高再生能源佔比下電動車充電站規劃對電網穩定性之研究

Abstract

隨著科技不斷發展，許多國家逐漸重視環保的議題，並希望未來可以朝向電氣化運輸的方向前進，許多政策開始透過限制傳統燃油車的產量來走向普及電動車(Electric Vehicle, EV)，然而比起傳統燃油車，電動車的車主更擔心車輛沒有足夠的續航以抵達其目的地，這項問題被稱為里程焦慮(Range Anxiety)。 為了解決這項問題，電網需要廣泛設置充電基礎設施來緩解車主的不安，然而，隨意地設置充電站勢必會對電網帶來負面影響，包括功率損失、電壓下降、三相不平衡率上升等問題。另一方面，隨著未來再生能源的持續發展，再生能源取代傳統燃油機組的趨勢也勢在必行，再生能源的節能減碳的同時，發電間歇性與不確定性會導致的電壓波動以及供電不穩，這些問題將會影響整個系統的穩定性以及可靠性。 本篇論文的主旨就是在未來高再生能源占比導致系統穩定度降低，在不影響電網穩定度的前提下，透過最佳化充電樁設置提供足夠的充電樁供電動車用戶使用。由於電動車資料的公開資料現在仍然相當缺乏，因此我們使用蒙地卡羅方法(Monte Carlo Method)來預測並統計電動車的負載資料，並將此資料當作模擬的基礎來計算所需的充電站負載量，之後定義我們的目標函數，透過粒子群演算法(Particle Swarm Optimization, PSO)來搜尋充電樁適合的設置地點。另一方面，本研究也提出一套改良的PSO演算法與傳統PSO進行比較，最後分析不同再生能源滲透率的前提下，研究充電樁設置策略對電網的影響。

With the continuous development of technology, many countries are gradually paying attention to environmental issues, and hope to move towards electrified transportation in the future. Many policies are beginning to popularize electric vehicles (EV) by restricting the production of pure gasoline vehicles. However, compared to traditional gasoline vehicles, electric vehicle owners are more worried that their vehicles do not have enough range to reach their destinations. This problem is known as Range Anxiety. To solve this problem, the power grid needs to extensively set up charging infrastructure to alleviate the anxiety of vehicle owners. However, arbitrarily setting up charging stations will inevitably have a negative impact on the power grid, including power loss, voltage drop, and an increase in the rate of three-phase imbalance. On the other hand, with the continuous development of renewable energy in the future, the trend of renewable energy replacing traditional fuel power units is also imperative. While renewable energy is energy-saving and carbon-reducing, the intermittency and uncertainty of power generation can lead to voltage fluctuations and unstable power supply. These issues will affect the stability and reliability of the entire system. The main purpose of this paper is to provide sufficient charger for electric vehicle users by optimizing the setup of charger, under the premise of not affecting the stability of the power grid in a future where a high proportion of renewable energy will reduce system stability. Since the public data of electric vehicles is still quite lacking, we use the Monte Carlo Method to predict and compile the load data of electric vehicles, and use this data as the basis for simulation to calculate the required load capacity of charging stations. We then define our objective function, and use the Particle Swarm Optimization (PSO) algorithm to search for suitable locations for the charging piles. On the other hand, this study also proposes an improved PSO algorithm and compares it with the traditional PSO. Finally, under the premise of different renewable energy penetration rates, we study the impact of charging pile setup strategies on the power grid.