Power System Optimization under Uncertainty Using GAMS and MATLAB Simulink
Introduction
The reliable operation of power systems is inherently challenged by uncertainties, including fluctuating demand, variable renewable generation, and unforeseen outages. This project addresses these challenges by applying advanced optimization techniques using General Algebraic Modeling System (GAMS) and MATLAB Simulink, aiming to minimize uncertainty impacts, ensuring robust power system operation and planning.
Objective
To develop and validate a robust optimization framework to mitigate uncertainties within power systems, integrating GAMS optimization strategies with dynamic simulations in MATLAB Simulink.
Project Overview and Methodology
The project leverages the strengths of two powerful tools—GAMS for optimization modeling and MATLAB Simulink for dynamic system simulation. The methodology is structured as follows:
  • Uncertainty Identification :
    Key uncertainties in demand and renewable generation are identified and characterized.
  • Mathematical Modeling :
    Robust optimization models are developed in GAMS to manage identified uncertainties effectively.
  • Dynamic Simulation :
    Developed optimization solutions are dynamically validated within MATLAB Simulink.
Suggested Figure: Schematic of integrated GAMS-MATLAB Simulink workflow.
Handling Uncertainty in Power Systems
Addressing uncertainty involves robust optimization, where the primary goal is to optimize system performance while accommodating variations. Key uncertainty management strategies in this project include:
  • Scenario-Based Approach :
    Creating diverse operational scenarios capturing potential uncertainty variations.
  • Robust Optimization :
    Formulating optimization problems to minimize worst-case scenario impacts.
  • Sensitivity Analysis :
    Evaluating system response under various uncertainty levels.
Suggested Figure: Illustration of scenario-based optimization approach.
Optimization with GAMS
GAMS facilitates comprehensive and flexible optimization problem formulations, crucial for addressing complex power system uncertainties. This involves:
  • Defining objective functions that minimize operational costs while maintaining reliability.
  • Incorporating constraints to capture system limitations and operational requirements.
  • Performing optimization under multiple scenarios to ensure robust solutions.
Suggested Figure: Example GAMS optimization model structure.
Dynamic Validation with MATLAB Simulink
Optimized solutions derived from GAMS are rigorously tested within MATLAB Simulink to ensure they perform effectively under realistic conditions:
  • Simulink simulations validate system stability, voltage regulation, and frequency control under uncertain conditions.
  • Real-time simulation scenarios help demonstrate the practical viability of optimized solutions.
Suggested Figure: Dynamic simulation results demonstrating system stability under uncertainty.
Application Impact and Advantages
The project's approach offers significant operational benefits to power systems:
  • Improved Reliability :
    Minimizing disruptions caused by uncertainties, enhancing overall system reliability.
  • Cost Efficiency :
    Reducing operational and contingency costs through proactive optimization.
  • Sustainable Integration :
    Better management and integration of renewable energy sources.
Additional Suggested Visual: Comparative analysis chart showing performance improvements before and after optimization.
Conclusion
This project successfully demonstrates how integrating robust optimization strategies with dynamic validation can significantly mitigate uncertainties in power system operations. By employing GAMS and MATLAB Simulink synergistically, the developed framework enhances reliability, reduces costs, and promotes sustainable energy integration, making it a valuable approach for modern power system management.