The Unit Commitment Problem
The Unit Commitment Problem
There exist several approaches to solve the common unit commitment problem. A very common solution is through dynamic programming as discussed in “Power Generation, Operation and Control” by Allen J. Wood and Bruce F. Wollenberg (Wood/Wollenberg). A more suitable and effective solution exist through Genetic Algorithm as discussed in the article “Unit Commitment Solution Methodology Using Genetic Algorithm” by K. S. Swarup and S. Yamashiro (Swarup/Yamashiro). Both methods will be discussed and compared against the methodology used and results presented and the significance, strengths and shortcomings of the approach.
The dynamic programming (Wood/Wollenberg) approach can be very effective for a small pool of generators. Generators are combined through strict priority and these combinations will be the only ones available through out the approach. For a single iteration, UC is performed first for every combination of the available generators without any constraints. Generator limits and minimum up and down times are then observed and unfeasible combinations are removed. Of the remaining combinations, ED is performed. Each combination is checked and evaluated for optimal solution which is minimizing all cost and committing to the load. The computation time of this approach is fast, but can slow down rapidly if the generator pool grows dramatically; as a generator is added to the pool more combinations need to be evaluated. Ramp rates and spinning reserves are not presented in this approach but would be great addition to the problem. These constraints could be easily integrated to the approach but are left out to make the computation simple.
The genetic algorithm (Swarup/Yamashiro) approach can be very effective for every generator pool size. All generators in the pool are ordered by cheapest generator and can be used throughout the approach. As an initial setup, random generators are selected using probabilistic variables for
There exist several approaches to solve the common unit commitment problem. A very common solution is through dynamic programming as discussed in “Power Generation, Operation and Control” by Allen J. Wood and Bruce F. Wollenberg (Wood/Wollenberg). A more suitable and effective solution exist through Genetic Algorithm as discussed in the article “Unit Commitment Solution Methodology Using Genetic Algorithm” by K. S. Swarup and S. Yamashiro (Swarup/Yamashiro). Both methods will be discussed and compared against the methodology used and results presented and the significance, strengths and shortcomings of the approach.
The dynamic programming (Wood/Wollenberg) approach can be very effective for a small pool of generators. Generators are combined through strict priority and these combinations will be the only ones available through out the approach. For a single iteration, UC is performed first for every combination of the available generators without any constraints. Generator limits and minimum up and down times are then observed and unfeasible combinations are removed. Of the remaining combinations, ED is performed. Each combination is checked and evaluated for optimal solution which is minimizing all cost and committing to the load. The computation time of this approach is fast, but can slow down rapidly if the generator pool grows dramatically; as a generator is added to the pool more combinations need to be evaluated. Ramp rates and spinning reserves are not presented in this approach but would be great addition to the problem. These constraints could be easily integrated to the approach but are left out to make the computation simple.
The genetic algorithm (Swarup/Yamashiro) approach can be very effective for every generator pool size. All generators in the pool are ordered by cheapest generator and can be used throughout the approach. As an initial setup, random generators are selected using probabilistic variables for