基于概率潮流的间歇性电源优化配置研究
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摘要
随着经济的发展和科技的进步,人类对能源需求日益增加的同时,对环境保护也越来越重视,基于可再生清洁能源的风力发电(Wind Power Generation, WG)、太阳能光伏电池(Photovoltaic, PV)等新能源发电具有节能减排等优点,因此得到了广泛的关注和应用。但由于这类电源的出力受自然气象条件的影响,具有明显的间歇性和随机波动性,当其并网运行后会给系统带来更多新的不确定因素,降低系统的安全可靠性。因此本论文深入研究了考虑多风电场风速相关性的概率潮流及风电的优化配置；基于出力互补性的分布式风电、太阳能光伏电池的优化配置；以及考虑无功补偿影响的间歇性分布式电源综合优化配置。具体研究工作如下：
1、针对不同风电场风速之间存在相关性,提出了一种考虑风速相关性的概率潮流模拟计算方法。在假设风电场风速满足正态分布的基础上,通过采用随机抽样技术和线性变换,运用蒙特卡罗模拟法进行了考虑风速相关性的概率潮流计算。基于异步发电机的稳态等值电路,在潮流计算中提出了一种描述其吸收无功的变系数二次多项式模型。分析了不同风速相关性对风电场并网点电压的概率密度和相关支路传输功率的概率分布的影响,以及考虑相关性后对风电场并网点无功补偿容量的配置。计算结果表明,在不同的风速相关性情况下,风电场并网节点电压及距风电场较近的支路潮流概率分布变化较大。
2、在随机变量的半不变量基础上,提出并推导了一种考虑风速相关性的概率潮流解析算法,即通过引入多个相互独立的随机变量,并根据各个风电场风速的相关系数,将其余风电场风速拟合成参考风电场风速与引入的随机变量之和,然后再将风速离散化,计算各种风速组合下的系统潮流及其概率,最后再采用VonMises函数求得所有风电场风速组合下的系统概率潮流。在假设风速满足正态分布时,通过与考虑风速相关性的概率潮流模拟法所求得的结果进行比较,验证了此解析算法的有效性和准确性。在进一步将此解析模型用来求解各风电场风速服从Weibull分布时,分别采用不同型号的风力发电机,并计算其在风速低度相关、中度相关、强正相关和强负相关下的系统概率潮流。计算结果表明,风力发电机的型号参数不同时,系统概率潮流亦会发生明显的变化。
3、在考虑风速相关性的概率潮流解析法的基础上,对风电场进行优化配置分别在3种不同的优化目标函数下,即系统最大风电安装容量配置、风电场经济运行效益最优配置和风电场无功优化配置,以风电场并网节点电压越限概率为机会约束条件,研究了不同型号的风力发电机,以及各风电场风速的相关性不同时,3种目标函数下的风电最优配置方案和无功补偿最优方案。优化结果表明,不同的风力发电机型号、不同的风速相关性情况下,其最优配置方案会出现较大的差别。
4、通过对间歇性电源出力在时域和地域上的互补性分析,构建了基于这种互补性的间歇性分布式电源优化配置模型。即根据分布式风电、太阳能光伏电池出力在典型日内各个时段内的概率分布情况,结合系统负荷功率在不同时段内的分布水平,将典型日划分为多个时段,以整个时段内间歇性分布式电源售电、投资、降损、废气减排以及系统电压水平综合最优为目标函数,并采用机会约束规划方法对每个时段进行处理。计算结果表明,系统负荷功率和间歇性DG输出功率在整个时空范围内的概率分布差异性和互补性的变化会严重影响间歇性DG的最优配置方案和系统运行状况。
5、由于间歇性DG和无功补偿电容都能改善系统电压水平和降低网损,而分别对间歇性DG和无功补偿电容进行独立的优化配置时,其最优配置方案并不能取得令人满意的效果。因此采用机会约束规划方法,对间歇性DG和补偿电容进行综合优化配置,能够充分利用补偿电容来改善系统概率潮流和提高节点电压期望值及其处于正常范围内的概率。计算结果表明,该方法降低了间歇性DG和补偿电容的总投资成本,取得了系统运行的经济效益、环境效益以及电压质量的综合最优。
Coupled with economic development and scientific advancement, human have placed greater stress on environmental protection while trying to meet the increasing demand of energy. New energy resources such as WG, PV, based on renewable clean resources, have thus received wide attention and application for their capacity to conserve energy and reduce gas emission. However, with obvious intermittence and randomicity of power output in consequence of natural climate changes, more uncertain factors are brought into system after integration decreasing stability and reliability. As a result, this paper are dedicated to probabilistic power flow and optimal allocation of WGs considering the correlation of wind velocity in various wind farms; optimal allocation of WGs and PVs based on output complementary; and comprehensive optimal allocation of intermittent DGs considering reactive compensation. The details are as follow:
1. As for the correlation of wind velocity in various wind farms, this paper has put forward a simulation calculation method considering this correlation. Assuming the wind velocity in wind farms are all in line with normal distribution, probabilistic power flow taking wind velocity correlation into account can be calculated through random sampling technology, linear transformation and Monte Carlo simulation method. And on the basis of steady state equivalent circuit of induction generator, a coefficient-variable quadratic polynomial model is proposed to describe its reactive consumption in power flow calculation. The influence of different wind velocity correlations is analyzed on probabilistic density of integration voltage and probabilistic distribution of transmission power on related branches, and also the allocation of reactive compensation capacity at integration point. Results have demonstrated relatively considerable changes occurred in integration voltage and power probabilistic distribution on branches closer to wind farms under the circumstance of different correlations.
