Parallel power system restoration planning using heuristic initialization and discrete evolutionary programming

Abstract

This paper proposes a sectionalizing planning for parallel power system restoration after a complete system blackout. Parallel restoration is conducted in order to reduce the total restoration process time. Physical and operation knowledge of the system, operating personnel experience, and computer simulation are combined in this planning to improve the system restoration and serve as a guidance for system operators/planners. Sectionalizing planning is obtained using discrete evolutionary programming optimization method assisted by heuristic initialization and graph theory approach. Set of transmission lines that should not be restored during parallel restoration process (cut set) is determined in order to sectionalize the system into subsystems or islands. Each island with almost similar restoration time is set as an objective function so as to speed up the resynchronization of the islands. Restoration operation and constraints (black start generator availability, load-generation balance and maintaining acceptable voltage magnitude within each island) is also taken into account in the course of this planning. The method is validated using the IEEE 39-bus and 118-bus system. Promising results in terms of restoration time was compared to other methods reported in the literature.

Optimal islands determination in power system restoration applying multi-objective populated simulated annealing

“…Then, the average of these distances is ascertained d. Afterwards, the first and the last solutions, known as the "extreme solutions," are determined. Consequently, Δ can be calculated by Equation 6:…”

“…Talib et al solved the islands determination problem in three different stages. At the first stage, the inherent power system specifications are addressed using the graph theory.…”

“…After modeling the network, utilizing the graph theory, the heuristic initialization is executed in the second phase. In Talib et al, the idea behind the second phase is to reduce the computational load by introducing a good starting point for determining the islands and simultaneously assuring the load‐generation balance. In the third step discrete evolutionary programming is used to minimize the restoration time and the number of tie lines between islands.…”

“…In the third step discrete evolutionary programming is used to minimize the restoration time and the number of tie lines between islands. In Talib et al, the number of islands is assumed to be known prior to the simulations.…”

Restauración de sistemas de potencia usando un modelo de programación lineal entera mixta

“…Por esta razón diferentes técnicas metaheurísticas también han sido empleadas para la solución de este problema. Estas incluyen algoritmos evolutivos (Talib et al, 2017), redes neuronales artificiales (Bretas y Phadke, 2003), lógica difusa (Hsiao y Chien, 2000), optimización basada en el comportamiento de las luciérnagas (El-Zonkoly, 2015) y redes de Petri (Wu et al, 2000). En (Liu et al, 2016) se presenta una revisión de técnicas de optimización aplicadas al problema de restablecimiento.…”