Geospatial Analysis and MILP Cost Minimisation of a Solar PV and BESS Connected Electric Truck Charging System in the United States : A Feasibility Study of Co-located Solar and BESS Charging System

Sammanfattning

The electrification of freight transport requires cost-effective and scalable infrastructure for charging electric trucks. This thesis investigates the technoeconomic feasibility of deploying co-located photovoltaic (PV) and battery energy storage systems (BESS) for electric truck charging in the United States, with the goal of minimising the cost of electricity (CoE). A two-stage methodology is applied: (1) a geospatial analysis to identify the promising regions of deployment based on regulatory, market, and locational factors, and (2) a MixedInteger Linear Programming (MILP) model to optimise system sizing and operation for a minimised CoE. The geospatial analysis identifies ERCOT South in Texas as the optimal location for minimising CoE, benefiting from deregulated market structures, a merchant-friendly policy environment, high solar irradiance, and cost of land. The energy-only market design of ERCOT enables revenue stacking through power purchase agreements (PPAs), participation in the spot market, and provision of ancillary services. The MILP model, optimised for one week per month for the first half of the year 2024, utilising weather and market data for that time, achieves a negative Cost of Electricity (CoE) of 13.60 USDc/kWh, a profit primarily driven by ancillary market prices. The CoE remains negative when increasing CAPEX and OPEX costs for the system under the same model period, removing the month of May, however, where ERCOT experiences a significant price spike in ancillary services, results in a positive CoE of 24.47 USDc/kWh, comparable to the cost of a diesel truck. Throughout a comparative analysis of other U.S. regions shows the tradeoffs between the ERCOT market and policy support. The findings underscore that optimal site selection is context-dependent and should align with investor risk appetite, policy preferences, and project objectives. This thesis presents an overarching framework for understanding site selection and modelling of CoE, providing insights for developers, policymakers, and logistics stakeholders seeking to accelerate the decarbonization of the U.S. freight sector.