间歇性能源输出功率预测与储能系统规划
发布时间：2018-12-13 16:59
【摘要】：间歇性能源出力预测和储能配置可有效减弱由于间歇性能源并网发电给电网的运行和管理带来的负面影响。本文围绕间歇性能源的出力预测以及储能规划开展了研究，主要研究成果如下： 1)针对风电出力预测，提出了一种基于改进经验模态分解法和遗传神经网络的风速预测组合模型和算法。通过与GA-BPNN模型、EMD与GA-BPNN组合模型的预测结果对比，表明所提组合模型和算法预测精度较高，且对超短期预测(10min)和短期预测(1hour)均能适用。 2)针对光伏出力预测，提出了一种基于相似日的对太阳辐照量进行逐时预测的EEMD和GA-BP组合模型和算法，还提出了一种基于相似日的对光伏出力进行直接预测的灰色神经网络组合预测模型和算法。算例结果表明，所提出的两种组合算法预测精度较高，具有潜在的应用价值。 3)分别提出了既定网架的含风电场输电网的储能规划模型和算法，以及储能电站与含风电场输电网的协调规划模型和算法，通过算例测试了各模型的合理性及方法的有效性，比较并分析了储能系统与含有风电场输电网规划不同方案的优劣，可为未来含间歇式能源电网的规划发展提供理论参考。 4)提出了考虑高渗透率光伏的配电网中储能系统优化配置模型和算法。以接入储能系统后产生的总费用的净现值为目标函数，满足储能分时控制策略约束以及配电网约束的条件下，提出了改进的自适应粒子群(APSO)算法来求解多类型储能在含高渗透率光伏配电网中优化配置问题。并对不同储能技术接入配电网的经济性能和节点电压变化情况进行了比较分析。最后，通过算例结果验证了所建模型的合理性和所提方法的有效性。 5)利用基于蒙特卡洛模拟的统计方法从电动汽车是否向电网输送电能和是否受电价控制两个方向出发建立了3种情境模型，仿真并分析了不同区域电动汽车负荷情境模型和不同规模的电动汽车对电网的影响。同时，提出满足系统和用户用电满意度的双目标的大规模电动汽车充电管理需求响应控制策略，并探讨电动汽车作为储能的一种形式实现系统辅助服务的调节能力，为间歇性能源提供辅助服务做准备。
[Abstract]:Intermittent energy output prediction and energy storage allocation can effectively reduce the negative effects of intermittent energy generation on the operation and management of power grid. The main research results of this paper are as follows: 1) aiming at wind power generation prediction, A combined model and algorithm for wind speed prediction based on improved empirical mode decomposition (EMD) and genetic neural network (GNN) is proposed. Compared with GA-BPNN model, EMD and GA-BPNN combined model, the proposed combination model and algorithm have high prediction accuracy, and can be applied to both ultra-short term prediction (10min) and short term prediction (1hour). 2) aiming at photovoltaic force prediction, a combined model and algorithm of EEMD and GA-BP is proposed to predict solar irradiance time by time based on similar days. A grey neural network combined prediction model and algorithm based on similar days for direct prediction of photovoltaic force is also proposed. The numerical results show that the two combined algorithms have high prediction accuracy and have potential application value. 3) the energy storage planning model and algorithm of the grid with wind farm and the coordinated planning model and algorithm of the energy storage power station and the transmission network with wind farm are put forward respectively. The rationality of each model and the validity of the method are tested by an example. The advantages and disadvantages of different schemes of energy storage system and transmission network planning with wind farm are compared and analyzed, which can provide a theoretical reference for the planning and development of energy grid with intermittent energy in the future. 4) the optimal configuration model and algorithm of energy storage system in distribution network considering high permeability photovoltaic are proposed. Taking the net present value of the total cost generated by access to the energy storage system as the objective function, the time-sharing control strategy constraint and the distribution network constraint are satisfied. An improved adaptive particle swarm optimization (APSO) algorithm is proposed to solve the problem of optimal configuration of multi-type energy storage in photovoltaic distribution networks with high permeability. The economic performance and node voltage change of different energy storage technology connected to distribution network are compared and analyzed. Finally, the rationality of the proposed model and the validity of the proposed method are verified by an example. 5) using the statistical method based on Monte Carlo simulation, three situation models are established from two directions: whether electric vehicles are supplying electric energy to the power grid and whether they are controlled by electricity price. Simulation and analysis of different regional electric vehicle load situation model and different size of electric vehicles on the power grid. At the same time, the demand response control strategy for charge management of large-scale electric vehicles is proposed, which can meet the requirements of both the system and the users' electricity satisfaction, and the regulation ability of the system auxiliary service is discussed as a form of energy storage. Prepare for intermittent energy support services.
【学位授予单位】：天津大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TM715
本文编号：2376873
[Abstract]:Intermittent energy output prediction and energy storage allocation can effectively reduce the negative effects of intermittent energy generation on the operation and management of power grid. The main research results of this paper are as follows: 1) aiming at wind power generation prediction, A combined model and algorithm for wind speed prediction based on improved empirical mode decomposition (EMD) and genetic neural network (GNN) is proposed. Compared with GA-BPNN model, EMD and GA-BPNN combined model, the proposed combination model and algorithm have high prediction accuracy, and can be applied to both ultra-short term prediction (10min) and short term prediction (1hour). 2) aiming at photovoltaic force prediction, a combined model and algorithm of EEMD and GA-BP is proposed to predict solar irradiance time by time based on similar days. A grey neural network combined prediction model and algorithm based on similar days for direct prediction of photovoltaic force is also proposed. The numerical results show that the two combined algorithms have high prediction accuracy and have potential application value. 3) the energy storage planning model and algorithm of the grid with wind farm and the coordinated planning model and algorithm of the energy storage power station and the transmission network with wind farm are put forward respectively. The rationality of each model and the validity of the method are tested by an example. The advantages and disadvantages of different schemes of energy storage system and transmission network planning with wind farm are compared and analyzed, which can provide a theoretical reference for the planning and development of energy grid with intermittent energy in the future. 4) the optimal configuration model and algorithm of energy storage system in distribution network considering high permeability photovoltaic are proposed. Taking the net present value of the total cost generated by access to the energy storage system as the objective function, the time-sharing control strategy constraint and the distribution network constraint are satisfied. An improved adaptive particle swarm optimization (APSO) algorithm is proposed to solve the problem of optimal configuration of multi-type energy storage in photovoltaic distribution networks with high permeability. The economic performance and node voltage change of different energy storage technology connected to distribution network are compared and analyzed. Finally, the rationality of the proposed model and the validity of the proposed method are verified by an example. 5) using the statistical method based on Monte Carlo simulation, three situation models are established from two directions: whether electric vehicles are supplying electric energy to the power grid and whether they are controlled by electricity price. Simulation and analysis of different regional electric vehicle load situation model and different size of electric vehicles on the power grid. At the same time, the demand response control strategy for charge management of large-scale electric vehicles is proposed, which can meet the requirements of both the system and the users' electricity satisfaction, and the regulation ability of the system auxiliary service is discussed as a form of energy storage. Prepare for intermittent energy support services.
【学位授予单位】：天津大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TM715
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