基于分布式优化的电动汽车充电能源枢纽网络最优控制策略

中文摘要 AI 辅助

本文研究了电动汽车充电能源枢纽网络的经济最优运行问题，这些枢纽提供现场可再生能源和固定电池储能，并通过直流线路相互连接，同时与配电网相连。具体而言，我们首先将整个网络的动态最优控制问题表述为一个凸二次规划，其中各车辆的充电功率曲线以及枢纽与电网之间的能量流动均作为优化变量。其次，我们提出了一种问题分解方法，通过ADMM算法实现分布式求解，该方法保持了全局最优性保证和各站点的隐私性。我们以荷兰为案例研究，考虑了一个具有完美预见性的两天前确定性公式，展示了我们的框架。结果表明，与预先固定充电功率的情况相比，优化其曲线（V1G）可以显著降低运营成本和排放，降幅超过25%。此外，我们将分布式算法与集中式解进行了验证，为大规模网络的最优运行和在线实施铺平了道路。

英文摘要

This paper studies the cost-optimal operation of a network of charging energy hubs for electric vehicles, which provide onsite renewable energy sources and stationary battery storage and are connected with each other via DC-lines as well as with the distribution grid. Specifically, we first formulate a dynamic optimal control problem for the entire network as a convex quadratic program, whereby the charging power profiles of the individual vehicles and the energy flows between hubs and the grid are subject to optimization. Second, we propose a problem decomposition that allows for a distributed solution via ADMM algorithms that preserves global optimality guarantees and privacy of the individual stations. We showcase our framework on a case-study for the Netherlands considering a two-day ahead deterministic formulation with perfect foresight. Our results show that compared to the case where charging powers are fixed a priori, optimizing their profiles (V1G) can significantly reduce the operational costs and emissions by more than 25%. Moreover, we verify our distributed algorithm against a centralized solution, paving the way to the optimal operation of large networks and online implementations.

发表机构

• Eindhoven University of Technology（埃因霍温理工大学）

机构由 AI 辅助整理，请以论文原文为准。