許多國家已經著手規劃淨零碳排的目標，因而帶起全球的電動車銷售浪潮，電動車的充電需求也日益增加。對電網來說，不穩定的電動車充電行為將造成能源在空間和時間上的供需差異擴大，進而影響電力供應上的安全性。而對於電動車使用者來說，大量不平衡的電動車使用充電站會讓熱門區域人潮壅擠、增加排隊等待的時間，充電站的用電也容易因此超出契約容量導致需要支付高昂的電力成本。因此我們提出 MISP 框架解決以上問題，這個框架設計為日前規劃排程的方式以避免排隊現象的發生，計算時考慮空間以及時間上的影響並結合 incentive 獎勵機制做個人化的充電推薦。這篇論文在實驗中利用開源資料集模擬實際情境，並且將我們提出的方法與其他多種基礎演算法比較，結果顯示 MISP 框架可以在顧及使用者的充電偏好下，又能夠最小化每個時刻充電站之間的充電位使用率變異度以維持供電上的平衡，且對於充電站來說收益也都有增加。

Many countries are planning to achieve net zero emissions, which will contribute to the growth of global EV sales. Meanwhile, it will increase the demand for EV charging. For the power grid, the unstable charging behavior of EVs stretches the spatial and temporal gap between energy supply and demand, compromising the security of the power supply. Furthermore, the large number of EVs using charging stations can cause congestion and waiting for a charger in popular areas. Uneven utilization of parking slots causes popular charging stations to exceed their contract capacity, which will pay for expensive electric bills. Therefore, we propose a framework to deal with the above problems called MISP. This framework is designed to avoid queue by day-ahead scheduling, considering the effect on the system under the spatial and temporal factors when making charging recommendations, and combing it with an incentive mechanism to achieve the personalized goal. This thesis uses the open dataset to simulate real-world situations and compares our proposed method with other basic algorithms. As a result of the experiment, we demonstrate that the MISP framework can minimize the variation of parking slots utilization among charging stations for each time period to achieve load balancing while considering users' charging preferences. In addition, the revenue of each charging station is increasing.

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