Smart algorithms for EV charging in SMEs: a Flemish case study on balancing peak demand reduction and dynamic pricing

The increasing deployment of smart meters and dynamic tariff structures offers new opportunities for cost optimization in electricity consumption. This paper investigates three smart charging algorithms for electric vehicles (EVs): peak demand reduction, dynamic pricing optimization, and a combined strategy balancing both mechanisms. Using a real-world case study of a medium-sized Flemish EV charging park with 40 AC and 3 DC chargers, we evaluate these algorithms under three years of dynamic tariffs. Results indicate that while peak demand reduction provides the most significant savings, integrating dynamic pricing optimization further enhances cost reductions, especially in the context of capacity-based grid tariffs. By assuming perfect knowledge of future charging sessions, the study applies computationally efficient rule-based algorithms, offering a practical alternative to data-intensive AI models for small and medium-sized enterprises (SMEs). A sensitivity analysis reveals that even modest capacity-based grid tariffs effectively incentivize peak shaving, highlighting the importance of regulatory structures in driving cost reductions. These findings underscore the value of combining peak reduction and dynamic pricing strategies to achieve substantial energy cost savings in EV charging for SMEs.