由於近年環保意識抬頭，電車取代 傳統燃油車輛漸成國際主流趨勢。工廠及商辦大樓 占 我國用電大戶比例 最多，在政府政策及國際區 趨勢 促使下，自設再生能源及充電樁之需求日益提升，然而導入分散式電力資源將對用戶之用電環境產生新的影響與衝擊，需要妥善實施系統規劃並導入電能管理系統 ，以促使降低投資成本與操作成本 並 穩定電力品質 。
本文主要以國內某大型三班制 工廠 為案例，使用 雙層之最佳化 規劃 方法。考慮快慢充電樁數量 、儲能系統與契約容量規劃 此外 亦 考慮工廠基本生產負載、多種的電動車類型、電動車 不確定性、 太陽能不確定性 及實際需量競價之得標情況。最佳化方法之 外層使用 差分進化 演算法求出最小 規劃 成本，於內層使用 混合整數線性規劃演算法(MILP)並 搭配三段式時間電價求出最小管理成本。結果顯示 雖然目前需量反應之結清價較低，導致總成本無法有效降低，但隨著智慧電網與電車取代燃油車的快速發展下，用電大戶場域規劃建置再生能源有助於降低用電成本以及參與多種輔助服務套利。本文所提出之 相關內容未來可供工廠或電動車充電站營運商作為系統規劃參考 。

In recent years, the governments around the world launched the policy to replace traditional generators by renewable energy and replace internal combustion engine vehicles (ICEVs) by electric vehicles (EVs) due to the increasing the awareness of environmental protection. And then, it is the aim of Net Zero Carbon Emissions (NZCE). In us country, the energy-heavy customers mainly include factories and commercial buildings. In order to encourage the customers to build the renewable energy, the government promote the policy is energy-heavy terms. The content in energy-heavy terms shows that the customers will build the 10% of contracted capacity before 2025. All of above, how to build the renewable energy is very important for each energy-heavy customer. Besides, building the charging points (CPs) also must be consider in renewable energy. If EVs aggregate a lot and stay for long time, sum of EV can be regard as energy storage systems (ESSs). EVs are not only the characteristic of shedding load within ESSs, they are the characteristic of mobility.
In this study, we proposed the two-tier planning and management. We consider as the number of workday and holiday for EVs, uncertainty with EVs, uncertainty with photovoltaics (PVs), replacement for battery of ESS, interest rate and the rate of descent with battery of ESSs. First tier is to optimize the minimal total cost by differential evolution (DE), and second tier is to optimize the minimal operating cost by mixed-integer linear programming (MILP).
In simulated resulted and analysis, we assume the clearing price as bidding price and compare four cases include participation with or without DR, different CP`s cost, different ESS`s battery cost and changing the bidding price in the future. In comparison of participation with or without DR, they show no participation in DR is the best because the clearing prices are quite lower. In comparison of different CP`s cost, they show that it replaces ESSs with DC CPs when the cost with CPs is 20% discount and over. In comparison of different ESS`s battery cost, they show that it expands the more capacity with ESSs when the cost with battery of ESSs is 20% discount and over. In comparison of changing the bidding price in the future, they show that the net income increases when bidding prices increase.

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