SECURITY CONSTRAINED OPTIMAL POWER FLOW BASED ON AN ARTIFICIAL INTELLIGENCE TECHNIQUE

In the past, artificial intelligence techniques were successfully adopted for obtaining optimal power flow in a power system. However, this optimality is limited to the economic aspects of the system's operating conditions. The other aspects of the operation, like security conditions, have been given limited attention. Hence, this paper presents an attempt to dispatch the power generation in electrical power systems optimally by taking into consideration both economic and secure operations, so that modern power systems can operate reliably and effectively. Security-constrained optimal power flow is addressed in this paper as a multi-objective optimization problem, consisting of four objective functions: minimizing power generation costs; minimizing voltage deviation; minimizing power losses; and alleviating the overloading on transmission lines. A detailed steady-state generator model is adopted in the present formulation. A metaheuristic optimization technique, namely, differential evolution, is used to obtain the security constraint optimal power dispatch. Additionally, the operating states of a power system have been addressed in this paper. The identification of the operating states is vital to the assessment of the security of the EPS. Improvements and appropriate security assessments have been made in some cases. The proposed algorithm is applied to a typical power system with different operating strategies. The obtained results are compared to those obtained from previous studies in the literature to demonstrate the suggested method's validity and effectiveness.