Data-Driven Game-Based Pricing for Sharing Rooftop Photovoltaic Generation and Energy Storage in the Residential Building Cluster Under Uncertainties

In this article, a novel machine learning based data-driven pricing method is proposed for sharing rooftop photovoltaic (PV) generation and energy storage in an electrically interconnected residential building cluster (RBC). In the studied problem, the energy sharing process is modeled by the leader-follower Stackelberg game where the owner of the rooftop PV system is responsible for pricing self-generated PV energy and operating ES devices. Meanwhile, local electricity consumers in the RBC choose their energy consumption with the given internal electricity prices. To track the stochastic rooftop PV panel outputs, the long short-term memory network based rolling-horizon prediction function is developed to dynamically predict future trends of PV generation. With system information, the predicted information is fed into a Q-learning based decision-making process to find near-optimal pricing strategies. The simulation results verify the effectiveness of the proposed approach in solving energy sharing problems with partial or uncertain information.

Automated summary

This article proposes a novel machine learning based data-driven pricing method for sharing rooftop photovoltaic generation and energy storage in an electrically interconnected residential building cluster. The energy sharing process is modeled by the leader-follower Stackelberg game, with the owner of the rooftop PV system responsible for pricing self-generated PV energy and operating ES devices. A long short-term memory network based rolling-horizon prediction function is developed to track stochastic PV panel outputs, and a Q-learning based decision-making process is used to find near-optimal pricing strategies. Simulation results confirm the effectiveness of the approach in solving energy sharing problems with partial or uncertain information.