Analysis of Tripartite Evolutionary Game in Marketization of New Energy Electricity Prices Based on Large Language Models
大規模言語モデルに基づく新エネルギー電力価格市場化における三者進化ゲームの分析 (AI 翻訳)
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）を用いて、新エネルギー電力価格の市場化改革における太陽光発電企業、電力網企業、政府の三者間の戦略選択を分析する。進化ゲームモデルとLLMのセマンティック解析を組み合わせ、市場価格変動や技術コストなどの要因が協力的な系統連系に与える影響を明らかにする。中国の江蘇省と湖北省の実データに基づき、政策立案と企業意思決定に資する理論的基盤を提供する。
English
This paper uses large language models (LLMs) to analyze the strategic choices of photovoltaic generators, grid enterprises, and government in the marketization of new energy electricity prices. It combines an evolutionary game model with LLM semantic parsing to identify key factors like price fluctuations and technology costs affecting grid connection. Using real data from Jiangxi and Hubei, it provides theoretical support for policy design and corporate decision-making.
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
中国の新エネルギー政策とLLM活用事例として、日本の系統連系や再エネ導入促進策（FIP制度など）の比較材料になる。特に、複数主体の協力を促進する政策設計の知見は、日本の系統混雑対策や需給調整市場にも応用可能。
In the global GX context
This paper offers insights into using LLMs for multi-stakeholder analysis in renewable energy policy, relevant to global energy transition. The game-theoretic approach can inform grid integration strategies under market reforms, applicable to contexts like the EU's renewable directives or US state-level policies.
👥 読者別の含意
🔬研究者:Demonstrates a novel application of LLMs in game-theoretic analysis of energy policy, useful for researchers in AI for sustainability.
🏢実務担当者:Provides a framework for analyzing grid-connection strategies under market pricing, relevant for energy companies and utilities.
🏛政策担当者:Offers data-driven insights on policy thresholds for renewable grid integration.
📄 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/2026313225first seen 2026-06-26 05:17:24
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