电网调峰能力对最大风电准入功率的影响研究
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摘要
电网调峰能力不足是限制我国风电并网的主要原因之一,研究调峰能力极限对解决电网调峰问题具有十分重要的意义。
目前,对我国电网调峰能力极限的研究都是在现有开机方式下,计算电网能够接受的最大风电功率。随着计及风电的发电调度方法的逐步实施,电网的调峰能力正在得到改善,用目前方法计算的调峰能力极限渐渐失去了实际意义。为了适应风电技术的发展趋势,论文突破了现有开机方式的限制,直接研究在所有开机组合方式下,常规电源设备具有的调峰能力极限,其结果不仅可以掌握改进发电调度方法后电网能够接受的最大风电功率,还可以了解现有电源设备的调峰能力缺陷,为进一步的设备改造提供科学依据。
论文研究的主要内容包括常规机组的调峰能力极限,以及与其紧密相关的风电功率预测方法,具体的研究内容和取得的成果如下：
研究了扩展常数和训练数据分布密度之间的关系,针对风速分布的不均匀性,提出了一种新的基于T-S(Takagi-Sugen, T-S)型模糊GRNN(General Regression Neural Network, GRNN)网络的风电功率预测方法。该方法首次以数据与密度中心的距离作为指标,把网络输入的数据空间划分成几个疏密程度不同的子空间,在每个子空间上建立一个GRNN模型,然后通过定义模糊规则,实现了模糊GRNN网络的风电功率预测。为了能够找到适用的数据中心,论文采用了数据场聚类方法,并提出了最优影响因子的概念和计算方法,为解决聚类分析中存在的聚类结果稳定性问题提供了有意义的探索。针对模糊参数训练,论文采用了性能良好的粒子群搜索算法。仿真结果表明了方法的有效性。
针对用正态分布计算风电功率预测误差区间时出现的问题,提出了一种基于Beta分布的风电功率预测误差区间的估计方法,该方法根据发电调度对系统备用容量安全性和经济性的要求,建立了一个能够计算任意概率水平的,Beta分布最小概率区间的优化模型,通过引入Beta分布函数的反函数形式,把等式约束的优化问题简化成一个无约束的优化问题,并根据Beta分布特点给出了一个快速的算法。仿真结果表明了模型、算法的正确性,和Beta分布的合理性。
针对目前研究我国电网调峰能力极限的方法都受到现有开机方式限制的情况,首次建立了一个在所有开机方式组成的状态空间中,计算常规机组的负调峰能力极限的优化模型。论文研究了在没有风电功率预测技术支持下,风电接入对电网造成的负调峰现象的本质,归纳出系统负荷、风电功率和机组状态之间的关系,以机组组合状态为控制变量,以最大化风电功率为目标,建立了一个能够计算常规机组负调峰能力极限的模型；针对模型中目标函数的双重性,设计了一个两层搜索算法。通过与现有研究结果的对比仿真,验证了模型的正确性和算法的有效性。
在研究了所有开机方式的基础上,进一步考虑到风电功率预测对电网调峰能力的影响,首次建立了一个计算常规机组调峰能力极限的优化模型。论文分析了风电功率预测对发电调度的影响,总结出建立调峰能力极限模型的原则,定义了调峰机组状态链的概念,在状态链的基础上推导出计算常规机组调峰能力极限的模型。针对多源树结构的调峰机组状态链,论文设计了快速的搜索算法。仿真结果说明了模型和算法的有效性。
Peak load regulation is one of the main problems that are holding back China's power grid being connected with large-scale wind farms. The research of regulation capacity limit of conventional generating units is a prerequisite to solve the problem.
At present, all of researches are not suitable for the future of wind power technology development because these don't consider the impact of wind power forecasting. In order to meet the development trend of wind power technology, this dissertation broke through the limitations of existing boot mode, and given a method that can calculate regulation capacity limit of conventional generators. The results can not only grasp on conventional generators acceptable to the largest wind power, can also learn peaking capacity deficiencies of existing power supplies. All that can provide a scientific basis for further equipment modification.
