【摘要】：隨著經(jīng)濟的發(fā)展,國民經(jīng)濟水平的提高,夏季空調(diào)負荷成為很多城市電力負荷峰谷差不斷增大的主要原因之一,加深了我國夏季高峰時段的電力供需矛盾。公共樓宇大規(guī)�？照{(diào)負荷具備一定的調(diào)峰能力,因此挖掘公共樓宇大規(guī)模空調(diào)負荷的調(diào)峰潛力,并采用合理的調(diào)控手段削減高峰負荷,對減緩電網(wǎng)的壓力具有很重要的意義。為解決公共樓宇大規(guī)�？照{(diào)的虛擬調(diào)峰問題,本文主要進行了了如下研究：(1)分析了公共樓宇大規(guī)模空調(diào)負荷的預(yù)測技術(shù)。準(zhǔn)確的公共樓宇大規(guī)�？照{(diào)負荷預(yù)測對于掌握公共樓宇空調(diào)負荷可調(diào)度容量從而參與電網(wǎng)調(diào)控具有重要意義。提出了一種考慮夏季氣溫積溫效應(yīng)的基于Elman神經(jīng)網(wǎng)絡(luò)預(yù)測模型。將有效積溫、溫度、濕度、五日滑動平均溫度作為氣象因素考慮進Elman神經(jīng)網(wǎng)絡(luò)預(yù)測模型中,預(yù)測預(yù)測歳的公共樓宇大規(guī)�？照{(diào)負荷值。(2)研究了公共樓宇大規(guī)模空調(diào)負荷參與電網(wǎng)虛擬調(diào)峰的調(diào)度方案的設(shè)計。公共樓宇大規(guī)模中央空調(diào)參與電網(wǎng)的調(diào)峰,需要充分考慮電網(wǎng)常規(guī)機組和公共樓宇的特點,所以立足于調(diào)度方案的設(shè)計,圍繞框架建立、評估等方面開展研究。研究分析了公共樓宇大規(guī)模空調(diào)負荷參與的電網(wǎng)的日前、日內(nèi)、實時的調(diào)度的調(diào)度技術(shù)框架,和有第三方負荷代理機構(gòu)參與的公共樓宇大規(guī)�？照{(diào)負荷參與調(diào)度架構(gòu),具體闡述了雙層調(diào)度目標(biāo)及約束條件,提出了公共樓宇大規(guī)�？照{(diào)負荷參與調(diào)度效果在線評估指標(biāo)及計算方法,可為公共樓宇大規(guī)�？照{(diào)負荷參與調(diào)度優(yōu)化協(xié)調(diào)系統(tǒng)提出參考,最后給出了公共樓宇大規(guī)�？照{(diào)負荷參與調(diào)度方案的優(yōu)化調(diào)整流程。(3)研究了公共樓大規(guī)�？照{(diào)負荷虛擬調(diào)峰的策略。首先分析了公共樓宇大規(guī)模中央空調(diào)系統(tǒng)的物理模型構(gòu)成以及主要功能,然后對中央空調(diào)進行物理建模,把中央空調(diào)的功率分為五大主要耗能部分,包含制冷機組耗能,冷凍水泵耗能,風(fēng)機盤管耗能,冷卻塔耗能和冷卻水泵的耗能,并對五大耗能部分主要影響因素進行建模分析；然后針對中央空調(diào)的主要耗能部分的分析得到中央空調(diào)的可優(yōu)化部分,分析了對公共樓宇大規(guī)模中央空調(diào)進行柔性調(diào)控的策略,包括全局的溫度控制、增加冷凍機水溫、風(fēng)機盤管的關(guān)閉等,并分析得到了公共樓宇大規(guī)模參與柔性調(diào)控的流程。(4)建立了公共樓宇大規(guī)�？照{(diào)負荷虛擬調(diào)峰組合優(yōu)化算例。公共樓宇大規(guī)�？照{(diào)組合參與電網(wǎng)日前調(diào)度,不同空調(diào)用戶,根據(jù)自身的實際特征,可采取的調(diào)控策略不同。首先分析了公共樓宇大規(guī)�？照{(diào)柔性調(diào)控組合方案,對中央空調(diào)典型用戶的空調(diào)負荷特性進行曲線擬合,然后得到典型的中央空調(diào)參與的電網(wǎng)調(diào)控預(yù)設(shè)方案。實例分析了針對公共樓宇大規(guī)模中央空調(diào)參與電網(wǎng)日前調(diào)度的雙層優(yōu)化組合方案,采用的CPLEX優(yōu)化軟件包進行組合優(yōu)化,得到早高峰、腰高峰和晚高峰的調(diào)控組合方案。
[Abstract]:With the development of economy and the improvement of national economy, air conditioning load in summer becomes one of the main reasons for the increasing peak and valley difference of power load in many cities, which deepens the contradiction between power supply and demand during the peak period of summer in China. The large-scale air conditioning load of public buildings has certain peak-shaving ability, so it is of great significance to excavate the peak-shaving potential of large-scale air-conditioner load of public buildings and to reduce the peak load by adopting reasonable control measures, which is of great significance to reduce the pressure of power grid. In order to solve the problem of virtual peak-shaving of large-scale air conditioning in public buildings, this paper has mainly carried out the following research: (1) the forecasting technology of large-scale air conditioning load in public buildings is analyzed. Accurate prediction of large-scale air conditioning load in public buildings is of great significance for grasping the dispatching capacity of air conditioning load in public buildings and participating in the regulation and control of power grid. This paper presents a prediction model based on Elman neural network considering the effect of accumulated temperature in summer. The effective accumulated temperature, humidity and five-day moving average temperature are taken into account as meteorological factors in the prediction model of Elman neural network. The large scale air conditioning load of public buildings is forecasted. (2) the design