine
Generator–load model
🎛️ PA + (1 + DD) Controller
🟩 Area 2
🌬️ Wind-based renewable source
⚙️ Thermal plant with:
Governor
Turbine
Generator–load model
🎛️ PA + (1 + DD) Controller
🔗 The two areas are interconnected through a tie-line, enabling power exchange and coordinated frequency regulation.
🎛️ Controller Structure: PA + (1 + DD)
The paper proposes a hybrid controller, consisting of:
🔹 PA Controller for initial regulation
🔹 (1 + DD) Controller with two derivative terms for enhanced dynamic response
📐 Controller Gains:
Area 1:
KP₁, KA₁, KD₁₁, KD₁₂
Area 2:
KP₂, KA₂, K₂₁, K₂₂
➡️ These eight parameters are optimally tuned using Grey Wolf Optimization.
🌞🌬️ Renewable Energy Modeling
PV and Wind power variations are applied as disturbance inputs
Random power profiles are generated for 0–100 seconds
Input patterns closely follow those used in the reference paper
📊 This realistic variability helps evaluate the robustness of the LFC strategy under fluctuating renewable generation.
🐺 Grey Wolf Optimization (GWO)
Grey Wolf Optimization is a nature-inspired metaheuristic algorithm, modeled on the social hierarchy and hunting behavior of grey wolves.
🔧 Optimization Setup
🔢 Number of decision variables: 8
🔁 Maximum iterations: 15
🧠 Optimization goal: Minimize frequency deviations
🎯 Objective Function
As defined in the reference paper, the objective function is based on the Integral of Time-weighted Absolute Error (ITAE):
📉 Minimizing this function ensures:
Faster damping of oscillations
Reduced overshoot
Improved steady-state performance
🔄 Optimization Process in MATLAB/Simulink
GWO iteratively updates controller gains
The Simulink model runs repeatedly during optimization
For each iteration:
Objective function value (ITAE) is calculated
Best solution (α wolf) is updated
📌 After convergence, the optimal controller gains are displayed in the MATLAB command window.
📊 Simulation Results & Validation
✅ Using the optimized gains:
Frequency deviations in both areas are significantly reduced
Tie-line power oscillations are well damped
System performance closely matches the results reported in the reference paper
📈 This confirms the effectiveness of GWO-based tuning for LFC in renewable-integrated power systems.
🧠 Key Takeaways
🐺 Grey Wolf Optimization efficiently tunes multi-parameter controllers
⚡ PA + (1 + DD) controller enhances LFC performance
🌞🌬️ Renewable integration introduces variability that must be carefully controlled
🔁 GWO-based LFC provides robust and stable frequency regulation
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