Coordinated Optimization of Logistics Electric Fleet and Energy Management System of Constrained Energy Hub

The electricity network has reached its transport capacity limits in various areas in the Netherlands and the challenge of granting connections became critical. The concept of energy hubs, where neighboring prosumers collaborate to optimize the available capacity, poses itself as a short-term alternative for grid reinforcement. This study presents a coordinated, MPC-based combined with the partheno-genetic algorithm, optimization approach enabling a smooth transition into an electric fleet for logistics companies, taking part of an energy hub, while respecting the grid’s limited capacity and taking into account uncertainties arising from load and generation profiles. The last-mile deliveries of the logistic company are depicted by the Electric Vehicle Routing Problem formulation, and the partheno-genetic algorithm is implemented to solve it, where the parameters of interest are fed to the energy management system that minimizes the overall energy costs at the energy hub while incorporating day-ahead market prices. The intermittency of renewable generation and load demand is tackled by adopting the model predictive control (MPC) framework, providing a corrective mechanism that ensures that the overarching objective of the system is met while respecting the grid’s limitation. Three charging strategies at the hub are investigated: dynamic charging, where charging power varies by magnitude and time, direct charging, having a fixed charging power and time, and delayed overnight charging, where the charging power is spread over a scheduled horizon. The results demonstrate the mitigation of the grid’s limited connection capacity while attaining cost savings with the proposed dynamic charging strategy, ranging between 11.5% and 52% compared to the delayed overnight charging and direct charging strategies.