The application of green innovation to technological innovation in building a low-carbon economic system
低炭素経済システム構築における技術革新へのグリーンイノベーションの応用 (AI 翻訳)
Yuxin Xing
🤖 gxceed AI 要約
日本語
本論文は、低炭素経済に向けたスマート交通、エネルギー、廃棄物管理の応用を分析し、二層のADNシナリオ計画モデルを提案。カッコウ探索アルゴリズムと内点法を用いて解決し、排出コストを40.44%削減。省Xの風力・太陽光・蓄電開発において、太陽光普及率と発電容量を迅速に向上させることを実証。
English
This paper analyzes smart transportation, energy, and waste management for a low-carbon economy, proposing a dual-layer ADN scenario planning model solved by Cuckoo Search and interior point method. It reduces emission costs by 40.44% compared to baseline models, and demonstrates rapid increases in photovoltaic penetration and capacity in Province X, offering a green innovation approach for regional low-carbon energy transition.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
In the global GX context
This paper presents a modeling framework for active distribution network planning that integrates renewable energy and energy storage, showing significant cost reductions. It contributes to the global literature on low-carbon energy system optimization, though its case study is specific to a Chinese province.
👥 読者別の含意
🔬研究者:Researchers in energy system optimization and low-carbon planning can leverage the dual-layer ADN model and its algorithmic solution for similar studies.
🏢実務担当者:Energy utilities and planners can use the proposed model for cost-effective integration of renewables and storage.
🏛政策担当者:Policymakers can note the potential for rapid photovoltaic deployment and cost reduction through advanced planning models.
📄 Abstract（原文）
The path to a low-carbon economy is an essential route for cities to become smart. This paper first analyzes the relevant applications of smart transportation, smart energy, and smart waste management systems within the low-carbon economy. Addressing the challenge of low energy efficiency utilization in cities, this paper proposes a dual-layer ADN scenario planning model to promote efficient energy use. It first establishes a fundamental framework for ADN planning within a low-carbon economic system. Recognizing the nonlinear dual-layer mixed-integer programming characteristics of the ADN planning model, it employs the Cuckoo Search algorithm—known for its strong global search capability—and the fast, efficient primal-dual interior point method to solve the upper and lower sub-layers of the model, respectively. Experimental results demonstrate that compared to models like PWL-MILP and SO-WGA, the ADN planning model reduces emission costs by 40.44% in both active distribution network operation and DHN independent operation scenarios, yielding resource allocation schemes with the lowest total configuration costs. In the development of wind power, photovoltaics, and energy storage in Province X, the AND planning model can rapidly increase photovoltaic penetration rates and power generation capacity while reducing photovoltaic costs, providing a feasible green innovation approach for regional low-carbon energy transition.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.65102/is2026386first seen 2026-05-17 05:38:01
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