2. With introduction of semi-invariant random variables, an analytical algorithm of probabilistic power flow considering correlation is proposed in this paper. In other words, several independent random variables are introduced in the first place. Then, according to those correlation coefficients of wind velocity in each wind farm, the wind velocity in the rest wind farms are fitted as the sum of that of a reference wind farm and the introduced random variables. After discretizing wind velocity, system power flow and its probability of different wind velocity combinations are worked out. Finally, with Von Mises function, the system probabilistic power flow under of all wind velocity combinations can be figured out. Assuming the wind velocity in wind farms are all in line with normal distribution and making comparison to the results of the simulation method considering wind velocity correlation, the validity and accuracy of this analytical algorithm are testified. Further this analytical model in the solution when wind velocity in each wind farm is under Weibull distribution, with different types of wind-fueled generator, calculation of system probabilistic power flow under low, median, strong positive and strong negative correlations are conducted. And the results have shown remarkable changes in system probabilistic power flow in compliance with parameters of different WGs.
3. Based on the probabilistic power flow analytic algorithm considering wind velocity correlation proposed above, optimal allocation of wind farms is proposed. Under three different optimization objective functions-allocation for system maximum wind power installation capacity, allocation for optimal economic operation of wind farms and allocation for optimal reactive compensation of wind farms, research on different types of WGs are conducted setting voltage limitation violation probability at integration point as constraints. Besides, the optimal allocation scheme and compensation scheme of three objective functions under different correlations between all the wind farms. The optimization has demonstrated relatively remarkable changes in optimal scheme due to the difference of WG and wind velocity correlations.
4. Complementarity analysis of the power output of intermittent DG on time domain and regional domain has given birth to its optimal allocation model. Given the probabilistic distribution of power output of DG and PV in each period of a typical day and the distribution level of system load in different periods, a typical day is divided into many parts. Then, an objective function aimed at comprehensive optimization of electricity sale, investment, line loss, gas emission and system voltage in an entire period is established with opportunity-constrained planning method applied in each part. The results have shown the great impact that difference and complementarity of the probabilistic distribution of system load and intermittent DGs' power output in the whole time-and-space area have on the optimal allocation scheme of DG and system operation.
5. Intermittent DG and reactive compensating capacitor can both help to improve system voltage and reduce network loss, nevertheless, separate independent allocations of them have hardly turned out to be satisfying. In order to solve this problem, the comprehensive optimization of DG and reactive compensating capacitor has been conducted through opportunity-constrained planning method, which has taken full advantage of compensating capacitor to improve system probabilistic power flow, raise expectation of node voltage and increase its probability of remaining in the normal scope. Results have favored this method for reducing overall investment cost of intermittent DG and compensating capacitor and achieving a comprehensive optimization of economy, environment and voltage quality in operation.
