Why Electric Bus Charging Optimization Matters
As electric mobility scales up, charging infrastructure becomes a critical pressure point for grid operators. Electric buses, with their predictable schedules and centralized charging locations, offer a unique opportunity to introduce smart charging strategies that reduce peak loads and carbon emissions.
But optimizing this behavior is complex. It requires algorithms that can handle multiple objectives, account for battery losses, and adapt to real-world constraints. It also demands incentive structures that align individual charging decisions with system-wide goals.
Algorithms in Action: Research Highlights from MEGAMIND
Leander’s research delivers practical tools and insights for stakeholders such as DSOs and transport operators:
✅ Algorithms for Nonconvex EV Charging
Developed new algorithms that extend beyond convex objectives, including solutions for concave and general objective functions, especially relevant when accounting for battery losses.
✅ Multi-objective Charging for Electric Buses
Created a scheduling algorithm that matches bus routes with charging slots, optimizing for both carbon emissions and peak load flattening. Applied to a real-world dataset, the method significantly improved both metrics.
✅ Flexibility Analysis of Charging Locations
Demonstrated that bus charging stations offer substantial flexibility, which can be leveraged to support grid stability.
✅ Incentive Mechanisms for Peak Flattening
Tested various price functions to influence charging behavior. Monomial price functions performed well, especially when accounting for collective behavior. Developed an alternative method that avoids post-hoc pricing, with only a slight performance trade-off.
✅ Next Steps
Ongoing work includes modeling local grid constraints and refining bus-line matching during the optimization phase.
Research in Practice: From Algorithms to Real-World Impact
A key strength of Leander van der Bijl’s work is how it connects advanced mathematical models with practical solutions for electric bus charging and grid optimization. His research doesn’t just stay in theory, it’s designed to help grid operators and transport companies make smarter, more sustainable decisions.
📄 Algorithms for Nonconvex EV Charging Problems – IEEE PowerTech 2025
Presents the technical foundation for solving EV charging problems with nonconvex objectives.
Link (proceedings soon online)
📄 Carbon, Cost and Capacity: Multi-objective Charging of Electric Buses – Arxiv preprint 2504.06078
Introduces the multi-objective charging framework and bus-line matching heuristic. Submitted for journal publication.
https://arxiv.org/abs/2504.06078
📄 Regulating AI in the ‘twin transitions’: Significance and shortcomings of the AI Act in the digitalised electricity sector – Review of European, Comparative & International Environmental Law, 2024
Co-authored with Espinosa Apráez and Noorman, this paper examines the AI Act’s applicability to the electricity sector. It concludes that current legislation covers only a narrow subset of AI systems, highlighting the need for additional regulation.
https://doi.org/10.1111/reel.12574
Looking Ahead: From Optimization to Implementation
Leander van der Bijl’s work exemplifies how mathematical modeling and data-driven insights can unlock new possibilities for grid coordination and sustainable transport. By combining technical depth with societal relevance, his research contributes to a smarter, more resilient energy system, one where electric buses not only move people, but also help balance the grid.
Key Takeaways
🔋 Smart charging of electric buses can reduce peak loads and carbon emissions.
📈 Optimization algorithms must reflect real-world constraints like battery losses and route schedules.
💡 Incentive mechanisms are essential to guide charging behavior toward system-wide goals.
🔍 Charging locations offer untapped flexibility that can support grid stability.
📚 Transparent and adaptable models accelerate the path from research to deployment.