基于改進粒子群算法的微電網(wǎng)多目標優(yōu)化運行研究
發(fā)布時間：2019-05-23 13:10
【摘要】：隨著全球能源危機加劇、環(huán)境的惡化以及大規(guī)模電力系統(tǒng)弊端的日益凸顯，分布式發(fā)電受到越來越多的關注，微電網(wǎng)作為多種分布式發(fā)電的有效集成，充分發(fā)揮了分布式發(fā)電的優(yōu)勢，能夠靈活地運行在并網(wǎng)狀態(tài)或孤島狀態(tài)，是解決分布式發(fā)電接入大電網(wǎng)的有效技術手段，提高了供電的可靠性和安全性。然而，分布式發(fā)電的多樣性以及組合的靈活性導致微電網(wǎng)在優(yōu)化運行、能量管理和運行控制與保護等方面存在較多亟須解決的問題，其中，微電網(wǎng)的優(yōu)化運行能有效提高能源利用率，減少發(fā)電成本和污染物的排放，對微電網(wǎng)系統(tǒng)的經(jīng)濟、環(huán)保、可靠運行有重要的意義，因此，研究微電網(wǎng)的優(yōu)化運行具有重要的實用價值和理論意義。 微電網(wǎng)的優(yōu)化運行問題是一個復雜的多目標，多約束，多變量的非線性優(yōu)化問題，智能優(yōu)化算法以其優(yōu)越的性能在微電網(wǎng)的優(yōu)化運行領域獲得了廣泛應用，如遺傳算法、免疫算法和粒子群算法等。其中，粒子群算法具有簡單、魯棒性強、精度高、收斂快等優(yōu)點。本文針對多目標粒子群算法存在的問題進行改進，提出基于全局最優(yōu)位置自適應選取和變尺度混沌局部搜索的改進多目標粒子群算法(Improved Multiobjective Particle Swarm Optimization Algorithm Based on GlobalBest Adaptive Selection and Mutative Scale Chaotic Local Search，IMOPSO-GL)，并采用ZDT系列標準測試函數(shù)和IEEE30節(jié)點電力系統(tǒng)無功優(yōu)化問題測試IMOPSO-GL算法的性能。結果表明，IMOPSO-GL具有良好的收斂性、且搜索到的Pareto最優(yōu)前端分布寬廣且均勻。IMOPSO-GL算法主要改進的策略有： ①全局最優(yōu)位置對多目標粒子群算法的收斂性和多樣性有很大的影響，本文提出了全局最優(yōu)位置自適應選取策略，在借鑒Sigma法原理的基礎上，，采用動態(tài)零點技術修正Sigma值的計算，同時引入擁擠距離機制，限制檔案粒子被選為全局最優(yōu)位置的最大次數(shù)，最終使種群均勻且快速地向當前Pareto最優(yōu)前沿飛行； ②外部檔案用來保留算法在搜索過程中找到的非劣解，對算法的性能有很大影響，本文通過對外部檔案設置最小規(guī)模，防止外部檔案規(guī)模過小時，粒子過于聚集在某些區(qū)域；當檔案規(guī)模超過最大規(guī)模時，采用循環(huán)擁擠排序策略刪除外部檔案多余成員，確保外部檔案在整個進化過程中的多樣性； ③由于多目標粒子群算法容易早熟收斂，本文提出變尺度混沌局部搜索策略，在種群尋優(yōu)能力減弱時，從外部檔案中選擇部分特殊粒子進行局部搜索，以提高算法的收斂性。 本文以包含光伏電池、風機、燃料電池、微型燃氣輪機、柴油發(fā)電機以及儲能系統(tǒng)的微電網(wǎng)為例，其中儲能系統(tǒng)采用由蓄電池和超級電容組成的蓄電池超級電容混合儲能系統(tǒng)。針對并網(wǎng)運行的微電網(wǎng)系統(tǒng)，建立了考慮經(jīng)濟收益與環(huán)境成本的微電網(wǎng)多目標優(yōu)化運行數(shù)學模型，針對孤島運行的微電網(wǎng)系統(tǒng)，建立了考慮發(fā)電成本與環(huán)保費用的微電網(wǎng)多目標優(yōu)化運行數(shù)學模型，并考慮了正負旋轉備用約束。在滿足功率平衡、分布式電源出力約束、蓄電池和超級電容荷電狀態(tài)和輸出功率約束以及與配電網(wǎng)傳輸功率約束的前提下，利用模糊專家系統(tǒng)和IMOPSO-GL進行模型求解，通過算例仿真，驗證了所建立模型的有效性。
[Abstract]:With the aggravation of the global energy crisis, the deterioration of the environment and the abuse of the large-scale power system, the distributed power generation has attracted more and more attention, and the micro-power grid is the effective integration of a variety of distributed power generation, and the advantages of the distributed power generation are fully realized, Can operate flexibly in a grid state or an isolated island state, and is an effective technical means for solving the distributed power generation access large power grid, and the reliability and the safety of the power supply are improved. However, the diversity of the distributed power generation and the flexibility of the combination lead to the problems that the micro-power grid has to solve in the aspects of optimal operation, energy management and operation control and protection, and the optimized operation of the micro-power grid can effectively improve the energy utilization rate, It is of great practical value and theory significance to study the optimization and operation of the micro-power grid, which is of great significance to the economic, environmental and reliable operation of the micro-grid system. The optimization and operation of the micro-power grid is a complicated multi-objective, multi-constraint and multi-variable nonlinear optimization problem. The intelligent optimization algorithm has been widely used in the optimization and operation of the micro-power grid with its superior performance, such as genetic algorithm, immune algorithm and particle