本文關(guān)鍵詞：基于協(xié)調(diào)優(yōu)化算法的有源配電網(wǎng)經(jīng)濟(jì)調(diào)度策略研究　出處：《南京郵電大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 微電網(wǎng) 經(jīng)濟(jì)調(diào)度 雙層優(yōu)化算法 粒子群算法 有源配電網(wǎng) 分解協(xié)調(diào)優(yōu)化

【摘要】：由于能源危機(jī)的日益嚴(yán)重以及大力推動(dòng)構(gòu)建環(huán)境友好型社會(huì),可再生能源技術(shù)得到快速發(fā)展。其中,風(fēng)力發(fā)電及光伏發(fā)電等可再生能源發(fā)電技術(shù)得到廣泛應(yīng)用,在環(huán)境效益等方面體現(xiàn)出獨(dú)特優(yōu)勢(shì),但同時(shí)也存在諸如接入成本高、控制技術(shù)不足,對(duì)主網(wǎng)產(chǎn)生消極影響等缺點(diǎn),為此,微電網(wǎng)技術(shù)應(yīng)運(yùn)而生。微電網(wǎng)將各可再生能源集中管理,構(gòu)成一個(gè)相對(duì)大電網(wǎng)友好的可控單元,充分發(fā)揮各微電源優(yōu)勢(shì)的同時(shí)也能保證不會(huì)對(duì)電網(wǎng)的安全穩(wěn)定產(chǎn)生影響。微電網(wǎng)并網(wǎng)運(yùn)行,配電網(wǎng)成為有源系統(tǒng),對(duì)與微電網(wǎng)的能量交換進(jìn)行合理調(diào)度可以起到節(jié)能降損,提高配電網(wǎng)電能質(zhì)量的積極作用。一方面,微電網(wǎng)中微電源的經(jīng)濟(jì)調(diào)度是微電網(wǎng)調(diào)度技術(shù)的一個(gè)重點(diǎn),如何消除風(fēng)電出力及光伏出力以及負(fù)荷等不確定因素對(duì)微電網(wǎng)安全穩(wěn)定運(yùn)行產(chǎn)生的隱患,本文對(duì)此進(jìn)行了研究。另一方面,并網(wǎng)模式下微電網(wǎng)與配電網(wǎng)之間的協(xié)調(diào)優(yōu)化方面的研究還比較缺乏,有源配電網(wǎng)與微電網(wǎng)之間如何實(shí)現(xiàn)互利共贏,本文對(duì)此進(jìn)行了試探性研究。對(duì)于微電網(wǎng)經(jīng)濟(jì)調(diào)度問(wèn)題,提出一種集中-分布式雙層能量?jī)?yōu)化算法,算法基于改進(jìn)粒子群算法實(shí)現(xiàn)。分別建立集中層及分布式層優(yōu)化模型,集中層以預(yù)測(cè)數(shù)據(jù)進(jìn)行日前調(diào)度,采用集中優(yōu)化方式。分布式層基于實(shí)時(shí)數(shù)據(jù),用于化解各不確定因素帶來(lái)的影響,采用分布式優(yōu)化的方式,運(yùn)用圖論的思想將計(jì)算任務(wù)分配至各參與調(diào)度的節(jié)點(diǎn),快速化解波動(dòng)。通過(guò)對(duì)一個(gè)中壓基準(zhǔn)微電網(wǎng)系統(tǒng)進(jìn)行雙層優(yōu)化仿真調(diào)度,驗(yàn)證了該算法的能快速收斂,有效得到最優(yōu)解。對(duì)于配電網(wǎng)與微電網(wǎng)的協(xié)調(diào)優(yōu)化調(diào)度,提出了一種分解協(xié)調(diào)優(yōu)化算法。首先構(gòu)建配電網(wǎng)及微電網(wǎng)的協(xié)調(diào)優(yōu)化模型,并對(duì)協(xié)調(diào)參數(shù)進(jìn)行設(shè)計(jì)。配電網(wǎng)基于改進(jìn)粒子群算法及二進(jìn)制粒子群算法,以網(wǎng)損最小為目標(biāo)進(jìn)行重構(gòu)及注入功率優(yōu)化。微電網(wǎng)依然以成本最小為目標(biāo)。算法通過(guò)傳遞協(xié)調(diào)參數(shù),協(xié)調(diào)兩個(gè)優(yōu)化目標(biāo)。用兩個(gè)算例對(duì)算法進(jìn)行驗(yàn)證,首先通過(guò)對(duì)IEEE33節(jié)點(diǎn)配電網(wǎng)系統(tǒng)進(jìn)行仿真,驗(yàn)證配電網(wǎng)的重構(gòu)及注入功率雙重優(yōu)化算法的有效性,然后對(duì)由中壓基準(zhǔn)配電網(wǎng)系統(tǒng)與IEEE33節(jié)點(diǎn)系統(tǒng)構(gòu)成的整體系統(tǒng)進(jìn)行協(xié)調(diào)優(yōu)化仿真,驗(yàn)證分解協(xié)調(diào)優(yōu)化算法,結(jié)果顯示協(xié)調(diào)優(yōu)化可以同時(shí)降低配電網(wǎng)網(wǎng)損及微電網(wǎng)的發(fā)電成本。
[Abstract]:Renewable energy technology has been developing rapidly due to the increasingly serious energy crisis and vigorously promoting the construction of an environment-friendly society. Among them, renewable energy generation technologies such as wind power generation and photovoltaic power generation have been widely used. In the aspect of environmental benefit, there are some disadvantages, such as high access cost, insufficient control technology, negative influence on the main network and so on. Microgrid technology emerged as the times require. Microgrid will be centralized management of renewable energy resources, constitute a relatively large power grid friendly controllable unit. While giving full play to the advantages of the micro-power supply, it can also ensure that the security and stability of the grid will not be affected. The distribution network