【摘要】：需求響應(yīng)能夠?qū)崿F(xiàn)供電側(cè)與用電側(cè)的有效資源互動(dòng),提高系統(tǒng)運(yùn)行效率和可靠性。有效利用需求側(cè)資源與傳統(tǒng)的單一增加調(diào)峰電源或加強(qiáng)電網(wǎng)建設(shè)的做法相比,可以極大減輕基礎(chǔ)設(shè)施的投資壓力。目前基于激勵(lì)的需求響應(yīng)項(xiàng)目大多針對(duì)工業(yè)用戶(hù)實(shí)施,而居民、商業(yè)和辦公等用戶(hù)雖然單體容量小但數(shù)量龐大、分布廣泛,具有很大的需求響應(yīng)潛力,且更適合開(kāi)展基于電價(jià)的需求響應(yīng)項(xiàng)目。因此在智能用電環(huán)境下,開(kāi)展用戶(hù)用電行為分析,是需求響應(yīng)應(yīng)用的基礎(chǔ)性工作。本文從單個(gè)用戶(hù)的用電情況監(jiān)測(cè)出發(fā),搭建多維度負(fù)荷分類(lèi)體系,根據(jù)典型用電負(fù)荷分類(lèi),對(duì)單個(gè)用戶(hù)的用電負(fù)荷進(jìn)行分解,并對(duì)多個(gè)用戶(hù)進(jìn)行用電行為分析,形成了具備不同價(jià)格敏感度的用戶(hù)聚類(lèi)。針對(duì)已得到的聚合用戶(hù),構(gòu)建實(shí)時(shí)市場(chǎng)環(huán)境,實(shí)現(xiàn)對(duì)聚合用戶(hù)的電價(jià)調(diào)度。首先,分析了用戶(hù)參與需求響應(yīng)的必要性和可行性,概述了目前需求響應(yīng)在國(guó)內(nèi)外的發(fā)展應(yīng)用現(xiàn)狀,并從電價(jià)調(diào)度、用電行為分析和模型參數(shù)辨識(shí)三個(gè)方面總結(jié)了學(xué)術(shù)界的研究現(xiàn)狀,為本文的后續(xù)研究奠定了基礎(chǔ)。其次,對(duì)于單個(gè)用戶(hù),針對(duì)目前常用負(fù)荷分解方法存在的不足,綜合考慮用戶(hù)常用設(shè)備的用途和特性,提出了多維度負(fù)荷分類(lèi)體系,分別從功能維度、時(shí)間維度和功率維度進(jìn)行設(shè)備分類(lèi),并基于模糊隸屬度函數(shù)構(gòu)建用戶(hù)負(fù)荷分解模型,以模糊隸屬度表征負(fù)荷分解結(jié)果的可信度,實(shí)現(xiàn)用戶(hù)用電負(fù)荷的分解,為需求響應(yīng)項(xiàng)目的潛力分析提供基礎(chǔ)。然后,對(duì)于多個(gè)用戶(hù),基于改進(jìn)K-Means聚類(lèi)算法,在峰谷電價(jià)環(huán)境下,選取峰谷平各時(shí)段的用電量占比和負(fù)荷率作為用戶(hù)的用電特征量,構(gòu)建用電行為聚類(lèi)模型,對(duì)用戶(hù)用電行為進(jìn)行聚類(lèi)分析,并針對(duì)價(jià)格轉(zhuǎn)換點(diǎn)前后的用戶(hù)用電量變化情況,提出了用戶(hù)價(jià)格敏感度的計(jì)算方法,構(gòu)建用戶(hù)篩選模型,從而確定適合于價(jià)格需求響應(yīng)的敏感對(duì)象。最后,對(duì)于聚合用戶(hù),在基于消費(fèi)者心理學(xué)構(gòu)建的實(shí)時(shí)市場(chǎng)環(huán)境下,應(yīng)用支持向量機(jī)進(jìn)行模型參數(shù)辨識(shí),構(gòu)建需求響應(yīng)電價(jià)的計(jì)算模型,并對(duì)電價(jià)調(diào)度誤差和應(yīng)用場(chǎng)景進(jìn)行分析。對(duì)比通過(guò)人工神經(jīng)網(wǎng)絡(luò)和回歸分析方法得到的誤差,提出將這三種參數(shù)辨識(shí)法進(jìn)行加權(quán)的組合分析法,構(gòu)建綜合調(diào)度模型,提高電價(jià)調(diào)度的準(zhǔn)確度和有效性。
[Abstract]:The demand response can realize the effective resource interaction between the power supply side and the power side, and improve the efficiency and reliability of the system. Compared with the traditional method of increasing peak-shaving power or strengthening power grid construction, the effective use of demand-side resources can greatly reduce the investment pressure of infrastructure. At present, most of the demand response projects based on incentives are aimed at industrial users. Although the individual capacity of residents, businesses and offices is small, the number of users is large, and they are widely distributed, so they have a great potential for demand response. And more suitable for the development of electricity-based demand response project. Therefore, it is the basic work for the application of demand response to carry out the analysis of the user's power consumption behavior in the intelligent power environment. In this paper, a multi-dimensional load classification system is built based on the power consumption monitoring of a single user. According to the typical power load classification, the power load of a single user is decomposed, and the power consumption behavior of multiple users is analyzed. A user cluster with different price sensitivity is formed. A real-time market environment is constructed for the aggregate users, and the electricity price scheduling for the aggregate users is realized. First of all, the necessity and feasibility of user participation in demand response are analyzed, and the current development and application of demand response at home and abroad are summarized. The analysis of electrical behavior and the identification of model parameters summarize the current research situation in academic circles and lay a foundation for the further study of this paper. Secondly, for a single user, considering the shortcomings of current load decomposition methods, a multi-dimensional load classification system is proposed, which is based on the functional dimension, considering the usage and characteristics of the equipment commonly used by users. Time dimension and power dimension are used to classify equipment, and user load decomposition model is constructed based on fuzzy membership function. The reliability of load decomposition result is represented by fuzzy membership degree, and user power load decomposition is realized. Provides the basis for potential analysis of demand response projects. Then, for multiple users, based on the improved K-Means clustering algorithm, under the peak-valley electricity price environment, the power consumption ratio and load rate of each period of peak and valley level are selected as the characteristics of the user, and the electricity behavior clustering model is constructed. Based on the clustering analysis of the user's electricity consumption behavior and the change of the user's electricity consumption before and after the price conversion point, the calculation method of the user's price sensitivity is put forward, and the user screening model is constructed. In order to determine the appropriate price response to the sensitive object. Finally, for aggregate users, in the real-time market environment based on consumer psychology, support vector machine is used to identify the model parameters, and the calculation model of demand response price is constructed. And the electricity price scheduling error and application scenario are analyzed. By comparing the errors obtained by artificial neural network and regression analysis, a combined analysis method weighted by these three parameter identification methods is proposed to construct a comprehensive scheduling model to improve the accuracy and effectiveness of electricity price scheduling.
【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TM73

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