Using AI to Monitor Robust Operation of the Grid
Using Complex-Valued Neural Networks (CVNNs) to forecast different parameter in the grid (such as solar irritation, load, etc) with high penetration level of uncertain renewable energies.
High penetration of renewable energy poses a significant challenge in operation of power system. Higher penetration of PV increases the concern about the potential impacts on the transmission system. Based on the uncertainty in the power grid caused by renewable energy generations, forecasting the parameters are really important to minimize the costs and power outage and increase the grid’s reliability.
Artificial neural networks (ANNs) have received considerable attention in academia for solving problems related to power systems. ANNs can single out the correlation pattern from input datasets to target datasets. They use the pattern that is identified to estimate the output for a new set of inputs. Back- propagation (BP) method is the workhorse for ANNs’ learning of network structures, which is used extensively for Short Term Load Forecasting (STLF), because of its extraordinary mapping capabilities.
Complex-Valued Neural Networks (CVNNs) prove their abilities for the identification of nonlinear systems. They can outperform their real counterparts in many ways. CVNNs give also the possibility of the simultaneous modeling and forecasting. The main motivation to use CVNNs is due to faster convergence, reduction in learning parameters, and ability to learn two dimension motion of signal. The CVNNs are simply the generalization of the
Hybrid Forecast And Load Commitment (HFALC) unit interaction with Energy Management System (EMS)
Top: Flowchart of series-parallel load forecasting method, down: Series-parallel method for 20 min look ahead forecast accompanied with actual load data
BESS connection to the distribution grid system
Architecture of CVNN including nodes, layers, and weights (Inputs: historical data, outputs: forecasting data)
CVNN forecasting method along with the actual load data (top: without filter, down: with Kalman filter)