Dynamic System Modeling & Optimization (MATLAB/Simulink)

This repository contains MATLAB scripts and Simulink models used for the modeling, simulation, and optimization of a dynamic energy system. Two different demand response (DR) schemes are implemented:

1. Cost / CO₂ minimization:
   The Grey Wolf Optimizer (GWO) is used to minimize the objective function related to energy cost and CO₂ emissions.

2. Loss minimization:
   The DR strategy aims at minimizing power losses in the network (power lines and transformer).

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Contents

• codes/
  MATLAB scripts and functions.

• models/
  Simulink models (.slx).

• results/
  Simulation results (.mat, .fig).

• flowcharts of the 2 scenarios ('.png')

Requirements

• MATLAB
• Simulink
• (Optional) Optimization / Global Optimization / Neural Network toolboxes, if required by the scripts.

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How to Run

A. Base case – No demand response

1. Open MATLAB.
2. Run initial_conditions.m.
3. Open and run no_demand_res.slx.
   After the simulation finishes, run no_demand_results.m to store the results.
4. Run filter_no_signals.m to extract the correct 24-hour values for each measurement.

B. Demand response – Cost / CO₂ minimization (cost_flowchart.png)

5. Run one of the following optimization scripts:
   • gwo_nn_comb_co2.m (CO₂-oriented objective), or
   • gwo_nn_comb_cost.m (cost-oriented objective).
6. Open and run with_demand_res_final_v5.slx.
   After the simulation finishes, run with_demand_results.m.
7. Run filter_with_signals.m to extract the correct 24-hour values for each measurement.
   → Cost/CO₂-minimizing DR scenario completed.

C. Demand response – Loss minimization (iterative GWO–simulation loop) (loss_flowchart.png)

8. Close and reopen MATLAB (to clear the workspace).
9. Run initial_conditions.m to initialize the first iteration.
10. Open only with_demand_res_final_v5_live.slx.
11. Run gwo_live_simulation_v2.m and wait until all simulations and the GWO optimization are completed.
12. After completion, you will obtain:
    • loss_history_figure.fig
    • load_compar.mat
    • load_history.mat
13. Now in order to have the simulation results from the load that minimize the losses open and run the "with_demand_res_loss_verify"
14. Run filter_with_loss_signals.m to extract the correct 24-hour values for each measurement.
15. Run calculate_cost_kgCO2.m to compute the total cost and CO₂ emissions for this scenario.

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Comparison

16. Compare the two DR scenarios (cost/CO₂ minimization vs. loss minimization) using the generated .mat files and figures in results/.