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專利授權區
專利名稱(中)	利用多目標強化學習之能源管理裝置及方法
專利名稱(英)	Device and method of energy management using multi-object reinforcement learning
專利家族	中華民國：I762128
專利權人	國立清華大學 100.00%
發明人	邱偉育,陳頌仁
技術領域	能源科技,資訊工程

專利摘要(中)
本發明主要揭示一種利用多目標強化學習之能源管理裝置，其耦接於至少一可再生能源裝置、一能源儲存裝置和複數個耗能裝置之間，且具有一第一強化學習單元以及一第二強化學習單元。其中，所述第一強化學習單元依據用戶給定的裝置運作需求(如裝置運作時間、功率)與電力公司之實時電價而執行強化學習運算以獲得包括複數個裝置控制參數的一耗能裝置運作排程。另一方面，所述第二強化學習單元依據電力公司之實時電價與能源儲存裝置之狀態參數而執行強化學習運算以獲得該能源儲存裝置的一充/放電運作排程。最終，所述能源管理裝置依據一耗能裝置運作排程控制複數個所述耗能裝置分時運作，且依據一充/放電運作排程控制能源儲存裝置之充/放電，從而在用電成本最小化的情況下使得用戶的用電滿意度能夠最大化。

專利摘要(中)

本發明主要揭示一種利用多目標強化學習之能源管理裝置，其耦接於至少一可再生能源裝置、一能源儲存裝置和複數個耗能裝置之間，且具有一第一強化學習單元以及一第二強化學習單元。其中，所述第一強化學習單元依據用戶給定的裝置運作需求(如裝置運作時間、功率)與電力公司之實時電價而執行強化學習運算以獲得包括複數個裝置控制參數的一耗能裝置運作排程。另一方面，所述第二強化學習單元依據電力公司之實時電價與能源儲存裝置之狀態參數而執行強化學習運算以獲得該能源儲存裝置的一充/放電運作排程。最終，所述能源管理裝置依據一耗能裝置運作排程控制複數個所述耗能裝置分時運作，且依據一充/放電運作排程控制能源儲存裝置之充/放電，從而在用電成本最小化的情況下使得用戶的用電滿意度能夠最大化。

專利摘要(英)
The present invention discloses a device of energy management system using multi-object reinforcement learning, which is coupled with a renewable energy device, an energy storage device and a plurality of energy using appliances. According to the present invention, the device is provided a first RL unit and a second RL device therein, wherein the first RL unit is configured for confucting a first reinforcement learning based on an electricity tariff provided by a power utility and a demand request provided by a user, so as to generate a first scheduling policy for controlling the energy using appliances. On the other hand, the second RL unit is configured for confucting a second reinforcement learning based on the electricity tariff and at least one state parameter of the energy storage device, so as to generate a second scheduling policy for controlling the charing process and the discharing process of the energy storage device. Consequently, level of the user’s satisfaction therefore rises in case of the electricity cost being significantly lowered.

專利摘要(英)

The present invention discloses a device of energy management system using multi-object reinforcement learning, which is coupled with a renewable energy device, an energy storage device and a plurality of energy using appliances. According to the present invention, the device is provided a first RL unit and a second RL device therein, wherein the first RL unit is configured for confucting a first reinforcement learning based on an electricity tariff provided by a power utility and a demand request provided by a user, so as to generate a first scheduling policy for controlling the energy using appliances. On the other hand, the second RL unit is configured for confucting a second reinforcement learning based on the electricity tariff and at least one state parameter of the energy storage device, so as to generate a second scheduling policy for controlling the charing process and the discharing process of the energy storage device. Consequently, level of the user’s satisfaction therefore rises in case of the electricity cost being significantly lowered.

聯絡資訊
承辦人姓名	李曉琪
承辦人電話	03-5715131 #31061
承辦人Email	hsiaochi@mx.nthu.edu.tw