A Bi-level stacked LSTM-DNN-based decoder network for AGC dispatch under regulation market framework in presence of VPP and EV aggregators

Abstract

The consideration of mileage settlement in the frequency regulation market has encouraged fast-acting units, such as converter-interfaced generators (CIG) and electric vehicle stations, to actively participate in load-generation balancing through automatic generation control (AGC). Conventional frequency regulation faces challenges in coping with the growing variability of CIGs and also lacks effective incentives for rapid-responding units. In this context, a bi-level AGC dispatch approach based on a stacked long short-term memory (LSTM)-deep neural network (DNN)-based decoder framework is proposed for a power system comprising diverse CIGs forming a virtual power plant and electric vehicle aggregators. The proposed decoder network is comprised of stacked LSTM and DNN, wherein the cascaded LSTM layers are introduced to accurately capture temporal information from time series input. The inclusion of a dropout mechanism further enhances the model’s generalisability in unforeseen environments. The proposed dispatch framework uses mileage-based compensation criteria to optimally allocate instructions among various participating units with differing regulation characteristics. The performance of the proposed method is analysed by considering packet loss, delay, unexpected generation failure, and denial of service attacks. The evaluation of the proposed approach reveals its superior performance compared to proportionality, particle swarm optimisation, decision tree, and DNN methods.