The main contents in this dissertation include studying regulation capacity limit of conventional generators, and studying wind power prediction method. The specific achievements are as follows:
Studied the relationship between spread and between data density, according to the uneven distribution of wind speed, a new T-S(Takagi-Sugen, T-S) fuzzy GRNN(General Regression Neural Network, GRNN) wind power prediction method is proposed. In this method, the distance between training data and density center is a criterion, the input data space is divided into several sub-space, a GRNN model is created in every sub-space, and a final prediction result is calculated by defined fuzzy rulers. In order to get accurate data center, the data field clustering method is used in this dissertation. In the method, an optimum objective function for reducing influence of sample deviation is constructed and an approximate solution is given of optimum affection factor. For training the fuzzy parameters, the dissertation uses a good performance PSO(Particle Swarm Optimization, PSO) search algorithm. The simulation results prove the correctness of the model and algorithm.
A normal distribution is usually used to model wind power forecast error, but it is not valid in some special cases. For solving this problem, an interval estimation method of wind power forecasts based on Beta distribution is proposed. In this method, according to the security and economy requirements of grid reserve capacity, a new universal optimization model is proposed to calculate minimum probability interval of Beta probability density function. By introducing the inverse function of Beta PDF, equality constrained optimization problem is simplified into an unconstrained optimization problem, and a smart algorithms is presented. The simulation result shows correctness of the algorithms and the rationality of Beta PDF.
According to the situation that no reliable wind power prediction results can be used, a new optimization model is created for calculating negative regulation capacity limit of conventional generators in the dissertation. The dissertation studies the mechanism of the negative peak load regulation of conventional generators based on active power balance equation., a new model is proposed to calculate the limit of capability of negative peak load regulation, and a practical two-tier algorithm is given. The simulation results prove the correctness of the model and algorithm.
According to the situation that wind power prediction is used in generation dispatch schedule method, a new optimization model is created for calculating regulation capacity limit of conventional generators in the dissertation. The influence of wind power prediction for generation dispatch schedule is analyzed. A principle is summarized to create model of regulation capacity limit. A concept of regulation units state chain is defined. On the concept, an optimization model is deduced. According to multi-source tree of regulation units state chain, an algorithm is proposed. The simulation results prove the correctness of the model and algorithm.
目前,对我国电网调峰能力极限的研究都是在现有开机方式下,计算电网能够接受的最大风电功率。随着计及风电的发电调度方法的逐步实施,电网的调峰能力正在得到改善,用目前方法计算的调峰能力极限渐渐失去了实际意义。为了适应风电技术的发展趋势,论文突破了现有开机方式的限制,直接研究在所有开机组合方式下,常规电源设备具有的调峰能力极限,其结果不仅可以掌握改进发电调度方法后电网能够接受的最大风电功率,还可以了解现有电源设备的调峰能力缺陷,为进一步的设备改造提供科学依据。
论文研究的主要内容包括常规机组的调峰能力极限,以及与其紧密相关的风电功率预测方法,具体的研究内容和取得的成果如下：
研究了扩展常数和训练数据分布密度之间的关系,针对风速分布的不均匀性,提出了一种新的基于T-S(Takagi-Sugen, T-S)型模糊GRNN(General Regression Neural Network, GRNN)网络的风电功率预测方法。该方法首次以数据与密度中心的距离作为指标,把网络输入的数据空间划分成几个疏密程度不同的子空间,在每个子空间上建立一个GRNN模型,然后通过定义模糊规则,实现了模糊GRNN网络的风电功率预测。为了能够找到适用的数据中心,论文采用了数据场聚类方法,并提出了最优影响因子的概念和计算方法,为解决聚类分析中存在的聚类结果稳定性问题提供了有意义的探索。针对模糊参数训练,论文采用了性能良好的粒子群搜索算法。仿真结果表明了方法的有效性。
针对用正态分布计算风电功率预测误差区间时出现的问题,提出了一种基于Beta分布的风电功率预测误差区间的估计方法,该方法根据发电调度对系统备用容量安全性和经济性的要求,建立了一个能够计算任意概率水平的,Beta分布最小概率区间的优化模型,通过引入Beta分布函数的反函数形式,把等式约束的优化问题简化成一个无约束的优化问题,并根据Beta分布特点给出了一个快速的算法。仿真结果表明了模型、算法的正确性,和Beta分布的合理性。
针对目前研究我国电网调峰能力极限的方法都受到现有开机方式限制的情况,首次建立了一个在所有开机方式组成的状态空间中,计算常规机组的负调峰能力极限的优化模型。论文研究了在没有风电功率预测技术支持下,风电接入对电网造成的负调峰现象的本质,归纳出系统负荷、风电功率和机组状态之间的关系,以机组组合状态为控制变量,以最大化风电功率为目标,建立了一个能够计算常规机组负调峰能力极限的模型；针对模型中目标函数的双重性,设计了一个两层搜索算法。通过与现有研究结果的对比仿真,验证了模型的正确性和算法的有效性。
