Generator Start-up Sequences Optimization for Network Restoration Using Genetic Algorithm and Simulated Annealing

Abstract

In the domain of power grid systems, scheduling tasks are widespread. Typically, linear programming (LP) techniques are used to solve these tasks. For cases with high complexity, linear system modeling is often cumbersome. There, other modeling approaches allow for a more compact representation being typically also more accurate as non-linear dependencies can be captured natively.In this work, we focus on the optimization of a power plant start-up sequence, which is part of the network restoration process of a power system after a blackout. Most large power plants cannot start on their own without cranking energy from the outside grid. These are the non-black start (NBS) units. As after a blackout we assume all power plants being shut down, self-contained power plants (black start (BS) units), such as the hydroelectric power plants, start first and boot the NBS units one after each other. Once a NBS unit is restored, it supports the restoration process and because an average NBS unit is much larger than a BS unit, NBS unit's impact on the restoration process is typically dominant. The overall restoration process can take, depending on the size of the blackout region and the damaged components, some hours to weeks. And as the blackout time corresponds directly to economic and life losses, its reduction, even by some minutes, is worthwhile.In this work we compare two popular metaheuristics, the genetic (GA) and simulated annealing (SA) algorithms on start-up sequence optimization and conclude that an efficient restoration plan can be evolved reliably and, depending on the implementation, in a very short period of time allowing for an integration into a real-time transmission system operation tool.