本文選題：消納風(fēng)電 + 風(fēng)電預(yù)測偏差　； 參考：《華北電力大學(xué)》2015年碩士論文

【摘要】：近年來風(fēng)電發(fā)展迅速,成為21世紀(jì)重要的綠色能源。隨著風(fēng)力發(fā)電在電網(wǎng)中的比重不斷提高,由于風(fēng)電具有隨機(jī)性、間歇性和波動(dòng)性的特點(diǎn),發(fā)電功率較難準(zhǔn)確預(yù)測,在時(shí)間和空間尺度上給接入風(fēng)電的系統(tǒng)備用容量優(yōu)化配置帶來了新的挑戰(zhàn)。日內(nèi)風(fēng)電預(yù)測的誤差以及超短期負(fù)荷預(yù)測偏差、機(jī)組故障等因素的存在,導(dǎo)致日內(nèi)功率預(yù)測與日前發(fā)電計(jì)劃存在較大偏差,嚴(yán)重影響了傳統(tǒng)的日前發(fā)電計(jì)劃在日內(nèi)的執(zhí)行。同時(shí)目前建立在高可靠性常規(guī)電源基礎(chǔ)上的備用配置方法還不能符合含大規(guī)模風(fēng)電的電力系統(tǒng)的可靠性要求。因此,需要依據(jù)發(fā)電偏差滾動(dòng)制定備用容量優(yōu)化分配策略,以更好地協(xié)調(diào)系統(tǒng)的可靠性和經(jīng)濟(jì)性,實(shí)現(xiàn)發(fā)電計(jì)劃由日前到日內(nèi)的平滑過渡。首先,本文介紹了引起發(fā)電偏差的各種因素,其中,重點(diǎn)介紹了風(fēng)電功率預(yù)測偏差較大的原因。在采用正態(tài)分布與拉普拉斯分布聯(lián)合的模型來擬合風(fēng)電預(yù)測偏差的基礎(chǔ)上,提出了基于置信度的風(fēng)電出力極值的求解方法,以確定在某一時(shí)段風(fēng)電出力極值。隨后,對含大規(guī)模風(fēng)電場的電力系統(tǒng)進(jìn)行隨機(jī)生產(chǎn)模擬,以求取含風(fēng)電場電力系統(tǒng)的最大備用容量。在對三種典型隨機(jī)生產(chǎn)模擬計(jì)算方法探討的基礎(chǔ)上,確定本文所用方法。將風(fēng)電機(jī)組視為固定出力為極值的常規(guī)電源優(yōu)先安排,然后安排系統(tǒng)中容量最大機(jī)組,采用等效持續(xù)負(fù)荷曲線法進(jìn)行隨機(jī)生產(chǎn)模擬,求取在某一可靠性指標(biāo)下的含風(fēng)電系統(tǒng)的最大備用容量。最后,在上述研究的前提下,確定了消納大規(guī)模風(fēng)電的備用容量在線滾動(dòng)決策,并建立了備用容量滾動(dòng)優(yōu)化模型。根據(jù)發(fā)電偏差與最大備用容量的關(guān)系,以備用容量購買成本最低為目標(biāo),建立優(yōu)化模型。采用改進(jìn)粒子群算法對模型進(jìn)行求解,計(jì)算得到了滾動(dòng)計(jì)劃各時(shí)段的最優(yōu)旋轉(zhuǎn)備用容量。在減少棄風(fēng)量的同時(shí),提高了系統(tǒng)運(yùn)行的經(jīng)濟(jì)性和可靠性,對各時(shí)段的備用容量調(diào)整具有指導(dǎo)意義。
[Abstract]:With the rapid development of wind power in recent years, it has become an important green energy in the twenty-first Century. With the increasing proportion of wind power in the power grid, due to the randomness, intermittence and volatility of wind power, the power generation power is difficult to accurately predict. The optimal allocation of standby capacity for wind power generation has brought a new choice on the time and space scale. The error of the intra day wind power forecast, the ultra short term load forecast deviation, the unit fault and other factors lead to the large deviation of the intra day power forecast and the pre day generation plan, which seriously affects the implementation of the traditional day forward generation plan in the day. At the same time, the standby configuration method based on the high reliability conventional power supply is also established. It can not meet the reliability requirements of the power system with large scale wind power. Therefore, it is necessary to formulate the optimal allocation strategy of reserve capacity according to the generation deviation, so as to better coordinate the reliability and economy of the system and realize the smooth transition from day to day. On the basis of fitting wind power forecast deviation with the combined model of normal distribution and Laplasse distribution, the method of solving the extreme value of wind power output based on confidence degree is proposed to determine the extreme value of wind power output at a certain period of time. On the basis of three typical stochastic production simulation calculation methods, the method used in this paper is determined on the basis of three typical stochastic production simulation calculation methods. The equivalent continuous load curve method is used for stochastic production simulation to obtain the maximum reserve capacity of the wind power system under a certain reliability index. Finally, on the premise of the above research, the on-line rolling decision of the reserve capacity of large scale wind power is determined, and a rolling optimization model of reserve capacity is established. With the relationship of capacity, the optimization model is set up with the minimum purchase cost of reserve capacity as the goal. The improved particle swarm optimization algorithm is used to solve the model, and the optimal rotation reserve capacity of each period of the rolling plan is calculated. At the same time, the economy and reliability of the system operation are improved and the reserve capacity of each period is adjusted. The whole is instructive.

【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TM614

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