Two-stage stochastic optimization for planning and operation of multiple types of mobile energy resources in distribution networks

Abstract

High penetrations of wind and photovoltaic (PV) generation increase temporal and scenario-dependent source–load variability in distribution networks, complicating the planning and operation of mobile energy resources. This paper proposes a multi-scenario-driven planning–operation co-optimization method that incorporates mobile energy storage vehicles (MESVs), transportable battery energy storage systems (TBESSs), mobile hydrogen fuel-cell power supply vehicles (MHSVs), power-to-hydrogen (P2H), and on-site hydrogen storage into a two-stage stochastic optimization framework. Probability-weighted representative scenarios characterize wind, photovoltaic, and load uncertainty. The first stage determines resource types, quantities, ratings, candidate-node activation, and P2H/hydrogen-storage capacities; the second stage determines scenario-dependent deployment and hourly operation. A second-order cone-relaxed DistFlow model represents battery operation, the P2H–storage–refueling–MHSV chain, road accessibility, and relocation costs. Representative cases for renewable energy integration, peak shaving, congestion mitigation, and black-start active-power support are tested on the IEEE 33-bus system. In the renewable-integration case, three TBESSs totaling 4.50 MW/11.25 MWh reduce monthly wind/PV curtailment from 571.908 to 461.385 MWh, a 19.3% reduction. In the peak-shaving case, road constraints increase the monthly economic objective from CNY 310,462.56 to CNY 344,079.39 (10.8%), showing that accessibility can materially alter deployment and operating outcomes. The results also show that MESVs provide peak-shaving and local network support, whereas MHSVs provide sustained active-power support through the electricity–hydrogen chain.