Open Access iconOpen Access

ARTICLE

A Two-Layer Optimal Scheduling Strategy for Rural Microgrids Accounting for Flexible Loads

Guo Zhao1,2, Chi Zhang1,2,*, Qiyuan Ren1,2

1 School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan, 430068, China
2 Key Laboratory of Solar Energy Efficient Utilization and Energy Storage Operation Control, Hubei University of Technology, Wuhan, 430068, China

* Corresponding Author: Chi Zhang. Email: email

Energy Engineering 2024, 121(11), 3355-3379. https://doi.org/10.32604/ee.2024.053130

Abstract

In the context of China’s “double carbon” goals and rural revitalization strategy, the energy transition promotes the large-scale integration of distributed renewable energy into rural power grids. Considering the operational characteristics of rural microgrids and their impact on users, this paper establishes a two-layer scheduling model incorporating flexible loads. The upper-layer aims to minimize the comprehensive operating cost of the rural microgrid, while the lower-layer aims to minimize the total electricity cost for rural users. An Improved Adaptive Genetic Algorithm (IAGA) is proposed to solve the model. Results show that the two-layer scheduling model with flexible loads can effectively smooth load fluctuations, enhance microgrid stability, increase clean energy consumption, and balance microgrid operating costs with user benefits.

Keywords


Cite This Article

APA Style
Zhao, G., Zhang, C., Ren, Q. (2024). A two-layer optimal scheduling strategy for rural microgrids accounting for flexible loads. Energy Engineering, 121(11), 3355-3379. https://doi.org/10.32604/ee.2024.053130
Vancouver Style
Zhao G, Zhang C, Ren Q. A two-layer optimal scheduling strategy for rural microgrids accounting for flexible loads. Energ Eng. 2024;121(11):3355-3379 https://doi.org/10.32604/ee.2024.053130
IEEE Style
G. Zhao, C. Zhang, and Q. Ren, “A Two-Layer Optimal Scheduling Strategy for Rural Microgrids Accounting for Flexible Loads,” Energ. Eng., vol. 121, no. 11, pp. 3355-3379, 2024. https://doi.org/10.32604/ee.2024.053130



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 288

    View

  • 74

    Download

  • 0

    Like

Share Link