Dynamic pricing systems for electric vehicle charging infrastructure represent a transformative solution to the challenges posed by rapid transportation electrification and its impact on power grid stability. This comprehensive framework integrates real-time wholesale electricity market prices, transformer loading conditions, and weather-driven renewable generation forecasts to optimize charging tariffs at five-minute intervals. The system architecture leverages OpenADR protocols for standardized communication, SCADA telemetry for grid monitoring, and advanced machine learning algorithms for demand prediction. Through a sophisticated two-stage optimization process combining ElasticNet regression for demand elasticity modeling and Mixed Integer Linear Programming for price determination, the framework achieves multiple objectives simultaneously: enhancing grid reliability, improving economic efficiency, and reducing consumer costs. Field trials conducted in the ERCOT market demonstrate significant improvements across all performance metrics, including complete elimination of transformer overload incidents and substantial operating margin increases for charging station operators. The implementation addresses critical technical challenges through robust data quality management, failsafe mechanisms for system reliability, and scalable computational architectures. This dynamic pricing paradigm offers utilities and grid operators a practical pathway to accommodate accelerating EV adoption without costly infrastructure upgrades, while aligning individual consumer incentives with system-wide efficiency goals through transparent, market-based pricing signals.
Keywords: demand response management, dynamic pricing systems, electric vehicle charging, real-time optimization, smart grid integration
