發(fā)布時間：2018-06-15 07:01

  本文選題：微電網(wǎng) + 自適應(yīng)人工魚群算法��； 參考：《西安理工大學》2017年碩士論文

【摘要】：微電網(wǎng)中包含多種分布式電源,可以靈活地運行于并網(wǎng)狀態(tài)和離網(wǎng)狀態(tài),有效地解決了新能源發(fā)電接入大電網(wǎng)的中出現(xiàn)的問題。但是,分布式電源的多樣性和組合的隨機性導致了微電網(wǎng)的運行控制難和運行成本高等問題,其中,微電網(wǎng)的優(yōu)化運行可以降低發(fā)電成本和環(huán)境污染費用,提升能源利用效率,對微電網(wǎng)系統(tǒng)的經(jīng)濟、可靠運行和環(huán)保至關(guān)重要,因此,研究微電網(wǎng)的優(yōu)化運行有十分重要的意義。本文首先介紹了微電網(wǎng)發(fā)展的背景和意義,分析了國內(nèi)外微電網(wǎng)及其優(yōu)化運行的研究現(xiàn)狀,闡述了典型微電網(wǎng)系統(tǒng)的結(jié)構(gòu)特點和工作原理。詳細地分析了微電網(wǎng)中的主要微電源:風力發(fā)電機、光伏電池、微型燃氣輪機、燃料電池及儲能系統(tǒng)的運行特性和成本函數(shù),為建立微電網(wǎng)多目標優(yōu)化運行模型奠定基礎(chǔ)。其次,針對基本人工魚群算法容易陷入局部最優(yōu)解、收斂速度慢和尋優(yōu)精度低的缺陷,提出一種改進的自適應(yīng)人工魚群算法方法:主要改進了算法覓食行為的視野和步長,人工魚同時按兩種視野進行搜索,確定兩個目標位置后,計算兩個目標的食物濃度并與當前位置的食物濃度進行比較,選擇向食物濃度低的位置移動。通過三個局部極值突出的函數(shù)測試,表明改進的算法要優(yōu)于叢本的人工魚群算法。針對含風、光、儲、微型燃氣輪機和燃料電池的并網(wǎng)型和離網(wǎng)型微電網(wǎng),綜合考慮發(fā)電成本(燃料成本、投資折舊成本、設(shè)備維護成本、并網(wǎng)時的電能交互成本)和環(huán)境成本,給出了在求解該模型時的安全運行的約束條件,使系統(tǒng)在一個優(yōu)化周期內(nèi)的總運行成本最低,建立了微電網(wǎng)多目標優(yōu)化數(shù)學模型,分別制定出微電網(wǎng)在并網(wǎng)運行時、孤網(wǎng)運行時分時段優(yōu)化運行策略,采用約束優(yōu)化自適應(yīng)人工魚群算法(AAFSA)對建立的數(shù)學模型進行求解,求得一個優(yōu)化周期內(nèi)各分布式電源的最佳出力及運行總成本,并與基本人工魚群算法求得的結(jié)果作對比。仿真結(jié)果表明:改進的算法具有避免陷入局部最優(yōu)解、收斂速度快和精度高的特點。
[Abstract]:The microgrid contains a variety of distributed power sources, which can operate flexibly in grid-connected and off-grid states, thus effectively solving the problem of new energy generation connected to large power grid. However, the diversity of distributed generation and the randomness of combination lead to the difficulty of operation control and high operating cost of microgrid. Among them, the optimal operation of microgrid can reduce the cost of generation and environmental pollution, and improve the efficiency of energy utilization. It is of great importance to the economy, reliable operation and environmental protection of microgrid system, so it is very important to study the optimal operation of microgrid. This paper first introduces the background and significance of the development of microgrid, analyzes the research status of microgrid and its optimal operation at home and abroad, and expounds the structural characteristics and working principle of typical microgrid system. The operating characteristics and cost function of the main micro-power sources in microgrid, such as wind turbine, photovoltaic cell, micro-gas turbine, fuel cell and energy storage system, are analyzed in detail, which lays the foundation for the establishment of multi-objective optimal operation model of microgrid. Secondly, aiming at the defects of the basic artificial fish swarm algorithm that it is easy to fall into the local optimal solution, the convergence speed is slow and the optimization accuracy is low, an improved adaptive artificial fish swarm algorithm method is proposed: the visual field and step size of the algorithm foraging behavior are mainly improved. The artificial fish searched according to two kinds of visual fields at the same time, determined the two target locations, calculated the food concentration of the two targets and compared with the food concentration of the current position, and chose to move to the position where the food concentration was low. The results show that the improved algorithm is better than the artificial fish swarm algorithm. For grid-connected and off-grid microgrids containing wind, light, storage, micro gas turbines and fuel cells, the cost of generating electricity (fuel cost, depreciation cost of investment, maintenance cost of equipment, interactive cost of electricity when connected to the grid) and environmental cost are considered synthetically. The constraint conditions for the safe operation of the system in solving the model are given. The total operating cost of the system is the lowest in a single optimization period. The multi-objective optimization mathematical model of the microgrid is established, and the microgrid in grid-connected operation is worked out respectively. In this paper, the algorithm of adaptive artificial fish swarm algorithm (AAFSAA) is used to solve the mathematical model of the isolated network. The optimal output and total operating cost of each distributed power source in an optimized period are obtained. The results are compared with those obtained by the basic artificial fish swarm algorithm. The simulation results show that the improved algorithm has the advantages of avoiding falling into local optimal solution, fast convergence speed and high precision.
【學位授予單位】：西安理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM732

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相關(guān)期刊論文 前10條

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本文編號：2021132