A Stackelberg game Based optimization strategy for Joint bidding of multi-agent wind power producers and multi-agent demand response aggregators

Aiming at the economy and accuracy of resource interaction matching between multi-agent wind power produc-ers(WPPs) and multi-agent demand response aggregators(DRAs) under the DR marketization scenario, a joint bidding optimization strategy between DRAs and WPPs based on the Stackelberg multi-leader multi-follower(MLMF) game is investigated. Firstly, a two-layer MLMF game framework with multi-agent DRAs as the leader and multi-agent WPPs as the follower is established. Secondly, a price mechanism based on non-cooperative game theory is established at the leader level; a resource matching mechanism based on bidding power difference and game price is established at the follower level. The optimization algorithm with particle swarm and commercial solver nested with each other is designed to simulate the model for distributed solving, which verifies the effectiveness and reasonableness of the proposed strategy in terms of the accuracy of resource matching between multi-agent DRAs and multi-agent WPPs and the stability of the bidding of WPPs, and gives the theoretical proof of the equilibrium solution of the MLMF game.