An ultra-fast optimization algorithm for unit commitment based on neural branching

Abstract

The computational efficiency of unit commitment (UC) is important for power system operations. Traditionally the unit commitment problem is solved per hour in a day, but with the scale of the power system and the electricity market continuing to expand, the large-scale UC problem will be hard to be solved within 1 h which will affect the power system operation and market clearing. To reduce the solving time of the large-scale UC problem, an ultra-fast optimization algorithm of neural branching for unit commitment (NBUC) is proposed. NBUC learns the branch and bound (B&B) decision made by full strong branching (FSB), which can generate the perfect B&B order to minimize the iterative process but take a lot of time to decide the perfect order by using graph convolutional neural network according to the historical data, and then makes the perfect order prediction with a certain precision without spending a lot of time to make B&B decision which minimizes the solving time including the iteration time and decision time in order to solve the large-scale UC problem quickly. A modified RTS-96 bus system is used to validate the effectiveness of the proposed NBUC. The results show 9.3% and 23.2% reductions in computational time compared with commercial software CPLEX and SCIP.

Keywords

• Unit commitment
• Mixed-integer linear program
• Branch-and-bound
• Graph convolutional neural network
• Neural branching