本文選題：電動汽車充換儲放電站 + 光伏發(fā)電預(yù)測�。� 參考：《華中科技大學(xué)》2014年博士論文

【摘要】：由于全世界都面臨著環(huán)境和能源危機,可再生能源和電動汽車受到了世界各國的高度重視。電動汽車充電基礎(chǔ)設(shè)施影響了電動汽車推廣和發(fā)展,多能源復(fù)合型電動汽車充換儲放電站(Electric Vehicle Battery Charge-Swap-Storge-Discharge Power Station, EV-BCSSDPS)作為重要的充電基礎(chǔ)設(shè)施,它將充電站、換電站和儲能電站的功能融合到一起。多能源復(fù)合型EV-BCSSDPS不僅可為電動汽車提供快速便捷的換電服務(wù),還可為電動汽車提供清潔的充電能源,另外還可利用梯次儲能作為后備電源。隨著電池技術(shù)的進一步發(fā)展和智能電網(wǎng)的建設(shè),EV-BCSSDPS作為智能電網(wǎng)的組成部分,合理利用其儲能特性將對平抑電網(wǎng)負荷波動、接納間歇性可再生能源及提高電網(wǎng)運行效率起到重大作用。本文以多能源復(fù)合的EV-BCSSDPS為對象,對其能量預(yù)測和能量管理的基本分析理論和設(shè)計方法進行了深入研究,包括有光伏發(fā)電預(yù)測、充電負荷需求預(yù)測、經(jīng)濟優(yōu)化運行管理和成本收益分析。 準(zhǔn)確的預(yù)知光伏系統(tǒng)的輸出功率,對EV-BCSSDPS在未來時段內(nèi)動力電池、梯次儲能的充放電及與電網(wǎng)交易有著非常重要的意義。針對分布式發(fā)電的隨機性問題,本文建立了光伏系統(tǒng)短期發(fā)電預(yù)測系統(tǒng)框架。首先從理論和數(shù)據(jù)上分析了氣象因素與光伏發(fā)電量之間的相關(guān)性,并給出了基于距離分析方法的相關(guān)性計算準(zhǔn)則,考慮了國內(nèi)太陽輻射站點稀少且預(yù)報能力較低的特點,確定了以氣溫和濕度作為基于神經(jīng)網(wǎng)絡(luò)預(yù)測模型的輸入因子。并給出了含隱含層節(jié)點的網(wǎng)絡(luò)結(jié)構(gòu)設(shè)計方法,建立了基于反傳播(Back Propagation, BP)神經(jīng)網(wǎng)絡(luò)的短期無輻照度輸出功率的預(yù)報模型,給出了定量評估模型精度的準(zhǔn)則。此外為增強模型對天氣突變的適應(yīng)能力,由云量預(yù)報信息對天氣類型聚類識別,采用自組織特征映射(Self-organizing Feature Map, SOM)方法聚類天氣類型,繼而對各天氣類型采用相應(yīng)的預(yù)測網(wǎng)絡(luò),避免單神經(jīng)網(wǎng)絡(luò)的過擬合問題。 針對電動汽車換電需求的隨機性問題,準(zhǔn)確預(yù)知電動汽車的換電及充電需求,開展EV-BCSSDPS充電負荷特性研究對于EV-BCSSDPS內(nèi)動力電池的有序、經(jīng)濟充電,電網(wǎng)安全運行,站內(nèi)其它微電源和電網(wǎng)的經(jīng)濟調(diào)度等都有重要意義。本文分析了某BCSSDPS的運營基礎(chǔ)數(shù)據(jù),利用BP及RBF神經(jīng)網(wǎng)絡(luò)和隨機建模方法建立了逐小時換電車輛數(shù)模型,基于此建立了電池箱充電起始時刻模型；此外,提出利用行駛里程作為電池充電量需求的度量標(biāo)準(zhǔn),分析了車輛行駛里程多樣性的特征,建立了基于高斯混合模型(Gaussian Mixture Model, GMM)的行駛里程模型,間接得出電池初始荷電狀態(tài)(Initial State-of-Charge, SOC0);另外,電池與充電機的特性決定了電池充電功率與充電時長。因此綜合考慮上述因素,建立了EV-BCSSDPS的充電負荷模型,并給出充電負荷功率計算及預(yù)測流程,并編寫了充電負荷預(yù)報軟件,實現(xiàn)了EV-BCSSDPS充電負荷需求的預(yù)報功能。最后基于非參數(shù)核密度估計方法分析了電動汽車充電負荷預(yù)報的不確定性。 針對光伏出力和電動汽車充換電需求的隨機性會對EV-BCSSDPS運營帶來不利的影響,本文建立了多能源復(fù)合型EV-BCSSDPS的能量管理模型和經(jīng)濟化調(diào)度策略。本文對EV-BCSSDPS內(nèi)微電源如動力電池、梯次儲能、光伏、非充電負荷等進行了分析和建模,優(yōu)化模型中充分考慮了電動汽車換電需求、動力電池充電需求、電池均衡使用約束、功率平衡約束、梯次儲能功率約束等,并在光伏發(fā)電預(yù)報和電動汽車充換電需求預(yù)報的基礎(chǔ)上,建立了涵蓋充電成本最小、系統(tǒng)負荷波動最小及兼顧二者的EV-BCSSDPS能量調(diào)度優(yōu)化模型。所建立的混合整數(shù)規(guī)劃和二次規(guī)劃模型可利用CPLEX求解,可給出包括每組卸載動力電池、梯次儲能、電網(wǎng)、光伏內(nèi)的出力組合。將仿真結(jié)果與無序充電方案對比,定量評估了不同優(yōu)化策略的能量管理模型對充電成本和系統(tǒng)負荷波動的影響。由于實際運行中,充換電需求預(yù)報會有誤差,本文也定量評估了換電需求預(yù)報誤差對EV-BCSSDPS優(yōu)化運行的影響。 EV-BCSSDPS是我國電動汽車應(yīng)用的重要充電基礎(chǔ)設(shè)施之一,得到了廣泛關(guān)注和示范推廣。本文探討了EV-BCSSDPS的組成結(jié)構(gòu)和運營模式,選擇以電池租賃運營模式的EV-BCSSDPS為研究對象,考慮了其投資成本、運營以及維護費用、人工薪酬等成本及充換電服務(wù)等收益,并給出評估EV-BCSSDPS成本效益模型的評價指標(biāo)。建立了基于GUI的EV-BCSSDPS全壽命周期成本收益分析軟件,并對該模型進行敏感性分析,得出影響EV-BCSSDPS收益的關(guān)鍵因素序列。該模型及分析結(jié)果為EV-BCSSDPS商業(yè)化運行提供了一些評估依據(jù)。
[Abstract]:As the world is facing the environmental and energy crisis, renewable and electric vehicles have been highly valued by all countries in the world. The electric vehicle charging infrastructure has affected the promotion and development of electric vehicles, and the Electric Vehicle Battery Charge-Swap-Storge-Discharge Power Station EV-BCSSDPS), as an important charging infrastructure, combines the functions of the charging station, the power station and the energy storage power station. The multi energy composite EV-BCSSDPS can not only provide fast and convenient switching services for electric vehicles, but also