本文選題：風(fēng)力發(fā)電 + 動態(tài)經(jīng)濟(jì)調(diào)度��； 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：隨著能源需求增長與化石燃料的日趨枯竭,風(fēng)力發(fā)電作為可再生能源受到各國的關(guān)注。但風(fēng)力發(fā)電本質(zhì)上具有波動性和隨機(jī)性,大規(guī)模風(fēng)電并網(wǎng)給電力系統(tǒng)的動態(tài)經(jīng)濟(jì)調(diào)度和安全運(yùn)行帶來新的挑戰(zhàn)和要求。傳統(tǒng)調(diào)度策略不再適用于包含大規(guī)模風(fēng)電并網(wǎng)的系統(tǒng),需要尋求新的調(diào)度決策方案。本文建立了基于機(jī)會約束規(guī)劃的含多風(fēng)電場動態(tài)經(jīng)濟(jì)調(diào)度模型,計入負(fù)荷和風(fēng)電出力的不確定性,考慮機(jī)組爬坡率、線路安全、旋轉(zhuǎn)備用等約束,以機(jī)會約束的形式保證正、負(fù)旋轉(zhuǎn)備用滿足負(fù)荷和風(fēng)電實際出力的波動,優(yōu)化常規(guī)機(jī)組、風(fēng)電計劃出力及預(yù)留的備用容量,確保系統(tǒng)失負(fù)荷和棄風(fēng)的風(fēng)險低于預(yù)定檻值。相比于現(xiàn)有的含風(fēng)電場動態(tài)經(jīng)濟(jì)調(diào)度,能更好地處理多風(fēng)電場接入的情形。針對機(jī)會約束的求解,采用兩種方法,將其轉(zhuǎn)化為確定性模型和利用基于隨機(jī)模擬的粒子群算法求解。求解機(jī)會約束規(guī)劃的傳統(tǒng)方法是將機(jī)會約束其轉(zhuǎn)化為確定性約束,重點和難點在于聯(lián)合變量的累積分布函數(shù)及其反函數(shù)的快速求解。本文提出采用FFT快速計算卷積獲得聯(lián)合變量的概率分布,以將機(jī)會約束轉(zhuǎn)化為確定性約束,使得模型轉(zhuǎn)化為確定性模型,避免了復(fù)雜的卷積運(yùn)算,用CPLEX求解得到的確定性模型,該方法大大減少了算法的運(yùn)行時間。以修改的IEEE39節(jié)點系統(tǒng)為算例驗證了所提調(diào)度模型的正確性及化簡方法的有效性。但當(dāng)機(jī)會約束的各變量之間不相互獨(dú)立時,很難將機(jī)會約束轉(zhuǎn)化為確定性約束,上述方法將不再可行;而基于隨機(jī)模擬的智能優(yōu)化算法不需要將機(jī)會約束轉(zhuǎn)化為確定性約束,通過生成大量試驗來模擬機(jī)會約束成立的概率。本文提出基于隨機(jī)模擬的改進(jìn)粒子群算法求解包含機(jī)會約束的含多風(fēng)電場電力系統(tǒng)動態(tài)經(jīng)濟(jì)調(diào)度問題,適用范圍廣,能應(yīng)用于求解非凸的調(diào)度優(yōu)化問題。在優(yōu)化過程中粒子群算法的學(xué)習(xí)因子設(shè)定為自適應(yīng)變化,慣性因子非線性變化以平衡全局優(yōu)化與局部優(yōu)化,避免陷入局部最優(yōu);同時,加入可行化調(diào)整策略及變異調(diào)整策略,以增強(qiáng)粒子群算法的優(yōu)化能力。本文采用機(jī)會約束規(guī)劃處理風(fēng)力發(fā)電帶來的不確定性,較為詳細(xì)地提出了模型的化簡、求解方法,對含大規(guī)模風(fēng)電場并網(wǎng)的電力系統(tǒng)優(yōu)化問題具有一定的借鑒意義。
[Abstract]:With the increase of energy demand and the depletion of fossil fuels, wind power as a renewable energy has attracted much attention. However, wind power generation is inherently volatile and stochastic, and large-scale wind power grid connection brings new challenges and requirements to dynamic economic dispatch and safe operation of power system. The traditional scheduling strategy is no longer suitable for large-scale wind power grid connected systems, so it is necessary to seek a new scheduling decision scheme. In this paper, a dynamic economic scheduling model with multiple wind farms based on chance constrained programming is established. The uncertainty of load and wind power output is taken into account, and the constraints such as slope climbing rate, line safety and rotation reserve are considered, and the positive is guaranteed in the form of chance constraints. The negative rotation reserve satisfies the fluctuation of load and actual output of wind power, optimizes the conventional unit, the planned output capacity of wind power and the reserved reserve capacity, so as to ensure that the risk of system losing load and abandoning wind is lower than the predetermined threshold value. Compared with the existing dynamic economic dispatching of wind farm, it can better deal with the situation of multiple wind farm access. For the solution of chance constraint, two methods are used to transform it into deterministic model and particle swarm optimization algorithm based on stochastic simulation. The traditional method to solve the chance-constrained programming is to transform the opportunistic constraints into deterministic constraints. The emphasis and difficulty lies in the quick solution of the cumulative distribution function and its inverse function of the joint variables. In this paper, the probability distribution of joint variables is obtained by using FFT to calculate convolution quickly, so that the chance constraints can be transformed into deterministic constraints, so that the model can be transformed into deterministic models, thus avoiding the complicated convolution operation and solving the deterministic model by CPLEX. This method greatly reduces the running time of the algorithm. The correctness of the proposed scheduling model and the effectiveness of the simplified method are verified by an example of the modified IEEE39 node system. However, when the variables of opportunity constraints are not independent from each other, it is difficult to transform the opportunity constraints into deterministic constraints, so the above methods will not be feasible, and the intelligent optimization algorithm based on stochastic simulation does not need to transform the opportunity constraints into deterministic constraints. The probability of chance constraint is simulated by generating a large number of experiments. In this paper, an improved particle swarm optimization algorithm based on stochastic simulation is proposed to solve the dynamic economic scheduling problem of multi-wind farm power systems with chance constraints. It has a wide range of applications and can be used to solve non-convex scheduling optimization problems. In the process of optimization, the learning factor of PSO is set as adaptive change, the nonlinear variation of inertial factor is used to balance global optimization and local optimization to avoid falling into local optimum, at the same time, feasible adjustment strategy and mutation adjustment strategy are added. In order to enhance the optimization ability of particle swarm optimization algorithm. In this paper, the opportunity-constrained programming is used to deal with the uncertainty caused by wind power generation, and the simplification and solution of the model are presented in detail, which can be used for reference in the optimization of power system with large-scale wind farms connected to the grid.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM73

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