基于灰色PSO算法的分布式電網(wǎng)多目標調峰優(yōu)化調度研究
發(fā)布時間：2018-09-14 08:53
【摘要】：目前我國新能源發(fā)展已經(jīng)走在了世界前列,成為全球風電規(guī)模最大、光伏發(fā)電增長最快的國家。然而風電、光伏這類分布式電源(Distributed Generation,DG)出力具有隨機性和間歇性,這種不確定性使系統(tǒng)的調峰調度任務更加艱巨。而傳統(tǒng)的依賴發(fā)電機組進行調峰的方式已經(jīng)無法完成目前新能源大規(guī)模并網(wǎng)所帶來的調峰任務。因此本文針對目前新能源大規(guī)模并網(wǎng),傳統(tǒng)的調峰調度運行方式不能滿足新能源出力消納要求的問題,探索一種用戶側主動參與調峰調度的可行方案。論文首先簡要介紹了我國現(xiàn)行的調峰調度模式,并且對其調峰調度模式的優(yōu)缺點進行分析。根據(jù)新能源發(fā)電的出力特性說明新能源大規(guī)模并網(wǎng)對電力系統(tǒng)所帶來的影響。論文針對目前調峰調度方式在新能源消納難以奏效的情況,挖掘需求側調峰資源、研究需求側管理方法,將新能源發(fā)電的特性與不同種類負荷的特性進行聯(lián)動,尋求一種可以實現(xiàn)功率互補,減小系統(tǒng)調峰壓力的協(xié)調調度模式。其次,論文針對DG、儲能單元、可控負荷、可中斷負荷等這些不同類型的分布式能源(Distributed Energy Resource,DER)數(shù)量多、分布分散、類型不一、難以管理的問題,提出利用虛擬電廠(Virtual Power Plant,VPP)將多個DER進行協(xié)調優(yōu)化,實現(xiàn)調峰資源合理優(yōu)化配置,構建以負荷方差最小以及運行成本最小為目標的多目標調峰調度模型。論文針對傳統(tǒng)多目標粒子群(Multi-objective Particle Swarm Optimization,MOPSO)算法精度低、容易陷入局極小、更新策略具有隨機性和對種群的全局最優(yōu)解選取缺乏指導所造成優(yōu)化結果的客觀性和可信度不足的缺點,提出了基于灰色關聯(lián)度的多目標粒子群改進算法。最后,論文以IEEE33節(jié)點配電系統(tǒng)為算例進行仿真,通過驗證表明論文提出的方法能夠有效增強系統(tǒng)調峰能力、提高新能源消納水平以及降低電網(wǎng)運行成本。
[Abstract]:At present, China's new energy development has been in the forefront of the world, become the world's largest wind power, photovoltaic power generation growth of the fastest country. However, the distributed power generation (Distributed Generation,DG), such as wind power and photovoltaic, has randomness and intermittency, which makes the task of peak-shaving scheduling more difficult. However, the traditional peak-shaving method based on generator sets has been unable to complete the peak-shaving task brought by large-scale grid connection of new energy sources. Therefore, in view of the problem that the traditional peak-shaving operation mode can not meet the demand of new energy, this paper explores a feasible scheme of active participation of user side in peak-shaving scheduling. Firstly, the paper briefly introduces the current peak-shaving scheduling mode in China, and analyzes the advantages and disadvantages of the peak-shaving scheduling mode. According to the power generation characteristics of new energy generation, the influence of large-scale grid connection of new energy on power system is explained. Aiming at the situation that the current peak-shaving dispatching mode is difficult to work in the new energy consumption, the paper excavates the demand-side peak-shaving resources, studies the demand-side management method, and links the characteristics of new energy generation with the characteristics of different kinds of loads. To seek a coordinated scheduling mode which can realize the complementary power and reduce the peak-shaving pressure of the system. Secondly, this paper aims at the problems of DG, energy storage unit, controllable load, interruptible load and so on, which are many in quantity, scattered in distribution, different in type and difficult to manage. A multi-objective peak-shaving scheduling model with minimum load variance and minimum operating cost is proposed by using virtual power plant (Virtual Power Plant,VPP) to coordinate and optimize multiple DER to realize rational allocation of peak-shaving resources. In this paper, the traditional multi-objective particle swarm optimization (Multi-objective Particle Swarm Optimization,MOPSO) algorithm has the disadvantages of low precision, easy to fall into local minima, randomness of update strategy and lack of guidance to select the global optimal solution of the population, which leads to the lack of objectivity and credibility of the optimization results. An improved multi-objective particle swarm optimization algorithm based on grey correlation degree is proposed. Finally, the simulation of IEEE33 node distribution system shows that the proposed method can effectively enhance the peak-shaving ability of the system, improve the level of new energy consumption and reduce the power grid operation costs.
