本文選題：塔式太陽能熱電系統(tǒng) + 鏡場(chǎng)調(diào)度�。� 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：塔式太陽能熱電系統(tǒng)是一種適合大規(guī)模發(fā)電的新能源利用方式,發(fā)展前景廣闊。其中,由定日鏡場(chǎng)和吸熱器等關(guān)鍵設(shè)備組成的聚光集熱子系統(tǒng)是整個(gè)電站的前端,其性能和效率對(duì)電站的運(yùn)行和總發(fā)電量產(chǎn)出都有很大影響。定日鏡場(chǎng)的調(diào)度直接決定每面定日鏡在吸熱器上聚焦點(diǎn)的位置,為保證電站平穩(wěn)、高效、安全運(yùn)行,對(duì)定日鏡場(chǎng)調(diào)度的優(yōu)化研究有著十分重要的現(xiàn)實(shí)意義,可為實(shí)際電站的運(yùn)行提供參考。本文對(duì)塔式太陽能電站的定日鏡場(chǎng)調(diào)度進(jìn)行了優(yōu)化,完成的主要工作如下:(1)建立定日鏡轉(zhuǎn)動(dòng)能耗模型,為了在保證吸熱器能流密度較均勻分布的前提下,實(shí)現(xiàn)吸熱器的太陽能吸收功率最大化和定日鏡因聚焦點(diǎn)變動(dòng)產(chǎn)生的轉(zhuǎn)動(dòng)能耗最小化,建立定日鏡場(chǎng)實(shí)時(shí)調(diào)度優(yōu)化問題。采用多變異位自適應(yīng)遺傳算法求解這一優(yōu)化問題,仿真結(jié)果表明優(yōu)化后的定日鏡場(chǎng)調(diào)度方案可以有效保障上述目標(biāo),有利于節(jié)能降耗。(2)考慮到定日鏡場(chǎng)調(diào)度優(yōu)化問題的求解速度需求,以及遺傳算法的潛在并行性,利用現(xiàn)有的GPU和CUDA平臺(tái),將遺傳算法并行實(shí)現(xiàn),大幅度提高調(diào)度方案產(chǎn)生的實(shí)時(shí)性。仿真結(jié)果表明,GPU加速后的優(yōu)化求解可以在保證遺傳算法求解質(zhì)量的前提下提高求解速度。(3)建立定日鏡場(chǎng)調(diào)度時(shí)間間隔優(yōu)化模型,在保證吸熱器能流密度標(biāo)準(zhǔn)差不超過上限值的基礎(chǔ)上,最優(yōu)化一段時(shí)間內(nèi)的吸熱器吸收能量和定日鏡轉(zhuǎn)動(dòng)能耗。為了便于求解,將時(shí)間間隔優(yōu)化問題在一定條件下轉(zhuǎn)化為嵌套形式的優(yōu)化問題,外層優(yōu)化問題利用禁忌算法求解,內(nèi)層優(yōu)化問題沿用鏡場(chǎng)實(shí)時(shí)調(diào)度優(yōu)化問題求解方法來求解。得到的優(yōu)化后的時(shí)間間隔可以有效提高電站效率,最后分析了不同參數(shù)對(duì)最優(yōu)調(diào)度時(shí)間間隔的影響。
[Abstract]:Tower solar thermal power system is a new energy utilization mode suitable for large-scale power generation.Among them, the collector subsystem composed of sun-fixing mirror field and heat absorber is the front end of the whole power plant. Its performance and efficiency have great influence on the operation of the power station and the output of the total power generation.In order to ensure the stable, efficient and safe operation of the power station, the scheduling of the sun-fixing mirror field directly determines the location of the focus of the sun-fixing mirror on the heat absorber. It is of great practical significance to optimize the scheduling of the sun-fixing mirror field.It can provide reference for actual power station operation.In this paper, the scheduling of the helioscope field of the tower solar power station is optimized. The main work accomplished is as follows: 1) the energy consumption model of the helioscope rotation is established, in order to ensure the uniform distribution of the energy flow density of the absorber.In order to maximize the solar energy absorption power of the absorber and minimize the rotational energy consumption caused by the focus change of the helioscope, the real-time scheduling optimization problem of the fixed day mirror field is established.The multi-mutation adaptive genetic algorithm is used to solve the optimization problem. The simulation results show that the optimized scheduling scheme of sun-fixed mirror field can effectively guarantee the above objectives.In consideration of the speed requirement of the scheduling optimization problem of fixed day mirror field and the potential parallelism of genetic algorithm, the genetic algorithm is implemented in parallel using the existing GPU and CUDA platforms.The real-time performance of the scheduling scheme is greatly improved.The simulation results show that the accelerated optimization of GPU can improve the speed of solution with the guarantee of the quality of genetic algorithm (GA)) and establish the optimal model of the scheduling time interval of the fixed day mirror field.On the basis of ensuring that the standard deviation of the energy flow density of the absorber does not exceed the upper limit, the energy absorption of the absorber and the energy consumption of the rotation of the helioscope are optimized for a period of time.In order to solve the problem easily, the time interval optimization problem is transformed into a nested optimization problem under certain conditions. The outer layer optimization problem is solved by Tabu algorithm, and the inner layer optimization problem is solved by the mirror field real-time scheduling optimization method.The optimized time interval can effectively improve the efficiency of the power station. Finally, the influence of different parameters on the optimal scheduling time interval is analyzed.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM615

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