4.3 Case Study 2: Network Reconfiguration for Loss Minimization

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4.3 Case Study 2: Network Reconfiguration for Loss Minimization

Problem Setup

  • Variables: Binary switches (0 = open, 1 = closed).
  • Objective: Minimize power loss while maintaining radial structure.

MATLAB Implementation

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% Fitness function: Calculate power loss for a switch configuration  
function loss = networkLoss(config)  
    % Run power flow analysis using config (e.g., MATPOWER)  
    results = runpf(config);  
    loss = sum(results.branch(:,14)); % Total real power loss  
end  

% GA Parameters  
nvars = 10; % Number of switches  
options = optimoptions('ga', 'PopulationType', 'bitstring', ...  
                       'MutationFcn', @mutationuniform, ...  
                       'MaxGenerations', 200);  

% Run GA  
[config_opt, loss_opt] = ga(@networkLoss, nvars, [], [], [], [], [], [], [], options);  
disp(['Optimal switch config: ', num2str(config_opt), ' | Loss: ', num2str(loss_opt), ' MW']);

Results

  • Compare losses before/after reconfiguration.
  • Visualize network topology changes.

5. Results and Analysis

5.1 OPF Performance

  • GA vs. Classical Methods:
    • GA achieves comparable cost with better constraint handling in non-convex cases.
  • Computation Time: Longer than gradient-based methods but robust for complex systems.

5.2 Network Reconfiguration

  • Loss Reduction: Typical 10–20% loss reduction in test systems (e.g., IEEE 33-bus).
  • Convergence: Plot fitness vs. generations to show optimization progress.

6. Discussion

  • Strengths of GAs:
    • Handles discrete variables (e.g., switches, capacitor banks).
    • Avoids local minima in non-linear problems.
  • Limitations:
    • Computationally intensive for large networks.
    • Requires tuning (population size, mutation rate).
  • MATLAB Advantages:
    • Parallel computing for faster fitness evaluation.
    • Integration with power flow tools (e.g., MATPOWER).

7. Conclusion

  • GAs are effective for complex electrical network optimization, especially with mixed variables.
  • MATLAB’s ga function provides flexibility for customization.
  • Future work: Hybrid algorithms (GA + PSO), real-time optimization in smart grids.

8. Appendix

MATLAB Code

  • Full OPF and reconfiguration scripts.
  • Custom power flow functions (if not using MATPOWER).

References

  1. Goldberg, D. E. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, 1989.
  2. Zimmerman, R. D., et al. MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems.
  3. MATLAB Global Optimization Toolbox Documentation.

Key MATLAB Functions/Toolboxes

  1. Global Optimization Toolbox: ga, gamultiobj (for multi-objective problems).
  2. MATPOWER: For power flow analysis.
  3. Parallel Computing Toolbox: Speed up fitness evaluation

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