CYCENESYMar 6, 2019

Tackling Unit Commitment and Load Dispatch Problems Considering All Constraints with Evolutionary Computation

arXiv:1903.09304v11 citations
Originality Incremental advance
AI Analysis

This addresses the complex operational scheduling in power systems, particularly with renewable integration, though it appears incremental in method adaptation.

The paper tackles the combined unit commitment and load dispatch problem in power systems without approximations, using an adaptive repair method that optimizes repair choices, and demonstrates it can surpass exact methods on simplified versions and solve the complete, otherwise intractable problem.

Unit commitment and load dispatch problems are important and complex problems in power system operations that have being traditionally solved separately. In this paper, both problems are solved together without approximations or simplifications. In fact, the problem solved has a massive amount of grid-connected photovoltaic units, four pump-storage hydro plants as energy storage units and ten thermal power plants, each with its own set of operation requirements that need to be satisfied. To face such a complex constrained optimization problem an adaptive repair method is proposed. By including a given repair method itself as a parameter to be optimized, the proposed adaptive repair method avoid any bias in repair choices. Moreover, this results in a repair method that adapt to the problem and will improve together with the solution during optimization. Experiments are conducted revealing that the proposed method is capable of surpassing exact method solutions on a simplified version of the problem with approximations as well as solve the otherwise intractable complete problem without simplifications. Moreover, since the proposed approach can be applied to other problems in general and it may not be obvious how to choose the constraint handling for a certain constraint, a guideline is provided explaining the reasoning behind. Thus, this paper open further possibilities to deal with the ever changing types of generation units and other similarly complex operation/schedule optimization problems with many difficult constraints.

Foundations

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