A Data-Driven Joint Chance-Constrained Game for Renewable Energy Aggregators in the Local Market

The local market can promote more self-generated electricity consumed locally. However, the unstable and intermittent generation of renewable energy sources brings uncertainty and overbidding risk to the local market. We proposed a competitive local energy market (LEM) where renewable energy aggregators (REAs) can submit bids instead of acting as price takers. A data-driven joint chance-constrained game of REAs is proposed for the optimal bidding problem under the network constraints and the risk-averse chance constraint. We consider the case that the maximum outputs of REAs are random variables whose probability distributions are unknown, but the decision-maker has access to finite samples. Each player’s strategy set is defined in a data-driven distributionally robust framework with the Wasserstein ambiguity set. The exact reformulation of the distributionally robust chance constraint and the corresponding inner approximation version are derived. We prove the existence of the Nash equilibrium in the proposed distributionally robust chance-constrained game. Finally, an efficient best response seeking algorithm is developed to find the normalized Nash equilibrium, and the convergence proof of the proposed algorithm is also provided. Numerical simulations demonstrate that the proposed game model can effectively increase the revenue of reliable players and reduce the overbidding rate of REAs.