【摘要】：化石能源發(fā)電所引起的環(huán)境污染問(wèn)題已經(jīng)成為制約國(guó)家能源可持續(xù)發(fā)展戰(zhàn)略的一大障礙,利用無(wú)污染、可再生的新能源代替化石能源發(fā)電,是未來(lái)電力發(fā)展趨勢(shì)之一。風(fēng)電作為新能源發(fā)電中的一種,具有清潔、儲(chǔ)存量大和易于開(kāi)發(fā)等優(yōu)點(diǎn),被廣泛開(kāi)發(fā)和利用。由于風(fēng)電的隨機(jī)不確定性,大規(guī)模風(fēng)電的接入,給電力系統(tǒng)穩(wěn)定運(yùn)行帶來(lái)了一定的挑戰(zhàn)。因此,研究含大規(guī)模風(fēng)電接入的電力系統(tǒng)動(dòng)態(tài)特性和風(fēng)功率預(yù)測(cè)以及多源區(qū)域電網(wǎng)的優(yōu)化調(diào)度,對(duì)提高風(fēng)電的開(kāi)發(fā)利用具有重要意義。本文對(duì)含大規(guī)模風(fēng)電接入的多源區(qū)域電網(wǎng)優(yōu)化調(diào)度問(wèn)題,展開(kāi)了如下研究:(1)構(gòu)建了含風(fēng)力發(fā)電機(jī)組、水力發(fā)電機(jī)組和汽輪發(fā)電機(jī)組的多源混合電力系統(tǒng)模型,在風(fēng)速波動(dòng)條件下,對(duì)該系統(tǒng)模型進(jìn)行了仿真分析。仿真結(jié)果表明,所建的多源混合電力系統(tǒng)的穩(wěn)定性受風(fēng)電機(jī)組輸出功率波動(dòng)性的影響,并能夠準(zhǔn)確描述該電力系統(tǒng)主要參數(shù)的動(dòng)態(tài)特性,為進(jìn)一步研究含大規(guī)模風(fēng)電接入的多源區(qū)域電網(wǎng)優(yōu)化調(diào)度研究提供支撐。(2)提出了基于粒子群神經(jīng)網(wǎng)絡(luò)(Particle Swarm Optimization and Back-propagation Neural Network,PSO-BP)的風(fēng)電功率預(yù)測(cè)方法,該方法利用粒子群算法的全局搜索能力來(lái)獲得BP(Back-propagation,BP)神經(jīng)網(wǎng)絡(luò)的初始權(quán)值和閾值,很好地解決了常規(guī)BP算法收斂速度慢、易陷入局部極小等問(wèn)題,并對(duì)PSO-BP算法和BP神經(jīng)網(wǎng)絡(luò)算法的預(yù)測(cè)結(jié)果進(jìn)行了對(duì)比分析。根據(jù)實(shí)例預(yù)測(cè)結(jié)果表明,PSO-BP算法較BP神經(jīng)網(wǎng)絡(luò)算法預(yù)測(cè)的絕對(duì)平均誤差(Mean Absolute Error,MAE)和均方根誤差(Root Mean Square Error,RMSE)分別減少了7.02%,和9.37%,證明粒子群神經(jīng)網(wǎng)絡(luò)(PSO-BP)算法在風(fēng)電場(chǎng)輸出功率預(yù)測(cè)方面具較理想的效果。(3)基于所建立的含風(fēng)電的多源混合電力系統(tǒng)模型和風(fēng)電功率預(yù)測(cè)的基礎(chǔ)上,研究了基于多智能體粒子群算法(Multi-agent and Particle Swarm Optimization,MA-PSO)的經(jīng)濟(jì)調(diào)度方法,該算法結(jié)合了粒子群(Particle Swarm Optimization,PSO)算法全局特性和多智能體系統(tǒng)(Multi-agent System,MAS)的智能特性,有效解決了高維數(shù)、非線性、多參數(shù)耦合的經(jīng)濟(jì)調(diào)度問(wèn)題;通過(guò)對(duì)MA-PSO算法與基本PSO算法優(yōu)化結(jié)果進(jìn)行對(duì)比分析,MA-PSO算法求出的一天的最優(yōu)值的發(fā)電成本為3.7964×10~4$,而PSO算法所求出的最優(yōu)值的發(fā)電成本為4.1787×10~4$。MA-PSO算法所求發(fā)電成本較PSO算法節(jié)省了3.823×10~3$,即節(jié)省率高達(dá)9.14%。證明MA-PSO算法搜索性能好,收斂精度高。同時(shí),MA-PSO算法應(yīng)用于解決經(jīng)濟(jì)調(diào)度問(wèn)題,能夠獲得較好的經(jīng)濟(jì)效益和環(huán)境效益。
[Abstract]:The problem of environmental pollution caused by fossil energy power generation has become a major obstacle to the national energy sustainable development strategy. The use of non-polluting renewable new energy to replace fossil energy power generation is one of the future power development trends. Wind power, as one of the new energy generation, has the advantages of clean, large storage and easy to develop, so it has been widely developed and used. Because of the random uncertainty of wind power and the connection of large-scale wind power, it brings some challenges to the stable operation of power system. Therefore, it is of great significance to study the dynamic characteristics and wind power prediction of power system with large-scale wind power access, as well as the optimal dispatching of multi-source regional power network, in order to improve the development and utilization of wind power. In this paper, the optimal dispatching problem of