考虑新能源不确定性的储能容量随机鲁棒优化方法

发布时间：2019-04-28 09:56

【摘要】：随着分布式发电、可控负荷、储能装置等在配电网的广泛应用,分布式能源的新型特性对传统配电网的运行与控制提出了更高的要求,传统的被动式接纳分布式电源的配电网不再满足未来技术发展需要,需要建立主动接纳分布式能源接入的主动配电网,从而有效提高分布式能源可用能力,提升配电网资产的利用率,提高用户的用电质量和供电可靠性。然而,高渗透率新能源接入配电网对配网安全稳定运行造成严重威胁。储能装置因其运行方式灵活、可充可放、与环境兼容等特点近年来被用于解决大规模风电并网问题。一方面,储能装置的引入能有效平滑新能源输出功率波动;另一方面,储能装置造价高、运行维护费用昂贵等特点使得很有必要进行储能经济配置研究。基于上述背景,本文主要完成了以下工作:1.考虑风电功率不确定性的鲁棒最优潮流计算方法风电接入会影响电力系统潮流分布,风电功率的随机性、间歇性、波动性可能导致线路过载及电压越限等问题,因此提出了一种考虑风电功率概率分布不确定性的最优潮流模型。模型可在风电功率任意可能分布下调度可调发电机组,保证系统安全经济运行。并将所得结果与传统的机会约束最优潮流进行对比,表明了所提方法的有效性。2.考虑概率分布鲁棒联合机会约束的储能容量优化配置方法提出了一种考虑风电功率预测误差概率分布不确定性的储能容量优化配置方法。该方法可保证在风电功率任意可能分布下通过调度可调发电机组及配置储能以保证系统安全运行,同时最小化储能配置成本。首先鉴于历史数据的不完备性,文中将根据历史数据获得的风电二阶矩信息描述为波动区间,然后采用概率分布鲁棒联合机会约束模型描述含风场系统储能最优配置问题,进而采用拉格朗日对偶消去优化模型中的随机变量,将鲁棒机会约束模型转化为确定性的线性矩阵不等式(linear matrix inequality,LMI)问题,最后采用凸优化算法求解。并分析了风电预测误差精度、机会约束置信度、风电功率波动性、系统网架结构、系统内灵活性资源等对储能配置容量的影响。3.基于新能源与随机负荷高阶矩信息的储能容量优化配置方法在主动配电网背景下,提出考虑主动配电网中分布式负荷与分布式可再生能源发电随机性的储能容量最小化模型。利用概率高阶矩对随机变量进行建模,并定义了主动配电系统供电可靠性的评价指标,应用数学优化算法获得了储能最小容量与系统供电充裕度之间的定量关系式。
[Abstract]:With the wide application of distributed power generation, controllable load and energy storage devices in distribution networks, the new characteristics of distributed energy sources put forward higher requirements for the operation and control of traditional distribution networks. The traditional passive distributed power distribution network no longer meets the needs of the future technology development, so it is necessary to establish the active distribution network which actively accepts the distributed energy access, so as to improve the distributed energy availability effectively. Improve the utilization of distribution network assets, improve the user's power quality and power supply reliability. However, high permeability new energy access to the distribution network is a serious threat to the safe and stable operation of the distribution network. In recent years, energy storage devices have been used to solve the problem of large-scale wind power grid connection due to its flexible operation mode, recharge and recharge, compatibility with environment and so on. On the one hand, the introduction of energy storage devices can effectively smooth the fluctuation of the output power of new energy sources; on the other hand, the high cost of energy storage devices and the high cost of operation and maintenance make it necessary to study the economic allocation of energy storage. Based on the above background, this paper mainly completed the following work: 1. Robust optimal power flow calculation method considering wind power uncertainty wind power access will affect power flow distribution, randomness, intermittency and volatility of wind power, which may lead to line overload and voltage out-of-limit, and so on. Therefore, an optimal power flow model considering the uncertainty of probability distribution of wind power is proposed. The model can dispatch adjustable generator set under arbitrary distribution of wind power to ensure the safe and economical operation of the system. The results obtained are compared with the traditional chance constrained optimal power flow, which shows the effectiveness of the proposed method. 2. A method of optimal allocation of energy storage capacity considering the uncertainty of probability distribution of wind power prediction error is presented in this paper. This method can ensure the safe operation of the system by dispatching adjustable generator sets and configuring energy storage under arbitrary distribution of wind power, while minimizing the cost of energy storage configuration. In view of the incompleteness of historical data, the second-order moment information of wind power obtained from historical data is described as fluctuation interval, and then the optimal allocation problem of energy storage in wind-field systems is described by probability distribution robust joint opportunity constraint model. Then the Lagrange dual elimination of random variables in the optimization model is used to transform the robust opportunity constraint model into deterministic linear matrix inequality (linear matrix inequality,LMI) problem. Finally the convex optimization algorithm is used to solve the problem. The influence of wind power prediction error accuracy, chance constraint confidence, wind power fluctuation, system grid structure and flexible resources on energy storage capacity is also analyzed. 3. Based on the high-order moment information of new energy sources and random loads, an energy storage capacity minimization model considering the randomness of distributed load and distributed renewable energy generation in active distribution network is proposed in the context of active distribution network. The probabilistic high-order moment is used to model the random variables and the evaluation index of the power supply reliability of the active distribution system is defined. The quantitative relationship between the minimum capacity of energy storage and the power supply abundance of the system is obtained by using the mathematical optimization algorithm.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM73;TM74

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