本文選題：需求響應(yīng) + 樓宇集群能量系統(tǒng)��； 參考：《上海電力學(xué)院》2017年碩士論文

【摘要】：隨著各種分布式電源和新型用電設(shè)備的日益推廣以及電力用戶對(duì)需求個(gè)性化要求的提升,智能用電的理念及其技術(shù)發(fā)展體系應(yīng)運(yùn)而生。在該背景下,高級(jí)量測(cè)和通信系統(tǒng)與智能負(fù)荷調(diào)控技術(shù)的日漸發(fā)展和完善,驅(qū)動(dòng)了以雙向互動(dòng)智能高效用能為目標(biāo)的需求響應(yīng)技術(shù)的應(yīng)用和發(fā)展。為獲得經(jīng)濟(jì)、環(huán)境效益的同時(shí)提升電能服務(wù)質(zhì)量,研究用電行為并主動(dòng)整合用戶側(cè)資源、優(yōu)化用能調(diào)度己成為一項(xiàng)積極而有效的措施。本文首先構(gòu)建了可實(shí)現(xiàn)集群內(nèi)部能源的互濟(jì)、儲(chǔ)用、控制及信息交互的樓宇集群能量管理系統(tǒng)。然后基于分時(shí)電價(jià)機(jī)制和系統(tǒng)內(nèi)可控元件的功耗模型,提出了一種電網(wǎng)友好型樓宇集群能量?jī)?yōu)化調(diào)度策略。以多形式儲(chǔ)能、冷熱電聯(lián)供系統(tǒng)(CCHP)、制冷機(jī)組等可控元件為控制目標(biāo),同時(shí)考慮系統(tǒng)各類約束條件建立了樓宇集群能量協(xié)同優(yōu)化調(diào)度模型。在分時(shí)電價(jià)機(jī)制下,以一天運(yùn)行成本最低為目標(biāo),利用機(jī)會(huì)約束規(guī)劃在處理隨機(jī)變量方面的優(yōu)勢(shì),建立了計(jì)及風(fēng)光出力不確定性的樓宇集群能量系統(tǒng)(BCES)經(jīng)濟(jì)優(yōu)化調(diào)度模型。在LINGO語(yǔ)言環(huán)境下編寫了相應(yīng)程序,并對(duì)模型求最優(yōu)解。算例結(jié)果表明通過(guò)優(yōu)化求解獲得的BCES中各單元的出力,可以較好的利用多形式儲(chǔ)能系統(tǒng),引導(dǎo)BCES對(duì)主網(wǎng)的削峰填谷的同時(shí)也有效地提高了自身的經(jīng)濟(jì)效益。然后,本文提出了一種基于用戶行為學(xué)的家庭日負(fù)荷曲線自下向上精細(xì)預(yù)測(cè)模型。在分析影響居民用電行為因素的基礎(chǔ)上,通過(guò)非齊次馬爾科夫鏈建立用戶狀態(tài)轉(zhuǎn)移矩陣并求取家庭中居民所處的狀態(tài);將居民活動(dòng)與相應(yīng)電器狀態(tài)關(guān)聯(lián),分別建立電器開啟及其使用持續(xù)時(shí)長(zhǎng)的聯(lián)合概率分布函數(shù);對(duì)居民家庭中常用電器進(jìn)行建模,并基于序貫抽樣法可進(jìn)行不同日類型、氣象數(shù)據(jù)、家庭人數(shù)下的居民日負(fù)荷曲線預(yù)測(cè)。算例仿真的結(jié)果表明負(fù)荷曲線預(yù)測(cè)精度較高,驗(yàn)證了該模型的有效性。最后,本文對(duì)電力用戶側(cè)的需求響應(yīng)行為展開研究。為了揭示用戶用電量對(duì)電價(jià)、氣象、日類型等相關(guān)變化因素的響應(yīng)程度,提出了一種需求響應(yīng)綜合模型。該模型實(shí)現(xiàn)了用戶在分時(shí)電價(jià)下的需求響應(yīng)行為規(guī)律的模擬,可為電力公司制定需求側(cè)管理策略提供基礎(chǔ)性指導(dǎo)。
[Abstract]:With the increasing promotion of various distributed power sources and new power equipment and the improvement of the demand individuation requirements of power users, the concept of intelligent power consumption and its technological development system have emerged as the times require. Under this background, the development and perfection of advanced measurement and communication system and intelligent load control technology drive the application and development of demand response technology which aims at two-way interactive intelligence and high utility. In order to achieve economic and environmental benefits and improve the quality of power service, it has become an active and effective measure to study the power consumption behavior and actively integrate the resources of the user side and optimize the energy use scheduling. In this paper, a building cluster energy management system which can realize the mutual aid, storage, control and information interaction of energy in the cluster is constructed. Then, based on the time-sharing pricing mechanism and the power consumption model of controllable components in the system, a grid friendly building cluster energy optimal scheduling strategy is proposed. Taking the controlled elements such as multi-form energy storage, combined cooling and heat supply system, refrigeration unit and so on, as the control target, and considering all kinds of constraints of the system, a building cluster energy cooperative optimal scheduling model is established. Under the time-sharing pricing mechanism, taking the lowest daily operating cost as the goal, and taking advantage of the advantage of opportunistic constrained programming in dealing with random variables, an economic optimal scheduling model of building cluster energy system (BCES) considering the uncertainty of wind power is established. The corresponding program is written in LINGO language environment, and the optimal solution of the model is obtained. The result of example shows that the output force of each unit in BCES can be obtained by optimization, which can make good use of multi-form energy storage system, and guide BCES to cut the peak and fill the valley of the main network, at the same time, it can effectively improve the economic benefit of itself. Then, this paper presents a fine forecast model of family daily load curve from bottom to bottom based on user behavior. Based on the analysis of the factors influencing the residents' electricity consumption behavior, the user state transfer matrix is established through the non-homogeneous Markov chain, and the state of the residents in the family is obtained, and the residents' activities are correlated with the corresponding electrical appliance states. Establishing the joint probability distribution function of the electric appliance opening and the duration of using, modeling the electrical appliances commonly used in the household, and based on the sequential sampling method can carry out the different types of day, meteorological data, Forecast of daily load curve under household size. The simulation results show that the accuracy of load curve forecasting is high, and the validity of the model is verified. Finally, the demand response behavior of power user side is studied in this paper. In order to reveal the response degree of electricity consumption to electricity price, weather, day type and other related factors, a comprehensive model of demand response is proposed. The model can simulate the demand response behavior of users under time-sharing price and can provide basic guidance for power companies to formulate demand-side management strategy.
【學(xué)位授予單位】：上海電力學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM73

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本文編號(hào)：1946449