【摘要】：在能源危機(jī)和環(huán)境污染日益嚴(yán)重的當(dāng)今社會(huì),具有清潔無(wú)污染、可循環(huán)利用等特點(diǎn)的風(fēng)能和太陽(yáng)能已引起廣泛關(guān)注。但由于風(fēng)能和太陽(yáng)能受到季節(jié)氣候的影響,具有間歇性和隨機(jī)性的特點(diǎn),則為了充分利用可再生能源,需要對(duì)電網(wǎng)中接入的可再生能源發(fā)電單元的容量和位置進(jìn)行合理配置。本論文針對(duì)可再生能源發(fā)電的隨機(jī)特性,對(duì)可再生能源電源的供電可靠性進(jìn)行評(píng)估,深入研究含間歇性能源的電網(wǎng)優(yōu)化配置問(wèn)題。主要研究?jī)?nèi)容及工作包含以下方面： 一、研究含儲(chǔ)能的可再生能源發(fā)電系統(tǒng)的供電可靠性評(píng)估方法,首先分析風(fēng)機(jī)和光伏電池發(fā)電功率的隨機(jī)特性,提出利用離散狀態(tài)馬爾科夫鏈對(duì)儲(chǔ)能裝置充放電過(guò)程的建模方法,基于馬爾科夫鏈的平穩(wěn)分布概率分析系統(tǒng)供電可靠性。最后,以蒙特卡羅仿真方法的計(jì)算結(jié)果作為檢驗(yàn)標(biāo)準(zhǔn),驗(yàn)證算法的準(zhǔn)確性。 二、研究分布式風(fēng)光互補(bǔ)發(fā)電系統(tǒng)的優(yōu)化配置,首先,以經(jīng)濟(jì)性為目標(biāo)應(yīng)用簡(jiǎn)單迭代法對(duì)系統(tǒng)容量的配置進(jìn)行優(yōu)化,提出了儲(chǔ)能單元剩余容量周期不變性原則,基于此原則使得滿足負(fù)荷需求的系統(tǒng)風(fēng)機(jī)個(gè)數(shù)、光伏個(gè)數(shù)和蓄電池容量三個(gè)優(yōu)化變量呈近似線性化關(guān)系,可大大減少迭代次數(shù)。其次,采用改進(jìn)的Pareto多目標(biāo)粒子群優(yōu)化算法,同時(shí)考慮供電可靠性和經(jīng)濟(jì)性兩個(gè)優(yōu)化指標(biāo),對(duì)系統(tǒng)中光伏單元、風(fēng)機(jī)和蓄電池的容量,及光伏陣列的安裝傾角和風(fēng)機(jī)的安裝高度進(jìn)行優(yōu)化配置。 三、基于概率最優(yōu)潮流算法實(shí)現(xiàn)風(fēng)電機(jī)組接入配電網(wǎng)系統(tǒng)的接入點(diǎn)和注入容量的優(yōu)化配置。以配電網(wǎng)的有功損耗為優(yōu)化目標(biāo),并計(jì)及新能源發(fā)電單元分散式接入到配電網(wǎng)中所引起的不確定因素,得到的最佳接入點(diǎn)和接入容量能夠減輕整個(gè)配電網(wǎng)的饋線壓力、提高節(jié)點(diǎn)電壓質(zhì)量且獲得環(huán)境和經(jīng)濟(jì)效益。提出了基于Cornish-Fisher級(jí)數(shù)展開(kāi)的交流概率潮流計(jì)算方法,該算法避免了傳統(tǒng)的蒙特卡羅模擬算法帶來(lái)的計(jì)算量大的問(wèn)題,提高了算法的效率。 四、基于凸二階錐優(yōu)化理論研究新能源發(fā)電單元在輻射狀配電網(wǎng)中的優(yōu)化配置問(wèn)題,首先,利用支路潮流方程對(duì)配電網(wǎng)系統(tǒng)進(jìn)行潮流計(jì)算,分析新能源接入后對(duì)配電網(wǎng)電壓分布及網(wǎng)絡(luò)損耗的影響；其次,針對(duì)支路潮流方程提出兩步松弛,即相角松弛和凸二階錐松弛,把含有非線性潮流方程約束的優(yōu)化問(wèn)題,轉(zhuǎn)換成凸二階錐優(yōu)化問(wèn)題進(jìn)行求解,避免了非線性優(yōu)化問(wèn)題求解復(fù)雜性隨電力系統(tǒng)規(guī)模增大呈指數(shù)上升的問(wèn)題；最后給出了松弛后問(wèn)題的最優(yōu)解具有保真性的充分條件,對(duì)于滿足條件的配電網(wǎng),可以基于凸二階錐優(yōu)化算法實(shí)現(xiàn)新能源電源在配電網(wǎng)中的優(yōu)化配置。
[Abstract]:In today's society where the energy crisis and environmental pollution are increasingly serious, wind and solar energy, which have the characteristics of clean and pollution-free and recyclable, have attracted wide attention. However, wind and solar energy are influenced by seasonal climate and have the characteristics of intermittence and randomness. In order to make full use of renewable energy, it is necessary to allocate the capacity and position of renewable energy generation units connected to power grid reasonably. According to the stochastic characteristics of renewable energy generation, this paper evaluates the reliability of renewable energy supply, and deeply studies the optimal configuration of power grid with intermittent energy. The main research contents and work include the following aspects: first, the reliability evaluation method of renewable energy generation system with energy storage is studied. Firstly, the stochastic characteristics of power generation of fan and photovoltaic cell are analyzed. A method of modeling the charging and discharging process of energy storage device by discrete Markov chain is proposed. The reliability of the power supply system is analyzed based on the stationary