Work

Distributed generation parameter optimization method based on fuzzy C-means clustering under the Internet of Things architecture

Year: 2021

Type: article

Abstract: Aiming at the problem that it is difficult to maximize market profits when various types of distributed generation work together in the power grid, this paper proposes a distributed generation parameter optimization method based on Internet of Things and fuzzy C-means clustering. Firstly, a grid distributed generation model is established under the Internet of Things architecture, and its internal parameter relationships are determined correspondingly. Secondly, the comprehensive evaluation criteria and constraints of distributed generation are proposed for maximizing market benefits. Finally, the distributed communication model based on Internet of Things and fuzzy C-means clustering are adopted to achieve the adaptive optimization of grid distributed generation parameters. The experiment simulation is carried out with IEEE standard test system. The results show that the total load and total revenue of the proposed algorithm are increased by 8.41% and 14.66% respectively compared with the traditional optimization algorithm considering generation side benefits and the algorithm considering energy loss only. Experimental results fully demonstrate the effectiveness of proposed algorithm.

Source: Energy Reports

DOI: 10.1016/j.egyr.2021.10.049

Authors: Xin Yao, Liyun Xing, Ping Xin

Institution: Beihua University

Language: English

Topic: Optimal Power Flow Distribution

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,090