Hybrid optimization for collaborative bidding strategy of renewable resources aggregator in day-ahead market considering competitors' strategies

Abstract

Fueled by the increasing penetration of renewable energy in the power system, it is a promising way to form renewable energy aggregators so as to improve their competitiveness in the electricity market. This paper focuses on aggregators integrating wind, solar, hydropower and storage, where hydro works to moderate fluctuations and storage helps improve the profitability of the aggregator. The paper establishes a two-phase stochastic information-gap-decision-theory (IGDT) based bidding framework for such aggregator. The interaction between the aggregator and the competitive market is modeled as a stochastic bi-level problem, in which the strategic behaviors of self-interested competitors are fully considered. A density-based K-means clustering algorithm combining seasonal factors is proposed to achieve predicted offering scenarios of rival generators based on their historical market data. The hybrid stochastic-IGDT handles competitors’ offer strategies through scenario-based stochastic programming, and manages uncertainties of wind and solar generation under risk-averse and risk-seeking strategies. The results from Elia’s data demonstrate that the proposed bidding model not only shows better adaptability to the competitors' strategic behaviors, but also generates more profit for the aggregator through collaborative bidding of multiple sources.