Optimal Sizing of Charging Energy Hubs for Heavy-Duty Electric Transport through Co-Optimization

TL;DR

This paper tackles the challenge of grid impacts from heavy‑duty electric vehicle electrification by proposing Charging Energy Hubs (CEHs) that couple local renewable generation, storage, and EV charging within a DC microgrid. A co‑design MILP optimizes discrete component sizing ($n_p$, $n_w$, $n_b$, $q_c$) and operational decisions over a one‑year horizon using scenario‑based weather and demand profiles to minimize the total cost of ownership, $J^{\mathrm{TCO}}$, while satisfying fleet energy needs and grid constraints. The methodology specifies detailed linear models for PV and wind generation, BESS dynamics, intelligent EV charging, grid interactions, and a comprehensive cost formulation including CapEx, OpEx, and degradation. The case study demonstrates how a carefully sized CEH can meet all fleet charging requirements without breaching daytime/nighttime grid limits, highlighting the value of integrated renewables and storage for scalable, grid‑compliant heavy‑duty electrification.

Abstract

Electrification of heavy-duty vehicles places substantial stress on distribution grids, and Charging Energy Hubs (CEHs) mitigate these impacts by integrating charging infrastructure with renewable energy sources and battery storage. Optimal sizing of CEH components is therefore a critical investment decision, yet challenging because design choices depend strongly on operational dynamics. This work presents a mixed-integer linear programming model for the optimal sizing of CEH components, using a co-design approach that jointly optimizes component sizing and operational decisions. A case study for a heavy-duty fleet demonstrates the effectiveness of the method for cost-efficient, scalable, and grid-compliant CEH planning.

Figures (6)

• Figure 1: Schematic configuration of the DC microgrid within the CEH, integrating PV systems, WTs, BESS, and EV chargers through AC/DC and DC/DC converters.
• Figure 2: Grid power across all representative scenarios (positive: withdrawals, negative: injections) with grid limits shown in orange.
• Figure 3: Aggregated EV charging power across all representative scenarios, showing charging-demand variability.
• Figure 4: Battery SOC across all representative scenarios, each day starting from the same initial SOC
• Figure 5: Zoomed-in view of the optimized EV charging schedule for the first two representative days.