will become an active system when the micro-grid is connected to the grid. Reasonable dispatch of energy exchange with microgrid can play an active role in reducing energy loss and improving power quality of distribution network. On the one hand, economic dispatch of micro-power supply in micro-grid is one of the emphases of micro-grid dispatching technology. How to eliminate the uncertain factors such as wind power output, photovoltaic output and load on the safe and stable operation of microgrid, this paper studies this. On the other hand. The research on coordination and optimization between microgrid and distribution network in grid-connected mode is still lacking, and how to achieve mutual benefit and win-win between active distribution network and micro-grid. This paper makes a tentative study on this. For the economic dispatch problem of microgrid, a centralized and distributed bilevel energy optimization algorithm is proposed. The algorithm is implemented based on improved particle swarm optimization algorithm. The optimization models of centralized layer and distributed layer are established respectively. The centralized layer is used to schedule the prediction data before the day, and the centralized optimization method is adopted. The distributed layer is based on real-time data. In order to resolve the influence of uncertain factors, the distributed optimization method is used to distribute the computing tasks to the nodes involved in the scheduling by using the idea of graph theory. Through the double-layer simulation and scheduling of a medium voltage reference microgrid system, it is verified that the algorithm can converge quickly and get the optimal solution effectively. For the coordination of distribution network and microgrid, the optimal dispatching is obtained. A decomposition and coordination optimization algorithm is proposed. Firstly, the coordination optimization model of distribution network and microgrid is constructed, and the coordination parameters are designed. The distribution network is based on improved particle swarm optimization algorithm and binary particle swarm optimization algorithm. The minimum loss is taken as the goal to reconstruct and optimize the injection power. The micro-grid still takes the minimum cost as the target. The algorithm coordinates the two optimization objectives through the transfer of coordination parameters. Two examples are used to verify the algorithm. First, the simulation of IEEE33 node distribution network system is carried out to verify the effectiveness of the dual optimization algorithm of distribution network reconfiguration and injection power. Then the overall system composed of medium voltage benchmark distribution network system and IEEE33 node system is simulated to verify the decomposition coordination optimization algorithm. The results show that coordinated optimization can reduce the generation cost of distribution network loss and microgrid simultaneously.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TM73

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