在研究了所有开机方式的基础上,进一步考虑到风电功率预测对电网调峰能力的影响,首次建立了一个计算常规机组调峰能力极限的优化模型。论文分析了风电功率预测对发电调度的影响,总结出建立调峰能力极限模型的原则,定义了调峰机组状态链的概念,在状态链的基础上推导出计算常规机组调峰能力极限的模型。针对多源树结构的调峰机组状态链,论文设计了快速的搜索算法。仿真结果说明了模型和算法的有效性。
Peak load regulation is one of the main problems that are holding back China's power grid being connected with large-scale wind farms. The research of regulation capacity limit of conventional generating units is a prerequisite to solve the problem.
At present, all of researches are not suitable for the future of wind power technology development because these don't consider the impact of wind power forecasting. In order to meet the development trend of wind power technology, this dissertation broke through the limitations of existing boot mode, and given a method that can calculate regulation capacity limit of conventional generators. The results can not only grasp on conventional generators acceptable to the largest wind power, can also learn peaking capacity deficiencies of existing power supplies. All that can provide a scientific basis for further equipment modification.
The main contents in this dissertation include studying regulation capacity limit of conventional generators, and studying wind power prediction method. The specific achievements are as follows:
Studied the relationship between spread and between data density, according to the uneven distribution of wind speed, a new T-S(Takagi-Sugen, T-S) fuzzy GRNN(General Regression Neural Network, GRNN) wind power prediction method is proposed. In this method, the distance between training data and density center is a criterion, the input data space is divided into several sub-space, a GRNN model is created in every sub-space, and a final prediction result is calculated by defined fuzzy rulers. In order to get accurate data center, the data field clustering method is used in this dissertation. In the method, an optimum objective function for reducing influence of sample deviation is constructed and an approximate solution is given of optimum affection factor. For training the fuzzy parameters, the dissertation uses a good performance PSO(Particle Swarm Optimization, PSO) search algorithm. The simulation results prove the correctness of the model and algorithm.
A normal distribution is usually used to model wind power forecast error, but it is not valid in some special cases. For solving this problem, an interval estimation method of wind power forecasts based on Beta distribution is proposed. In this method, according to the security and economy requirements of grid reserve capacity, a new universal optimization model is proposed to calculate minimum probability interval of Beta probability density function. By introducing the inverse function of Beta PDF, equality constrained optimization problem is simplified into an unconstrained optimization problem, and a smart algorithms is presented. The simulation result shows correctness of the algorithms and the rationality of Beta PDF.
According to the situation that no reliable wind power prediction results can be used, a new optimization model is created for calculating negative regulation capacity limit of conventional generators in the dissertation. The dissertation studies the mechanism of the negative peak load regulation of conventional generators based on active power balance equation., a new model is proposed to calculate the limit of capability of negative peak load regulation, and a practical two-tier algorithm is given. The simulation results prove the correctness of the model and algorithm.
According to the situation that wind power prediction is used in generation dispatch schedule method, a new optimization model is created for calculating regulation capacity limit of conventional generators in the dissertation. The influence of wind power prediction for generation dispatch schedule is analyzed. A principle is summarized to create model of regulation capacity limit. A concept of regulation units state chain is defined. On the concept, an optimization model is deduced. According to multi-source tree of regulation units state chain, an algorithm is proposed. The simulation results prove the correctness of the model and algorithm.
引文
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