This study proposes a new trust-region based sequential linear programming algorithm to solve the AC optimal power flow (OPF) problem. The OPF problem is solved by linearizing the cost function, power balance and engineering constraints of the system, followed by a trust-region to control the validity of the linear model. To alleviate the problems associated with the infeasibilities of a linear approximation, a feasibility restoration phase is introduced. This phase uses the original nonlinear constraints to quickly locate a feasible point when the linear approximation is infeasible. The algorithm follows convergence criteria to satisfy the first order optimality conditions for the original OPF problem. Studies on standard IEEE systems and large-scale Polish systems show an acceptable quality of convergence to a set of best-known solutions and a substantial improvement in computational time, with linear scaling proportional to the network size.The OPF problem optimizes the total operating cost to support efficient 2 operation of power systems while satisfying system constraints for a nominal 3 state [1]. In practice one needs to solve a security-constrained OPF (SC-OPF) 4 problem which takes into account the possibility of a sudden failure of a single 5 component (generator, transmission line, transformer, etc) in the system. This 6 is known as the N−1 security criterion [1, 2]. The OPF problem without se-7 curity constraints has been extensively investigated in the literature (see, for 8 instance [2], and references therein). This paper addresses the OPF problem 9 for simplicity, but the benefits of our approach extend to the context of the 10 SC-OPF problem as well. It is well-known that the OPF problem is nonlinear 11 and nonconvex in nature, potentially having multiple equilibrium points. Hence 12 searching for a global solution is in principle 3, 4, 5]). Electricity 13 market clearing strategies are mainly based on nodal prices, which are the dual 14 variables of power balance constraints of the OPF problem. This highlights 15 the importance of the convexity and scalability features for any algorithm to 16 use in OPF calculations [1, 6]. In addition to this, real-world OPF problems 17 involve very large numbers of decision variables. This makes them challenging 18 for a solution technique, both in terms of memory and computational time re-19 quirements. Consequently there is a great need for computationally efficient 20 techniques which can handle the nonconvex AC network constraints. 21 In the context of OPF, solution approaches, such as linear programming 22 (LP) [6, 7, 8], quadratic programming (QP) [9], Lagrangian relaxation [10], 23 and interior-point (IP) methods [11] have been extensively investigated in the 24 literature. It is worth noting that, among all these approaches, IP methods 25 have emerged as a promising direct solution approach for OPF problems. IP 26 methods have proven to be a viable computational alternative for the solution of 27 large-scale OPF problems [12]. The primal-dual log...