【摘要】：在全球能源互聯(lián)網(wǎng)的大環(huán)境下,作為新能源、新材料、新方向、新技術(shù)等現(xiàn)代高科技技術(shù)綜合集成品的電動(dòng)汽車將在能源、交通、電力、通信等行業(yè)掀起巨大變革,這一變革或?qū)⒊蔀樾履茉锤锩闹匾苿?dòng)力量。各國為鼓勵(lì)電動(dòng)汽車行業(yè)的發(fā)展相繼出臺(tái)了大量優(yōu)惠政策。電動(dòng)汽車相關(guān)優(yōu)惠、支持政策的落地,勢(shì)必會(huì)帶動(dòng)電動(dòng)汽車行業(yè)迅速發(fā)展,所以充電站等基礎(chǔ)配套充電設(shè)施的發(fā)展和規(guī)劃是否科學(xué)、合理將成為影響電動(dòng)汽車行業(yè)能否平衡、快速發(fā)展的關(guān)鍵因素。而目前作為基礎(chǔ)配套設(shè)施的充電站建設(shè)存在著認(rèn)識(shí)不統(tǒng)一、政策配套不完善、協(xié)調(diào)推進(jìn)難度大、標(biāo)準(zhǔn)規(guī)范不健全等問題,嚴(yán)重制約著電動(dòng)汽車行業(yè)的規(guī)模型發(fā)展。為了更科學(xué)、規(guī)范化的建設(shè)充電站,本文在充分考慮電動(dòng)汽車與能源互聯(lián)網(wǎng)融合的基礎(chǔ)上針對(duì)目前充電站規(guī)劃存在的問題從理論和實(shí)踐意義論述了充電站科學(xué)優(yōu)化選址的必要性和緊迫性。首先從電網(wǎng)安全、經(jīng)濟(jì)發(fā)展、交通便利性、環(huán)境影響、技術(shù)發(fā)展和規(guī)劃協(xié)調(diào)性六個(gè)方面分析了影響充電站選址的因素,構(gòu)建了基于能源互聯(lián)網(wǎng)的充電站選址評(píng)價(jià)指標(biāo)體系并根據(jù)系統(tǒng)論的觀點(diǎn)從系統(tǒng)的角度探討了經(jīng)濟(jì)、交通、環(huán)境、技術(shù)、電網(wǎng)安全等各規(guī)劃因素之間的邏輯關(guān)系及相互作用,構(gòu)建了充電站選址DPSIR模型,并將各系統(tǒng)指標(biāo)分別劃分在驅(qū)動(dòng)力系統(tǒng)、壓力系統(tǒng)、狀態(tài)系統(tǒng)、影響系統(tǒng)和響應(yīng)系統(tǒng)五個(gè)不同的系統(tǒng)中,通過系統(tǒng)間的相互關(guān)聯(lián)作用分析了各要素之間的邏輯影響。其次,提出了基于遺傳算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的充電站選址模型并使用熵值法對(duì)各指標(biāo)的權(quán)重進(jìn)行了計(jì)算。再次,為避免傳統(tǒng)物元可拓模型出現(xiàn)指標(biāo)值超出節(jié)域?qū)е玛P(guān)聯(lián)函數(shù)無法計(jì)算的情況,利用改進(jìn)的物元可拓模型對(duì)充電站進(jìn)行了選址規(guī)劃。另外,本文基于遺傳變異改進(jìn)蟻群聚類算法的RBF神經(jīng)網(wǎng)絡(luò)對(duì)充電站進(jìn)行了選址評(píng)價(jià)。使用基于遺傳變異改進(jìn)的蟻群聚類算法來確定RBF神經(jīng)網(wǎng)絡(luò)隱含層的個(gè)數(shù),從而解決RBF神經(jīng)網(wǎng)絡(luò)初始參數(shù)沒有科學(xué)方法難以精確選取的問題并使用實(shí)例證明了此方法的科學(xué)性和有效性。最后比較了各方法的優(yōu)缺點(diǎn),為充電站選址規(guī)劃問題提供了新的求解方法和思路。
[Abstract]:In the global energy Internet environment, as new energy, new materials, new directions, new technologies and other modern high-tech integrated products of electric vehicles in energy, transportation, electricity, communications and other industries will bring a huge change. This change may become an important driving force of the new energy revolution. In order to encourage the development of electric vehicle industry, countries have introduced a large number of preferential policies. The development and planning of the basic charging facilities such as charging stations and other basic supporting facilities will inevitably lead to the rapid development of the electric vehicle industry. Whether the development and planning of the basic charging facilities, such as charging stations, is scientific or not, will affect the balance of the electric vehicle industry. Key factors for rapid development. At present, the construction of charging station as the basic supporting facilities has some problems, such as the lack of unity of understanding, the imperfect policy, the difficulty of coordination and promotion, the unsound standard and so on, which seriously restrict the scale development of the electric vehicle industry. In order to build a more scientific and standardized charging station, On the basis of considering the fusion of electric vehicle and energy internet, this paper discusses the necessity and urgency of the scientific optimization of charging station location from the theoretical and practical significance in view of the problems existing in the current charging station planning. First of all, the factors influencing the location of charging stations are analyzed from six aspects: power grid safety, economic development, convenience of transportation, environmental impact, technological development and coordination of planning. The evaluation index system of charging station location based on energy Internet is constructed, and the logical relationship and interaction among various planning factors, such as economy, traffic, environment, technology, power grid security, etc., are discussed from the viewpoint of system theory. The DPSIR model of charging station location is constructed, and the system indexes are divided into five different systems, namely, driving force system, pressure system, state system, influence system and response system. The logical influence of each element is analyzed by the interrelation between systems. Secondly, a charging station location model based on genetic algorithm to optimize BP neural network is proposed and the weight of each index is calculated by entropy method. Thirdly, in order to avoid the problem that the index value is beyond the domain and the correlation function can not be calculated in the traditional matter-element extension model, an improved matter-element extension model is used to plan the location of charging station. In addition, RBF neural network based on genetic variation improved ant colony clustering algorithm is used to evaluate the location of charging station. Ant colony clustering algorithm based on genetic variation is used to determine the number of hidden layers in RBF neural network. In order to solve the problem that the initial parameters of RBF neural network are difficult to select accurately without scientific method, the scientific and effective method is proved by an example. Finally, the advantages and disadvantages of each method are compared, which provides a new method and train of thought for the location planning of charging station.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18;F426.471;F426.61

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本文編號(hào)：2398533