Optimal Power Flow, Sizing and Location of Thermal Generating Units Using Meta – Heuristic Soft Computing Algorithms

This paper presents an approach on stochastically approximated optimal power flow (OPF) in thermal generation system. It also considers the optimal sizing and location of thermal generation units for finding the optimal generator with minimal losses in distributed environment. The proposed hybrid approach optimizes the power flow using stochastic approximation technique with Flower Pollination Algorithm (FPA). With OPF as a key objective, the system chooses the optimal location and sizing of distributed thermal generators using Krill Herd Algorithm (KH). Here, the objective function for FPA is concerned with minimal cost, emission and voltage stability index. Likewise, KH algorithm is concerned with minimal loss and installation of thermal generators. Proposed hybrid system that combines optimal power flow, sizing and location is compared with conventional techniques to prove its effectiveness. The comparative analysis of proposed hybrid approach is implemented over standard IEEE 30-bus and IEEE 57-bus distribution system. Simulation results proved that the meta-heuristic hybrid approach performs well when compared with available conventional techniques.