Day-ahead strategic bidding of multi-energy microgrids participating in electricity, thermal energy, and hydrogen markets: A stochastic bi-level approach
Jiahua Wang, Zhentong Shao, Jiang Wu, Lei Wu
Abstract
This paper proposes a stochastic strategic bidding approach for a multi-energy microgrid (MEMG) to optimize its participation across electricity, thermal energy, and hydrogen markets. A MEMG powered entirely by renewable energy and integrating these three energy forms is designed using advanced energy conversion and storage technologies. A bi-level model is developed: in the upper level, the MEMG’s bidding strategies are optimized to maximize profits under operational constraints and market demands; in the lower level, detailed pricing mechanisms for each energy market are modeled, incorporating physical constraints and market competition. To address uncertainties in renewable energy generation, a chance-constrained approach is employed to mitigate potential market penalties. Moreover, a novel cost estimation method enables the MEMG to effectively price energy during trading. The bi-level problem is transformed into a tractable mixed-integer linear programming (MILP) problem using the Karush–Kuhn–Tucker conditions and linearization techniques. Numerical results show that the MEMG efficiently participates in multiple energy markets, reducing renewable energy curtailment and adjusting its trading strategies based on market conditions, thereby improving overall economic benefits. • An energy trading model for multi-energy microgrids (MEMGs) is developed. • The MEMG participates in the electricity, thermal energy, and hydrogen markets. • A stochastic bi-level approach is proposed to provide strategic bidding. • An energy cost estimation method is proposed to guide energy pricing for the MEMG. • The KKT conditions and linearization methods are used to compute effective solutions.