of dispatching scheme for large-scale air conditioning load of public buildings to participate in the virtual peak-shaving of power grid is studied. The large scale central air conditioning of public buildings takes part in the peak-shaving of power grid, which needs to fully consider the characteristics of conventional units and public buildings. Therefore, based on the design of dispatching scheme, the research is carried out around the establishment of framework, evaluation and so on. This paper studies and analyzes the pre-day, in-day, real-time scheduling technical framework of large-scale air-conditioning load in public buildings, and the large-scale air-conditioning load scheduling framework of public buildings with third-party load agents. In this paper, the double layer dispatching target and constraint condition are expounded, and the online evaluation index and calculation method of large-scale air conditioning load participating in dispatching of public buildings are put forward, which can be used as a reference for the optimization and coordination system of large-scale air conditioning load participation in public buildings. Finally, the optimization and adjustment process of large-scale air conditioning load in public buildings is given. (3) the strategy of virtual peak-shaving for large-scale air conditioning load in public buildings is studied. Firstly, the physical model and main functions of large-scale central air conditioning system in public buildings are analyzed, and then the physical model of central air conditioning system is built. The power of central air conditioning system is divided into five main energy consumption parts, including the energy consumption of refrigeration units. Cooling pump energy consumption, fan coil energy consumption, cooling tower energy consumption and cooling pump energy consumption. Then, according to the analysis of the main energy consumption part of the central air conditioning system, the optimizable part of the central air conditioning system is obtained. The flexible control strategy for large-scale central air conditioning in public buildings is analyzed, including the overall temperature control, increasing the water temperature of the freezer. The process of large-scale participation in flexible control of public buildings is obtained. (4) A virtual peak-shaving optimization example of large-scale air conditioning load in public buildings is established. Large scale air conditioning combinations of public buildings participate in daily dispatching of power grid. According to their actual characteristics, different air conditioning users can adopt different control strategies. Firstly, the large-scale flexible control scheme of public buildings is analyzed, and the curve fitting of the load characteristics of typical central air conditioning users is carried out, and then the presupposition scheme of power grid regulation and control in which typical central air conditioners participate is obtained. An example is given to analyze the bilevel optimal combination scheme for large-scale central air conditioning in public buildings to participate in the daily dispatching of power network. The CPLEX optimization software package is used to optimize the combination of the early peak, middle peak and late peak to obtain the control combination of early peak, low peak and late peak.
【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TM73