multi-source regional power network with large-scale wind power access is studied as follows: (1) the model of multi-source hybrid power system with wind turbine generator, hydrogenerator and turbine generator is constructed. Under the condition of wind speed fluctuation, the system model is simulated and analyzed. The simulation results show that the stability of the multi-source hybrid power system is affected by the fluctuation of the output power of the wind turbine, and the dynamic characteristics of the main parameters of the power system can be accurately described. It provides support for further research on optimal dispatching of multi-source regional power network with large-scale wind power access. (2) A wind power prediction method based on particle swarm optimization neural network (Particle Swarm Optimization and Back-propagation Neural Network,PSO-BP) is proposed. This method utilizes the global searching ability of particle swarm optimization algorithm to obtain the initial weights and thresholds of BP (Back-propagation,BP) neural network, which solves the problems of slow convergence speed and easy to fall into local minima of conventional BP algorithm. The prediction results of PSO-BP algorithm and BP neural network algorithm are compared and analyzed. The prediction results show that the absolute mean error (Mean Absolute Error,MAE) and root mean square error (Root Mean Square Error,RMSE) of the PSO-BP algorithm are 7.02 and 9.37 less than those of the BP neural network algorithm, respectively. It is proved that the particle swarm optimization neural network (PSO-BP) algorithm is effective in predicting the output power of wind farm. (3) based on the model of multi-source hybrid power system with wind power and the prediction of wind power, The economic scheduling method based on multi-agent particle swarm optimization (Multi-agent and Particle Swarm Optimization,MA-PSO) is studied. The algorithm combines the global characteristics of particle swarm optimization (Particle Swarm Optimization,PSO) algorithm and multi-agent system (Multi-agent System,). The intelligent characteristic of MAS effectively solves the economic scheduling problem with high dimension, nonlinear and multi-parameter coupling. By comparing and analyzing the optimization results of MA-PSO algorithm and basic PSO algorithm, it is found that the optimal value of MA-PSO algorithm is 3.7964 脳 10 ~ (4) 脳 10 ~ (4) / day. The optimal value of the PSO algorithm is 4.1787 脳 10~4$.MA-PSO, and the cost is 3.823 脳 10 ~ (-3) less than that of the PSO algorithm, that is, the saving rate is as high as 9.14%. It is proved that MA-PSO algorithm has good searching performance and high convergence accuracy. At the same time, the MA-PSO algorithm is applied to solve the economic scheduling problem, which can obtain better economic and environmental benefits.
【學(xué)位授予單位】：華北水利水電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18;TM73

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本文編號(hào)：2317144