Optimization of environmental benefits and mathematical modeling of photovoltaic-coal hybrid power generation system in low-carbon environment
低炭素環境下における太陽光・石炭ハイブリッド発電システムの環境便益最適化と数学的モデリング (AI 翻訳)
Ying Liu
🤖 gxceed AI 要約
日本語
本論文は、太陽光発電と石炭発電を組み合わせたハイブリッドシステムの環境便益を最適化するため、粒子群最適化（PSO）アルゴリズムを改良したモデルを提案。システム同定とモデル縮約により両発電モデルを統合し、慣性重みを導入した改良PSOが標準PSOよりも優れた収束率（最大99.21%）を示した。最適化により環境便益が向上し、ハイブリッドシステムのグリーン持続可能な発展に寄与する。
English
This paper proposes an optimization model for environmental benefits of a photovoltaic-coal hybrid power generation system using an improved particle swarm optimization (PSO) algorithm. The hybrid system integrates PV and coal models via system identification and model reduction. The improved PSO with inertia weight achieves convergence rates up to 99.21%, outperforming standard PSO, and demonstrates significant environmental benefit optimization, guiding green sustainable development.
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
日本でも石炭火力の段階的削減と太陽光の大量導入が進む中、ハイブリッドシステムの最適化は既存インフラ活用とCO2削減の両立に示唆を与える。ただし、日本の政策文脈（GX基本方針等）への直接的な言及はなく、汎用的なモデル研究として位置づけられる。
In the global GX context
As many countries seek to balance coal phase-down with renewable integration, this hybrid optimization model offers a methodological framework for maximizing environmental benefits. While not explicitly tied to global disclosure standards (e.g., TCFD, ISSB), it provides quantitative approaches relevant to energy transition planning and asset optimization in coal-dependent regions.
👥 読者別の含意
🔬研究者:Provides a novel improved PSO algorithm with application to hybrid energy system optimization; useful for researchers in energy systems modeling.
🏢実務担当者:Offers a practical optimization tool for designing PV-coal hybrid plants to reduce emissions while maintaining generation stability.
📄 Abstract（原文）
The mathematical modeling of the coal power generation model is completed by combining the relevant information in the field of coal power generation and thermal power generation, followed by the mathematical modeling of the photovoltaic power generation model by using the physical properties of the junction, and the photovoltaic grid-connected inverter control principle, so that the photovoltaic power generation model maintains a stable performance performance. On this basis, the PV power generation model is combined with the coal power generation model using a combination of system identification and model downscaling to obtain a hybrid PV-coal power generation system. The improvement of the standard particle swarm optimization algorithm is achieved by introducing the inertia weight way, and the solution process of the improved particle swarm optimization algorithm is supplemented, and finally an optimization model of the environmental benefits of the PV-coal hybrid power generation system based on the improved particle swarm optimization algorithm is designed. On the three test functions, the improved particle swarm algorithm outperforms the standard PSO algorithm with convergence rates of 99.21%, 97.57% and 92.68%, i.e., it verifies that the inertia weights improve the standard PSO algorithm. In addition the optimal solutions of the three objective functions are 2,077,000 yuan, 23,910,000 yuan and 235,310,000 yuan, i.e., to demonstrate the application effect of the environmental benefit optimization model of PV-coal hybrid power generation system with improved particle swarm optimization algorithm, which is of guiding value for the green sustainable development of PV-coal hybrid power generation system.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.65102/is2026635first seen 2026-05-17 06:06:35 · last seen 2026-05-20 05:10:26
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