Allocation of FACTS devices in hydrothermal systems

Locating series FACTS devices for congestion management in deregulated electricity markets

“…Since, LMP difference is highest for the congested/overloaded lines (i.e. line operating at limit) and overloaded lines are not always the best locations for placement of series FACTS devices [16], a neighborhood search method is required which will be taken care by formation of priority list. Hence, in this method, a priority list is formed based on the magnitude of the difference in LMPs.…”

Thyristor-controlled switch capacitor placement in large-scale power systems via mixed integer linear programming and Taylor series expansion

“…However, to deal with the nonlinear characteristic of the problem, the authors simplified nonlinear equality constraints to inequalities. In [15], in order to linearize the allocation problem, the author assumes that the voltage angles of two adjacent buses would not change before and after TCSC installation. In this paper, we formulate the allocation problem as an MILP; and the first order Taylor series expansion is employed to linearize the problem.…”

A Comprehensive Survey of Optimal Placement and Coordinated Control Techniques of FACTS Controllers in Multi-Machine Power System Environments

“…Oliveira et al suggested a dynamic optimization programming algorithm for allocation of FACTS devices in hydrothermal systems in order to minimize the expected thermal generation costs and the investments on FACTS devices in a pre-specified time interval [44]. Chang and Huang et al showed that a hybrid optimization programming algorithm for optimal placement of SVC for voltage stability reinforcement [45].…”

“…The various optimization based methods have been proposed in literatures that includes non-linear optimization programming techniques [38], [39], [173], mixed integeroptimization programming techniques [40]- [42], [170]- [171], dynamic optimization programming algorithms [44], hybrid optimization programming algorithms [45], bellmann's optimization principle [46], decomposition coordination methods [47]- [48], curved space optimization techniques [174]. The various artificial intelligence (AI) based methods proposed in literature includes genetic algorithms (GA) [49]- [64], [175]- [176], [180], tabu search algorithms [65], [66], simulated annealing (SA) based approach [69]- [70], [177], particle swarm optimization (PSO) techniques [71]- [73], [80], artificial neural network (ANN) based algorithms [74]- [76], ant colony optimization (ACO) algorithms [77]- [78], graph search algorithms [79], fuzzy logic based approach [81]- [82], other techniques such as norm forms of diffeomorphism techniques [83], evolution strategies algorithms [84], [86], improved evolutionary programming [68], gravitational optimization techniques [85], benders decomposition techniques [42], augmented Lagrange multiplier approach …”