發(fā)布時(shí)間：2018-05-04 16:02

  本文選題：冷熱電聯(lián)供 + 碳排放權(quán)　； 參考：《長(zhǎng)沙理工大學(xué)》2014年碩士論文

【摘要】：近年來(lái),冷熱電聯(lián)供(Combined Cold Heat and Power, CCHP)由于其不僅能夠減少有害氣體的排放,緩解環(huán)境壓力,而且能實(shí)現(xiàn)能源梯級(jí)利用,提高能源綜合利用率。因此,在能源危機(jī)和環(huán)境日益惡化的雙重壓力下,冷熱電聯(lián)供系統(tǒng)在我國(guó)經(jīng)濟(jì)可持續(xù)發(fā)展中扮演著重要的角色。為了實(shí)現(xiàn)不同污染氣體排放量之間的轉(zhuǎn)化,引入了碳排放當(dāng)量折算系數(shù),實(shí)現(xiàn)了s02和NOx與C02間排放量的等價(jià)轉(zhuǎn)化,簡(jiǎn)化了環(huán)境成本模型,進(jìn)而建立了兼顧環(huán)境成本和燃料成本的CCHP環(huán)保經(jīng)濟(jì)優(yōu)化調(diào)度模型；針對(duì)粒子群算法易陷入局部最優(yōu)、收斂過(guò)早的缺陷,提出了一種參數(shù)空間粒子群算法,通過(guò)附加一類高度參數(shù),使粒子移動(dòng)的方向和距離由單一速度決定轉(zhuǎn)變成還受高度的共同作用,構(gòu)成位置、速度、高度三類參數(shù)空間,從而每一維待優(yōu)化變量由原來(lái)速度、位置兩類參數(shù)所組成的尋優(yōu)區(qū)域變成新增高度參數(shù)的全新尋優(yōu)空間,將其用于求解CCHP系統(tǒng)兼顧環(huán)境成本和燃料成本優(yōu)化調(diào)度模型。為了綜合考慮碳排放對(duì)冷熱電聯(lián)供系統(tǒng)的影響,引入了碳排放權(quán)交易成本函數(shù),建立了考慮碳排放權(quán)交易成本、燃料成本、環(huán)境成本的CCHP系統(tǒng)多目標(biāo)優(yōu)化調(diào)度模型；針對(duì)粒子群算法將慣性權(quán)重作為全局變量來(lái)更新賦值,進(jìn)而容易導(dǎo)致了早熟的問(wèn)題,為此提出了一種模糊自修正粒子群算法,通過(guò)利用模糊推理機(jī)制建立了粒子適應(yīng)度值隸屬度函數(shù),使得在每次尋優(yōu)過(guò)程中粒子可以充分根據(jù)自身當(dāng)前適應(yīng)度隸屬度函數(shù)值來(lái)修正慣性權(quán)重的取值,可以進(jìn)一步改善早熟的缺陷,增強(qiáng)全局搜索能力,并將其用于求解CCHP系統(tǒng)多目標(biāo)優(yōu)化調(diào)度模型。通過(guò)算例仿真結(jié)果表明,參數(shù)空間粒子群算法對(duì)比經(jīng)典粒子群算法和改進(jìn)粒子群算法有較強(qiáng)的全局搜索能力,早熟缺陷得到了明顯改善,可以得到更可靠的優(yōu)化結(jié)果；考慮碳排放權(quán)交易成本后可以有效控制CO2排放總量和獲取額外的收益,進(jìn)而降低聯(lián)供系統(tǒng)的綜合運(yùn)行成本。CCHP環(huán)保經(jīng)濟(jì)調(diào)度的研究對(duì)CCHP系統(tǒng)節(jié)能減排的推進(jìn)有著重要意義,而且也為該系統(tǒng)的推廣應(yīng)用提供一定的參考依據(jù)；同時(shí)參數(shù)空間粒子群算法和模糊自修正粒子群算法的提出為優(yōu)化問(wèn)題的求解開辟了新的途徑,能夠更好地滿足對(duì)優(yōu)化結(jié)果精準(zhǔn)性的要求。
[Abstract]:In recent years, combined Cold Heat and Power (CCHPs) can not only reduce the emission of harmful gases and relieve the environmental pressure, but also realize the cascade utilization of energy and improve the comprehensive utilization of energy. Therefore, under the dual pressure of energy crisis and environmental deterioration, the combined cooling and heat supply system plays an important role in the sustainable economic development of our country. In order to realize the conversion between the emissions of different pollution gases, the equivalent conversion coefficient of carbon emissions is introduced. The equivalent conversion of emissions between S2, NOx and CO2 is realized, and the environmental cost model is simplified. Furthermore, a CCHP environmental and economic optimal scheduling model with both environmental cost and fuel cost is established, and a parameter space particle swarm optimization (PSO) algorithm is proposed to solve the problem that particle swarm optimization (PSO) is easy to fall into local optimum and converge prematurely. By attaching a class of height parameters, the direction and distance of particle movement are changed from a single velocity to a co-action of height, forming three parameter spaces of position, velocity and height, so that each one-dimensional variable to be optimized is changed from the original velocity. The optimization region composed of two types of location parameters becomes a new optimization space for new height parameters, which is used to solve the optimal scheduling model of CCHP system with both environmental cost and fuel cost. In order to comprehensively consider the effect of carbon emissions on the CCHP system, the carbon emission trading cost function is introduced, and the multi-objective optimal scheduling model of CCHP system considering carbon emissions trading cost, fuel cost and environmental cost is established. Aiming at the problem that the inertia weight is used as a global variable to update the assignment in particle swarm optimization (PSO) algorithm, a fuzzy self-correcting PSO algorithm is proposed to solve the problem of premature convergence. By using fuzzy reasoning mechanism, the membership function of particle fitness value is established, so that the particle can modify the value of inertia weight according to its current fitness membership function value in every optimization process. It can further improve the defects of precocity, enhance the global search ability, and apply it to solve the multi-objective optimal scheduling model of CCHP system. The simulation results show that the parameter space particle swarm optimization algorithm has a strong global search ability compared with the classical particle swarm optimization algorithm and the improved particle swarm optimization algorithm, and the premature defects are obviously improved, and more reliable optimization results can be obtained. Considering the cost of carbon emission trading can effectively control the total amount of CO2 emissions and obtain additional benefits, and then reduce the integrated operating cost of the co-supply system. The study of environmental protection and economic scheduling of CCHP system is of great significance to the promotion of energy saving and emission reduction of CCHP system. It also provides a certain reference for the popularization and application of the system, and opens up a new way for solving the optimization problem by using the parameter space particle swarm optimization algorithm and the fuzzy self-correcting particle swarm optimization algorithm. Can better meet the requirements of the accuracy of the optimization results.
【學(xué)位授予單位】：長(zhǎng)沙理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F426.61;TP18
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本文編號(hào)：1843643