Adaptive Congestion Management in Power Grids Using Grey Wolf Optimization and Multi-Agent Communication

This dataset contains the complete Python source code for the implementation of an Adaptive Congestion Management strategy in Power Grids using the Grey Wolf Optimization (GWO) algorithm in coordination with a Multi-Agent System (MAS) developed using the SPADE framework.

The code simulates a distributed decision-making mechanism for economic dispatch and congestion alleviation in the IEEE 57-bus test system. The optimization layer is driven by the PSO, GWO metaheuristics, which minimizes generation cost while mitigating line overloads. Agents representing the grid manager and individual generators communicate dynamically over an XMPP-based network to coordinate redispatch actions.