Situation Awareness and Sensitivity Analysis for Absorption of Grid-Connected Renewable Energy Power Generation Integrating Robust Optimization and Radial Basis Function Neural Network

Abstract

The significance of situation awareness (SA) in power systems has increased to enhance the utilization of grid-connected renewable energy power generation (REPG). This paper proposes a real-time calculation architecture based on the integration of robust optimization (RO) and artificial intelligence. First, the time-series simulation of the REPG consumption capacity is carried out under the current grid operating conditions. RO is employed in this simulation, given the randomness of the REPG output and the grid load. Then, the radial basis function neural network (RBFNN) is trained with the results under different parameters using the artificial fish swarm algorithm (AFSA), enabling the neural network (NN) to be the replacement for the time-series simulation model. The trained NN can quickly perceive the REPG absorption situation within the predefined grid structure and period. Moreover, the Sobol' method is adopted to conduct the global sensitivity analysis for different parameters based on the input-output samples obtained by the trained NN. Finally, the simulation experiments based on the modified IEEE 14-bus system prove the real-time performance and accuracy of the proposed SA architecture.

Keywords

• Renewable energy
• radial basis function neural network
• robust optimization
• sensitivity analysis
• situation awareness