swarm optimization. And the like, wherein the particle swarm algorithm has the advantages of simplicity, strong robustness, high precision, fast convergence, and the like. An improved multi-objective particle swarm optimization algorithm based on global optimal position adaptive selection and variable-scale hybrid local search is proposed in this paper. The improved Multiobjective Particle Swarm Optimization Algorithm Based on GlobalBest Adaptive Selection and Mutative Scale Chaotic Local Search is proposed. and the performance of the IMOPPSO-GL algorithm is tested by adopting a ZDT series standard test function and an IEEE30 node power system reactive power optimization problem The results show that the IMAOPSO-GL has good convergence and the Pareto optimal front-end distribution is wide and uniform. An improved strategy of the IMAOPSO-GL algorithm It is shown that the global optimal position has great influence on the convergence and diversity of the multi-objective particle swarm optimization algorithm. In this paper, a global optimal position adaptive selection strategy is proposed, which is based on the principle of the Sigma method. on the basis of the calculation of the sigma value by adopting the dynamic zero-point technique, the congestion distance mechanism is introduced, the maximum number of the file particles is restricted to the global optimal position is limited, the population is finally made to be uniform and fast, In flight, an external file is used to keep the non-inferior solution found in the search process, and has a great impact on the performance of the algorithm. in that case of the most large scale, the redundant members of the external file are removed by a circular congestion sort policy to ensure that the external files are throughout the evolution process In this paper, the local search strategy of variable-scale hybrid is presented in this paper, because the multi-objective particle swarm optimization algorithm is easy to converge. when the optimization capability is reduced, part of the special particles are selected from the external files for partial search, The convergence of the high algorithm is as an example of the micro-power grid which contains the photovoltaic cell, the fan, the fuel cell, the micro-gas turbine, the diesel generator and the energy storage system. Aiming at the micro-grid system running in grid-connected operation, a multi-objective optimal operation mathematical model of a micro-power grid considering the economic benefit and the environmental cost is established, and the micro-grid multi-objective optimization operation mathematical model which takes into account the economic benefit and the environmental cost is established, and the micro-grid multi-objective optimization operation model which takes into account the power generation cost and the environmental protection cost is established for the micro-grid system running on the island. The Mathematical Model of the Optimization of the Standard Operation and the Consideration Under the premise of satisfying the power balance, the distributed power output constraint, the charge state and the output power of the storage battery and the super capacitor and the transmission power of the power distribution network, the fuzzy expert system and the IMO-GL are used for the model. The solution is simulated and verified by an example.
