PyPSA-Earth · Taiwan

Model data模型資料

What the model is built from, in full detail: the grid and its six regions, every power plant, where renewables can go and how much they could produce, and what each technology costs. These are the inputs of today's system (2013 weather) and of the MOEA 2030 and 2034 plans; results are on the model dashboard.模型的完整輸入資料：電網與六個區域、每座電廠、再生能源可設置的位置與可發電量，以及各技術的成本。資料涵蓋現況系統（2013 年氣象）與經濟部 2030、2034 年規劃；模擬結果請見模型儀表板。

Where the inputs come from輸入資料的來源

This fork runs PyPSA-Earth, an open model with its own global data. Some inputs are PyPSA-Earth's defaults, some are Taiwan data this fork adds, and some are other sources or settings added here. Every source, cost row and setting on this page carries one of these tags:本分支執行開源模型 PyPSA-Earth，它有自己的全球資料。部分輸入沿用 PyPSA-Earth 預設，部分為本分支加入的台灣資料，另有部分為本分支加入的其他來源或設定。本頁每個來源、成本項目與設定都標示以下其中一種：

Changes to PyPSA-Earth itself (code and data files) are listed in notes/DATA_PROVENANCE.md.對 PyPSA-Earth 本身（程式與資料檔）的修改列於 notes/DATA_PROVENANCE.md。

Grid and regions電網與區域 PyPSA-Earth defaultPyPSA-Earth 預設

The six regions六個區域

Each region is one bus in the model; its substations are merged. Named after the two largest county seats inside it.每個區域在模型中為一個節點，區內變電所合併。以區內人口最多的兩個縣市治所命名。

Links between regions區域間輸電

Demand: the GEGIS hourly profile for 2030 (SSP2-2.6, 2013 weather), scaled by 0.749 so that the year matches Taipower-system generation in 2024 (251.4 TWh; this total is still to confirm, see data/README.md), and split between regions by population and GDP (PyPSA-Earth's build_demand_profiles).需求：GEGIS 2030 年逐時曲線（SSP2-2.6、2013 年氣象），乘以 0.749 使全年總量符合 2024 年台電系統發電量（251.4 TWh；此總量仍待確認，見 data/README.md），並依人口與 GDP 分配至各區（PyPSA-Earth 的 build_demand_profiles）。

Power plants電廠 Taiwan data台灣資料

Where the plants are電廠位置

Marker area proportional to capacity圓點面積與裝置容量成正比

Capacity by region各區裝置容量

GW, plants in the list (renewables in the plant list are the large registered sites only)GW，列表中的電廠（清單中的再生能源僅為大型登記場址）

From the model's power plant files, built by data/build_custom_powerplants.py (today) and build_future_powerplants.py (plans); method and checks in data/README.md. The Sources column gives each plant's sources for capacity, location and commissioning year. Regions are assigned by location.資料取自模型的電廠檔，由 data/build_custom_powerplants.py（現況）與 build_future_powerplants.py（規劃）產生；方法與檢核見 data/README.md。「來源」欄列出每座電廠容量、位置與商轉年的來源。區域依位置指定。

Renewable potential再生能源潛力 PyPSA-Earth defaultPyPSA-Earth 預設 Added in this fork本分支新增

By region各區域

Capacity factor by month各月容量因數

Average output per MW installed, 2013 weather每 MW 裝置的平均出力，2013 年氣象

How the potential is computed潛力的計算方式

Technology costs技術成本 PyPSA-Earth defaultPyPSA-Earth 預設 Added in this fork本分支新增

Cost of electricity by technology各技術發電成本

Investment cost over time投資成本變化

€ per kW, as used in the model for each cost year每 kW 歐元，模型各成本年份所用數值

PyPSA's cost data compared with Taiwan'sPyPSA 成本資料與台灣資料比較 PyPSA-Earth defaultPyPSA-Earth 預設 Taiwan data台灣資料

The model uses PyPSA's technology-data, mostly European (Danish Energy Agency) figures. Taiwan publishes its own: the Ministry of Economic Affairs sets feed-in tariffs from an installed cost, an O&M share, an annual output and a cost of capital for each technology, and Taipower reports the actual cost of each source. Below, the Taiwan figures sit next to the PyPSA rows of cost year .模型使用 PyPSA 的 technology-data，多為歐洲（丹麥能源署）數據。台灣有自己的資料：經濟部依各技術的期初設置成本、運維比例、年售電量與平均資金成本率訂定躉購費率；台電公布各種發電方式的實際成本。以下將台灣數據與 PyPSA 年成本並列比較。

Installed cost: Taiwan vs PyPSA設置成本：台灣與 PyPSA

€ per kW. Taiwan: feed-in tariff parameters (2026; offshore wind 2023)每 kW 歐元。台灣：躉購費率參數（2026 年；離岸風電 2023 年）

Cost per MWh: three views每 MWh 成本：三種觀點