Graph-Based Unified Settlement Framework for Complex Electricity Markets: Data Integration and Automated Refund Clearing

The increasing complexity of China’s electricity market creates substantial challenges for settlement automation, data consistency, and operational scalability. Existing provincial settlement systems are fragmented, lack a unified data structure, and depend heavily on manual intervention to process high-frequency and retroactive transactions. To address these limitations, a graph-based unified settlement framework is proposed to enhance automation, flexibility, and adaptability in electricity market settlements. A flexible attribute-graph model is employed to represent heterogeneous multi-market data, enabling standardized integration, rapid querying, and seamless adaptation to evolving business requirements. An extensible operator library is designed to support configurable settlement rules, and a suite of modular tools—including dataset generation, formula configuration, billing templates, and task scheduling—facilitates end-to-end automated settlement processing. A robust refund-clearing mechanism is further incorporated, utilizing sandbox execution, data-version snapshots, dynamic lineage tracing, and real-time change-capture technologies to enable rapid and accurate recalculations under dynamic policy and data revisions. Case studies based on real-world data from regional Chinese markets validate the effectiveness of the proposed approach, demonstrating marked improvements in computational efficiency, system robustness, and automation. Moreover, enhanced settlement accuracy and high temporal granularity improve price-signal fidelity, promote cost-reflective tariffs, and incentivize energy-efficient and demand-responsive behavior among market participants. The method not only supports equitable and transparent market operations but also provides a generalizable, scalable foundation for modern electricity settlement platforms in increasingly complex and dynamic market environments.

Authors

Publication Details

Journal
Energy Engineering
Published
2025-10-02
DOI
https://doi.org/10.32604/ee.2025.069820
Primary Topic
Electric Power System Optimization
Type
article
Field-Weighted Citation Impact
0.00
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article

Graph-Based Unified Settlement Framework for Complex Electricity Markets: Data Integration and Automated Refund Clearing

Ziyu Yue, Xiao Song Guo, Yifan Wang, Zhaoyuan Wu et al.
Energy Engineering
Electric Power System Optimization
article

Graph-Based Unified Settlement Framework for Complex Electricity Markets: Data Integration and Automated Refund Clearing

Ziyu Yue, Xiao Song Guo, Yifan Wang, Zhaoyuan Wu, Yuyang Wang, Suyan Long, Guanting Yin
article en

Abstract

The increasing complexity of China’s electricity market creates substantial challenges for settlement automation, data consistency, and operational scalability. Existing provincial settlement systems are fragmented, lack a unified data structure, and depend heavily on manual intervention to process high-frequency and retroactive transactions. To address these limitations, a graph-based unified settlement framework is proposed to enhance automation, flexibility, and adaptability in electricity market settlements. A flexible attribute-graph model is employed to represent heterogeneous multi-market data, enabling standardized integration, rapid querying, and seamless adaptation to evolving business requirements. An extensible operator library is designed to support configurable settlement rules, and a suite of modular tools—including dataset generation, formula configuration, billing templates, and task scheduling—facilitates end-to-end automated settlement processing. A robust refund-clearing mechanism is further incorporated, utilizing sandbox execution, data-version snapshots, dynamic lineage tracing, and real-time change-capture technologies to enable rapid and accurate recalculations under dynamic policy and data revisions. Case studies based on real-world data from regional Chinese markets validate the effectiveness of the proposed approach, demonstrating marked improvements in computational efficiency, system robustness, and automation. Moreover, enhanced settlement accuracy and high temporal granularity improve price-signal fidelity, promote cost-reflective tariffs, and incentivize energy-efficient and demand-responsive behavior among market participants. The method not only supports equitable and transparent market operations but also provides a generalizable, scalable foundation for modern electricity settlement platforms in increasingly complex and dynamic market environments.

Energy EngineeringVol. 123(1)
Openalex Percentile: Top 73%
Electric Power System Optimization
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Graph-Based Unified Settlement Framework for Complex Electricity Markets: Data Integration and Automated Refund Clearing — Ziyu Yue, Xiao Song Guo, et al. · Energy Engineering (2025) | TGRS Research Map | TGRS