本文關(guān)鍵詞：智能電網(wǎng)下需求響應(yīng)機(jī)理及其短期負(fù)荷預(yù)測(cè)模型研究　出處：《青島大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 智能電網(wǎng) 需求響應(yīng) 峰谷分時(shí)電價(jià) 短期負(fù)荷預(yù)測(cè)

【摘要】：隨著電力市場(chǎng)的不斷發(fā)展和日益完善,其利益主體逐漸呈現(xiàn)出多元化,電價(jià)機(jī)制也得到調(diào)整和更新。為綜合優(yōu)化整個(gè)電力系統(tǒng)的資源配置,緩解電網(wǎng)短期負(fù)荷容量不足,需求響應(yīng)成為電力領(lǐng)域研究熱點(diǎn)。在智能電網(wǎng)的技術(shù)支撐下,需求響應(yīng)可通過制定峰谷電價(jià)促進(jìn)電網(wǎng)與用戶之間的實(shí)時(shí)性互動(dòng),有利于電力資源合理分配,配合短期負(fù)荷預(yù)測(cè)技術(shù),最終實(shí)現(xiàn)雙方獲益。因此,量化分析峰谷電價(jià)下用戶需求響應(yīng)機(jī)理和研究計(jì)及需求響應(yīng)的短期負(fù)荷預(yù)測(cè)新方法意義重大。根據(jù)消費(fèi)者心理學(xué)原理,建立基于分段函數(shù)的需求響應(yīng)機(jī)理模型,對(duì)峰谷電價(jià)進(jìn)行量化分析,由于模型未充分考慮非電價(jià)因素的影響,因此賦予用戶響應(yīng)模糊屬性。引入基于數(shù)據(jù)挖掘的負(fù)荷時(shí)間序列聚類方法,以負(fù)荷序列間歐式距離和差分序列標(biāo)準(zhǔn)差構(gòu)成的交集約束作為聚類過程的綜合判據(jù),對(duì)目標(biāo)電網(wǎng)全年的歷史負(fù)荷數(shù)據(jù)做聚類處理。根據(jù)聚類結(jié)果,同時(shí)從負(fù)荷數(shù)據(jù)大小和曲線形態(tài)變化兩個(gè)方面挖掘識(shí)別負(fù)荷特性,制定次日動(dòng)態(tài)峰谷電價(jià)。通過實(shí)際算例,擬合預(yù)測(cè)日需求響應(yīng)負(fù)荷曲線,結(jié)果表明實(shí)施動(dòng)態(tài)峰谷電價(jià)削峰填谷效果顯著,且參與需求響應(yīng)項(xiàng)目的用戶可根據(jù)參與度從中獲取相應(yīng)收益。另外,本文以曲線的形式協(xié)助描述了需求響應(yīng)機(jī)理,基于時(shí)變函數(shù)建立了用戶實(shí)際需求響應(yīng)模型。通過頻譜分析研究了需求響應(yīng)負(fù)荷的基本特性,并以此為依據(jù)確定預(yù)測(cè)模型輸入量組成,引入需求響應(yīng)量化結(jié)果,分別建立了計(jì)及需求響應(yīng)的RNN、Elman-NN和RBF-NN預(yù)測(cè)模型。通過實(shí)際算例,對(duì)比在三種預(yù)測(cè)模型中計(jì)及需求響應(yīng)因素前后的預(yù)測(cè)性能,結(jié)果表明RBF-NN模型預(yù)測(cè)性能最佳,且將需求響應(yīng)量化結(jié)果引入預(yù)測(cè)模型可顯著提高其預(yù)測(cè)精度。分析上述實(shí)際算例結(jié)果,結(jié)合動(dòng)態(tài)峰谷電價(jià)機(jī)制,根據(jù)消費(fèi)者心理學(xué)原理描述了基于Logistic函數(shù)的用戶需求響應(yīng)機(jī)理。通過量化電價(jià)、用戶響應(yīng)程度以及溫度等外界因素,構(gòu)建了考慮需求響應(yīng)綜合影響因素的RBF-NN短期負(fù)荷預(yù)測(cè)模型。基于Logistic函數(shù)的需求響應(yīng)機(jī)理充分考慮了電力用戶應(yīng)對(duì)不同電價(jià)的心理響應(yīng)狀態(tài),且其響應(yīng)度曲線在不同電價(jià)差分段點(diǎn)處連續(xù)可導(dǎo),與描述需求響應(yīng)機(jī)理的分段函數(shù)相比,更符合客觀事實(shí)。通過實(shí)際算例,分析了本文構(gòu)建模型在不同電價(jià)機(jī)制下的預(yù)測(cè)性能,證明了在RBF-NN模型中綜合考慮電價(jià)、用戶響應(yīng)度等因素的重要性,為計(jì)及需求響應(yīng)的短期負(fù)荷預(yù)測(cè)研究提供了一定的理論依據(jù)。
[Abstract]:With the development of power market and the increasingly perfect, the stakeholders gradually diversified, the price mechanism can be adjusted and updated. For the comprehensive optimization of the whole power system of the allocation of resources, alleviate the power short-term load capacity, demand response has become the research focus in the field of electric power. The technical support of the smart grid, demand response by make tou promote real-time interaction between grid and users, is conducive to the rational allocation of power resources, short term load forecasting technology, realize the benefit of both parties. Therefore, the quantitative analysis and Research on the response mechanism and demand response to short term load forecasting method is significant user demand under peak valley electricity price. According to the principle of consumer psychology. To establish a mechanism model of piecewise function response based on the needs of quantitative analysis of the peak valley price, because the model does not take into account the non price for The influence, therefore gives the user response fuzzy attributes. The introduction of load time series clustering method based on data mining, the Euclidean distance between the load sequence and the difference series of standard deviation constitute the intersection of constraints as the comprehensive criteria of the clustering process, the historical load data of the annual target grid clustering processing. According to the clustering results, at the same time from two a load curve data size and morphological changes of mining to identify load characteristics, formulate the dynamic tou. Through practical examples, the fitting and prediction of demand response on load curve, the results show that the dynamic peak electricity peak effect, and participate in the project according to customer demand response participation from which to obtain the corresponding revenue. In addition, this paper describes the demand curve in the form of Assistance Response Mechanism Based on time-varying function to establish the actual needs of users. The frequency spectrum response model Analysis of the basic characteristics of load demand response, and on this basis to determine the model input. The introduction of demand response quantitative results, considering demand response RNN are established respectively, Elman-NN and RBF-NN prediction model. Through practical examples, contrast response prediction performance factors before and after three kinds of prediction model considering demand the results show that the RBF-NN model is the best prediction performance, and the quantitative results into the prediction model can significantly improve the prediction accuracy. In response to the needs of the actual analysis results, combined with the dynamic peak valley electricity price mechanism, according to the principle of consumer psychology describes the Logistic function of the user demand response based on the mechanism. By quantifying the price, external factors and temperature etc. in response to the user, based on the consideration of comprehensive effect of short term load forecasting model RBF-NN factors in response to demand. The Logistic function of the demand response based on machine And take full account of the power users with different price psychological response state, and the response curve of price difference in different sub points continuously differentiable, and describe the demand response mechanism of piecewise function compared to more accord with the objective facts. Through practical examples, this paper constructs the performance prediction model in different price mechanism is analyzed. It is proved that RBF-NN model considering the price, the user response degree of importance and other factors, provide a theoretical basis for the research of demand response to short-term load forecasting.

【學(xué)位授予單位】：青島大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM715

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