Dynamic Pricing for Electric Vehicle Charging Operators: A Sequential Game Framework With a Task‐Specific SAC Stabilisation Design

ABSTRACT

With the rapid expansion of electric vehicles (EVs), effective charging‐service‐fee (CSF) pricing is increasingly important for coordinating charging‐station operation and user‐side charging decisions. This paper proposes an operator‐side dynamic CSF pricing model within a simulation‐driven sequential leader–follower framework. The charging operator acts as the leader and jointly adjusts the CSFs of its self‐operated electric vehicle charging stations (EVCSs), whereas EV users respond through route‐ and queue‐informed station selection. The operator objective coordinates revenue, charging‐pile utilisation and a station‐level power distribution network (PDN) imbalance proxy. Standard soft actor‐critic (SAC) is retained as the pricing‐policy backbone and is equipped with three task‐specific stabilisation modules: the demand‐ and congestion‐aware stratified experience pool (DCA‐SEP), the critic‐disagreement‐regularised joint Bellman loss (CDR‐JBL) and the constraint‐triggered adaptive delayed update (CT‐ADU). In a Wuxi case study based on five independent experiment‐level runs, the proposed model increases the mean revenue of self‐operated EVCSs by 45.70%, reduces the PDN‐imbalance proxy by 18.40% and lowers total charging time under the reference demand setting.