本文選題：信息物理能源系統(tǒng) + 需求側(cè)用電能效優(yōu)化　； 參考：《清華大學(xué)》2014年博士論文

【摘要】：信息物理能源系統(tǒng)是由大量感知測(cè)量節(jié)點(diǎn)、嵌入式計(jì)算設(shè)備和大尺度異構(gòu)通信網(wǎng)絡(luò)等構(gòu)成的應(yīng)用于智能電網(wǎng)的閉環(huán)感知聯(lián)控系統(tǒng)，其具有協(xié)作測(cè)量，信息分析處理，實(shí)時(shí)靈活交互通信，復(fù)雜系統(tǒng)動(dòng)態(tài)控制等功能。本論文研究基于信息物理能源系統(tǒng)的需求側(cè)協(xié)作測(cè)量與能效優(yōu)化方法，論文的主要研究?jī)?nèi)容包括： 研究了瞬態(tài)穩(wěn)態(tài)特征協(xié)作的非侵入式負(fù)荷辨識(shí)測(cè)量方法，解決了需求側(cè)復(fù)雜動(dòng)態(tài)的用電狀態(tài)背景下的負(fù)荷辨識(shí)問題。面向負(fù)荷平穩(wěn)特征提出了貝葉斯經(jīng)驗(yàn)學(xué)習(xí)辨識(shí)測(cè)量方法。面向負(fù)荷非平穩(wěn)瞬態(tài)特征提出了基于高斯過程的概率辨識(shí)測(cè)量方法。為解決單辨識(shí)模型易受干擾問題，提出了多辨識(shí)模型協(xié)作的非侵入用電負(fù)荷辨識(shí)測(cè)量方法，提高了負(fù)荷辨識(shí)測(cè)量的準(zhǔn)確性和魯棒性。 為了實(shí)現(xiàn)對(duì)需求側(cè)用電負(fù)荷的高精度可靠預(yù)測(cè)，提出了信息物理關(guān)聯(lián)用電負(fù)荷概率預(yù)測(cè)方法。為降低冗余信息對(duì)預(yù)測(cè)模型干擾，研究了基于信息物理相關(guān)性加權(quán)的數(shù)據(jù)約簡(jiǎn)方法；針對(duì)需求側(cè)用電負(fù)荷異頻段特征，研究了基于經(jīng)驗(yàn)?zāi)B(tài)分解的用電負(fù)荷多分辨率分析方法；提出了分層特征加權(quán)稀疏貝葉斯負(fù)荷概率預(yù)測(cè)方法，實(shí)現(xiàn)了對(duì)用電負(fù)荷的概率分布預(yù)測(cè)，預(yù)測(cè)精度高，算法魯棒性好。 面向信息物理能源系統(tǒng)需求側(cè)協(xié)作測(cè)量網(wǎng)絡(luò)，提出了分層客戶端-服務(wù)器協(xié)作測(cè)量網(wǎng)絡(luò)模型。研究了智能水滴無線數(shù)據(jù)傳輸能效優(yōu)化算法，解決協(xié)作測(cè)量網(wǎng)絡(luò)無線數(shù)據(jù)傳輸能耗降低問題。為了進(jìn)一步降低協(xié)作測(cè)量網(wǎng)絡(luò)無線通信能耗，提出基于最優(yōu)解反饋的半監(jiān)督式智能水滴路由優(yōu)化算法，提高了協(xié)作測(cè)量網(wǎng)絡(luò)能效性。 提出了虛擬力監(jiān)督粒子群能效性優(yōu)化方法，解決需求側(cè)用電網(wǎng)絡(luò)的用電時(shí)間能效優(yōu)化問題和負(fù)荷削峰填谷問題。面向未來開放的電力市場(chǎng)，，提出了基于負(fù)荷預(yù)測(cè)的峰谷動(dòng)態(tài)電價(jià)理論計(jì)算模型，研究了基于延時(shí)成本修正和福利函數(shù)的用電能效性評(píng)價(jià)指標(biāo)。針對(duì)需求側(cè)用電時(shí)間優(yōu)化問題，提出了虛擬力監(jiān)督粒子群能效性優(yōu)化算法，實(shí)現(xiàn)了對(duì)峰值用電負(fù)荷的平滑，優(yōu)化了能效性評(píng)價(jià)指標(biāo)。 研制了測(cè)量電能信息的低功耗電能信息測(cè)量節(jié)點(diǎn)，設(shè)計(jì)了具有網(wǎng)絡(luò)組態(tài)監(jiān)測(cè)、測(cè)量數(shù)據(jù)顯示、數(shù)據(jù)預(yù)處理、用電統(tǒng)計(jì)信息查詢與負(fù)荷預(yù)測(cè)等功能的需求側(cè)協(xié)作測(cè)量軟件平臺(tái)�；谠O(shè)計(jì)需求側(cè)測(cè)量系統(tǒng)軟硬件平臺(tái)驗(yàn)證了需求側(cè)用電負(fù)荷在線監(jiān)測(cè)、非侵入式負(fù)荷協(xié)作辨識(shí)測(cè)量和用電負(fù)荷預(yù)測(cè)在實(shí)際工作環(huán)境中的性能。
[Abstract]:The information physical energy system is a closed loop sensing system which is composed of a large number of sensor nodes, embedded computing devices and large scale heterogeneous communication networks, which is applied to the smart grid. It has cooperative measurement, information analysis and processing, etc. Real-time flexible interactive communication, complex system dynamic control and other functions. In this paper, the demand-side collaborative measurement and energy efficiency optimization methods based on information physical energy system are studied. The main contents of this paper are as follows: the non-invasive load identification and measurement method based on transient steady-state characteristic collaboration is studied. The problem of load identification under the background of complex dynamic power consumption on the demand side is solved. A Bayesian empirical learning identification method for load stationary features is proposed. A probabilistic identification and measurement method based on Gao Si process is proposed for load nonstationary transient characteristics. In order to solve the problem that single identification model is vulnerable to interference, a multi-identification model cooperative non-invasive load identification method is proposed. The accuracy and robustness of load identification and measurement are improved. In order to achieve high accuracy and reliability prediction of demand side load, a probability forecasting method of information physics correlation is proposed. In order to reduce the interference of redundant information to the prediction model, the data reduction method based on the weight of information physics correlation is studied, and the multi-resolution analysis method based on empirical mode decomposition is studied for the different frequency band characteristics of the demand side load. A hierarchical feature weighted sparse Bayesian load probability forecasting method is proposed to predict the probability distribution of power load. The prediction accuracy is high and the algorithm is robust. A hierarchical client-server collaborative measurement network model is proposed. The energy efficiency optimization algorithm of intelligent water droplet wireless data transmission is studied to solve the problem of reducing the energy consumption of cooperative measurement network wireless data transmission. In order to further reduce the energy consumption of cooperative measurement network, a semi-supervised intelligent water droplet routing optimization algorithm based on optimal solution feedback is proposed to improve the energy efficiency of collaborative measurement network, and a virtual force supervised particle swarm optimization method is proposed. To solve the problem of energy efficiency optimization of demand side network and load peak filling. For the future open power market, a theoretical calculation model of peak-valley dynamic electricity price based on load forecasting is proposed, and the evaluation index of power efficiency based on delay cost correction and welfare function is studied. A virtual force supervised particle swarm optimization algorithm for energy efficiency optimization is proposed to solve the problem of time optimization of power consumption on the demand side, which can smooth the peak load. The energy efficiency evaluation index is optimized, the low power consumption power information measurement node is developed, and the network configuration monitoring, measurement data display, data preprocessing are designed. Demand-side collaborative measurement software platform for power statistics information query and load forecasting. Based on the software and hardware platform of the DSM system, the performance of DSM, non-intrusive load identification and load forecasting is verified in the actual working environment.
【學(xué)位授予單位】：清華大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM715

