Planning a robust echelon utilization network for used electric vehicle batteries based on two decision-making criteria

The echelon utilization of electric vehicle batteries offers opportunities to mitigate pollution from used batteries and decrease costs in energy storage and low-speed electric vehicles. Based on two decision-making criteria, this study addresses the echelon utilization network planning problem about used batteries. Our problem plans the locations and battery transportation to meet diverse quality requirements in the secondary market. First, we develop a risk-neutral adaptive distributionally robust optimization (ADRO) model under uncertainty in secondary market demand and quantity of high-quality batteries. The proposed model is reformulated as a mixed-integer second-order conic programming (SOCP) model and solved by accelerated Benders decomposition (BD). Second, we propose a risk-averse ADRO model based on the mean-conditional value-at-risk (CVaR). Subsequently, we devise a tailored BD algorithm to solve a pair of subproblems in each iteration. The results of our numerical experiments demonstrate the following: (i) The application of echelon utilization can reduce the operational costs of the battery remanufacturing network by 3.7%. (ii) Our ADRO model exhibits better out-of-sample performance compared with the sample average approximation (SAA) model, which achieves the balance between economy and robustness for echelon utilization network planning. (iii) The accelerated BD algorithm significantly outperforms the classical BD method. (iv) The sensitivity analysis on key model parameters reveals trade-offs among optimality and recovery quantity, ambiguity set size, and risk preference to the informed energy managers. These results suggest that ADRO echelon utilization network planning models can be applied to energy management problems.