A novel risk-averse multi-energy Management for Effective Offering Strategy of integrated energy production units in a real-time electricity market

Transforming a coal-fired power generation unit into a so-called integrated energy production unit (IEPU) is considered a promising low-carbon technology. An IEPU produces not only "greener" electricity but also hydrogen and natural gas by capturing carbon emissions from its coal-fired unit. Moreover, an IEPU has an enhanced capacity to adjust its electricity output, i.e., it has better flexibility, and can play a significant role in real-time electricity market (RTM) by trading its flexibility to recover its initial outlay. However, because of the complicated production process and temporal coupling constraint, it is challenging to effectively manage the multi-energy production of IEPU to reach its RTM offering strategy. To solve this problem, this work proposes a novel risk-averse multi-energy management method for effective offering strategy of the IEPU in the RTM. This multi-energy management process comprises three phases. In the first phase, a novel distributionally robust optimal self-scheduling IEPU model is established to determine its desired operating point, which has the best expected profit. This model accurately captures the complicated multi-vector energy production process and leverages the discounted mean entropic value-at-risk to improve the IEPU's ability to withstand risks with an acceptable economic compromise. A novel uncertainty decoupling algorithm is also proposed to improve computational efficiency. In the second phase, an offering strategy making method is proposed to maximize the probability of IEPU winning bids at the desired point. In the third phase, a post-clearing IEPU scheduling method is proposed to ensure that the IEPU operates following the winning bid power. Case studies demonstrate that the proposed method enables the IEPU operator to participate in the RTM at its desired operating point at the maximum probability, with properly balanced profit and risk. Additionally, the proposed solution algorithm is verified to significantly accelerate the computation process, thus enhancing the practicality of this multi-energy management method for the RTM participant, who often faces a strict time constraint in decision-making.