A comparison between mixed-integer linear programming and dynamic programming with state prediction as novelty for solving unit commitment

•Two unit commitment appraoches for a combined-cycle power plant are investigated.•State prediction is used with dynamic programming to reduce calculation time.•The benefit of the implementation is shown by several simulation cases.•The results are verified by comparing it with a conventional approa...

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Bibliographic Details
Published in:International journal of electrical power & energy systems Vol. 125; p. 106426
Main Authors: Putz, Dominik, Schwabeneder, Daniel, Auer, Hans, Fina, Bernadette
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.02.2021
Subjects:
Dynamic programming
Mixed-integer linear programming
Optimisation approach
Prediction algorithms
State optimisation
Unit commitment
Optimisation approach
Prediction algorithms
Dynamic programming
State optimisation
Mixed-integer linear programming
Unit commitment
ISSN:0142-0615, 1879-3517
Online Access:Get full text
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Summary:•Two unit commitment appraoches for a combined-cycle power plant are investigated.•State prediction is used with dynamic programming to reduce calculation time.•The benefit of the implementation is shown by several simulation cases.•The results are verified by comparing it with a conventional approach. Recently, the increasing prevalence of renewable energies has faced the challenge of operating power supply systems to efficiently plan electricity generation on a daily basis, since renewable energies are generated intermittently and the decisions of the individual generation units are discrete. The Unit Commitment (UC) problem, which determines the dispatch of generation units, is one of the critical problems in the operation of power supply systems. A long list of formulation proposals have been made that claim to solve this problem. For this purpose, two established approaches, mixed-integer linear programming (MILP) and backward dynamic programming (DP), are used as basis for a deterministic single-generator unit with general convex cost function in this paper. The DP algorithm is enhanced by a so-called state prediction, which reduces the time to find the optimal solution. The proposed formulation is tested empirically on the basis of existing formulations at long-term profit based UC instance derived from real data. Finally, the calculation results show that the derived approach significantly shortens the computation time, which confirms the effectiveness of state prediction. The comparison of the approaches shows that the DP algorithm with state prediction delivers a satisfying solution in significantly less time than DP and MILP. Furthermore, the given linearity of the dependence of the computation time on number of steps is a superior advantage of the DP strategy. This superiority becomes even more evident when the planning horizon extends over a longer period of time.
ISSN:0142-0615
1879-3517
DOI:10.1016/j.ijepes.2020.106426