Analysis of Tripartite Evolutionary Game in Marketization of New Energy Electricity Prices Based on Large Language Models
Hui MAO, Benyan Tan, Ribesh Khanal, Obaid ULLAH, Meizhong Huang
🤖 gxceed AI 要約
日本語
本論文は、大規模言語モデル（LLM）を統合した三者進化ゲームモデルを構築し、太陽光発電事業者、電力網事業者、政府の戦略選択と影響要因を分析する。市場化された電気料金変動、技術革新コスト、評価ペナルティなどの要因をLLMの多段階意味解析により特定し、江西省と湖北省の実データを用いてシミュレーション検証を行った。政策立案と企業の意思決定最適化にデータ駆動型の理論的基盤を提供する。
English
This paper constructs a tripartite evolutionary game model integrated with large language models (LLMs) to analyze strategy selection and influencing factors among photovoltaic power generation enterprises, grid enterprises, and government agencies. Using multi-round semantic parsing, it identifies key factors such as market-oriented electricity price fluctuations, technological innovation costs, and assessment penalties. Numerical simulations based on actual data from Jiangxi and Hubei provinces validate the model, providing a data-driven theoretical basis for policy implementation and enterprise decision optimization.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
中国の新エネ電力市場化改革に関する研究だが、日本でも再生可能エネルギーの市場統合やFIT制度の見直しが進む中、類似の政策課題に対するゲーム理論とLLMを活用した分析手法は参考になる。特に、SSBJ対応や有報でのリスク開示において、ステークホルダー間の戦略的相互作用をモデル化する手法は応用可能。
In the global GX context
While focused on China's renewable energy market reform, this study demonstrates a novel integration of LLMs with evolutionary game theory to model stakeholder interactions in energy transition. Globally, it offers a methodological template for policy design and regulatory impact assessment, particularly relevant for markets implementing competitive renewable energy pricing.
👥 読者別の含意
🔬研究者:Provides a framework combining LLMs and game theory for analyzing energy transition policy, which can be extended to other regions and stakeholder configurations.
🏢実務担当者:Offers insights into key factors (e.g., electricity price fluctuations, penalty mechanisms) that affect collaborative grid-connection strategies for renewable energy firms.
🏛政策担当者:Highlights how LLMs can enhance policy modeling by capturing heterogeneous agent cognition, aiding in designing effective market-oriented reforms.
📄 Abstract（原文）
The newly-issued 2025 policy on deepening the market-oriented reform of new energy feed-in tariffs has exerted a profound impact on reshaping the development pattern of new energy industries, such as photovoltaic power. In this evolving context, collaborative grid-connection among photovoltaic power generation enterprises, power grid enterprises, and government agencies is crucial for enhancing the competitiveness of the new energy industry and achieving energy transition. This paper constructs a tripartite evolutionary game model to deeply explore the strategy selection and key influencing factors of each subject in the grid-connection process. It integrates large language models (LLMs) to analyze factors affecting strategy selection among different stakeholders and utilizes LLMs to capture the heterogeneous cognitive characteristics of different subjects, thereby overcoming the limitations of "strong assumptions" commonly found in traditional game models. Through multi-round semantic parsing, it identifies key influencing factors such as market-oriented electricity price fluctuations, technological innovation costs, and assessment penalty. Furthermore, based on the actual data of photovoltaic industry development in Jiangxi and Hubei Provinces, numerical simulations are employed to analyze the impact of key factors (e.g., market-oriented electricity price fluctuations) on the strategic choices of the three stakeholder parties under the new policy framework and verify the model's effectiveness. The study clarifies the critical thresholds affecting collaborative grid connection, providing a data-driven theoretical basis for the government to implement targeted policies and enterprises to optimize decision-making.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1051/wujns/2026313225/pdffirst seen 2026-07-17 04:50:02
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