Name
Abstract | ||
|---|---|---|
Power plant start-up scheduling is aimed at minimizing the start-up time while limiting maximum turbine-rotor stresses. A shorter start-up time not only reduces fuel and electricity consumption during the start-up process, but also increases its capability of adapting to changes in electricity demand. The start-up scheduling problem can be formulated as a function optimization problem with constraints. We have constructed an efficient and robust search model-a genetic algorithm (GA) with an enforcement operation-which forces the search along the edge of the feasible space, where the optimal schedule is supposed to exist. However, this model has to perform a prior Monte Carlo test to obtain the enforcement gains used for the implementation of the enforcement operation. In this paper, we attempt to eliminate the Monte Carlo test by proposing a self-reliant search model by introducing a GA with an adaptive enforcement operation that can generate and adapt enforcement gains during the search process. The test results of this proposed model show that the overall number of time-consuming dynamic simulations for the constraints calculation can be reduced further, thus increasing the overall efficiency of finding the optimal or near-optimal schedules |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/5326.798766 | IEEE Transactions on Systems, Man, and Cybernetics, Part C |
Keywords | DocType | Volume |
power plant start-up scheduling,enforcement gain,start-up process,enforcement operation-which,enforcement operation,adaptive enforcement operation,robust search model-a,start-up scheduling problem,adaptive-edge search,start-up time,shorter start-up time,efficiency,robustness,minimisation,power generation,testing,power plants,monte carlo methods,indexing terms,genetic algorithm,genetic algorithms,dynamic simulation,fuel consumption,power plant,scheduling problem,stress,constraint optimization,adaptive control | Journal | 29 |
Issue | ISSN | Citations |
4 | 1094-6977 | 2 |
PageRank | References | Authors |
0.44 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Kamiya | 1 | 2 | 0.44 |
| K. Kawai | 2 | 27 | 6.66 |
| I. Ono | 3 | 2 | 0.44 |
| S. Kobayashi | 4 | 2 | 0.44 |