Dual-layer scheduling coordination algorithm for power supply guarantee using multi-objective optimization in spot market environment

As the global electricity market continues to evolve, power dispatch in the spot market environment faces unprecedented challenges. Price fluctuations, the intermittency and uncertainty of renewable energy sources, and stringent environmental constraints make traditional dispatch methods inadequate. To address this, this work proposes a two-layer scheduling strategy based on a multi-objective enhanced genetic algorithm. This strategy aims at balancing multiple objectives such as cost efficiency, environmental impact, and system stability to optimize power dispatch in the spot market. The upper-layer scheduling of this strategy focuses on strategic decisions at the macro level, including generation planning and electricity market transactions. Its lower-layer scheduling concentrates on operational execution at the micro level, specifically power transmission and distribution. To validate the model’s effectiveness, this work designs a regional grid model that includes wind, solar, and several conventional generation units. The experimental results show that, compared to the benchmark strategy, the proposed algorithm achieves a cost savings of 8.33% while ensuring a reliable power supply. Additionally, the algorithm reduces carbon dioxide emissions by approximately 15.1% and significantly increases the average utilization rate of renewable energy to 93.4%. The algorithm is iterated 100 times, each simulating a 24-hour scheduling cycle. The experiment demonstrates its excellent performance in high-dimensional decision spaces and multi-objective optimization problems. This work not only provides an innovative multi-objective optimization solution for power dispatch in the spot market environment but also achieves significant improvements in terms of economic efficiency, environmental sustainability, and long-term viability. Through this two-layer scheduling strategy, the dispatch efficiency of the power system is significantly enhanced, and this provides strong support for the development of a green, low-carbon power supply system.

Authors

Institutions

Publication Details

Journal
Energy Informatics
Published
2025-03-18
DOI
https://doi.org/10.1186/s42162-025-00485-w
Citations
2
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
1.46
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Dual-layer scheduling coordination algorithm for power supply guarantee using multi-objective optimization in spot market environment

Bohao Sun, Zhen Ji, Wei Ming Sun, Xuanyuan Wang et al.
2 citations
Energy Informatics
Smart Grid Energy Management
1.46
article

Dual-layer scheduling coordination algorithm for power supply guarantee using multi-objective optimization in spot market environment

Bohao Sun, Zhen Ji, Wei Ming Sun, Xuanyuan Wang, Bo Yan, Xu Gao
article en
2 citations

Abstract

As the global electricity market continues to evolve, power dispatch in the spot market environment faces unprecedented challenges. Price fluctuations, the intermittency and uncertainty of renewable energy sources, and stringent environmental constraints make traditional dispatch methods inadequate. To address this, this work proposes a two-layer scheduling strategy based on a multi-objective enhanced genetic algorithm. This strategy aims at balancing multiple objectives such as cost efficiency, environmental impact, and system stability to optimize power dispatch in the spot market. The upper-layer scheduling of this strategy focuses on strategic decisions at the macro level, including generation planning and electricity market transactions. Its lower-layer scheduling concentrates on operational execution at the micro level, specifically power transmission and distribution. To validate the model’s effectiveness, this work designs a regional grid model that includes wind, solar, and several conventional generation units. The experimental results show that, compared to the benchmark strategy, the proposed algorithm achieves a cost savings of 8.33% while ensuring a reliable power supply. Additionally, the algorithm reduces carbon dioxide emissions by approximately 15.1% and significantly increases the average utilization rate of renewable energy to 93.4%. The algorithm is iterated 100 times, each simulating a 24-hour scheduling cycle. The experiment demonstrates its excellent performance in high-dimensional decision spaces and multi-objective optimization problems. This work not only provides an innovative multi-objective optimization solution for power dispatch in the spot market environment but also achieves significant improvements in terms of economic efficiency, environmental sustainability, and long-term viability. Through this two-layer scheduling strategy, the dispatch efficiency of the power system is significantly enhanced, and this provides strong support for the development of a green, low-carbon power supply system.

Energy InformaticsVol. 8(1)
State Grid Corporation of China (China) (CN)
Openalex Percentile: Top 19%
Smart Grid Energy Management
1.46
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.