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

Xu Xu, Yan Xu, Ming Hao Wang, Jiayong Li, Zhao Xu, Songjian Chai, Yufei He

Research output: Journal article publicationJournal articleAcademic researchpeer-review

30 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number9166747
Pages (from-to)4480-4491
Number of pages12
JournalIEEE Transactions on Industrial Informatics
Volume17
Issue number7
DOIs
Publication statusPublished - Jul 2021

Keywords

  • Energy sharing
  • energy storage (ES)
  • long short-term memory (LSTM) network
  • photovoltaic (PV) generation
  • pricing method
  • Q-learning algorithm
  • residential building cluster (RBC)
  • Stackelberg game

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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