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專利授權區
專利名稱(中)	基於強化學習的充電站能源使用規劃方法及裝置
專利名稱(英)	METHOD AND APPARATUS FOR PLANNING ENERGY USAGE OF CHARGING STATION BASED ON REINFORCEMENT LEARNING
專利家族	中華民國：I767868
專利權人	國立清華大學 100%
發明人	江坤諺,邱偉育
技術領域	能源科技,資訊工程,電子電機

專利摘要(英)
A method and an apparatus for planning energy usage of charging station based on reinforcement learning are provided. In the method, multiple system states are defined by using a power demand and a remaining battery energy of a charging station itself, and a global power demand and an internal power price of an energy sharing area, and expected returns for arranging energy use actions under each system state are estimated to construct a reinforcement learning table. According to the reinforcement learning table, an energy use action adapted for a current system state is selected and uploaded to a coordinator device, and a trading electricity arranged by the coordinator device and a reward of adopting the power use action calculated by the coordinator device are used to update the reinforcement learning table. The current system state, the power use action, the reward and a number of times the system state being selected are recorded and used to generate a simulation environment, so as to calculate an overall benefit of arranging the power use action under each system state and accordingly update the reinforcement learning table.

專利摘要(英)

A method and an apparatus for planning energy usage of charging station based on reinforcement learning are provided. In the method, multiple system states are defined by using a power demand and a remaining battery energy of a charging station itself, and a global power demand and an internal power price of an energy sharing area, and expected returns for arranging energy use actions under each system state are estimated to construct a reinforcement learning table. According to the reinforcement learning table, an energy use action adapted for a current system state is selected and uploaded to a coordinator device, and a trading electricity arranged by the coordinator device and a reward of adopting the power use action calculated by the coordinator device are used to update the reinforcement learning table. The current system state, the power use action, the reward and a number of times the system state being selected are recorded and used to generate a simulation environment, so as to calculate an overall benefit of arranging the power use action under each system state and accordingly update the reinforcement learning table.

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