【學位授予單位】：蘭州理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM73
[Abstract]:At present, China's new energy development has been in the forefront of the world, become the world's largest wind power, photovoltaic power generation growth of the fastest country. However, the distributed power generation (Distributed Generation,DG), such as wind power and photovoltaic, has randomness and intermittency, which makes the task of peak-shaving scheduling more difficult. However, the traditional peak-shaving method based on generator sets has been unable to complete the peak-shaving task brought by large-scale grid connection of new energy sources. Therefore, in view of the problem that the traditional peak-shaving operation mode can not meet the demand of new energy, this paper explores a feasible scheme of active participation of user side in peak-shaving scheduling. Firstly, the paper briefly introduces the current peak-shaving scheduling mode in China, and analyzes the advantages and disadvantages of the peak-shaving scheduling mode. According to the power generation characteristics of new energy generation, the influence of large-scale grid connection of new energy on power system is explained. Aiming at the situation that the current peak-shaving dispatching mode is difficult to work in the new energy consumption, the paper excavates the demand-side peak-shaving resources, studies the demand-side management method, and links the characteristics of new energy generation with the characteristics of different kinds of loads. To seek a coordinated scheduling mode which can realize the complementary power and reduce the peak-shaving pressure of the system. Secondly, this paper aims at the problems of DG, energy storage unit, controllable load, interruptible load and so on, which are many in quantity, scattered in distribution, different in type and difficult to manage. A multi-objective peak-shaving scheduling model with minimum load variance and minimum operating cost is proposed by using virtual power plant (Virtual Power Plant,VPP) to coordinate and optimize multiple DER to realize rational allocation of peak-shaving resources. In this paper, the traditional multi-objective particle swarm optimization (Multi-objective Particle Swarm Optimization,MOPSO) algorithm has the disadvantages of low precision, easy to fall into local minima, randomness of update strategy and lack of guidance to select the global optimal solution of the population, which leads to the lack of objectivity and credibility of the optimization results. An improved multi-objective particle swarm optimization algorithm based on grey correlation degree is proposed. Finally, the simulation of IEEE33 node distribution system shows that the proposed method can effectively enhance the peak-shaving ability of the system, improve the level of new energy consumption and reduce the power grid operation costs.
【學位授予單位】：蘭州理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM73
【參考文獻】
相關期刊論文 前10條
1 張高;王旭;蔣傳文;張裕;王正宇;;采用雙層優(yōu)化調度的虛擬電廠經(jīng)濟性分析[J];電網(wǎng)技術;2016年08期
2 袁桂麗;陳少梁;劉穎;房方;;基于分時電價的虛擬電廠經(jīng)濟性優(yōu)化調度[J];電網(wǎng)技術;2016年03期
3 薛美東;趙波;張雪松;江全元;;并網(wǎng)型微網(wǎng)的優(yōu)化配置與評估[J];電力系統(tǒng)自動化;2015年03期
4 劉聰;劉文穎;王維洲;朱丹丹;文晶;孟s，
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