沿海配电网与光伏-储能-电动汽车充电站的分布式鲁棒联合规划
Data-Driven Distributionally Robust Planning of Coastal Distribution Networks and PV-Storage-EV Stations for Low-Carbon Energy Transition
AI总结：
针对沿海配电网在可再生能源整合与电动汽车充电设施部署下的协同规划需求，提出三层分布式鲁棒优化框架联合优化光伏-储能-电动汽车充电站选址与配电网配置，开发i-CCG算法求解，在实际47节点电网验证其降本及鲁棒低碳效果。
摘要：
潮汐能、光伏（PV）等可再生能源的快速整合，以及电动汽车（EV）充电基础设施的日益普及，使得沿海城市配电网（DN）的协同规划成为必要。本文提出一种三层分布式鲁棒优化框架，联合优化光伏-储能-电动汽车充电站（PSES）的选址与沿海配电网的配置，以应对电力负荷、光伏出力及电动汽车充电需求的不确定性。上层决策PSES最优选址与网络拓扑，以最小化总投资和运行成本；中层通过最优潮流模型处理最坏不确定性场景，利用模糊集捕捉相关不确定性；针对储能系统二进制变量引入的非凸性，提出并严格证明一种新型松弛方法的精确性；下层考虑潮汐能波动驱动的动态定价，优化购电、碳排放等运行决策。开发不精确列约束生成（i-CCG）算法高效求解问题。基于中国某实际47节点沿海配电网的数值结果表明，该方法有效降低成本，确保大幅不确定性下的鲁棒低碳规划。
英文摘要：
Coastal distribution networks (DNs) are becoming key infrastructure for the low-carbon energy transition as electric vehicle (EV) charging, photovoltaic (PV) generation, energy storage, and marine renewable procurement are increasingly coupled at the planning stage. Existing planning models, however, usually treat network expansion, PV-storage-EV station (PSES) deployment, low-carbon operation, and coastal asset degradation separately. This paper proposes a data-driven distributionally robust planning framework for coastal DNs and PSESs. The framework jointly optimizes radial network topology and PSES siting while incorporating salt-spray degradation costs, carbon-emission-flow (CEF)-based nodal carbon accounting, and tidal-generation-driven procurement costs. To represent heterogeneous uncertainty, the model combines continuous box uncertainty for load and PV availability with probability ambiguity over EV-charging scenarios generated from historical charging profiles. The resulting two-stage distributionally robust optimization problem is reformulated through exact relaxation of ESS mode binaries and KKT-based decomposition, and is solved by an inexact column-and-constraint generation (i-C&CG) algorithm. Case studies on a realistic 47-node coastal feeder show that salt-spray exposure can alter the selected topology and PSES siting, while the proposed planning scheme improves voltage quality, reduces annualized total cost, and reveals the cost--carbon trade-off of tidal procurement. The tailored i-C&CG algorithm obtains the same optimal design as conventional C&CG with about an order-of-magnitude reduction in solution time.
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