A novel strategy for coordinated regulating the coupled distribution grid-transportation network system through adjusting hydrogen flow via a joint electricity price-hydrogen price-charging price mechanism

Currently, the proportion of distributed renewable energy sources in distribution networks is continuously increasing, while electric vehicles (pure electric and hydrogen-powered) are experiencing substantial growth within the transportation network. Addressing how to coordinate the regulation of renewable power generation and guide electric vehicle charging, thereby ensuring secure distribution network operations and enhancing traffic network efficiency, has become an urgent issue. This paper proposes a novel strategy for coordinated regulating the coupled system through adjusting hydrogen flow via a joint electricity price-hydrogen price- charging price mechanism. Electricity prices serve to control the electrical energy flow; hydrogen prices control the hydrogen energy flow; and charging prices control the charging patterns of electric vehicles. Two pricing mechanisms are compared: one considering only electricity prices and charging prices (without hydrogen energy flow), and another incorporating electricity prices, hydrogen prices, and charging prices (with hydrogen energy flow). Based on these pricing mechanisms, two methods are contrasted: a rule-based price updating approach and a reinforcement learning-based DDPG (Deep Deterministic Policy Gradient) price decision method. Results show that in the proposed price mechanism (with hydrogen energy flow) DDPG strategy, the voltage deviation has decreased by 0.007 (p.u.) compared to the old price mechanism (without hydrogen energy flow) rule-based strategy. However, there is no significant improvement observed in terms of transportation network regulation capability, in the proposed price mechanism DDPG strategy, the waiting time and time loss have increased by 2.2% and 1.2% compared to the old price mechanism rule-based strategy. Results reveal that the proposed joint electricity price-hydrogen price-charging price mechanism exhibits superior performance, which can effectively control the electrical energy flow-hydrogen energy flow-traffic flow. However different coupling devices exhibit varying degrees of sensitivity to price changes. In addition, the DDPG method proves more effective. In future research, the main focus is on addressing the insufficient explainability of deep reinforcement learning.