provide a clean charging energy source for electric vehicles. In addition, the cascade energy storage can be used as a backup power supply. With the further development of battery technology and the construction of smart grid, EV-BCSSDPS as a component of the smart grid, the rational use of its energy storage characteristics will play an important role in reducing the load fluctuation of the power grid, accepting intermittent renewable energy and improving the efficiency of the power grid operation. The basic analysis theory and design method of measurement and energy management are studied in depth, including photovoltaic power generation forecast, charge load demand forecast, economic optimization operation management and cost income analysis.
The accurate prediction of the output power of the photovoltaic system is of great significance to the charging and discharging of the power battery, the cascade energy storage and the power grid transaction in the future period of time. Aiming at the randomness of the distributed generation, the framework of the short-term generation forecasting system for the photovoltaic system is set up in this paper. First, the meteorology is analyzed in theory and data from the theory and data. The correlation between the factors and the photovoltaic power generation is given, and the correlation calculation criterion based on the distance analysis method is given. Considering the rare and low prediction ability of the domestic solar radiation stations, the temperature and humidity as the input factor based on the neural network prediction model are determined, and the network structure containing the hidden layer nodes is given. The prediction model of short term irradiance output power based on Back Propagation (BP) neural network is established, and the criterion for quantitative evaluation of the model accuracy is given. In addition, the adaptability of the model to weather mutation, the clustering recognition of cloudiness forecast information to the sky gas type, and the self organizing feature mapping (Self-or) are used. Ganizing Feature Map (SOM) method clustering weather types, and then adopt corresponding prediction network for different weather types to avoid over fitting of single neural network.
In view of the randomness of the demand for electric vehicles, it is accurate to predict the demand for electric vehicles switching and charging. It is of great significance to carry out the study of the EV-BCSSDPS charging load characteristics for the order of the power battery in the EV-BCSSDPS, the economic charging, the safe operation of the power grid, the other micro power supply and the economic dispatch of the power grid in the station. This paper analyzes a BCS SDPS's basic operation data, using BP and RBF neural network and random modeling method to establish an hour Model for the number of tramcars. Based on this, the starting time model of battery tank charging is established. In addition, the characteristics of the range diversity of vehicle driving range are analyzed by using the driving mileage as the measurement standard of the battery charge demand. The initial charge state of the battery (Initial State-of-Charge, SOC0) is indirectly obtained by the driving mileage model of the Gauss Mixture Model (GMM Model, GMM). In