The electrification of heavy-duty vehicles is a critical step toward reducing greenhouse gas emissions. While the electrification of passenger transport is steadily approaching maturity, the development of megawatt-scale charging infrastructure for trucks and heavy-duty vehicles remains a significant challenge, primarily due to distribution grid constraints and high investment costs. Traditional approaches to connecting large new loads, such as grid reinforcement, are often costly and time-demanding, posing a socio-economic challenge to the rapid rollout of EV charging infrastructure.

This work presents a decision-support framework designed to evaluate and optimize conditional connection agreements (CCAs) between Distribution System Operators (DSOs) and Charging Point Operators (CPOs), which allow connecting high-power charging stations to the distribution grid under flexible terms. The framework is designed to support both parties in negotiating and planning these agreements, balancing the technical limits of the grid with the commercial necessities of charging operators.

The methodology is based on a bi-level, multi-objective optimization approach, employing a genetic algorithm (NSGA-II) to explore a range of conditional connection agreements, while nested inner optimizations evaluate grid reinforcement and storage investment choices based on a set of available investment strategies. By incorporating discrete investment options and allowing for temporal sequencing of upgrades, the methodology reflects realistic planning scenarios and regulatory constraints in the European context.

The approach is applied on a use case based on a Norwegian scenario with a connection request of a CPO. The results are expected to offer insights into how CCAs may offer mutual benefits for both DSOs and CPOs, potentially reducing infrastructure bottlenecks and improving economic outcomes.

This work is part of the ongoing Norwegian research project MegaCharge, and is intended to support policy and industry efforts toward efficient, scalable electrification of heavy-duty transport.