本文選題：微網(wǎng)　切入點：光伏　出處：《華北電力大學(北京)》2017年碩士論文

【摘要】：微網(wǎng)利用光伏、風機等可再生能源發(fā)電并與儲能系統(tǒng)配合向本地負荷供電是解決能源危機和環(huán)境污染問題的方法之一。然而,光伏、風機等電源的出力具有間歇性,且與用戶負荷需求呈不對等分布,難以被本地用戶直接利用。因此,通過能量管控實現(xiàn)能源就地高效利用,避免大量過剩功率入網(wǎng),對負荷起到削峰效果是當前微網(wǎng)研究重點之一。目前,已有不少微網(wǎng)能量管控策略,然而大部分控制策略依賴于精確的負荷需求和分布式電源出力預測數(shù)據(jù)或通過短時的預測修正來減小對預測數(shù)據(jù)的依賴,但精準預測和反復預測修正的過程受到現(xiàn)有預測技術和軟件計算能力的限制。此外,隨著電動汽車的快速發(fā)展,其充電隨機性高、瞬時充電功率較大且難以精確預測的特性會降低依賴精確預測數(shù)據(jù)的能量管控策略的控制效果。針對上述問題,論文對包含光伏、風機和微型燃氣輪機分布式電源的小區(qū)微網(wǎng)提出了冷/熱電聯(lián)產和儲能系統(tǒng)聯(lián)合運行的能量管控策略,實現(xiàn)了對各類能源的高效利用和對用戶冷/熱、電負荷的分類削減,改善能源供給和負荷需求分布不對等的現(xiàn)狀。其中,考慮小區(qū)內電動汽車充電負荷不斷增長的情況,論文對儲能系統(tǒng)的充放電控制提出了一種基于預測數(shù)據(jù)的實時修正算法。該算法利用預測數(shù)據(jù)但不依賴于預測數(shù)據(jù)的精確性,也不需要對數(shù)據(jù)預測部分進行反復計算修正,直接通過對儲能系統(tǒng)充放電功率的修正計算實現(xiàn)最大程度的沖擊負荷削減。論文利用實測和模擬數(shù)據(jù),對提出的微型燃氣輪機冷/熱電聯(lián)產和儲能系統(tǒng)聯(lián)合運行的能量管控策略進行編程實現(xiàn),并與儲能系統(tǒng)充放電控制采用固定閾值算法和自適應控算法的控制策略進行結果比較。結果表明,論文設計的能量管控策略較好的實現(xiàn)了各類能源的本地利用,并在保證運行最優(yōu)經(jīng)濟性的基礎上實現(xiàn)最大程度的沖擊負荷削減效果。
[Abstract]:One of the ways to solve the problem of energy crisis and environmental pollution is to use photovoltaic, blower and other renewable energy sources to generate electricity and cooperate with energy storage system to supply local load. However, the output of photovoltaic, blower and other power sources is intermittent. And the distribution is not equal to the demand of user load, so it is difficult to be directly utilized by local users. Therefore, energy can be efficiently utilized in place through energy control, and a large amount of excess power can be avoided. Peak-cutting effect on load is one of the key points in microgrid research. At present, there are many microgrid energy control strategies. However, most control strategies rely on accurate load demand and distributed power generation prediction data or reduce the dependence on prediction data by short-term forecasting correction. But the process of accurate prediction and repeated prediction correction is limited by existing prediction techniques and software computing capabilities. In addition, with the rapid development of electric vehicles, their charging randomness is high. The characteristics of high instantaneous charge power and difficult to predict accurately will reduce the control effect of energy control strategy which depends on accurate predictive data. In this paper, the microgrid of distributed power system of fan and micro gas turbine has put forward the energy control strategy of combined operation of cold / heat power generation and energy storage system, which realizes the efficient utilization of all kinds of energy and the classification and reduction of user's cold / heat and electric load. Improving the unequal distribution of energy supply and load demand. Among them, considering the increasing charge load of electric vehicles in the district, In this paper, a real-time correction algorithm based on predictive data is proposed for charge and discharge control of energy storage system, which utilizes the prediction data but does not depend on the accuracy of the prediction data, nor does it need to calculate and modify the prediction part repeatedly. The maximum impact load reduction is realized by modifying the charge and discharge power of the energy storage system. The proposed energy control strategy for the combined operation of cooling / heat and power generation and energy storage system of micro gas turbine is realized by programming. The results are compared with the control strategies of fixed threshold algorithm and adaptive control algorithm in charge and discharge control of energy storage system. The results show that the energy control strategy designed in this paper can achieve the local utilization of all kinds of energy. The maximum impact load reduction effect is realized on the basis of ensuring the optimal operation economy.
【學位授予單位】：華北電力大學(北京)
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM73

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