石炭火力統合エネルギーシステムの炭素貯留と移行可能な電気負荷を伴う協調パラメータ最適化のためのデータ
Data for: Collaborative Parameter Optimization of Coal-Fired Integrated Energy System with Carbon Storage and Shiftable Electrical Load (原題)
沙文慧
🤖 gxceed AI 要約
日本語
本データセットは、350MWの亜臨界石炭火力ユニットに溶剤ベースの炭素貯留と移行可能な電気負荷を組み合わせ、時間帯別料金下での柔軟なスケジューリングを最適化するための入力データ、数値結果、ソースコードを提供する。PSOと限界費用反復法を用いた2層フレームワークにより、貯留規模と分単位の運用を決定する。中国北部の代表的な冬の平日の1分分解能データを含む。
English
This dataset provides input data, numerical results, and source code for flexible scheduling of a 350 MW subcritical coal-fired unit integrated with solvent-based carbon storage and shiftable electrical load under time-of-use tariffs. A two-layer framework using PSO and marginal-cost iterative dispatch optimizes storage sizing and minute-level operation. Data cover one representative winter weekday in North China at one-minute resolution.
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
日本の石炭火力はフェードアウト政策下にあるが、既存設備のCO2回収・貯留（CCS）や負荷調整は、移行期間中の安定供給と排出削減の両立に寄与し得る。本データは、日本の石炭火力の柔軟運用やCCS統合の技術的知見を提供する。
In the global GX context
Globally, this dataset contributes to the literature on integrating carbon capture with flexible coal-fired power generation, which is relevant for transition strategies in coal-dependent economies. The optimization framework and data can inform similar modeling efforts for CCS retrofits and demand-side flexibility.
👥 読者別の含意
🔬研究者:Provides a reproducible dataset and code for modeling coal-CCS integration and load shifting, useful for benchmarking optimization algorithms.
🏢実務担当者:Offers insights into operational flexibility and cost optimization for coal plants with carbon capture, potentially relevant for plant operators considering CCS retrofits.
📄 Abstract（原文）
This dataset provides the input data, numerical results, and source code for the flexible scheduling of a 350 MW subcritical coal-fired unit coupled with solvent-based carbon storage and a shiftable electrical load under a time-of-use tariff. Results were produced in Python (NumPy, Numba, pandas, openpyxl, SciPy) with a two-layer framework: an outer adapted particle swarm optimization (30 particles × 35 iterations, 30 independent runs per algorithm) sizes the storage, an inner marginal-cost iterative dispatch determines minute-level operation, and a grid search (10 MW / 10 min steps) optimizes load shifting; regressions and an independent verifier complete the analysis.The data cover one representative winter weekday at a uniform one-minute resolution (1440 records, Time_min = 1–1440); the only spatial object is the single unit in North China, with no geographic coordinates. In each time-series sheet, rows are minute indices and columns are named variables, with units given by suffix—power in MW, storage mass in tonnes (t), energy in MWh, cost in CNY, tariff in CNY/kWh, duration in min; IsValley/IsPeak/IsFlat are 0/1 period flags. There are no missing values (input AGC range 200.44–277.81 MW). The model is deterministic apart from PSO initialization (CV = 0.027% over 30 runs); fitted rules report R² and sample size n, and displayed values are rounded to two decimals.AGC_Load.xlsx is the sole input. The outputs correspond one-to-one to the manuscript figures/tables: 4.1 baseline economics; 4.2 PSO convergence and 30-run statistics (plus a .json record); 4.3 storage dispatch and power difference; Table3a penalty sensitivity; 4.4 load-shift surface (50×50 grid) and time series; 4.5 coal-price/tariff sensitivity; and 4.6 period/mode-separated correlations with fitted curves and R². Files are open .xlsx/.json/.py formats, readable in Excel, LibreOffice Calc (https://www.libreoffice.org/download/), or Python; code/run_all.py regenerates all outputs and verify/verify_final.py reproduces every reported value.
🔗 Provenance — このレコードを発見したソース
- scidb https://doi.org/10.57760/sciencedb.010a1first seen 2026-09-09 06:00:52 · last seen 2026-09-21 05:54:29
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