Integrated energy scheduling and routing for a network of mobile prosumers

Due to the spatio-temporal complexity in energy consumption profiles of multiple consumers at different regions, it is expected that more cost savings and higher resiliency to power disruptions can be achieved using a network of mobile prosumers. In this paper, the prosumer is assumed to be an autonomous vehicle equipped with different distributed energy resources (e.g., solar panel, battery) that cannot only provide but also consume energy. An integrated vehicle routing and energy scheduling decision model is developed to adaptively dispatch vehicles to balance the temporally and spatially distributed energy requests subject to vehicle mobility constraints, and thus to maximally exploit the potentials of mobile prosumer network for cost savings and carbon emission reductions. The performances of the integrated decision model are evaluated using three metrics including operational costs, energy requested from power grids, and carbon emissions. The simulation results demonstrate that compared to disjoint vehicle routing and energy scheduling model, the proposed integrated decision model (i) is more efficient to shift energy loads at both temporal and spatial scales, and (ii) can save up to 38% of operational costs, reduce up to 29% of energy requested from power grids, and reduce up to 27% of carbon emissions.