Non-Convex Optimal Power Flow Implementation by Distributed Meta-Heuristic Optimization Algorithm

Abstract

To achieve more accurate operation of power system, the models are sometime represented as a non-linear, non-convex and even multi-peak function, such as the fuel cost function with valve-point effects. However, to the best of the author’s knowledge, the existing distributed optimization algorithms can only solve the convex problem. It is urged that proposing a novel distributed optimization technique to solve non-convex optimization problem. Due to the strong generalization ability of meta-heuristic algorithm, a novel distributed meta-heuristic optimization algorithm is proposed in this paper. In the meantime, a distributed power flow solution algorithm is embedded into each iteration. These two algorithms constitute a novel double-layer optimization mechanism to solve the non-convex optimal power flow (NCOPF), in which a set of optimizers (operators essentially) across the network to optimize the operation of each generation area in parallel while minimizing the total operational cost of the entire multi-area power system. The experimental results in two test systems demonstrate that the proposed algorithm does not implement distributed NCOPF solution only but also improve the solution accuracy, accelerate the convergence speed and enhance the robustness, especially in the large-scale system.