Uncertainty-Aware Deployment of Mobile Energy Storage Systems for Distribution Grid Resilience
Uncertainty-Aware Deployment of Mobile Energy Storage Systems for Distribution Grid Resilience
复制标题
面向配电网弹性的移动的储能系统的不确定性感知部署
DOI：
10.1109/tsg.2021.3064312
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发表时间：
2021-03
影响因子：
9.6
通讯作者：
Mostafa Nazemi;P. Dehghanian;Xiaonan Lu;Chen Chen-Chen
中科院分区：
文献类型：
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作者：
Mostafa Nazemi;P. Dehghanian;Xiaonan Lu;Chen Chen-Chen
随着空间灵活性在整个网络中的交换，移动的能量存储系统（MESS）提供了有前途的机会，以提高配电系统对紧急情况的弹性。尽管可再生能源（RESs）在配电系统（PDS）中的整合有了显着的增长，但由于其固有的不确定性和随机性，大多数恢复和恢复策略并没有释放这些资源的全部潜力。本文提出了一种新的恢复机制，在PDS的路由和调度MESS集成随机RES，以实现敏捷的系统响应和恢复，面对高影响低概率（HILP）事件的后果。建议的综合模型是作为一个非凸的非线性随机优化公式与联合概率约束（JPC）。该问题等价地重新表述为一个易于处理的混合整数线性规划（MILP）模型，可以通过商业现成的求解器来解决。IEEE 33节点和123节点测试系统的案例研究表明，所提出的框架在提高系统弹性的有效性和可扩展性。这是通过有效的路由和调度的MESS联合管理的动态网络重新配置中存在的随机RES。
With the spatial flexibility exchange across the network, mobile energy storage systems (MESSs) offer promising opportunities to elevate power distribution system resilience against emergencies. Despite the remarkable growth in integration of renewable energy sources (RESs) in power distribution systems (PDSs), most recovery and restoration strategies do not unlock the full potential in such resources due to their inherent uncertainty and stochasticity. This paper develops a novel restoration mechanism in PDSs for routing and scheduling of MESSs integrated with stochastic RESs to achieve agile system response and recovery in facing the aftermath of high-impact low-probability (HILP) incidents. The proposed integrated model is presented as a non-convex non-linear stochastic optimization formulation with joint probabilistic constraints (JPCs). The problem is equivalently reformulated to a tractable mixed-integer linear programming (MILP) model that can be solved by commercial off-the-shelf solvers. Case studies on the IEEE 33-node and 123-node test systems demonstrate the effectiveness and scalability of the proposed framework in boosting the system resilience. This is achieved via effective routing and scheduling of MESSs jointly managed with dynamic network reconfiguration in presence of stochastic RESs.