Optimal regulation of demand-side resource aggregation involving load aggregators under low-carbon background

Large-scale new energy access to power system brings great challenge under double-carbon background, and demand-side resource (DSR) provides an important solution for power system regulation. However, a large number of distributed DSRs are limited to participating power system regulation because of their market size and the ability to participate in the scheduling, and thus lead to the lack of market competitive and low actual response of these DSRs at present. An optimal regulation method of DSR aggregation with the participation of load aggregators (LAs) is proposed, considering carbon emission and multi-type demand response. Firstly, the multi-level management mode of DSRs is introduced. Secondly, aiming at the lowest economic cost and the lowest carbon emission, a DSR aggregation optimization regulation model was established considering carbon emission and multi-type demand response. Finally, the validity of the optimal scheduling model is verified by simulation solution. The simulation results show that the model can effectively reduce the economic cost and carbon emissions of the resource subject on the demand side, reduce the energy cost of distributed DSRs by 5%, improve the level of resource management on the demand side, and provide an effective means for the economic optimization and regulation of the power grid under the requirements of low carbon and toughness.

Authors

Institutions

Publication Details

Published
2024-10-16
DOI
https://doi.org/10.1117/12.3034097
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

Optimal regulation of demand-side resource aggregation involving load aggregators under low-carbon background

Dan Li, Ziteng Liu, Chengcheng Fu, Zhenlan Dou et al.
Smart Grid Energy Management
article

Optimal regulation of demand-side resource aggregation involving load aggregators under low-carbon background

Dan Li, Ziteng Liu, Chengcheng Fu, Zhenlan Dou, Bo Pan, Yichen Liu, Dexiang Jia
article en

Abstract

Large-scale new energy access to power system brings great challenge under double-carbon background, and demand-side resource (DSR) provides an important solution for power system regulation. However, a large number of distributed DSRs are limited to participating power system regulation because of their market size and the ability to participate in the scheduling, and thus lead to the lack of market competitive and low actual response of these DSRs at present. An optimal regulation method of DSR aggregation with the participation of load aggregators (LAs) is proposed, considering carbon emission and multi-type demand response. Firstly, the multi-level management mode of DSRs is introduced. Secondly, aiming at the lowest economic cost and the lowest carbon emission, a DSR aggregation optimization regulation model was established considering carbon emission and multi-type demand response. Finally, the validity of the optimal scheduling model is verified by simulation solution. The simulation results show that the model can effectively reduce the economic cost and carbon emissions of the resource subject on the demand side, reduce the energy cost of distributed DSRs by 5%, improve the level of resource management on the demand side, and provide an effective means for the economic optimization and regulation of the power grid under the requirements of low carbon and toughness.

Energy Research Institute (CN), Shanghai Electric (China) (CN)
Openalex Percentile: Top 84%
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.