Improved Differential Evolution Algorithm for Power System Optimization

In power systems, reactive power optimization and distribution network reconfiguration represent critical operational tasks. The Improved Differential Evolution Algorithm (IDEA) serves as an efficient optimization technique for addressing these challenges. The algorithm implementation typically involves mutation, crossover, and selection operations, where candidate solutions evolve through generations using difference vectors between population members. For reactive power optimization, IDEA minimizes power losses by optimizing reactive power compensation devices through objective functions that model system constraints. In distribution network reconfiguration, the algorithm modifies network topology by opening/closing switches while maintaining radial structure constraints through specialized encoding schemes. The core algorithm employs adaptive parameter control where mutation factors and crossover rates dynamically adjust based on population diversity metrics. Beyond power systems, IDEA demonstrates versatility in applications like economic dispatch (optimizing generator outputs subject to load demands) and transportation network optimization (route planning with capacity constraints). The algorithm's robustness stems from its balance between exploration and exploitation phases, making it a promising approach worthy of further research and practical implementation.