Gravitational search algorithm optimization algorithm for grid distributed energy storage resource pool regulation matching load peak–valley and operation constraints

The precise regulation of distributed energy storage resource pools can enhance the capacity to stabilize the peak-valley load difference of the power grid, mitigate load fluctuations, ensure efficient utilization of renewable energy, and reduce power grid losses. Consequently, this study investigates the GSA optimization algorithm for regulating distributed energy storage resource pools in the power grid, which can address load peaks and valleys while adhering to operational constraints. The objective function is defined as the load peak-valley difference, load variance, renewable energy utilization rate, and grid losses. The operational constraints encompass aspects such as power storage, operation, voltage, and load reduction limitations. Following the construction of a distributed energy storage resource pool regulation model, the GSA optimization algorithm is employed to solve it, identify the global optimal solution from the optimal regulation solution set, and achieve the regulation of the energy storage resource pool. The results demonstrate that after applying the algorithm, the average peak-valley difference and load variance of the power grid are 1.27 kW and 0.088 kW2, respectively. The average utilization rate and loss of renewable energy are 99.085% and 114.809 kW, respectively. This method effectively reduces the peak load of the power grid, addresses the issue of low valley load, and significantly reduces load fluctuations while maintaining low losses and high utilization of renewable energy, thereby ensuring stable operation of the power grid.

Authors

Institutions

Publication Details

Journal
Discover Internet of Things
Published
2025-07-12
DOI
https://doi.org/10.1007/s43926-025-00172-y
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Gravitational search algorithm optimization algorithm for grid distributed energy storage resource pool regulation matching load peak–valley and operation constraints

Zhichao Lin, Yu‐Ting Huang, Xiangyang Su, Busheng Luo et al.
Discover Internet of Things
Smart Grid Energy Management
article

Gravitational search algorithm optimization algorithm for grid distributed energy storage resource pool regulation matching load peak–valley and operation constraints

Zhichao Lin, Yu‐Ting Huang, Xiangyang Su, Busheng Luo, Liangbo Xie, Yingmin Wang, Gang Chen
article en

Abstract

The precise regulation of distributed energy storage resource pools can enhance the capacity to stabilize the peak-valley load difference of the power grid, mitigate load fluctuations, ensure efficient utilization of renewable energy, and reduce power grid losses. Consequently, this study investigates the GSA optimization algorithm for regulating distributed energy storage resource pools in the power grid, which can address load peaks and valleys while adhering to operational constraints. The objective function is defined as the load peak-valley difference, load variance, renewable energy utilization rate, and grid losses. The operational constraints encompass aspects such as power storage, operation, voltage, and load reduction limitations. Following the construction of a distributed energy storage resource pool regulation model, the GSA optimization algorithm is employed to solve it, identify the global optimal solution from the optimal regulation solution set, and achieve the regulation of the energy storage resource pool. The results demonstrate that after applying the algorithm, the average peak-valley difference and load variance of the power grid are 1.27 kW and 0.088 kW2, respectively. The average utilization rate and loss of renewable energy are 99.085% and 114.809 kW, respectively. This method effectively reduces the peak load of the power grid, addresses the issue of low valley load, and significantly reduces load fluctuations while maintaining low losses and high utilization of renewable energy, thereby ensuring stable operation of the power grid.

Discover Internet of ThingsVol. 5(1)
Advanced Energy (United States) (US), China Southern Power Grid (China) (CN), Power Grid Corporation (India) (IN)
Openalex Percentile: Top 85%
Smart Grid Energy Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.