Optimization Research on Provincial Users' Participation in Regional Electricity Spot Market Bidding Based on Hierarchically Gated Recurrent Neural Network

Abstract

The creation of strategic pricing models is crucial in the highly competitive centralized energy markets in order to maximize revenues. In order to achieve maximum profit, market participants must act intelligently while bidding on electrical energy purchases or sales. Notwithstanding the many restructuring changes of the Chinese electricity sector, market participants are nonetheless exposed to risks associated with volatility in market prices and uncertainty over demand behavior. The data are collected from New South Wales electricity market. Data-adaptive Gaussian average filtering (DAGAF) is used to pre-process the data during the pre-processing step. Hierarchically Gated Recurrent Neural Networks (HGRNNs) are successfully used to classify both long-term and spot transactions. The neural network's weight parameter is optimized by the Lotus Effect Optimization Algorithm (LEA), which enhances the HGRNN. The RESMB-HGRNN-LEA proposed is implemented on the Python working platform. To calculate the suggested approach, performance measures including accuracy, precision, sensitivity, computation time, and recall were looked at.In comparison to the existing technique, the proposed RESMB-HGRNN-LEA method yields better results in terms of accuracy (16.65% and 18.85%), sensitivity (16.34% and 12.23%), precision (14.89% and 16.89%), and computing time (82.37% and 94.47%).