Optimal Power Flow using An Optimally Tuned Pattern Search Algorithm

Optimal power flow (OPF) is a critical optimization application in power system planning and operation. Numerous studies employ metaheuristic techniques to address OPF problems of varying complexity. However, these techniques often suffer from slow convergence due to their dependence on the quality of initial solutions. To overcome this limitation, initial solutions must be optimally tuned to achieve good outcomes with faster convergence. This paper proposes an optimally tuned pattern search (OPS) algorithm to solve OPF problems in medium and large power systems. The tuning process, performed using the classical interior point method (IPM), provides optimal initial control variable values for the standard pattern search (PS) algorithm. The proposed technique is applied to three test systems: IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems. The OPF problem is formulated to minimize four objectives: total active power loss, total generator fuel cost, total generator emission, and total deviation in load bus voltage magnitude. The performance of the OPS algorithm is evaluated based on objective function values and computation times and is compared with IPM and two popular metaheuristic techniques, particle swarm optimization (PSO) and genetic algorithm (GA). Results indicate that the OPS algorithm's performance varies across test systems but generally balances optimization performance with computational efficiency.