Two-stage ANN-based bidding strategy for a load aggregator using decentralized equivalent rival concept

As an intermediator between the wholesale electricity market and retail market, a typical load aggregator submits an optimal bid to the system operator to meet the expected demands of its customers. In this regard, the provision of an effective optimal bidding strategy is very crucial for a load aggregator to increase its profit. Within this context, this paper proposes a two-stage artificial neural network based adaptive bidding strategy procedure for an LA by revealing, modelling, and predicting the aggregative behaviour of the competitors in an hourly electricity market. To this end, we develop the concept of decentralized equivalent rival whose behaviour in the electricity market reflects the aggregation of behaviours of all individual competitors. Also, an equivalent market which its outcomes are approximately equal to those of the real market is modelled. The equivalent market's participants are the load aggregator and its corresponding DER. The proposed approach is capable enough to consider transmission constraints. The performance of the proposed approach has been examined on an illustrative example and the IEEE 30-bus test system by considering transmission network constraints. The proposed artificial neural network-based adaptive bidding strategy has compared with a Q -learning-based bidding approach and the results are analysed.