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 曾鳴;沈又幸;盧鍵明;李娜;魏陽(yáng);;面向智能電網(wǎng)的需求側(cè)管理決策支持系統(tǒng)[J];華東電力;2011年02期

2 王會(huì)梅;李旭;鮮明;王國(guó)玉;;網(wǎng)絡(luò)抗攻擊性能的遺傳投影尋蹤評(píng)估模型[J];計(jì)算機(jī)科學(xué);2010年06期

3 田磊;馮丹;岳銀亮;吳素貞;毛波;;磁盤存儲(chǔ)系統(tǒng)節(jié)能技術(shù)研究綜述[J];計(jì)算機(jī)科學(xué);2010年09期

4 韓希先;楊東華;李建中;;TKEP:海量數(shù)據(jù)上一種有效的Top-K查詢處理算法[J];計(jì)算機(jī)學(xué)報(bào);2010年08期

5 任倩倩;李建中;程思瑤;;無線傳感器網(wǎng)絡(luò)中可容錯(cuò)的事件監(jiān)測(cè)算法[J];計(jì)算機(jī)學(xué)報(bào);2012年03期

6 高振華;楊帆;陳聞杰;柴志雷;;CPS處理器中時(shí)間指令擴(kuò)展研究及實(shí)現(xiàn)[J];計(jì)算機(jī)應(yīng)用;2012年06期

7 蔣國(guó)平;樊春霞;宋玉蓉;邵斐;;復(fù)雜動(dòng)態(tài)網(wǎng)絡(luò)同步控制及其在信息物理系統(tǒng)中的應(yīng)用[J];南京郵電大學(xué)學(xué)報(bào)(自然科學(xué)版);2010年04期

8 李國(guó)華;李建中;高宏;;ε-近似和加權(quán)公平性保證的無線傳感器網(wǎng)絡(luò)擁塞控制算法[J];計(jì)算機(jī)學(xué)報(bào);2011年11期

9 梁東方;王宇英;周興社;劉廈;;基于異構(gòu)模型融合的CPS系統(tǒng)仿真建模方法研究[J];計(jì)算機(jī)科學(xué);2012年11期

10 王小樂;黃宏斌;鄧蘇;;處理順序約束的信息物理融合系統(tǒng)靜態(tài)任務(wù)表調(diào)度算法[J];自動(dòng)化學(xué)報(bào);2012年11期

本文編號(hào)：1996009