• Energy Research
• 2021-2025close
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• UNSW Sydney close

Risk preference is an important factor in electricity market strategy analysis and decision-making. The existing methods of risk preference analysis need to design and execute questionnaires or experiments on the subjects, and hence are costly and time-consuming for bidding in electricity markets. This article proposes a new method of data-driven risk preference analysis for power generation plants based on historical data and inverse reinforcement learning. Historical data are transformed to the transition function model according to the specific market mechanism. An adjusted inverse reinforcement learning model is thereafter proposed along with the optimization objective and technical constraints. The proposed method is tested in a simulated electricity market environment using the Australian Energy Market Operator (AEMO) day-ahead bidding data. Simulation results show that 1) thermal power plants prefer to adjust risk preferences within the day; 2) apart from the thermal power plants, the rest types of power plants are risk-neutral; 3) the daily risk preference trend of the thermal power plants varies in different seasons and is closely related to the load level.

Risk preference is an important factor in electricity market strategy analysis and decision-making. The existing methods of risk preference analysis need to design and execute questionnaires or experiments on the subjects, and hence are costly and time-consuming for bidding in electricity markets. This article proposes a new method of data-driven risk preference analysis for power generation plants based on historical data and inverse reinforcement learning. Historical data are transformed to the transition function model according to the specific market mechanism. An adjusted inverse reinforcement learning model is thereafter proposed along with the optimization objective and technical constraints. The proposed method is tested in a simulated electricity market environment using the Australian Energy Market Operator (AEMO) day-ahead bidding data. Simulation results show that 1) thermal power plants prefer to adjust risk preferences within the day; 2) apart from the thermal power plants, the rest types of power plants are risk-neutral; 3) the daily risk preference trend of the thermal power plants varies in different seasons and is closely related to the load level.

Abstract Hydrogen, as a carbon-free fuel and reducing agent, has the potential to mitigate carbon dioxide emission in BF ironmaking and the shaft injection is one of the promising and feasible processes; however, its effects on non-renewable fossil fuels saving in ironmaking process and the in-furnace phenomena are still not clear. In this study, a multi-fluid BF model is adopted to study hydrogen shaft injection's influence on in-furnace phenomena and BF performance, including flow-thermal-chemical behaviors and fossil fuel rate saving. The computational domain includes the industrial-scale BF regions from the slag surface in the hearth to the stock line near the furnace top, and the study is carried out under a fixed bosh gas flow rate. The simulation results show that compared with regular BF operation, hydrogen shaft injection can considerably affect BF performance, including the gas flow field, thermal field, and reduction behaviors. It is found that, as the injection rate increases, the penetration depth of hydrogen shows an increasing trend; the apex of cohesive zone shifts to a higher position with a more concave-shaped profile; moreover, both the reduction degree of iron oxides and thermal conditions in the shaft can be improved, which are accompanied with a significantly decreased fossil fuel rate. This study provides a quantitative tool to understand and optimize the hydrogen shaft injection into BFs towards a low-carbon ironmaking process.

Abstract Hydrogen, as a carbon-free fuel and reducing agent, has the potential to mitigate carbon dioxide emission in BF ironmaking and the shaft injection is one of the promising and feasible processes; however, its effects on non-renewable fossil fuels saving in ironmaking process and the in-furnace phenomena are still not clear. In this study, a multi-fluid BF model is adopted to study hydrogen shaft injection's influence on in-furnace phenomena and BF performance, including flow-thermal-chemical behaviors and fossil fuel rate saving. The computational domain includes the industrial-scale BF regions from the slag surface in the hearth to the stock line near the furnace top, and the study is carried out under a fixed bosh gas flow rate. The simulation results show that compared with regular BF operation, hydrogen shaft injection can considerably affect BF performance, including the gas flow field, thermal field, and reduction behaviors. It is found that, as the injection rate increases, the penetration depth of hydrogen shows an increasing trend; the apex of cohesive zone shifts to a higher position with a more concave-shaped profile; moreover, both the reduction degree of iron oxides and thermal conditions in the shaft can be improved, which are accompanied with a significantly decreased fossil fuel rate. This study provides a quantitative tool to understand and optimize the hydrogen shaft injection into BFs towards a low-carbon ironmaking process.