基于无场景不确定性感知DLMP的配电网电动汽车充电与无功功率支持双层协调
Scenario-Free Uncertainty-Aware DLMP-Based Bilevel Coordination of EV Charging and Reactive Power Support in Distribution Networks
AI总结：
研究配电网电动汽车充电与无功功率支持协调问题，提出基于DLMP的无场景不确定性感知双层优化框架，用紧凑鲁棒对偶公式处理不确定性，通过非单位功率因数运行提供无功支持，仿真表明可提高电压安全性、有效协调且降低计算复杂度。
AI中文摘要：
本文针对配电网中电动汽车充电与无功功率支持的协调问题，提出了一种基于分布边际电价（DLMP）的无场景不确定性感知双层优化框架。上层电动汽车聚合器联合调度有功和无功充电功率以最小化充电成本，下层能源管理系统进行网络约束经济调度并确定DLMP。为捕捉负荷需求和光伏发电的不确定性，开发了一种紧凑鲁棒对偶（RC）公式，避免了大规模随机规划和传统鲁棒优化的计算负担。与主要假设高斯不确定性的现有鲁棒对偶方法不同，该方法为净需求不确定性导出了确定性公式，更真实地反映了不对称负荷和可再生能源的变化。精确性引理在KKT重新公式化和大M线性化后保留了DLMP的经济解释。电动汽车充电器还通过非单位功率因数运行提供无功功率支持以改善电压调节。在IEEE 33节点配电系统上的仿真结果表明，与传统随机和鲁棒优化方法相比，该方法提高了电压安全性，实现了有效的不确定性感知电动汽车协调，且计算复杂度显著降低。
英文摘要：
This paper develops a scenario-free uncertainty-aware bilevel optimization framework for coordinated electric vehicle (EV) charging and reactive power support in distribution networks using distribution locational marginal prices (DLMPs). The upper-level EV aggregator jointly schedules active and reactive charging power to minimize charging costs, while the lower-level energy management system performs network-constrained economic dispatch and determines DLMPs subject to feeder and voltage constraints. To capture uncertainties in load demand and photovoltaic (PV) generation, a compact robust counterpart (RC) reformulation is developed that avoids the computational burden of large-scale stochastic programming and conventional robust optimization. Unlike existing robust counterpart methods that primarily assume Gaussian uncertainties, the proposed approach derives a deterministic reformulation for net-demand uncertainty modeled by a normal-minus-beta distribution, providing a more realistic representation of asymmetric load and renewable variability. An exactness lemma preserves the economic interpretation of DLMPs after KKT reformulation and Big-M linearization. EV chargers also provide reactive power support through non-unity power factor operation to improve voltage regulation. Simulation results on the IEEE 33-bus distribution system demonstrate improved voltage security, effective uncertainty-aware EV coordination, and significantly lower computational complexity than conventional stochastic and robust optimization approaches.
