Search

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.

This paper presents seven parameters of double diode model of the photovoltaic module under different weather conditions are extracted using differential development with an integrated mutation per iteration (DEIM) algorithm. The algorithm is produced by integrating of two other algorithms namely, electromagnetism like (EML) and differential evolution (DE) algorithms. DEIM enhances the mutation step of the original DE by using the attraction-repulsion principle found in the EML algorithm. Meanwhile, a novel strategy based on adjusting mutation and crossover rate factors for each iteration is adopted in this paper. The implemented scheme's success is confirmed by comparing its results with those obtained by techniques cited in the literature. Along with the results, the DEIM suggests high closeness with the experimental I–V characteristic. For the proposed algorithm an average Root Mean Square Error (RMSE), absolute error (AE), mean bias Error (MBE), and execution time values were 0.0608, 0.044, 0.0053 and 23.333, respectively. The comparisons and evaluations results proved that the DEIM is better in terms of precision and rapid convergence. Furthermore, fewer control parameters are needed in comparison to EML and DE algorithms.

This paper presents seven parameters of double diode model of the photovoltaic module under different weather conditions are extracted using differential development with an integrated mutation per iteration (DEIM) algorithm. The algorithm is produced by integrating of two other algorithms namely, electromagnetism like (EML) and differential evolution (DE) algorithms. DEIM enhances the mutation step of the original DE by using the attraction-repulsion principle found in the EML algorithm. Meanwhile, a novel strategy based on adjusting mutation and crossover rate factors for each iteration is adopted in this paper. The implemented scheme's success is confirmed by comparing its results with those obtained by techniques cited in the literature. Along with the results, the DEIM suggests high closeness with the experimental I–V characteristic. For the proposed algorithm the average Root Mean Square Error ( MSE), Absolute Error (AE ), Mean Bias Error ( MBE), and execution time values were 0.0608, 0.044, 0.0053, and 23.333, respectively. The comparisons and evaluation results proved that the DEIM is better in terms of precision and rapid convergence. Furthermore, fewer control parameters are needed in comparison to EML and DE algorithms.