This paper is engrossed in the usage of metaheuristic optimization methods named JAYA algorithm and teacher learning based (TLBO) algorithm, which deal with objective function optimal power flow (OPF) problem. TLBO Jaya are parameter-free algorithms, reduce complexity algorithms. The this to real losses maintain voltages tap positions within their limits. IEEE-39 bus system data considered for ...

Abstract Because of the enormous economic gain it may offer, unit commitment (UC) has been a primary optimization job in daily operation planning contemporary power systems. The AC optimal flow (ACOPF) constraint is method for determining ideal load arrangement alternating current (AC) Using and ACOPF limitations to determine distribution an important approach ensure steady functioning with exa...

Machine learning assisted optimal power flow (OPF) aims to reduce the computational complexity of these non-linear and non-convex constrained optimization problems by consigning expensive (online) offline training. The majority work in this area typically employs fully connected neural networks (FCNN). However, recently convolutional (CNN) graph (GNN) have also been investigated, effort exploit...

The rapid uptake of natural gas-fired units in energy systems poses significant challenges coordinating the electricity and gas systems. Besides, uncertainty caused by integrated renewable such as wind power raises more requirements on robustness operation for system (IEGS). To address these challenges, this paper investigates distributed adjustable robust optimal flow (OPGF) model IEGS. Using ...

Optimal power flow (OPF) problems are mathematical programs to determine how distribute over networks subject and operational constraints. In this article, we treat an OPF problem as operator that maps user demand generated power, allow the parameters take values in some admissible set. We formalize theoretic approach, define characterize restricted parameter sets under which mapping has a sing...