Measurement and Analysis Methods for Adjustable Loads in Megacities

Demand Side Management (DSM) has become an important solution to address the power supply-demand imbalance, and flexible loads (such as cooling loads and electric vehicle (EV) charging loads) are core components of DSM. This study focuses on a megacity and analyzes the regulation potential, calculation methods, and impacts of cooling loads and EV loads on the power system. Using the Peak Load Comparison Method (PLCM) and the Baseline Load Comparison Method (BLCM), the cooling load in a southern city was quantitatively measured. The results indicate that cooling loads are increasing year by year, with cooling loads during summer peak periods exerting significant pressure on the power grid. Additionally, the charging behavior of electric vehicles was modeled and analyzed, estimating the charging demand for different types of electric vehicles and predicting future growth trends in charging loads. The findings show that cooling loads and EV charging loads are significant flexible resources in the power systems of megacities, and their reasonable regulation is crucial for enhancing the operational efficiency and stability of the grid. Finally, the study highlights current challenges and suggests future research directions, including incorporating more climate data and user behavior data to improve load estimation accuracy.

Authors

Institutions

Publication Details

Published
2025-04-15
DOI
https://doi.org/10.1109/acpee64358.2025.11040555
Primary Topic
Structural Analysis and Optimization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Measurement and Analysis Methods for Adjustable Loads in Megacities

Yongtao Chen, Yuqing Zhou, Rui Zhu, Xiaotian Li et al.
Structural Analysis and Optimization
article

Measurement and Analysis Methods for Adjustable Loads in Megacities

Yongtao Chen, Yuqing Zhou, Rui Zhu, Xiaotian Li, Pengfei Yan, Yuan Zhang
article en

Abstract

Demand Side Management (DSM) has become an important solution to address the power supply-demand imbalance, and flexible loads (such as cooling loads and electric vehicle (EV) charging loads) are core components of DSM. This study focuses on a megacity and analyzes the regulation potential, calculation methods, and impacts of cooling loads and EV loads on the power system. Using the Peak Load Comparison Method (PLCM) and the Baseline Load Comparison Method (BLCM), the cooling load in a southern city was quantitatively measured. The results indicate that cooling loads are increasing year by year, with cooling loads during summer peak periods exerting significant pressure on the power grid. Additionally, the charging behavior of electric vehicles was modeled and analyzed, estimating the charging demand for different types of electric vehicles and predicting future growth trends in charging loads. The findings show that cooling loads and EV charging loads are significant flexible resources in the power systems of megacities, and their reasonable regulation is crucial for enhancing the operational efficiency and stability of the grid. Finally, the study highlights current challenges and suggests future research directions, including incorporating more climate data and user behavior data to improve load estimation accuracy.

Chongqing University (CN), State Grid Corporation of China (China) (CN)
Openalex Percentile: Top 84%
Structural Analysis and Optimization
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.

Measurement and Analysis Methods for Adjustable Loads in Megacities — Yongtao Chen, Yuqing Zhou, et al. · (2025) | TGRS Research Map | TGRS