I am currently working on a power flow optimization problem using the PandaPower library. My research aims to minimize power losses and improve the overall efficiency of the power transmission network. In addition, I am also interested in analyzing the multiple slack operation of the system. For this purpose, I am using metaheuristic algorithms such as Particle Swarm Optimization (PSO), Cuckoo Search Algorithm (CSA), and Grey Wolf Optimization (GWO) to find the optimal power generation values for each generator.
To explore the multiple slack operation, I have implemented the distributed slack model available in PandaPower with NR for power flow. However, I am encountering an issue where the slack bus power goes beyond its maximum limits even after setting constraints on the generator power outputs. I have tried adding constraints to the optimization problem, but it seems to have little to no impact on the slack bus power limits.
Here's a brief outline of my approach:
- I have defined the PandaPower network with the necessary buses, generators, and loads.
- I have set up an objective function that minimizes power losses and penalizes power imbalance.
- I have defined constraints for the optimization problem, such as active/reactive power limits, voltage limits, and generator power output limits.
- I have used the metaheuristic algorithms to find the optimal generator power outputs.
- I have attempted to use the distributed slack model in PandaPower to analyze multiple slack operation.
Despite following this approach, I am unable to keep the slack bus power within the desired limits while analyzing the multiple slack operation using the distributed slack model. I would appreciate any suggestions or guidance on how to address this issue effectively. Are there any alternative methods or modifications to the PandaPower model that could help in this situation, specifically for the multiple slack operation?