计及碳交易和备用容量变化的风光互补电力系统经济调度

发布时间：2018-04-03 11:04

本文选题：风电　切入点：光伏　出处：《山东大学》2015年硕士论文

【摘要】：发展风电、光伏等新能源是解决中国能源紧缺问题的主要途径。风电和光伏具有随机性大、波动性强和不易调度的特点,大规模的新能源接入电网会对电网造成冲击,影响电力系统的安全运行。把风电和光伏互补接入电网能够有效削弱新能源的波动性,有利于缓解中国目前大规模存在的弃风问题。降低碳排放,走可持续发展道路是全世界的共识。火力发电机组是碳排放的重要排放源,碳交易是以降低全世界的碳排放总量为目的的市场机制。在中国逐步建立和完善碳交易市场机制的大背景下,研究计及碳交易的含有风电、光伏发电的电力系统经济调度具有重大的理论意义和现实意义。本文具体研究内容如下：(1)研究了风电和光伏发电互补接入电网后的经济调度建模问题。引入环境惩罚成本来计及环境效益,引入备用容量惩罚成本来计及风电和光伏发电接入电网对备用容量的影响,建立了计及环境惩罚成本和备用容量惩罚成本的风光互补电力系统经济调度模型,利用遗传算法进行经济调度优化,并进行仿真计算,验证了方法的有效性。(2)碳交易是以降低全世界的碳排放总量为目的的市场机制。在计及环境惩罚成本和备用容量惩罚成本的风光互补电力系统经济调度模型的基础上,引入碳交易模型,并且对风光互补接入电网后的备用容量变化进行了深入探讨,建立了计及碳交易和备用容量变化的风光互补电力系统的经济调度模型(简称“计及碳交易的模型”)。针对基本遗传算法收敛速度慢、容易陷入局部最优解的问题,对遗传算法的杂交概率和变异概率进行了自适应改进,并且在算法中引入了免疫机制,建立了一种自适应免疫遗传算法。在深入讨论了碳交易价格和单位电量碳排放分配系数对总成本和碳排放总量的影响,确定了最优碳交易价格和最优单位电量碳排放分配系数的方法的基础上,采用自适应免疫遗传算法进行经济调度的优化计算,仿真计算表明,计及碳交易的模型不仅能够有效削弱新能源的波动性,而且能够有效降低碳排放总量和降低发电成本。(3)采用计及碳交易的模型和自适应免疫遗传算法对枣庄电网进行了经济调度的仿真计算和分析。首先,对夏季某日华电台儿庄风电场和光伏电站进行了功率预测,其次,采用计及碳交易的模型和自适应免疫遗传算法进行了经济调度的仿真计算。实例仿真计算结果进一步验证了所提方法的有效性。
[Abstract]:Developing wind power, photovoltaic and other new energy is the main way to solve the problem of energy shortage in China.Wind power and photovoltaic have the characteristics of high randomness, strong volatility and difficult to dispatch. Large scale new energy access to the grid will impact the power grid and affect the safe operation of the power system.Connecting wind power and photovoltaic to the power grid can effectively reduce the volatility of new energy and help alleviate the current large-scale wind abandonment problem in China.It is the consensus of the world to reduce carbon emissions and follow the path of sustainable development.Thermal generating units are important sources of carbon emissions, and carbon trading is a market mechanism aimed at reducing the total amount of carbon emissions in the world.Under the background of the gradual establishment and improvement of carbon trading market mechanism in China, it is of great theoretical and practical significance to study the economic dispatch of power system with wind power and photovoltaic power generation taking into account carbon trading.The main contents of this paper are as follows: (1) the economic dispatching modeling problem of wind power and photovoltaic power is studied after the complementary connection of wind power and photovoltaic power.Environmental penalty cost is introduced to take into account environmental benefits, and reserve capacity penalty cost is introduced to consider the impact of wind power and photovoltaic power grid access on reserve capacity.The economic dispatching model of wind-wind complementary power system considering environmental penalty cost and reserve capacity penalty cost is established. Genetic algorithm is used to optimize economic scheduling, and the simulation calculation is carried out.The effectiveness of the method is verified. 2) carbon trading is a market mechanism aimed at reducing the total amount of carbon emissions around the world.Based on the economic dispatching model of wind-wind complementary power system considering environmental penalty cost and reserve capacity penalty cost, carbon trading model is introduced, and the change of reserve capacity after wind complementary connection to power grid is discussed.The economic dispatching model of wind and wind complementary power system considering carbon trading and reserve capacity is established.Aiming at the problem that the convergence speed of the basic genetic algorithm is slow and it is easy to fall into the local optimal solution, the hybrid probability and mutation probability of the genetic algorithm are improved adaptively, and the immune mechanism is introduced into the algorithm.An adaptive immune genetic algorithm is established.In this paper, the effects of carbon trading price and carbon allocation coefficient per unit electric quantity on total cost and total carbon emission are discussed in depth, and the optimal carbon trading price and the optimal carbon emission allocation coefficient per unit electricity quantity are determined.Adaptive immune genetic algorithm is used to optimize the economic scheduling. The simulation results show that the carbon trading model can not only effectively reduce the volatility of new energy.Moreover, it can effectively reduce the total carbon emission and reduce the cost of generating electricity.) the economic dispatch of Zaozhuang power network is simulated and analyzed by using the model of carbon trading and the adaptive immune genetic algorithm.Firstly, the power prediction of Huadian Taierzhuang wind farm and photovoltaic power station is carried out on a certain day in summer. Secondly, the simulation calculation of economic dispatch is carried out by using a carbon trading model and an adaptive immune genetic algorithm.The effectiveness of the proposed method is further verified by the simulation results.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TM73;TM61

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