Data-Driven Distributionally Robust Planning of Coastal Distribution Networks and PV-Storage-EV Stations for Low-Carbon Energy Transition

Abstract

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.