Multi-regional energy sharing approach for shared energy storage and local renewable energy resources considering efficiency optimization

• Proposes a two-layer master-slave game framework for cross-regional energy sharing and economic optimization. • Develops dynamic interaction model enabling flexible electricity pricing and strategic consumption. • Balances stakeholder interests to boost returns for users (+28%) and MREO (+19%). • Enhances photovoltaic self-consumption by 36% while stabilizing grid load fluctuations. • Establishes sustainable energy synergy through mutual-benefit mechanisms. As distributed photovoltaic and shared energy storage systems expanded on the user side, developing an energy-sharing mechanism across different regions became crucial for fully utilizing local renewable energy resources and maximizing the system’s overall economic performance. This paper established a multi-regional energy operator (MREO) model considering shared energy storage, and a two-layer trading and optimization framework based on a master–slave game was developed. Initially, a trading system was devised to evaluate the interests of the power grid, MREO, and end-users. Next, an optimization model was formulated to capture the dynamic interactions between MREO decisions and user responses. The top-layer model was managed by MREO and focused on energy sharing among regions, which is used to set flexible electricity prices according to regional demand and optimize the use of shared energy storage. Meanwhile, the bottom-layer model addressed user demand response, allowing users to modify their energy consumption and select more advantageous trading areas based on information provided by the MREO. Simulation results confirmed that the proposed model accurately evaluated each party’s income, iteratively balanced their interests, and increased economic returns for both users and MREO. Additionally, the proposed approach supported greater local photovoltaic energy consumption, reduced grid load fluctuations, and fostered mutually beneficial outcomes for all stakeholders.

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Publication Details

Journal
International Journal of Electrical Power & Energy Systems
Published
2025-03-18
DOI
https://doi.org/10.1016/j.ijepes.2025.110592
Citations
24
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
29.15
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article

Multi-regional energy sharing approach for shared energy storage and local renewable energy resources considering efficiency optimization

Wenyang Deng, Muhammad Faizan Tahir, Dongliang Xiao, Dibin Zhu et al.
24 citations
International Journal of Electrical Power & Energy Systems
Microgrid Control and Optimization
29.15
article

Multi-regional energy sharing approach for shared energy storage and local renewable energy resources considering efficiency optimization

Wenyang Deng, Muhammad Faizan Tahir, Dongliang Xiao, Dibin Zhu, Mingli Chen
article en
24 citations

Abstract

• Proposes a two-layer master-slave game framework for cross-regional energy sharing and economic optimization. • Develops dynamic interaction model enabling flexible electricity pricing and strategic consumption. • Balances stakeholder interests to boost returns for users (+28%) and MREO (+19%). • Enhances photovoltaic self-consumption by 36% while stabilizing grid load fluctuations. • Establishes sustainable energy synergy through mutual-benefit mechanisms. As distributed photovoltaic and shared energy storage systems expanded on the user side, developing an energy-sharing mechanism across different regions became crucial for fully utilizing local renewable energy resources and maximizing the system’s overall economic performance. This paper established a multi-regional energy operator (MREO) model considering shared energy storage, and a two-layer trading and optimization framework based on a master–slave game was developed. Initially, a trading system was devised to evaluate the interests of the power grid, MREO, and end-users. Next, an optimization model was formulated to capture the dynamic interactions between MREO decisions and user responses. The top-layer model was managed by MREO and focused on energy sharing among regions, which is used to set flexible electricity prices according to regional demand and optimize the use of shared energy storage. Meanwhile, the bottom-layer model addressed user demand response, allowing users to modify their energy consumption and select more advantageous trading areas based on information provided by the MREO. Simulation results confirmed that the proposed model accurately evaluated each party’s income, iteratively balanced their interests, and increased economic returns for both users and MREO. Additionally, the proposed approach supported greater local photovoltaic energy consumption, reduced grid load fluctuations, and fostered mutually beneficial outcomes for all stakeholders.

International Journal of Electrical Power & Energy SystemsVol. 167
New York University Abu Dhabi (AE), Guangdong University of Technology (CN), North China Electric Power University (CN)
Affordable and clean energy
Openalex Percentile: Top 0%
Microgrid Control and Optimization
29.15
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