Acting as a key to future environmentally friendly transportation systems, electric vehicles (EVs) have attached importance to develop fast charging technologies to accomplish the requirement of vehicle users. However, fast charging behaviors would cause degradations in EVs’ batteries, as well as negative effects like new demand peak and feeder overloads to the connected distribution network, especially when plugging in large scale EVs. Decentralized coordination is encouraged and our goal is to achieve an optimal strategy profile for EVs in a decentralized way considering both the need of fast charging and reducing degradations in batteries and the distribution network. In this article, we innovatively model the EV fast charging problem as an optimization coordination problem subject to the coupled feeder capacity constraints in the distribution network. The need of fast charging is expressed by the total charging time, and the relative tendency to fully charge within the desired time period. We introduce a $\ell _{0}$ -norm of the charging strategy which is non-convex to represent the total charging time, and apply the $\ell _{1}$ -norm minimization to approximate the sparse solution of $\ell _{0}$ -norm minimization. The shorter the charging horizon is the stronger willing of fast charging the user has. The objective of the optimization problem tradeoffs the EVs’ battery degradation cost, the load regulation in the distribution network, the satisfaction of charging and the total charging time, which is non-separable among individual charging behaviors. Even though alternating direction method of multipliers (ADMM) has been widely applied in distributed optimization with separable objective and coupled constraints, its decentralized scheme cannot be applied directly to the underlying non-separable EV charging coordination problem. Hence, a hierarchical algorithm based on ADMM is proposed such that the convergence to the optimal strategies is guaranteed under certain step-size parameter. Furthermore, a receding horizon based algorithm is proposed considering the forecast errors on the base demand and the EV arrival distribution. The results are demonstrated via some simulation results.

中文翻译：

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考虑快速充电和退化的约束配电网络中基于 ADMM 的电动汽车协调

作为未来环保交通系统的关键，电动汽车（EV）重视开发快速充电技术，以满足车辆用户的需求。然而，快速充电行为会导致电动汽车电池性能下降，以及出现新的需求高峰和馈线过载等负面影响，尤其是在接入大型电动汽车时。鼓励分散协调，我们的目标是考虑到快速充电的需要以及减少电池和配电网络的退化，以分散的方式实现电动汽车的最佳战略配置。在本文中，我们创新地将电动汽车快速充电问题建模为受配电网络中耦合馈线容量约束的优化协调问题。快速充电的需要用总充电时间和在所需时间段内充满电的相对趋势来表示。我们引入了一个非凸的充电策略的 $\ell _{0}$ -norm 来表示总充电时间，并应用 $\ell _{1}$ -norm 最小化来近似 $\ell _{1}$ -norm 的稀疏解\ell _{0}$ - 范数最小化。充电时间越短，用户对快速充电的意愿就越强。优化问题的目标是在电动汽车的电池退化成本、配电网负载调节、充电满意度和总充电时间之间进行权衡，这在单个充电行为之间是不可分离的。尽管乘法器交替方向法（ADMM）已广泛应用于具有可分离目标和耦合约束的分布式优化中，但其分散方案不能直接应用于底层不可分离的电动汽车充电协调问题。因此，提出了一种基于 ADMM 的分层算法，以便在一定步长参数下保证收敛到最优策略。此外，考虑到基本需求和电动汽车到达分布的预测误差，提出了一种基于后退范围的算法。通过一些模拟结果证明了结果。尽管乘法器交替方向法（ADMM）已广泛应用于具有可分离目标和耦合约束的分布式优化中，但其分散方案不能直接应用于底层不可分离的电动汽车充电协调问题。因此，提出了一种基于 ADMM 的分层算法，以便在一定步长参数下保证收敛到最优策略。此外，考虑到基本需求和电动汽车到达分布的预测误差，提出了一种基于后退范围的算法。通过一些模拟结果证明了结果。尽管乘法器交替方向法（ADMM）已广泛应用于具有可分离目标和耦合约束的分布式优化中，但其分散方案不能直接应用于底层不可分离的电动汽车充电协调问题。因此，提出了一种基于 ADMM 的分层算法，以便在一定步长参数下保证收敛到最优策略。此外，考虑到基本需求和电动汽车到达分布的预测误差，提出了一种基于后退范围的算法。通过一些模拟结果证明了结果。提出了一种基于ADMM的分层算法，在一定的步长参数下保证收敛到最优策略。此外，考虑到基本需求和电动汽车到达分布的预测误差，提出了一种基于后退范围的算法。通过一些模拟结果证明了结果。提出了一种基于ADMM的分层算法，在一定的步长参数下保证收敛到最优策略。此外，考虑到基本需求和电动汽车到达分布的预测误差，提出了一种基于后退范围的算法。通过一些模拟结果证明了结果。