addition, the characteristics of the battery and the charger determine the battery charging power and the length of the charge. Therefore, the charging load model of EV-BCSSDPS is established, and the charge load model of EV-BCSSDPS is established, and the charge load model of EV-BCSSDPS is established. The charging load power calculation and prediction process are calculated, and the charging load forecasting software is written. The forecast function of the EV-BCSSDPS charging load demand is realized. Finally, the uncertainty of the electric vehicle charging load forecasting is analyzed based on the non parameter kernel density estimation method.
In this paper, the energy management model of multi energy complex EV-BCSSDPS and the economic scheduling strategy are established in this paper. This paper analyzes and builds the micro power supply in EV-BCSSDPS, such as power battery, cascade energy storage, photovoltaic, non charge load, etc. in this paper, aiming at the adverse effects of the photovoltaic power and the randomness of the electric vehicle charging and switching demand on the operation of EV-BCSSDPS. In the model, the optimization model takes full account of the electric vehicle switching demand, the power battery charging demand, the battery balance constraint, the power balance constraint, the cascade energy storage power constraints and so on. On the basis of the photovoltaic power forecast and the electric vehicle replacement demand forecast, the minimum charge cost, the minimum load fluctuation of the system and the balance of two are established. The EV-BCSSDPS energy scheduling optimization model. The mixed integer programming and the two programming model can be solved by CPLEX. The combination of power battery, cascade energy storage, power grid and PV can be given. The simulation results are compared with the disordered charging scheme, and the energy management model of different optimization strategies is evaluated by the fixed quantity. The effect of charging cost and system load fluctuation. Due to the error of the demand forecast for charge transfer in actual operation, this paper also quantitatively evaluates the influence of the demand forecast error on the optimized operation of EV-BCSSDPS.
EV-BCSSDPS is one of the important charging infrastructure for electric vehicles in China, and it has received extensive attention and demonstration and promotion. This paper discusses the structure and operation mode of EV-BCSSDPS, chooses the EV-BCSSDPS of battery leasing operation mode as the research object, and takes into account the cost of investment, operation and maintenance, artificial compensation and so on. And the benefit of charge transfer service, and the evaluation index of EV-BCSSDPS cost benefit model is given. A GUI based EV-BCSSDPS full life cycle cost benefit analysis software is set up, and the sensitivity analysis of the model is carried out to get the key factors affecting the EV-BCSSDPS income. The model and the analysis result are commercial operation of EV-BCSSDPS. Some basis for evaluation is provided.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TM615;U469.72

【引證文獻】

相關(guān)碩士學(xué)位論文 前1條

1 鄒福強;電動汽車光伏充電站的在線能量管理方法[D];華北電力大學(xué)(北京);2016年

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本文編號：2072265