本文選題：魯棒優(yōu)化 + 不確定性��； 參考：《南京郵電大學(xué)》2017年碩士論文

【摘要】：近年來(lái),可再生能源得到迅速發(fā)展,其中以風(fēng)電尤為突出�？稍偕茉茨苡行У馗纳骗h(huán)境污染問(wèn)題,緩解能源危機(jī)。風(fēng)電預(yù)測(cè)技術(shù)的局限性,氣候、地形分布的多樣性,風(fēng)電本身間歇性、不確定性以及部分可預(yù)測(cè)性的特點(diǎn),對(duì)電力系統(tǒng)規(guī)劃與運(yùn)行帶來(lái)挑戰(zhàn)。風(fēng)電的概率分布函數(shù)本身就是不確定的,如果將其作為已知量進(jìn)行處理不能反映風(fēng)電出力的真實(shí)情況,無(wú)法從根本上保證電力系統(tǒng)規(guī)劃與運(yùn)行方法的有效性。魯棒優(yōu)化方法可解決上述隨機(jī)變量的概率分布函數(shù)不完全已知的情況,本文討論兩種魯棒優(yōu)化方法,其一是盒式魯棒優(yōu)化方法,該方法將風(fēng)速以及風(fēng)機(jī)輸出功率設(shè)定為隨機(jī)變量,使用區(qū)間不確定集刻畫風(fēng)速,采用隨機(jī)變量的分布情況刻畫風(fēng)電輸出功率的隨機(jī)特性,把隨機(jī)問(wèn)題改寫成確定型問(wèn)題。另一種是概率分布魯棒優(yōu)化方法,已知不確定量的一階和二階矩,采用條件風(fēng)險(xiǎn)價(jià)值(CVaR,Conditional value at risk),推演出包含不確定量的CVaR約束模型,再采用對(duì)偶優(yōu)化理論、Schur補(bǔ)和S-lemma,把CVaR約束轉(zhuǎn)變?yōu)殡p線性矩陣不等式(BMI,Bi-linear Matrix Inequality)約束,把基于BMI約束的不確定模型化為確定模型。最后,確定性模型的求解使用基于BMI的免疫粒子群算法。本文研究了上述魯棒優(yōu)化方法在輸電系統(tǒng)柔性負(fù)荷調(diào)度、輸電系統(tǒng)無(wú)功規(guī)劃和輸電系統(tǒng)可用輸電容量評(píng)估問(wèn)題中的應(yīng)用。本文把風(fēng)電作為新能源的代表進(jìn)行研究,新能源出力的不確定性,可使用柔性負(fù)荷調(diào)度予以平衡,然而柔性負(fù)荷的互動(dòng)具有自主性、隨機(jī)性與無(wú)序性,將會(huì)進(jìn)一步增加電力系統(tǒng)中的不確定特性,本文在上述背景下提出基于魯棒優(yōu)化方法的輸電系統(tǒng)柔性負(fù)荷調(diào)度策略。采用概率分布魯棒優(yōu)化方法解決輸電網(wǎng)無(wú)功規(guī)劃問(wèn)題,可有效應(yīng)對(duì)于風(fēng)電函數(shù)概率分布集合中的任意可能分布,在給定的概率約束下確保輸電網(wǎng)的運(yùn)行安全,并且將輸電網(wǎng)的網(wǎng)損和無(wú)功設(shè)備的投資成本之和最小化。采用魯棒優(yōu)化方法解決輸電系統(tǒng)可用輸電容量評(píng)估問(wèn)題,可應(yīng)對(duì)于風(fēng)電概率分布不完全可知的情況,該方法可以保證網(wǎng)絡(luò)安全運(yùn)行約束前提下,最大化可用傳輸容量。仿真結(jié)果以及算例分析表明所提方法在輸電系統(tǒng)規(guī)劃與運(yùn)行中應(yīng)用的可行性和有效性。
[Abstract]:In recent years, renewable energy has been developed rapidly, especially wind power. Renewable energy can effectively improve the problem of environmental pollution and alleviate the energy crisis. The limitation of wind power forecasting technology, the diversity of climate and terrain distribution, the intermittent, uncertainty and partial predictability of wind power bring challenges to power system planning and operation. The probability distribution function of wind power is itself uncertain. If it is treated as a known quantity can not reflect the true situation of wind power generation, it can not fundamentally guarantee the effectiveness of power system planning and operation methods. Robust optimization method can solve the problem that the probability distribution function of random variables is not completely known. In this paper, we discuss two robust optimization methods, one is boxed robust optimization method, and the other is boxed robust optimization method. In this method, wind speed and fan output power are set as random variables, interval uncertainty sets are used to describe wind speed, and the distribution of random variables is used to describe the stochastic characteristics of wind power output power. The other is the probabilistic distribution robust optimization method, in which the first and second moments of the uncertainty are known, and the conditional risk value (Cvar) conditional value at riskn is used to deduce the CVaR constraint model with uncertainty. Then by using the dual optimization theories such as Schur complement and S-lemma, the CVaR constraint is transformed into a bilinear matrix inequality constraint, and the uncertain model based on BMI constraint is transformed into a deterministic model. Finally, the BMI-based immune particle swarm optimization algorithm is used to solve the deterministic model. In this paper, the application of the robust optimization method to the problems of flexible load scheduling, reactive power planning and the evaluation of the available transmission capacity of transmission systems is studied. This paper studies wind power as the representative of new energy. The uncertainty of new energy output can be balanced by flexible load dispatching. However, the interaction of flexible load has autonomy, randomness and disorder. It will further increase the uncertainty of power system. In this paper, a robust optimization method based flexible load scheduling strategy is proposed. Using the robust optimization method of probability distribution to solve the problem of reactive power planning in transmission network can effectively deal with any possible distribution in the set of probability distribution of wind power functions and ensure the safety of transmission network under given probability constraints. The sum of network loss and investment cost of reactive power equipment is minimized. The robust optimization method is used to solve the problem of evaluation of the available transmission capacity of transmission systems. It can be used to maximize the available transmission capacity under the condition that the probability distribution of wind power is not completely known. Simulation results and numerical examples show that the proposed method is feasible and effective in transmission system planning and operation.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM715;TM732

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