<p>This paper presents a straightforward approach for determining the total transfer capability (TTC) between two power system areas, incorporating security constraints. A multi-objective methodology is proposed to optimize the generator variables—active power generation and terminal voltage magnitude—to maximize the TTC and the minimum damping ratio, which is an index associated with small-signal stability, considering contingency scenarios and static constraints. The formulation is based on optimal power flow (OPF), and the multi-objective approach facilitates the identification of the conflicting relationship between the two objectives via the Pareto front. To solve the optimization problem, two metaheuristics are employed: NSGA-II and MOCS. Each individual, from the metaheuristic perspective, is evaluated using conventional power flow and modal analysis tools, implemented in MATLAB with the PSAT toolbox. The methodology is tested on the New England test system. A systematic comparison of Pareto fronts, computational time, and performance metrics is conducted to evaluate the methods. Nonlinear time-domain simulations confirm that some solutions obtained are feasible even under transient stability conditions.</p>

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Multi-objective approach for dynamic transmission capability calculation

  • Junior N. N. Costa,
  • Wesley Peres,
  • Francisco C. R. Coelho

摘要

This paper presents a straightforward approach for determining the total transfer capability (TTC) between two power system areas, incorporating security constraints. A multi-objective methodology is proposed to optimize the generator variables—active power generation and terminal voltage magnitude—to maximize the TTC and the minimum damping ratio, which is an index associated with small-signal stability, considering contingency scenarios and static constraints. The formulation is based on optimal power flow (OPF), and the multi-objective approach facilitates the identification of the conflicting relationship between the two objectives via the Pareto front. To solve the optimization problem, two metaheuristics are employed: NSGA-II and MOCS. Each individual, from the metaheuristic perspective, is evaluated using conventional power flow and modal analysis tools, implemented in MATLAB with the PSAT toolbox. The methodology is tested on the New England test system. A systematic comparison of Pareto fronts, computational time, and performance metrics is conducted to evaluate the methods. Nonlinear time-domain simulations confirm that some solutions obtained are feasible even under transient stability conditions.