冷热电联供系统环保经济优化调度及参数空间PSO算法研究

发布时间：2018-05-04 16:02

  本文选题：冷热电联供 + 碳排放权　； 参考：《长沙理工大学》2014年硕士论文

【摘要】：近年来,冷热电联供(Combined Cold Heat and Power, CCHP)由于其不仅能够减少有害气体的排放,缓解环境压力,而且能实现能源梯级利用,提高能源综合利用率。因此,在能源危机和环境日益恶化的双重压力下,冷热电联供系统在我国经济可持续发展中扮演着重要的角色。为了实现不同污染气体排放量之间的转化,引入了碳排放当量折算系数,实现了s02和NOx与C02间排放量的等价转化,简化了环境成本模型,进而建立了兼顾环境成本和燃料成本的CCHP环保经济优化调度模型；针对粒子群算法易陷入局部最优、收敛过早的缺陷,提出了一种参数空间粒子群算法,通过附加一类高度参数,使粒子移动的方向和距离由单一速度决定转变成还受高度的共同作用,构成位置、速度、高度三类参数空间,从而每一维待优化变量由原来速度、位置两类参数所组成的寻优区域变成新增高度参数的全新寻优空间,将其用于求解CCHP系统兼顾环境成本和燃料成本优化调度模型。为了综合考虑碳排放对冷热电联供系统的影响,引入了碳排放权交易成本函数,建立了考虑碳排放权交易成本、燃料成本、环境成本的CCHP系统多目标优化调度模型；针对粒子群算法将惯性权重作为全局变量来更新赋值,进而容易导致了早熟的问题,为此提出了一种模糊自修正粒子群算法,通过利用模糊推理机制建立了粒子适应度值隶属度函数,使得在每次寻优过程中粒子可以充分根据自身当前适应度隶属度函数值来修正惯性权重的取值,可以进一步改善早熟的缺陷,增强全局搜索能力,并将其用于求解CCHP系统多目标优化调度模型。通过算例仿真结果表明,参数空间粒子群算法对比经典粒子群算法和改进粒子群算法有较强的全局搜索能力,早熟缺陷得到了明显改善,可以得到更可靠的优化结果；考虑碳排放权交易成本后可以有效控制CO2排放总量和获取额外的收益,进而降低联供系统的综合运行成本。CCHP环保经济调度的研究对CCHP系统节能减排的推进有着重要意义,而且也为该系统的推广应用提供一定的参考依据；同时参数空间粒子群算法和模糊自修正粒子群算法的提出为优化问题的求解开辟了新的途径,能够更好地满足对优化结果精准性的要求。
[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.
【学位授予单位】：长沙理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F426.61;TP18
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本文编号：1843643