Real time probabilistic power system state estimation

Smartening of contemporaneous power delivery systems in conjunction with the increased penetration of renewable energies (REs), change the way to energize consumers who are willing to maximize their utility from energy consumption. However, there is a high degree of uncertainty in the electricity markets of such systems. Moreover, the unprecedented ascending penetration of distributed energy resources (DERs) mainly harvesting REs is a direct consequence of environmentally friendly concerns. This type of energy resources brings about more uncertainties into power system operation resulting in, necessitates probabilistic analysis of the system performance. In the smarter power markets, encountered the restructuring and deregulation, the online studies of system performance is of huge interest. This paper proposes a new methodology for real time state estimation, e.g. energy pricing by probabilistic optimal power flow (P-OPF) studies using the concept of hybrid artificial neural networks (ANN) and differential evolutionary (DE) method. In order to examine the effectiveness and applicability of the proposed method, two case studies are conducted and the obtained results are compared against those of Monte Carlo simulation (MCS) technique. Comparison of the results reveals the impressiveness of the method regards to both accuracy and execution time criteria.

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