本文選題：智能電網(wǎng)　切入點(diǎn)：多目標(biāo)優(yōu)化　出處：《蘭州理工大學(xué)》2014年碩士論文

【摘要】：智能電網(wǎng)是應(yīng)對(duì)全球能源、氣候、環(huán)境以及經(jīng)濟(jì)和可持續(xù)發(fā)展的綜合解決方案,也是未來(lái)電網(wǎng)研究與發(fā)展的方向。輸配電網(wǎng)的智能化是堅(jiān)強(qiáng)智能電網(wǎng)的重要內(nèi)容。智能電網(wǎng)規(guī)劃是電力系統(tǒng)規(guī)劃的重要組成部分,對(duì)其進(jìn)行科學(xué)合理的規(guī)劃,能夠給社會(huì)帶來(lái)巨大的經(jīng)濟(jì)效益和社會(huì)效益,同時(shí)也是發(fā)展智能電網(wǎng)的重要基礎(chǔ)。電網(wǎng)規(guī)劃是一個(gè)非線性、多目標(biāo)、多約束的問(wèn)題,其涉及潮流計(jì)算、無(wú)功補(bǔ)償、網(wǎng)絡(luò)損耗、供電可靠性及經(jīng)濟(jì)性等約束。 用傳統(tǒng)的電網(wǎng)優(yōu)化方法求解電網(wǎng)規(guī)劃時(shí),算法往往容易陷入局部最優(yōu)解,使得最終不能尋到全局最優(yōu)解。近年來(lái),現(xiàn)代啟發(fā)式算法被廣泛應(yīng)用于各個(gè)領(lǐng)域并且取得很好的效果,這些算法具有全局尋優(yōu)能力強(qiáng)及通用性強(qiáng)等特點(diǎn),但是容易產(chǎn)生“維數(shù)災(zāi)”問(wèn)題。本文根據(jù)粒子群算法的基本思想,結(jié)合量子理論,提出一種改進(jìn)的量子粒子群優(yōu)化算法,并將其應(yīng)用在輸電網(wǎng)網(wǎng)架規(guī)劃和含分布式電源的智能電網(wǎng)多目標(biāo)優(yōu)化規(guī)劃中。研究表明,該算法對(duì)智能電網(wǎng)多目標(biāo)優(yōu)化規(guī)劃是有效的。 本文根據(jù)電網(wǎng)規(guī)劃的多目標(biāo)性,選擇合適的目標(biāo)建立電網(wǎng)規(guī)劃的數(shù)學(xué)模型。對(duì)量子粒子群算法進(jìn)行改進(jìn),使其能應(yīng)用在離散問(wèn)題的求解中。改進(jìn)后的量子粒子群算法提高了算法的運(yùn)行速度和收斂速度。以18節(jié)點(diǎn)輸電網(wǎng)系統(tǒng)擴(kuò)展規(guī)劃和8節(jié)點(diǎn)含分布式電源的配電網(wǎng)擴(kuò)展規(guī)劃為例,驗(yàn)證該算法求解多目標(biāo)規(guī)劃的有效性及高效性。對(duì)于多目標(biāo)Pareto最優(yōu)解集,采用擁擠距離排序方法進(jìn)行構(gòu)造。 最后應(yīng)用MATLAB軟件對(duì)算例進(jìn)行仿真研究并得出相應(yīng)規(guī)劃結(jié)果。結(jié)果表明本文應(yīng)用的量子粒子群算法在智能電網(wǎng)多目標(biāo)規(guī)劃時(shí)能夠在保證計(jì)算速度的前提下,很好的完成電網(wǎng)規(guī)劃的任務(wù)。
[Abstract]:Smart grid is a comprehensive solution to global energy, climate, environment, economic and sustainable development. It is also the direction of grid research and development in the future.Intelligent transmission and distribution network is an important content of strong smart grid.Smart grid planning is an important part of power system planning. Scientific and reasonable planning can bring great economic and social benefits to the society, and it is also an important basis for the development of smart grid.Power network planning is a nonlinear, multi-objective, multi-constraint problem, which involves power flow calculation, reactive power compensation, network loss, power supply reliability and economic constraints.When the traditional power network optimization method is used to solve the power network planning, the algorithm is easily trapped in the local optimal solution, so that the global optimal solution can not be found in the end.In recent years, modern heuristic algorithms have been widely used in various fields and achieved good results. These algorithms have the characteristics of strong global optimization ability and strong versatility, but they are easy to produce the problem of "dimension disaster".Based on the basic idea of particle swarm optimization and quantum theory, an improved quantum particle swarm optimization algorithm is proposed in this paper. It is applied to transmission network planning and smart grid multi-objective optimization planning with distributed generation.The research shows that the algorithm is effective for multi-objective optimization planning of smart grid.In this paper, according to the multi-objective of power network planning, the mathematical model of power network planning is established by selecting suitable targets.The quantum particle swarm optimization (QPSO) algorithm is improved so that it can be used to solve discrete problems.The improved Quantum Particle Swarm Optimization (QPSO) algorithm improves the speed of operation and convergence of the algorithm.Taking the expansion planning of 18-node transmission network system and the distribution network expansion planning with 8-node distributed generation as examples, the effectiveness and efficiency of the algorithm for solving multi-objective programming are verified.For the multi-objective Pareto optimal solution set, the congestion distance sorting method is used to construct the optimal solution set.Finally, the MATLAB software is used to simulate the example and the corresponding programming results are obtained.The results show that the quantum particle swarm optimization (QPSO) algorithm applied in this paper can accomplish the task of power network planning well under the premise of ensuring the computing speed when the smart grid multi-objective programming is carried out.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM715

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