Bi-level day-ahead and real-time hybrid pricing model and its reinforcement learning method

Youmeng He, Chunhua Gu, Yan Gao and Jingqi Wang

Energy, 2025, vol. 322, issue C

Abstract: Both multi-time slot (day-ahead) pricing and single-time slot (real-time) pricing are vital parts of real-time pricing. The widespread utilization of renewable energy sources has increased flexibility and uncertainty of the grid system. A single pricing strategy is unable to meet the demand of the grid. This paper design a hybrid pricing strategy for smart grids that combines real-time and day-ahead pricing. This strategy considers multi-source energy generation on the supply side, also, the distributed energy generation and load transfer on the demand side. Within the framework of the Markov Decision Process, a bi-level stochastic model for real-time demand response is formulated to maximize the benefits of both the supply and demand sides. Subsequently, a deep deterministic policy gradient algorithm relying on prioritized experience replay is used to formulate a real-time price plan and user’s power consumption. Through the information interaction between the upper and lower levels, the real-time prices are decided adaptively. Meanwhile, the optimal strategy of power supply and consumption are obtained. Our simulation results demonstrate that the proposed hybrid pricing strategy guarantee the benefits of both the supply and demand sides, while achieving the balance between supply and demand.

Keywords: Hybrid pricing; Bi-level programming; Reinforcement learning; Per-DDPG; Demand response (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:322:y:2025:i:c:s0360544225009582

DOI: 10.1016/j.energy.2025.135316

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