A Game-Theoretic approach for deep valley pricing strategy in the demand side

• To handle the load curve issues caused by renewable energy sources, a deep valley price for multi-type users is proposed to stimulate different users individually, which include residential users, industrial user, and so on, conduct the load shift for shaving the peak, mitigating the peak-to-valley difference, and improving the utility for all stakeholders. • To delineate the deep valley price intervals, we leverage the Fuzzy C Means clustering method to achieve the time division of deep valley price. • For the pricing model, the two-layer game-theoretic deep valley price model, resolved by backward induction approach, is proposed to simulate the interaction of multi-users and utility company for maximizing their utility and maximizing overall system efficiency. The introduction of large-scale renewable energy will inevitably bring significant challenges to the security and stability of power systems. Toward this end, this paper proposes a demand-side deep valley pricing (DVP) model based on game theory. First, we conduct the fuzzy affiliation-based deep valley period division by Fuzzy C Means (FCM). Subsequently, we leverage the Bayesian discriminant method to calculate the posterior probability of results to verify the proposed division method. The model incorporates a game-theoretic framework, elucidating dynamics between electric users and the utility company within the proposed DVP pricing regimen. In addition, the utility function of participators is calculated through the fluctuation of user demand load, and the Nash equilibrium (NE) is obtained using the inverse induction method. Finally, the effectiveness of the proposed DVP pricing strategy is validated by the simulation results, which can reduce the peak-to-valley difference of the load curve and promote the characteristics of users’ electricity consumption behavior.