【學位授予單位】：重慶大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM732
本文編號：2483921
[Abstract]:With the aggravation of the global energy crisis, the deterioration of the environment and the abuse of the large-scale power system, the distributed power generation has attracted more and more attention, and the micro-power grid is the effective integration of a variety of distributed power generation, and the advantages of the distributed power generation are fully realized, Can operate flexibly in a grid state or an isolated island state, and is an effective technical means for solving the distributed power generation access large power grid, and the reliability and the safety of the power supply are improved. However, the diversity of the distributed power generation and the flexibility of the combination lead to the problems that the micro-power grid has to solve in the aspects of optimal operation, energy management and operation control and protection, and the optimized operation of the micro-power grid can effectively improve the energy utilization rate, It is of great practical value and theory significance to study the optimization and operation of the micro-power grid, which is of great significance to the economic, environmental and reliable operation of the micro-grid system. The optimization and operation of the micro-power grid is a complicated multi-objective, multi-constraint and multi-variable nonlinear optimization problem. The intelligent optimization algorithm has been widely used in the optimization and operation of the micro-power grid with its superior performance, such as genetic algorithm, immune algorithm and particle swarm optimization. And the like, wherein the particle swarm algorithm has the advantages of simplicity, strong robustness, high precision, fast convergence, and the like. An improved multi-objective particle swarm optimization algorithm based on global optimal position adaptive selection and variable-scale hybrid local search is proposed in this paper. The improved Multiobjective Particle Swarm Optimization Algorithm Based on GlobalBest Adaptive Selection and Mutative Scale Chaotic Local Search is proposed. and the performance of the IMOPPSO-GL algorithm is tested by adopting a ZDT series standard test function and an IEEE30 node power system reactive power optimization problem The results show that the IMAOPSO-GL has good convergence and the Pareto optimal front-end distribution is wide and uniform. An improved strategy of the IMAOPSO-GL algorithm It is shown that the global optimal position has great influence on the convergence and diversity of the multi-objective particle swarm optimization algorithm. In this paper, a global optimal position adaptive selection strategy is proposed, which is based on the principle of the Sigma method. on the basis of the calculation of the sigma value by adopting the dynamic zero-point technique, the congestion distance mechanism is introduced, the maximum number of the file particles is restricted to the global optimal position is limited, the population is finally made to be uniform and fast, In flight, an external file is used to keep the non-inferior solution found in the search process, and has a great impact on the performance of the algorithm. in that case of the most large scale, the redundant members of the external file are removed by a circular congestion sort policy to ensure that the external files are throughout the evolution process In this paper, the local search strategy of variable-scale hybrid is presented in this paper, because the multi-objective particle swarm optimization algorithm is easy to converge. when the optimization capability is reduced, part of the special particles are selected from the external files for partial search, The convergence of the high algorithm is as an example of the micro-power grid which contains the photovoltaic cell, the fan, the fuel cell, the micro-gas turbine, the diesel generator and the energy storage system. Aiming at the micro-grid system running in grid-connected operation, a multi-objective optimal operation mathematical model of a micro-power grid considering the economic benefit and the environmental cost is established, and the micro-grid multi-objective optimization operation mathematical model which takes into account the economic benefit and the environmental cost is established, and the micro-grid multi-objective optimization operation model which takes into account the power generation cost and the environmental protection cost is established for the micro-grid system running on the island. The Mathematical Model of the Optimization of the Standard Operation and the Consideration Under the premise of satisfying the power balance, the distributed power output constraint, the charge state and the output power of the storage battery and the super capacitor and the transmission power of the power distribution network, the fuzzy expert system and the IMO-GL are used for the model. The solution is simulated and verified by an example.
【學位授予單位】：重慶大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM732
【參考文獻】
相關期刊論文 前10條
1 江岳文;陳沖;溫步瀛;;基于隨機模擬粒子群算法的含風電場電力系統(tǒng)經(jīng)濟調度[J];電工電能新技術;2007年03期
2 張國強;張伯明;;基于組合預測的風電場風速及風電機功率預測[J];電力系統(tǒng)自動化;2009年18期
3 陳達威;朱桂萍;;微電網(wǎng)負荷優(yōu)化分配[J];電力系統(tǒng)自動化;2010年20期
4 王銳;顧偉;吳志;;含可再生能源的熱電聯(lián)供型微網(wǎng)經(jīng)濟運行優(yōu)化[J];電力系統(tǒng)自動化;2011年08期
5 曹一家;苗軼群;江全元;;含電動汽車換電站的微電網(wǎng)孤島運行優(yōu)化[J];電力自動化設備;2012年05期
6 牛銘;黃偉;郭佳歡;蘇玲;;微網(wǎng)并網(wǎng)時的經(jīng)濟運行研究[J];電網(wǎng)技術;2010年11期
7 劉天琪;江東林;;基于儲能單元運行方式優(yōu)化的微電網(wǎng)經(jīng)濟運行[J];電網(wǎng)技術;2012年01期
8 張雙樂;李鵬;陳超;施儒昱;;基于改進變尺度混沌優(yōu)化算法的微網(wǎng)優(yōu)化運行[J];電力自動化設備;2013年01期
9 洪博文;郭力;王成山;焦冰琦;劉文建;;微電網(wǎng)多目標動態(tài)優(yōu)化調度模型與方法[J];電力自動化設備;2013年03期
10 石慶均;江全元;;包含蓄電池儲能的微網(wǎng)實時能量優(yōu)化調度[J];電力自動化設備;2013年05期
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