distribution probability of Markov chain. Finally, the accuracy of the algorithm is verified by the results of Monte Carlo simulation. Secondly, the optimal configuration of distributed wind-wind complementary generation system is studied. Firstly, the simple iterative method is applied to optimize the configuration of system capacity, and the principle of cycle invariance of residual capacity of energy storage unit is proposed. Based on this principle, the number of system fans, the number of photovoltaic cells and the capacity of battery are approximately linearized, which can greatly reduce the number of iterations. Secondly, using the improved Pareto multi-objective particle swarm optimization algorithm, considering the reliability and economy of power supply, the capacity of PV unit, fan and battery in the system is studied. And the installation angle of photovoltaic array and the installation height of fan are optimized. Thirdly, based on the probabilistic optimal power flow algorithm, the optimal configuration of the access point and injection capacity of wind turbine to the distribution network system is realized. Taking the active power loss of the distribution network as the optimization objective, and taking into account the uncertainty caused by decentralized access to the distribution network by new energy generation units, the optimal access point and access capacity can reduce the feeder pressure of the whole distribution network. The voltage quality of the node is improved and the environmental and economic benefits are obtained. An AC probabilistic power flow calculation method based on Cornish-Fisher series expansion is proposed. The algorithm avoids the problem of large amount of computation brought by the traditional Monte Carlo simulation algorithm and improves the efficiency of the algorithm. Fourthly, based on convex second-order cone optimization theory, the optimal allocation of new energy generation units in radial distribution network is studied. Firstly, the power flow calculation of distribution network system is carried out by using branch power flow equation. The influence of new energy on distribution network voltage distribution and network loss is analyzed. Secondly, for the branch power flow equation, a two-step relaxation, namely phase angle relaxation and convex second-order cone relaxation, is proposed. The optimization problem with nonlinear power flow equation constraints is transformed into convex second-order cone optimization problem. The problem that the complexity of nonlinear optimization problem increases exponentially with the increase of power system scale is avoided. Finally, the sufficient condition that the optimal solution of the relaxation problem has fidelity is given. For the distribution network that satisfies the condition, the optimal configuration of new energy power supply in distribution network can be realized based on convex second-order cone optimization algorithm.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TM61

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本文編號(hào)：2407069