Cooperative Distributed Demand Management for Community Charging of PHEV/PEVs Based on KKT Conditions and Consensus Networks
Cooperative Distributed Demand Management for Community Charging of PHEV/PEVs Based on KKT Conditions and Consensus Networks
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DOI：
10.1109/tii.2014.2304412
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发表时间：
2014-02
影响因子：
12.3
通讯作者：
Navid Rahbari Asr;M. Chow
中科院分区：
文献类型：
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作者：
Navid Rahbari Asr;M. Chow
随着大量此类车辆被引入电网，需要针对插电式混合动力汽车 (PHEV) 和插电式电动汽车 (PEV) 的社区充电提供高效可靠的需求侧管理技术。为了避免过载并在充电时间和成本方面最大化客户偏好，可以制定约束非线性优化问题。在本文中，我们开发了一种用于 PHEV/PEV 充电控制的新型协作分布式算法，该算法使用 Karush-Kuhn-Tucker (KKT) 条件和共识网络以分布式方式解决约束非线性优化问题。在我们的设计中，通过充电站的点对点协调来实现所有本地和全局约束下的全局最优功率分配。因此，不再需要中央控制单元。这样，当问题规模增加时，可以避免单节点拥塞，并且系统获得针对单链路/节点故障的鲁棒性。此外，通过蒙特卡罗模拟，我们证明了所提出的分布式方法可以随着充电点和返回解决方案的数量而扩展，这与最多具有 2% 次优性的集中式优化算法相当。因此，我们方法的主要优点是消除了对中央能源管理/协调单元的需求，获得了针对单链路/节点故障的鲁棒性，并且在单节点计算方面具有可扩展性。
Efficient and reliable demand side management techniques for community charging of plug-in hybrid electrical vehicles (PHEVs) and plug-in electrical vehicles (PEVs) are needed, as large numbers of these vehicles are being introduced to the power grid. To avoid overloads and maximize customer preferences in terms of time and cost of charging, a constrained nonlinear optimization problem can be formulated. In this paper, we have developed a novel cooperative distributed algorithm for charging control of PHEVs/PEVs that solves the constrained nonlinear optimization problem using Karush-Kuhn-Tucker (KKT) conditions and consensus networks in a distributed fashion. In our design, the global optimal power allocation under all local and global constraints is reached through peer-to-peer coordination of charging stations. Therefore, the need for a central control unit is eliminated. In this way, single-node congestion is avoided when the size of the problem is increased and the system gains robustness against single-link/node failures. Furthermore, via Monte Carlo simulations, we have demonstrated that the proposed distributed method is scalable with the number of charging points and returns solutions, which are comparable to centralized optimization algorithms with a maximum of 2% sub-optimality. Thus, the main advantages of our approach are eliminating the need for a central energy management/coordination unit, gaining robustness against single-link/node failures, and being scalable in terms of single-node computations.