Bayesian Deep Learning for Dynamic Power System State Prediction Considering Renewable Energy Uncertainty

Abstract

Modern power systems are incorporated with distributed energy sources to be environmental-friendly and costeffective. However, due to the uncertainties of the system integrated with renewable energy sources, effective strategies need to be adopted to stabilize the entire power systems. Hence, the system operators need accurate prediction tools to forecast the dynamic system states effectively. In this paper, we propose a Bayesian deep learning approach to predict the dynamic system state in a general power system. First, the input system dataset with multiple system features requires the data pre-processing stage. Second, we obtain the dynamic state matrix of a general power system through the Newton-Raphson power flow model. Third, by incorporating the state matrix with the system features, we propose a Bayesian long short-term memory (BLSTM) network to predict the dynamic system state variables accurately. Simulation results show that the accurate prediction can be achieved at different scales of power systems through the proposed Bayesian deep learning approach.

Deterministic and Robust Volt-var Control Methods of Power System Based on Convex Deep Learning

“…The lower model mainly uses the worst scenario method (WSM) to determine the set of random variables corresponding to the two extreme scenarios, i.e., when the maximum and minumim extreme node voltages occur, respectively. It takes the node voltage as the target function, the random variables as the control variables, and the forecast error range as the upper and lower bounds of control variables, which can be expressed as: (13) where PFC(•) is the function for PF calculation;…”

“…Because of the time-consuming nature of traditional PF methods (such as Newton-Raphson method) serving as basic tools for VVC, voltage stability analysis, etc., deep learning (DL) method has been introduced into PF in recent years [11]- [13]. The well-trained deep learning power flow (DLPF) model can realize the end-to-end and high-precision mapping between the state parameters and PF results (such as node voltage and phase angle), eliminating the PF constraints and iterative process required by the traditional methods.…”

Two-level optimal scheduling strategy of demand response-based microgrids based on renewable energy forecasting

“…The uncertainty of WT, PV, and load forecasting errors has a signi cant impact on the scheduling and operation of MG. Therefore, how to express and quantify these uncertainty factors is key to improve the reliability and economy of MG. Common prediction methods include physical models [5,6] and machine learning [7][8][9]. In physical prediction models, the predicted results are calculated based on meteorological information obtained from numerical weather prediction (NWP) and geographical location, but the error of these information will gradually increase with the aging of hardware [10,11].…”