Simulation-Based Dynamic Pricing Optimization for Integrating Electricity and Hydrogen in Local Energy Markets

Abstract

The growing need to reduce carbon emissions and promote sustainable energy use is driving the transition toward environmentally friendly energy systems. The integration of distributed renewable energy sources and advancements in smart grid technologies are paving the way for innovative solutions to enhance energy efficiency and reduce dependence on fossil fuels. This study introduces an agent-based simulation model to optimize a charging station system that seamlessly integrates electricity and hydrogen infrastructure. The charging station is equipped with solar and wind generation, battery storage, and an advanced electricity-hydrogen transfer system, allowing it to generate electricity, store hydrogen, and support both electric vehicle (EV) and hydrogen fuel-cell vehicle (HFCV) charging, as well as supply electricity to building prosumers. To optimize the operations of this integrated system, we develop a leader-follower optimization model, which is then evaluated through simulations. The upper level maximizes the charging station’s profit, while the lower level addresses users’ decisions on energy consumption and charging behaviors in response to dynamic pricing. By simulating different scenarios with varying building loads, EV behaviors, and uncertainties, we analyze their impact on energy pricing and transaction dynamics. The results demonstrate that the integrated electricity-hydrogen system, when optimized through simulation, can achieve significant cost savings and enhance energy-sharing efficiencies, contributing to resilient and sustainable local energy networks.