Route planning algorithm based on dynamic programming for electric vehicles delivering electric power to a region isolated from power grid

In this study, we considered the electric power delivery problem when using electric vehicles (EVs) for multiple households located in a remote region or a region isolated by disasters. Two optimization problems are formulated and compared; they yield the optimal routes that minimize the overall traveling distance of the EVs and their overall electric power consumption, respectively. We assume that the number of households requiring power delivery and the number of EVs used for power delivery in the region are given constants. Subsequently, we divide the households into groups and assign the households in each group to one EV. Each EV is required to return to its initial position after delivering electric power to all the households in the assigned group. In the first method, the benchmark method, the optimal route that minimizes the overall traveling distance of all the EVs is determined using the dynamic programming method. However, owing to traffic congestion on the roads, the optimal path that minimizes the overall traveling distance of all the EVs does not necessarily yield their minimum overall electric power consumption. In this study, to directly minimize the overall electric power consumption of all the considered EVs, we propose an optimization method that considers traffic congestion. Therefore, a second method is proposed, which minimizes the overall electric power consumption considering traffic congestion. The electric power consumed during the travel of each EV is calculated as a function of the length of each road section and the nominal average speed of vehicles on the road section. A case study in which four EVs are assigned to deliver electric power to serve eight households is conducted to validate the proposed method. To verify the effectiveness of the proposed method, the calculation results considering traffic congestion are compared with the benchmark method results, which minimizes the traveling distance. The comparison of the results from the two different methods shows that the optimal solution for the proposed method reduces the overall electric power consumption of all the EVs by 236.5(kWh) (9.4%) compared with the benchmark method. Therefore, the proposed method is preferable for the reduction of the overall electric power consumption of EVs.