【相似文獻】

相關(guān)期刊論文 前10條

1 鄭就;;中央空調(diào)負荷采用計算機軟件的計算與分析[J];制冷與空調(diào)(四川);2008年04期

2 劉馨宜;;空調(diào)負荷控制策略分析[J];科技資訊;2009年35期

3 盧麗;宗通;王國磊;;建筑遮擋對空調(diào)負荷影響的分析與探討[J];中國住宅設(shè)施;2011年02期

4 李斌;林弘宇;徐石明;顏慶國;肖文舉;張磊;楊永標(biāo);;智能電網(wǎng)框架下公共樓宇空調(diào)負荷資源化應(yīng)用[J];供用電;2014年03期

5 李英娜,顧平道,莊琛;大中型商場空調(diào)負荷影響因素的探討[J];應(yīng)用能源技術(shù);2004年02期

6 溫權(quán),李敬如,趙靜;空調(diào)負荷計算方法及應(yīng)用[J];電力需求側(cè)管理;2005年04期

7 李東梅;李敬如;趙靜;溫權(quán);;北京空調(diào)負荷結(jié)構(gòu)及調(diào)控措施研究[J];電力技術(shù)經(jīng)濟;2005年06期

8 李民;朱慰慈;;2005年夏季鎮(zhèn)江地區(qū)空調(diào)負荷特點分析[J];江蘇電機工程;2006年02期

9 楊潔;張旭;王凌飛;;上海世博會展館建筑空調(diào)負荷指標(biāo)的影響因素分析[J];暖通空調(diào);2006年09期

10 陶勇;沈穎;;夏季氣象條件對地區(qū)空調(diào)負荷的影響[J];華東電力;2006年10期

相關(guān)會議論文 前10條

1 張春路;李灝;丁國良;陳芝久;;空調(diào)負荷新型計算方法研究[A];上海市制冷學(xué)會一九九七年學(xué)術(shù)年會論文集[C];1997年

2 林真國;付祥釗;張素云;;論由照明引起的空調(diào)負荷計算[A];全國暖通空調(diào)制冷2000年學(xué)術(shù)年會論文集[C];2000年

3 蔣騫;龍惟定;;雙層立面建筑的空調(diào)負荷計算[A];第13屆全國暖通空調(diào)技術(shù)信息網(wǎng)技術(shù)交流大會文集[C];2005年

4 龍洋波;吳祥生;孫秀平;;幾種常見空調(diào)負荷動態(tài)計算方法介紹[A];2005西南地區(qū)暖通空調(diào)熱能動力年會論文集[C];2005年

5 何大四;張旭;;改進的季節(jié)性指數(shù)平滑法預(yù)測空調(diào)負荷實例研究[A];全國暖通空調(diào)制冷2004年學(xué)術(shù)年會資料摘要集（2）[C];2004年

6 周娟;陳友明;胡敏;;空調(diào)負荷計算新方法的應(yīng)用研究[A];全國暖通空調(diào)制冷2004年學(xué)術(shù)年會資料摘要集（2）[C];2004年

7 高立新;陸亞俊;;智能化空調(diào)負荷計算軟件的開發(fā)[A];全國暖通空調(diào)制冷2004年學(xué)術(shù)年會資料摘要集（2）[C];2004年

8 張春路;李灝;丁國良;陳芝久;;空調(diào)負荷新型計算方法研究[A];全國暖通空調(diào)制冷1998年學(xué)術(shù)年會論文集（2）[C];1998年

9 何大四;張旭;;改進的季節(jié)性指數(shù)平滑法預(yù)測空調(diào)負荷的實例研究[A];上海市制冷學(xué)會二○○三年學(xué)術(shù)年會論文集[C];2003年

10 王春麗;李曉冬;;分層空調(diào)負荷與溫度場動態(tài)多區(qū)熱質(zhì)平衡模型探討[A];全國暖通空調(diào)制冷2004年學(xué)術(shù)年會資料摘要集（2）[C];2004年

相關(guān)重要報紙文章 前2條

1 藍旺;給空調(diào)負荷“降溫”[N];中國電力報;2004年

2 ;空調(diào)負荷來勢兇猛 電網(wǎng)企業(yè)應(yīng)對有力[N];國家電網(wǎng)報;2012年

，

本文編號：2170450