Topological considerations on peer-to-peer energy exchange and distributed energy generation in the smart grid

Energy Informatics

article

Sha, Ang¹  Aiello, Marco² 

[1] University of Groningen;University of Stuttgart

关键词: Energy distribution;    Simulation;    Distributed energy generation;    Energy trading;    Power routing;    Smart grid;   

DOI  :  10.1186/s42162-020-00109-5

来源: Springer

【 摘 要 】

The vision of the future Smart Grid considers end-users connected to it as both consuming and generating energy. Equipped with small-scale renewable energy generators and storage systems, end-users, also known as prosumers, engage in a local energy market for procuring and selling energy, in turn disrupting the traditional utility model. The appeal of this vision lies in the engagement of end-users, in facilitating the introduction and optimization of renewable energy sources, with the overall expectation of optimizing the global energy generation and distribution process. To handle the peer-to-peer energy exchange and distributed energy generation in the digitalized Smart Grid, we proposed an optimization strategy. In the present work, we propose a Monte Carlo based simulation model to investigate the role of the topology in facilitating the peer-to-peer energy exchanges and distributed energy generation. We consider a 37-node distribution network and evaluate four topological models: radial, complete graph, random graph, and small-world. The results indicate that the random graph model is better than other models in reducing the average delivery path length and energy losses in the energy transfer between providers and consumers. The small-world model has higher efficiency than other models in reducing the maximum power load in the distribution network and the cost of buying energy for end-users. We scale up the investigation by considering a 100-node network and evaluate the random graph and the small-world models by varying the rewiring probabilities. The results show that the small-world model outperforms the random graph model on most efficiency metrics, even when considering infrastructural costs. This work provides the foundation for a decision support system for analysis and high level planning of the distribution network.

【 授权许可】

CC BY