Theoretical proof of edge search strategy applied to power plant start-up scheduling

Power plant start-up scheduling is aimed at minimizing the start-up time while limiting maximum turbine rotor stresses. This scheduling problem is highly nonlinear and has a number of local optima. In our previous research, we proposed an efficient search model: genetic algorithms (GAs) with enforce...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2002-06, Vol.32 (3), p.316-331
Main Authors: Kamiya, A., Kawai, K., Ono, I., Kobayashi, S.
Format: Article
Language:English
Subjects:
Dynamic scheduling
Enforcement
Equations
Gain
Genetic algorithms
Guidelines
Inverse
Mathematical analysis
Mathematical models
Optimal scheduling
Optimization
Power generation
Schedules
Scheduling
Searching
Stress
Studies
Testing
Turbines
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Summary:Power plant start-up scheduling is aimed at minimizing the start-up time while limiting maximum turbine rotor stresses. This scheduling problem is highly nonlinear and has a number of local optima. In our previous research, we proposed an efficient search model: genetic algorithms (GAs) with enforcement operation to focus the search along the edge of the feasible space where the optimal schedule is supposed to stay. Based on a nonlinear dynamic simulation and a linear inverse calculation with the iteration method, the enforcement operation is applied to move schedules generated by GA toward the edge. We prove that the optimal schedule lies on the edge, ensuring that searching along the edge instead of the entire space can improve the search efficiency significantly without missing the optimum. Furthermore, we provide a theoretical setting equation for the inverse enforcement gains of the linear inverse calculation, intended to move schedules closer to the edge at each iteration of the enforcement operation. The theoretical setting equation is verified and discussed with the test results. We propose the theoretical setting equation with the test results as a guideline for the use of our proposed search model: GA with enforcement operation.
ISSN:1083-4419
2168-2267
1941-0492
2168-2275
DOI:10.1109/TSMCB.2002.999808