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BACKGROUND: The role of CO and adsorption sequence of CO and N 2 O in the adsorption and reduction of N 2 O on char was investigated based on density functional theory at the M06-2X/6-311G(d) theoretical level. This study lays a foundation for reaction mechanisms of the influence of CO on heterogeneous N 2 O reduction on char. RESULTS: Zigzag and armchair models were used as char models. The first adsorption of CO increases the limiting-step energy barrier of the reaction by 44.5 kJÁmol −1 compared with the first adsorption of N 2 O for zigzag char. However, the adsorption sequence does not have an effect for armchair char. The rate-limiting steps of the overall reaction for zigzag and armchair char models occur in the stages of N 2 O adsorption and N 2 O reduction, respectively. The introduction of CO has little effect on the N 2 O adsorption stage but increases the energy barrier of the N 2 O reduction stage. During the reduction of N 2 O on coal char, zigzag char releases greater heat than armchair char; moreover, zigzag char has a much lower activation energy than armchair char. CONCLUSION: In the simultaneous presence of CO and N 2 O, the char model prefers to adsorb N 2 O. The rate-limiting step of the two char models are located in different reaction stages, and CO mainly affects the N 2 O reduction stage. The presence of CO hinders the reduction of N 2 O on the surface of both zigzag and armchair char models. A rise of temperature can promote the reduction of N 2 O by char. Zigzag char is superior to armchair char in both thermodynamics and kinetics.

Bilevel optimization problems can be used to represent the collaborative interaction between a power system and grid-connected entities, called the followers, such as data centers. Most existing approaches assume that such followers' response behaviors are made available to the power system in the operation decision-making, which may be untenable in reality. This work presents a novel idea of solving bilevel optimization problems without assuming power systems' omniscience. The followers' responses will be represented by a function of the power system's decisions using Gaussian Process Regression. Then the two layers in the bilevel problem can be solved separately by the power system and its followers. This not only avoids the omniscience assumption, but also significantly increases the computational efficiency without compromising accuracy, especially for the problems with a complex lower layer. Moreover, a bilevel critical load restoration model is developed to test the proposed technique. Compared to the conventional methods, the proposed restoration model considers the load-side operation and the varying load marginal value, and can accurately estimate load-side loss and achieve better restoration solutions. Two case studies validate the advantages of the proposed approaches from different perspectives.