Coordinated Optimal Dispatch of Source–Grid–Load–Storage Based on Dynamic Electricity Price Mechanism

Under the backdrop of the “dual carbon” strategy, the rapid increase in renewable energy penetration has exacerbated challenges such as widening peak–valley load gaps and insufficient grid regulation capacity, highlighting the urgent need to establish a market-oriented collaborative dispatching mechanism. This paper proposes a peak-shaving and valley-filling dispatching approach based on a multi-agent system (MAS) to enhance both the regulatory capability and economic efficiency of power grids. A multi-agent collaborative architecture is established on the generation side, where behavioral modeling and interaction simulations of generation, load, and energy storage agents are conducted using the NetLogo platform to emulate dynamic responses under market conditions. On the grid side, dynamic electricity pricing and energy storage control strategies are implemented. An integrated time-of-use electricity pricing mechanism is designed that incorporates environmental pollution factors, supply–demand state factors, and price-smoothing factors to dynamically adjust tariffs. A price-responsive load demand model and a dynamic threshold-based energy storage control strategy are developed to facilitate flexible regulation. On the load side, an optimized dispatch model is formulated with dual objectives of minimizing system operating costs and reducing the standard deviation of the net load profile. The Beetle Antennae Search (BAS) algorithm is employed to solve the model, striking a balance between economic efficiency and stability. Case study results demonstrate that, compared with traditional dispatch methods, the coordinated optimization of the BAS algorithm and the dynamic pricing mechanism proposed in this paper achieves a dual improvement in solution efficiency and economy. This ultimately reduces the system’s peak-to-valley difference by 10.92% and operating costs by 66.2%, proving its effectiveness and superiority in power grids with high renewable energy penetration.

Authors

Institutions

Publication Details

Journal
Energies
Published
2025-11-28
DOI
https://doi.org/10.3390/en18236277
Primary Topic
Electric Power System Optimization
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Coordinated Optimal Dispatch of Source–Grid–Load–Storage Based on Dynamic Electricity Price Mechanism

Xiangdong Meng, Hui Luan, Chenggang Li, Xianglan Piao et al.
Energies
Electric Power System Optimization
article

Coordinated Optimal Dispatch of Source–Grid–Load–Storage Based on Dynamic Electricity Price Mechanism

Xiangdong Meng, Hui Luan, Chenggang Li, Xianglan Piao, Haifeng Zhang, Dexin Li
article en

Abstract

Under the backdrop of the “dual carbon” strategy, the rapid increase in renewable energy penetration has exacerbated challenges such as widening peak–valley load gaps and insufficient grid regulation capacity, highlighting the urgent need to establish a market-oriented collaborative dispatching mechanism. This paper proposes a peak-shaving and valley-filling dispatching approach based on a multi-agent system (MAS) to enhance both the regulatory capability and economic efficiency of power grids. A multi-agent collaborative architecture is established on the generation side, where behavioral modeling and interaction simulations of generation, load, and energy storage agents are conducted using the NetLogo platform to emulate dynamic responses under market conditions. On the grid side, dynamic electricity pricing and energy storage control strategies are implemented. An integrated time-of-use electricity pricing mechanism is designed that incorporates environmental pollution factors, supply–demand state factors, and price-smoothing factors to dynamically adjust tariffs. A price-responsive load demand model and a dynamic threshold-based energy storage control strategy are developed to facilitate flexible regulation. On the load side, an optimized dispatch model is formulated with dual objectives of minimizing system operating costs and reducing the standard deviation of the net load profile. The Beetle Antennae Search (BAS) algorithm is employed to solve the model, striking a balance between economic efficiency and stability. Case study results demonstrate that, compared with traditional dispatch methods, the coordinated optimization of the BAS algorithm and the dynamic pricing mechanism proposed in this paper achieves a dual improvement in solution efficiency and economy. This ultimately reduces the system’s peak-to-valley difference by 10.92% and operating costs by 66.2%, proving its effectiveness and superiority in power grids with high renewable energy penetration.

EnergiesVol. 18(23)
Jilin University (CN), Jilin Electric Power Research Institute (China) (CN)
Electric Power Research Institute
Openalex Percentile: Top 61%
Electric Power System 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.