本文選題：主動配電網(wǎng) + 儲能系統(tǒng)�。� 參考：《南京理工大學(xué)》2017年碩士論文

【摘要】：隨著新能源發(fā)電技術(shù)的不斷發(fā)展和大規(guī)模應(yīng)用,在緩解傳統(tǒng)能源的同時造成了新能源利用率低、成本浪費(fèi)等一系列問題,如何針對主動配電網(wǎng)中提高分布式能源利用率、保證供電可靠性來對儲能系統(tǒng)進(jìn)行優(yōu)化配置是亟待解決的問題。本文針對主動配電網(wǎng)中分布式電源和儲能系統(tǒng)的容量配置問題進(jìn)行研究,主要進(jìn)行以下幾個方面的研究:首先,對分布式電源、可控能效負(fù)荷以及儲能系統(tǒng)進(jìn)行研究,建立了風(fēng)力發(fā)電和光伏發(fā)電系統(tǒng)的數(shù)學(xué)模型,建立了可控能效負(fù)荷包括空調(diào)、熱水器和照明負(fù)荷的能耗響應(yīng)數(shù)學(xué)模型以及儲能系統(tǒng)和逆變器的數(shù)學(xué)模型。針對需求側(cè)能效負(fù)荷管理,結(jié)合分時電價和用戶滿意度的評價指標(biāo)研究了單體用戶可控能效負(fù)荷優(yōu)化調(diào)度策略,為了對儲能系統(tǒng)進(jìn)行合理利用制定了儲能系統(tǒng)充放電管理策略。其次,為了選取典型的自然資源樣本,采用KMO和Bartlett球度檢驗選取相關(guān)性最佳的樣本數(shù)據(jù)用于主成分分析,并且對小波-BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型中的權(quán)值、伸縮因子和平移因子采用動量-自適應(yīng)學(xué)習(xí)速率進(jìn)行修正。針對多目標(biāo)分布式電源和儲能系統(tǒng)容量優(yōu)化配置問題,采用和聲搜索算法進(jìn)行求解,對于算法所存在的收斂性能和容易陷入局部最優(yōu)等問題進(jìn)行了改進(jìn),對搜索過程中的記憶保留概率、和聲微調(diào)幅度和微調(diào)擾動概率采用動態(tài)參數(shù)模式進(jìn)行更新調(diào)整,與遺傳算法進(jìn)行融合提高其收斂性能。而后,以夏季典型日和冬季典型日的風(fēng)速為例進(jìn)行短期風(fēng)功率預(yù)測,并與傳統(tǒng)BP神經(jīng)網(wǎng)絡(luò)預(yù)測和WNN網(wǎng)絡(luò)預(yù)測進(jìn)行對比,結(jié)果表明了基于主成分分析的改進(jìn)小波-BP神經(jīng)網(wǎng)絡(luò)預(yù)測方法的快速性和準(zhǔn)確性。在分布式發(fā)電預(yù)測的基礎(chǔ)上,以主動配電網(wǎng)中某地區(qū)夏季典型日和冬季典型日的風(fēng)速、太陽光照和負(fù)荷需求數(shù)據(jù)為例,以分布式電源和儲能系統(tǒng)的配置成本、新能源棄電率和負(fù)荷缺電率為多目標(biāo)進(jìn)行優(yōu)化配置,通過算例驗證了該模型和改進(jìn)的和聲搜索算法的正確性和有效性。最后,為了進(jìn)一步降低配置容量和成本,通過需求側(cè)能效負(fù)荷優(yōu)化管理對分布式電源和儲能進(jìn)行容量優(yōu)化配置,其配置結(jié)果與能效負(fù)荷優(yōu)化管理前相比,其配置成本下降了 7.1%,負(fù)荷缺電率下降了 2.8%,新能源棄電率下降了 2.1%,驗證了所制定的單體用戶可控能效負(fù)荷優(yōu)化管理策略的可行性和正確性,表明了在對需求側(cè)可控能效負(fù)荷進(jìn)行優(yōu)化管理后,在降低用戶用電成本的同時,能夠進(jìn)一步減少分布式電源和儲能系統(tǒng)的配置成本,而且能夠有效提高主動配電網(wǎng)的供電可靠性。
[Abstract]:With the continuous development and large-scale application of new energy generation technology, a series of problems, such as low utilization rate of new energy and cost waste, have been caused while alleviating traditional energy sources. How to improve distributed energy utilization efficiency in active distribution network? It is an urgent problem to ensure the reliability of power supply to optimize the configuration of energy storage system. In this paper, the capacity configuration of distributed generation and energy storage system in active distribution network is studied. The following aspects are studied: firstly, distributed power generation, controllable energy efficiency load and energy storage system are studied. The mathematical models of wind power generation and photovoltaic power generation system are established. The mathematical model of energy consumption response of controllable energy efficiency load including air conditioning, water heater and lighting load, and the mathematical model of energy storage system and inverter are established. According to the demand side energy efficiency load management, combined with the evaluation index of time-sharing price and customer satisfaction, the optimal scheduling strategy of single user controllable energy efficiency load was studied, and the charge and discharge management strategy of energy storage system was established in order to make rational use of energy storage system. Secondly, in order to select a typical natural resource sample, KMO and Bartlett sphericity test are used to select the best correlation sample data for principal component analysis, and the weights in the wavelet BP neural network model are predicted. The scaling factor and the translation factor are modified by the momentum-adaptive learning rate. In order to solve the problem of capacity optimization of multi-objective distributed power supply and energy storage system, the harmonic search algorithm is used to solve the problem. The convergence performance of the algorithm and the problem of falling into local optimum are improved. The memory retention probability, the amplitude of harmonic fine tuning and the probability of fine tuning disturbance are updated and adjusted by dynamic parameter mode, and the convergence performance is improved by fusion with genetic algorithm. Then, taking the wind speed of typical days in summer and winter as an example, the short-term wind power prediction is carried out, and compared with the traditional BP neural network and WNN neural network prediction. The results show that the improved wavelet BP neural network prediction method based on principal component analysis is fast and accurate. On the basis of distributed generation prediction, the wind speed, solar illumination and load demand data of a typical day in summer and typical day in winter in an active distribution network are taken as an example, and the configuration cost of distributed generation and energy storage system is taken as an example. The new energy loss rate and load power shortage rate are optimized for multi-objective configuration. The correctness and effectiveness of the model and the improved harmonic search algorithm are verified by an example. Finally, in order to further reduce the configuration capacity and cost, the energy efficiency load optimization management on the demand side is used to optimize the configuration of distributed power generation and energy storage, and the configuration results are compared with those before the energy efficiency load optimization management. The allocation cost has decreased by 7.1, the load power shortage rate has decreased by 2.8 percent, the new energy consumption rate has dropped by 2.1 percent, and the feasibility and correctness of the optimized management strategy of controllable energy efficiency load for individual users has been verified. It is shown that after optimized management of demand-side controllable energy efficiency load, it can further reduce the configuration cost of distributed power generation and energy storage system while reducing the cost of consumer electricity consumption. And it can effectively improve the power supply reliability of the active distribution network.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM73

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