Multi-energy scheduling study under uncertainties in iron and steel industry based on multi-objective two-stage robust optimization

Current energy scheduling optimization relies on prefect energy prediction, such as byproduct gases, etc. However, the inherent prediction uncertainties in byproduct gases remain existing, presenting challenges to the safety and reliability of energy system. Meanwhile, renewable energy sources, such as solar energy, are expected to be widely used in a carbon-neutral steel industry. Nonetheless, renewable energy output itself is inherently uncertain. Therefore, considering solar energy for optimal dispatch further increases modeling complexity and uncertainty. The main problem addressed in this study is the robust joint scheduling of the integrated steel energy system with distributed PV under multiple uncertainties. To address this issue, this paper proposes a multi-objective two-stage robust optimization model considering multiple uncertainties for multi-energy scheduling optimization. This model handles uncertainties in byproduct gases prediction and solar energy generation, aiming to concurrently realize minimum economic operating cost (EOC) and total carbon emission (TCE). The effectiveness of the proposed energy optimization model is evaluated using a real steelworks dataset. Comparing to results without uncertainty, the EOC and TCE have average 4.42 %, and 0.25 % reduction with considering multiple uncertainties, respectively. Sensitivity analysis reveals that the decreased electricity price could enhance the EOC and TCE reduction, and the coal price influence EOC and TCE depending on the weight coefficient between the objectives. These findings demonstrate that robust joint scheduling under uncertainty improves energy flexibility through collaborative load scheduling strategies.