On decentralized convex optimization in a multi-agent setting with separable constraints and its application to optimal charging of electric vehicles
On decentralized convex optimization in a multi-agent setting with separable constraints and its application to optimal charging of electric vehicles
复制标题
可分离约束多智能体环境下的分散凸优化及其在电动汽车优化充电中的应用
DOI：
10.1109/cdc.2016.7799197
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
M. Prandini
中科院分区：
文献类型：
--
作者：
Luca Deori;Kostas Margellos;M. Prandini
我们开发了一个分散的算法多代理，凸优化程序，受到可分离的约束，每个代理的约束函数只涉及其本地的决策向量，而所有代理的决策向量耦合通过一个共同的目标函数。我们构造了一个变种的所谓的Jacobi算法，并表明，当目标函数是二次的，收敛到一些最小的集中问题对应的实现。然后，我们的算法作为一个有效的替代梯度为基础的方法。我们说明了它的有效性，将其应用到电动汽车的最佳充电的问题，在那里，而不是以前的方法，我们显示收敛到一个最佳的充电方案有限的，可能很大，数量的车辆。
We develop a decentralized algorithm for multi-agent, convex optimization programs, subject to separable constraints, where the constraint function of each agent involves only its local decision vector, while the decision vectors of all agents are coupled via a common objective function. We construct a variant of the so called Jacobi algorithm and show that, when the objective function is quadratic, convergence to some minimizer of the centralized problem counterpart is achieved. Our algorithm serves then as an effective alternative to gradient based methodologies. We illustrate its efficacy by applying it to the problem of optimal charging of electric vehicles, where, as opposed to earlier approaches, we show convergence to an optimal charging scheme for a finite, possibly large, number of vehicles.