Carbon-aware mobile energy storage system scheduling in active power distribution systems under PV and traffic uncertainties

Carbon emission flow (CEF) is a promising approach for assessing both generation-and consumption-side carbon footprints in the power system sector. In this study, we propose a carbon-aware mobile energy storage system (MESS) scheduling framework that reduces the total carbon emissions via the CEF approach while ensuring the economical and robust operation of coupled power distribution and transportation systems under uncertainties. To manage uncertainties in photovoltaic (PV) generation outputs and traffic user equilibrium model-based traffic flow demands, the proposed framework is formulated as a distributionally robust optimization (DRO) problem using the Wasserstein metric. Thus, the Wasserstein-based DRO problem is reformulated as a tractable deterministic optimization problem by calculating distributionally robust bounds of the uncertainties. The proposed framework was tested on coupled IEEE 33-bus power distribution and 24-node Sioux Falls transportation systems, including three carbon-free PV systems, three gas-turbine generators, and three stationary energy storage systems (SESSs) or MESSs. The results show that MESSs decrease system-wide carbon emissions significantly in the CEF model and, compared to SESSs, further reduce them at the cost of a slight increment in real power losses due to the mobility characteristics of MESSs.