Design of a New Energy Microgrid Optimization Scheduling Algorithm Based on Improved Grey Relational Theory
In order to solve the problem of the large-scale integration of new energy into power grid output fluctuations, this paper proposes a new energy microgrid optimization scheduling algorithm based on a two-stage robust optimization and improved grey correlation theory. This article simulates the fluct...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/18/1/36 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | In order to solve the problem of the large-scale integration of new energy into power grid output fluctuations, this paper proposes a new energy microgrid optimization scheduling algorithm based on a two-stage robust optimization and improved grey correlation theory. This article simulates the fluctuation of the outputs of wind turbines and distributed photovoltaic power plants by changing their robustness indicators, generates economic operating cost data for microgrids in multiple scenarios, and uses an improved grey correlation theory algorithm to analyze the correlation between new energy and various scheduling costs. Subsequently, a weighted analysis is performed on each correlation degree to obtain the correlation degree between new energy and total scheduling operating costs. The experimental results show that the improved grey correlation theory optimization scheduling algorithm for new energy microgrids proposed obtains weighted correlation degrees of 0.730 and 0.798 for photovoltaic power stations and wind turbines, respectively, which are 3.1% and 4.6% higher than traditional grey correlation theory. In addition, the equipment maintenance costs of this method are 0.413 and 0.527, respectively, which are 25.1% and 5.4% lower compared to the traditional method, respectively, indicating that the method effectively improves the accuracy of quantitative analysis. |
|---|---|
| ISSN: | 1999-4893 |