李龙锡 Peer-to-peer multi-energy sharing for home microgrids: An integration of data-driven and model-driven approaches

我校经济管理学院李龙锡老师在T2级别期刊——《International Journal of Electrical Power and Energy Systems》上发表题为“Peer-to-peer multi-energy sharing for home microgrids: An integration of data-driven and model-driven approaches”。论文第一作者李龙锡为经济管理学院特任教授。

Abstract /摘要：

To form the economic and sustainable communities, peer-to-peer energy sharing is becoming one of the most promising ways for coordinating home microgrids. This paper presents a double auction-based peer-to-peer multi-energy sharing mechanism, to achieve the coordination of the intelligent, self-interested, and privacy-conscious home microgrids, and conduct the electricity sharing and heat sharing simultaneously. Based on a combination of model-driven optimization and data-driven prediction, an integrated model is developed, which can analyze historical transaction data, excavate the hybrid market trading rules, accurately and stably predict energy sharing strategies, and efficiently optimize the operation strategies of uncontrollable and controllable equipment. A hybrid genetic algorithm–extreme learning machine approach is proposed to realize data-driven prediction. According to the cost minimization objective, a joint optimization of the home microgrid electricity and heat energy sharing strategies and distributed energy operation strategy is conducted, to facilitate the energy interaction among microgrids and enhance the coordination between electricity and heat systems. In addition, we have evaluated the accuracy and stability of the genetic algorithm–extreme learning machine model and proved the validity of the model. Numerical results demonstrate that the proposed model can efficiently solve the multi-energy sharing problem, obtain high cost savings, improve the independence of microgrids, realize complementary advantages of multi-energy, and address the complicated relationships among the peers, which are regarded as stakeholders with separate privacy and interests。

论文信息;

Title/题目:

Peer-to-peer multi-energy sharing for home microgrids: An integration of data-driven and model-driven approaches

Authors/作者:

Li Longxi;Zhang Sen

Key Words /关键词：

Multi-energy sharing;Peer-to-peer;Data-driven approach;Model-driven approach;Home microgrid

Indexed by /核心评价：

SCI;EI;INSPEC; WAJCI; Scopus; AHCI;

Highlights/研究要点

• The peer to peer multi-energy sharing is realized based on a double auction scheme.;

• A genetic algorithm–extreme learning machine method is adopted for trade prediction.;

• The sharing strategy is got by data-driven prediction and model-driven optimization.;

• A post-matching adjustment process ensures the Pareto optimality of the participants.;

• The proposed method can reduce the energy cost and promote the local energy balance.

DOI：10.1016/J.IJEPES.2021.107243

全文链接：https://www.sciencedirect.com/science/article/pii/S0142061521004828?via%3Dihub