Adaptive Intelligent Migration Multi-Task Multi-Objective Evolutionary Algorithm for Solving Optimal Power Flow Problems with Wind-Solar Photovoltaic Fields and FACTS Devices

For the constrained multi‑objective power flow optimization (MOOPF), an adaptive intelligent migration multi-task multi-objective evolutionary algorithm (MTCMO-AIMS) was proposed for optimal power flow (OPF) problems with wind-solar photovoltaic fields and FACTS devices. A mathematical model is established to describe the OPF formulation while explicitly accounting for the variability and unpredictability of wind and solar generation. The essential constraints include the nodal power balance relationships, the permissible operating ranges of conventional generator active power outputs, the allowable rating bounds of shunt reactive power compensation equipment together with the operational limits imposed on Flexible AC Transmission System (FACTS) apparatus. The adaptive migration probability mechanism (AMPM) incorporates additional modules for adaptive migration probability calculation and dynamic updating, enabling knowledge migration to be dynamically adapted to the real-time state of the population. Replacing blind random migration with directional elite-guided transfer, the intelligent elite-guided migration strategy (IEGMS) makes better use of complementary information across distinct optimization tasks. MTCMO-AIMS is applied to complex constrained MOOPF problems that integrate wind farms, solar farms and FACTS equipment, with detailed analysis on the IEEE 30-bus system and IEEE 57-bus system. The obtained results are compared with those of the AFSEA, TPCMaO, TSTI and CCMO algorithms. Experimental results demonstrate that it can find excellent solutions and generate a satisfactory Pareto frontier in both system scales, verifying its effectiveness and scalability in larger-scale power systems.