Abstract
Generators startup sequence plays a significant role in achieving a suitable and effective restoration strategy. This paper outlines an ant colony search algorithm in order to determine the generator starting times during the bulk power system restoration. The algorithm attempts to maximize the system generation capability over a restoration period, where the dynamic characteristics of different types of units and system constraints are considered. Applying this method for the 39-bus New England test system, and comparing the results with backtracking-search and P/t methods, it is found that proposed algorithm improved generation capability.
Highlights
In recent years, power systems are operated fairly close to their limits primarily due to economic competition and deregulation
As the Black-start units themselves can only supply a small fraction of the system load, these units must be used to assist in the starting of larger units, which need their station service loads to be supplied by outside
The tour must be closed and contain each node exactly once. This can be represented as a sequence of n items, where the actual order of the sequence determines a particular solution to the problem
Summary
Power systems are operated fairly close to their limits primarily due to economic competition and deregulation. They have increased in size and complexity Both factors increase the risk of major power outages 1. Black-start units are units that do not require off-site power to start, such as: diesel generator sets and hydroelectric units 5. It should be pointed out that a proper sequence of generators start-up can increase the system MW outputs and keep the constraints satisfied. Optimal generators start-up strategy in system restoration is a multistage decision optimization problem. A number of studies have been carried out to determine generators start-up sequence 7–10 using heuristic methods which do not guarantee their optimality. The goal of the proposed method is to maximize the total system generation capability over a restoration period whilst considering the corresponding static and dynamic constraints including the cranking power, critical maximum interval, and critical minimum interval constraints. The simulation results for a 39-bus New England test system are illustrated and compared with those obtained by backtracking search and p/t methods
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