Abstract: The rapid development of data centers makes it possible for them to participate in power system dispatching as demand response. By dispatching the computing resources in data centers between regions, it is possible to achieve the goal of energy saving and emission reduction and cost saving. However, considering the demand response of data center computing resources in power system dispatching faces the problem of insufficient computing speed. Based on this, a neural branching power-computation network fast optimization method considering computing demand response is proposed. First, a unit commitment double-layer model considering computing power resource demand response is established. Then, graph convolutional neural network and branch and bound method are combined and applied to the double-layer model. Through historical data training, the neural branching power-computation network fast optimization method considering computing demand response has the ability to quickly determine the order of branch and bound variables and minimize the number of iterations, which significantly improves the solution speed and realizes the fast solution of unit combination demand response considering data center computing resources. The performance of the proposed method is verified in the simulation scenario of "East Data West Computing" engineering. Compared with the pseudo cost branching algorithm, the average solution time is reduced by 39.1%. Compared with the commercial solver CPLEX, the average solution time is reduced by 38.1%. Compared with the machine learning-based optimization acceleration algorithm Extratrees, the average solution time is reduced by 13.5%. In addition, the system coordinated dispatching frequency is increased from 1 time/h to 4 times/h, and the average renewable energy absorption is increased by 17.4%.

Key words: MILP, unit commitment, demand response, IDC, neural branching