Open Access

ARTICLE

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

Xiaozhe Guo¹, Suyan Long², Ziyu Yue², Yifan Wang², Guanting Yin¹, Yuyang Wang¹, Zhaoyuan Wu^1,*

1 School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, 102206, China
2 China Electric Power Research Institute, Nanjing, 210003, China

* Corresponding Author: Zhaoyuan Wu. Email:

Energy Engineering 2026, 123(1), . https://doi.org/10.32604/ee.2025.069820

Received 01 July 2025; Accepted 04 September 2025; Issue published 27 December 2025

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.

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Keywords

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