因應現今再生能源成長快速的背景下，電力供需平衡議題早已成是能源管理中的一個重要挑戰。若可以預先觀察不同電力情境下的供需平衡現象，將能及早提出相應情境的解決方案。
本文建置一應用儲能搭配火力機組執行削峰填谷之模擬的台灣電力即時資訊的數位孿生，使用Python之開源環境進行開發，藉由Flask建立網頁伺服器讓使用者於異地操作該數位孿生平台，經由網路爬蟲(Web Crawler)將即時電力資訊及氣象資料存儲至資料庫做為後續能源情境之模擬資料源。
本數位孿生係基於台電開放資料的裝置容量資訊設定再生能源成長率作及儲能容量作為情境變數，供研究者可藉由開平台實現不同情境的即時電力需量與經後台管理系統套入多元回歸預測得出情境淨負載的尖、離峰閥值，搭配儲能系統執行9個不同能源情境執行削峰填谷的模擬及成效指標功能。

In response to today's rapid growth of renewable energy, the issue of balancing power supply and demand has long become an important challenge in energy management. If the supply and demand balance phenomenon under different power scenarios can be observed, solutions to the corresponding scenarios will be proposed as early as possible.
This article builds a digital twin of Taiwan’s real-time electric power information that applies energy storage and thermal power units to simulate peak shaving and valley filling. It is developed using the Python open-source environment and uses Flask to create a web server to allow users to operate the digital twin remotely. The platform stores real-time power information and meteorological data in the database through a Web Crawler as a simulation data source for subsequent energy scenarios.
This digital twin sets the renewable energy growth rate and energy storage capacity as situational variables based on the device capacity information of Taipower's open data, allowing researchers to realize real-time power demand in different situations by opening the platform and integrating it into the backend management system. Multiple regression prediction is used to obtain the peak and off-peak thresholds of the scenario netload, and the energy storage system is used to perform the simulation and performance indicator functions of peak-shaving and valley-filling in nine different energy scenarios.

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