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This paper proposes a novel active power redispatch sequential approach to solve the transient stability-constrained optimal power flow (TSC-OPF) problem for the preventive control of transient stability. Based on an independent transient stability assessment of the power system for a given contingency scenario, two proposed power redispatch constraints are formulated and embedded into a conventional OPF formulation. Since these two new constraints only depend on steady-state variables, the dimension of the resulting TSC-OPF model is similar to that of a conventional OPF model and can be solved by standard optimization methods to perform a nonheuristic active power redispatch. The stabilization process is performed by sequentially solving the optimization and the transient stability problems until a suitable generation dispatch that provides a transiently stable equilibrium point is assessed. The validity and the effectiveness of the proposed method are numerically demonstrated in the WSCC three-machine, nine-bus system and an equivalent model of the Mexican power system.

This paper presents a new, significantly improved, approach to formulate a global transient stability-constrained optimal power flow (TSC-OPF), where the sets of dynamic and transient stability constraints to be considered in the optimization process are reduced to one single stability constraint. This constraint is derived from dynamic information provided by the SIngle Machine Equivalent (SIME) method and is only expressed in terms of steady-state variables, which allows us to diminish the length of the time-domain simulation to be included into the global TSC-OPF to a single (initial) time step. In this way, the size of the resulting optimization problem is reduced to one very similar to that of a conventional OPF, overcoming the main drawback of global TSC-OPF techniques (its huge dimension) while maintaining its accuracy and improving its practical application to real power networks. Effectiveness of the proposal is demonstrated by numerical examples on the WSCC three-machine, nine-bus system and the Mexican 46-machine, 190-bus system.

This paper proposes a new approach to model stability and security constraints in optimal power flow (OPF) problems based on a neural network (NN) representation of the system security boundary (SB). The novelty of this proposal is that a closed form, differentiable function derived from the system's SB is used to represent security constraints in an OPF model. The procedure involves two main steps: First, an NN representation of the SB is obtained based on back-propagation neural network (BPNN) training. Second, a differentiable mapping function extracted from the BPNN is used to directly incorporate this function as a constraint in the OPF model. This approach ensures that the operating points resulting from the OPF solution process are within a feasible and secure region, whose limits are better represented using the proposed technique compared to typical security-constrained OPF models. The effectiveness and feasibility of the proposed approach is demonstrated through the implementation, as well as testing and comparison using the IEEE two-area and 118-bus benchmark systems, of an optimal dispatch technique that guarantees system security in the context of competitive electricity markets.

This paper presents a new approach to transient stability control using global transient stability-constrained optimal power flow (TSC-OPF) methods. Its novelty consists in using the single machine equivalent (SIME) method to perform (and improve) two main important functions of global TSC-OPF approaches: first, SIME is used to efficiently perform the power system transient stability analysis; second, SIME determines a stable one machine infinite bus equivalent rotor angular trajectory that is used as the reference stability constraint, at one specific integration step. In this way, the stability constraint is adjusted by SIME, at each iteration of the TSC-OPF method, in order to accurately reflect power system dynamic behavior. The prowess and main characteristics of the proposed approach are shown by numerical examples.

The paper presents the model of a voltage source converter -high voltage direct current (VSC-HVDC) suitable for optimal power flow (OPF) solutions using Newton's algorithm. The VSC-HVDC's ability to provide independent control of the converters ac voltage magnitudes and phase angles relative to the system voltage, which allows the use of separate active and reactive power control loops for system regulation, is well represented by the model. In this new development in Newton OPF, the VSC-HVDC system equations are incorporated directly into the matrix W for a unified optimal solution in a single frame-of-reference. The multipliers method is used to handle all inequality constraints of variables, leading to highly efficient OPF solutions of constrained power networks. The solution approach does not require structural changes in the linearised system of equations during the iterative process using Newton's method. The effectiveness of the VSC-HVDC model and its proposed implementation in Newton OPF is demonstrated by means of two sample systems.