本文選題：風電場 + 機組優(yōu)化調度�。� 參考：《重慶大學》2014年碩士論文

【摘要】：隨著全球范圍內能源需求的增長和環(huán)境問題的日益突出，風能作為可再生能源中最具經濟發(fā)展前景的清潔能源，逐漸受到世界各國的重視和青睞，風電并網容量逐年增加。但是，與常規(guī)發(fā)電機組不同，由于風電功率具有隨機性和波動性，大規(guī)模風電的接入必然會給電力系統(tǒng)機組的優(yōu)化調度和運行帶來一系列的挑戰(zhàn)和要求。因此，合理制定含風電場電力系統(tǒng)的機組調度計劃對于提高風電的利用率具有重要意義。本文圍繞含風電場電力系統(tǒng)的機組優(yōu)化調度問題，開展了如下的研究工作： 計及負荷和風電功率的不確定性，結合拉丁超立方抽樣和場景技術分析了負荷和風電功率的聯(lián)合多時段場景模型。針對風電功率預測誤差缺乏統(tǒng)一的概率分布模型，通過建立風電功率預測誤差的經驗分布函數，結合樣條插值法建立了風電功率預測誤差總體分布函數的解析表達式，并采用拉丁超立方技術對負荷和風電功率的預測誤差進行抽樣，，結合場景削減技術分析了負荷和風電功率了負荷和風電功率的聯(lián)合多時段場景模型。算例分析結果表明了該方法的可行性和有效性。 計及不同場景間負荷和風電功率的波動性對機組優(yōu)化調度的影響，以所有場景下發(fā)電成本的期望值和方差的加權和為目標函數，建立了綜合考慮負荷及風電功率不確定性影響的機組優(yōu)化調度模型，并采用改進的粒子群優(yōu)化算法對模型進行求解。針對負荷和風電功率的不確定性影響，基于不同場景之間負荷和風電功率的極限波動區(qū)間確定系統(tǒng)的正負旋轉備用需求。為提高算法迭代過程中的收斂性能，提出以種群最優(yōu)值為引導動態(tài)調整機組的出力范圍。以某典型10機測試系統(tǒng)為例進行算例分析，驗證了所提模型和算法的正確性和有效性。 考慮到機組優(yōu)化調度與環(huán)境成本、負荷預測誤差以及風電滲透率等參數密切相關，為分析這些參數變化對機組優(yōu)化調度的影響，給出了含風電場電力系統(tǒng)機組優(yōu)化調度的算例仿真分析。算例分析結果表明：環(huán)境成本對機組調度的發(fā)電成本影響不大，但對機組調度的環(huán)境效益影響顯著；負荷預測誤差和風電滲透率對機組調度的發(fā)電成本影響較大。
[Abstract]:With the increase of global energy demand and the increasingly prominent environmental problems, wind energy, as the most promising clean energy in renewable energy, has been paid more and more attention and favor in the world, and the wind power grid capacity is increasing year by year. However, unlike conventional generators, because of the randomness and volatility of wind power, large-scale wind power access will inevitably bring a series of challenges and requirements to the optimal scheduling and operation of power system units. Therefore, it is of great significance to make the dispatching plan of wind farm power system reasonably for improving the utilization ratio of wind power. In this paper, the following research work is carried out on the optimal dispatching of units with wind farm power system: Considering the uncertainty of load and wind power, combined with Latin hypercube sampling and scenario technology, the combined multi-period scenario model of load and wind power is analyzed. In view of the lack of a unified probability distribution model for wind power prediction error, an analytical expression of wind power prediction error overall distribution function is established by establishing the empirical distribution function of wind power prediction error and combining spline interpolation method. The prediction error of load and wind power is sampled by using Latin hypercube technique, and the combined multi-period scenario model of load and wind power is analyzed with scene reduction technology. The results of an example show that the method is feasible and effective. Considering the effect of fluctuation of load and wind power between different scenarios on the optimal scheduling of generating units, the weighted sum of expected value and variance of generation cost in all scenarios is taken as the objective function. An optimal scheduling model considering the uncertainty of load and wind power is established, and the improved particle swarm optimization algorithm is used to solve the model. According to the uncertainty of load and wind power, the requirement of positive and negative rotation reserve is determined based on the limit fluctuation range of load and wind power between different scenarios. In order to improve the convergence performance of the iterative algorithm, the optimal population value is proposed to guide the dynamic adjustment of the generating range. Taking a typical 10-machine test system as an example, the correctness and validity of the proposed model and algorithm are verified. Considering that the optimal scheduling of the unit is closely related to the environmental cost, load forecasting error and wind power permeability, the influence of these parameters on the optimal scheduling of the unit is analyzed. An example of optimal dispatching of power system units with wind farm is presented. The result of example analysis shows that the environmental cost has little effect on the generation cost of unit dispatching, but it has a significant effect on the environmental benefit of unit dispatching, and the load forecasting error and wind power permeability have great influence on the generation cost of unit dispatching.
【學位授予單位】：重慶大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM614;TM73

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本文編號：1945012