1、针对不同风电场风速之间存在相关性,提出了一种考虑风速相关性的概率潮流模拟计算方法。在假设风电场风速满足正态分布的基础上,通过采用随机抽样技术和线性变换,运用蒙特卡罗模拟法进行了考虑风速相关性的概率潮流计算。基于异步发电机的稳态等值电路,在潮流计算中提出了一种描述其吸收无功的变系数二次多项式模型。分析了不同风速相关性对风电场并网点电压的概率密度和相关支路传输功率的概率分布的影响,以及考虑相关性后对风电场并网点无功补偿容量的配置。计算结果表明,在不同的风速相关性情况下,风电场并网节点电压及距风电场较近的支路潮流概率分布变化较大。
2、在随机变量的半不变量基础上,提出并推导了一种考虑风速相关性的概率潮流解析算法,即通过引入多个相互独立的随机变量,并根据各个风电场风速的相关系数,将其余风电场风速拟合成参考风电场风速与引入的随机变量之和,然后再将风速离散化,计算各种风速组合下的系统潮流及其概率,最后再采用VonMises函数求得所有风电场风速组合下的系统概率潮流。在假设风速满足正态分布时,通过与考虑风速相关性的概率潮流模拟法所求得的结果进行比较,验证了此解析算法的有效性和准确性。在进一步将此解析模型用来求解各风电场风速服从Weibull分布时,分别采用不同型号的风力发电机,并计算其在风速低度相关、中度相关、强正相关和强负相关下的系统概率潮流。计算结果表明,风力发电机的型号参数不同时,系统概率潮流亦会发生明显的变化。
3、在考虑风速相关性的概率潮流解析法的基础上,对风电场进行优化配置分别在3种不同的优化目标函数下,即系统最大风电安装容量配置、风电场经济运行效益最优配置和风电场无功优化配置,以风电场并网节点电压越限概率为机会约束条件,研究了不同型号的风力发电机,以及各风电场风速的相关性不同时,3种目标函数下的风电最优配置方案和无功补偿最优方案。优化结果表明,不同的风力发电机型号、不同的风速相关性情况下,其最优配置方案会出现较大的差别。
4、通过对间歇性电源出力在时域和地域上的互补性分析,构建了基于这种互补性的间歇性分布式电源优化配置模型。即根据分布式风电、太阳能光伏电池出力在典型日内各个时段内的概率分布情况,结合系统负荷功率在不同时段内的分布水平,将典型日划分为多个时段,以整个时段内间歇性分布式电源售电、投资、降损、废气减排以及系统电压水平综合最优为目标函数,并采用机会约束规划方法对每个时段进行处理。计算结果表明,系统负荷功率和间歇性DG输出功率在整个时空范围内的概率分布差异性和互补性的变化会严重影响间歇性DG的最优配置方案和系统运行状况。
5、由于间歇性DG和无功补偿电容都能改善系统电压水平和降低网损,而分别对间歇性DG和无功补偿电容进行独立的优化配置时,其最优配置方案并不能取得令人满意的效果。因此采用机会约束规划方法,对间歇性DG和补偿电容进行综合优化配置,能够充分利用补偿电容来改善系统概率潮流和提高节点电压期望值及其处于正常范围内的概率。计算结果表明,该方法降低了间歇性DG和补偿电容的总投资成本,取得了系统运行的经济效益、环境效益以及电压质量的综合最优。
Coupled with economic development and scientific advancement, human have placed greater stress on environmental protection while trying to meet the increasing demand of energy. New energy resources such as WG, PV, based on renewable clean resources, have thus received wide attention and application for their capacity to conserve energy and reduce gas emission. However, with obvious intermittence and randomicity of power output in consequence of natural climate changes, more uncertain factors are brought into system after integration decreasing stability and reliability. As a result, this paper are dedicated to probabilistic power flow and optimal allocation of WGs considering the correlation of wind velocity in various wind farms; optimal allocation of WGs and PVs based on output complementary; and comprehensive optimal allocation of intermittent DGs considering reactive compensation. The details are as follow:
1. As for the correlation of wind velocity in various wind farms, this paper has put forward a simulation calculation method considering this correlation. Assuming the wind velocity in wind farms are all in line with normal distribution, probabilistic power flow taking wind velocity correlation into account can be calculated through random sampling technology, linear transformation and Monte Carlo simulation method. And on the basis of steady state equivalent circuit of induction generator, a coefficient-variable quadratic polynomial model is proposed to describe its reactive consumption in power flow calculation. The influence of different wind velocity correlations is analyzed on probabilistic density of integration voltage and probabilistic distribution of transmission power on related branches, and also the allocation of reactive compensation capacity at integration point. Results have demonstrated relatively considerable changes occurred in integration voltage and power probabilistic distribution on branches closer to wind farms under the circumstance of different correlations.
2. With introduction of semi-invariant random variables, an analytical algorithm of probabilistic power flow considering correlation is proposed in this paper. In other words, several independent random variables are introduced in the first place. Then, according to those correlation coefficients of wind velocity in each wind farm, the wind velocity in the rest wind farms are fitted as the sum of that of a reference wind farm and the introduced random variables. After discretizing wind velocity, system power flow and its probability of different wind velocity combinations are worked out. Finally, with Von Mises function, the system probabilistic power flow under of all wind velocity combinations can be figured out. Assuming the wind velocity in wind farms are all in line with normal distribution and making comparison to the results of the simulation method considering wind velocity correlation, the validity and accuracy of this analytical algorithm are testified. Further this analytical model in the solution when wind velocity in each wind farm is under Weibull distribution, with different types of wind-fueled generator, calculation of system probabilistic power flow under low, median, strong positive and strong negative correlations are conducted. And the results have shown remarkable changes in system probabilistic power flow in compliance with parameters of different WGs.
3. Based on the probabilistic power flow analytic algorithm considering wind velocity correlation proposed above, optimal allocation of wind farms is proposed. Under three different optimization objective functions-allocation for system maximum wind power installation capacity, allocation for optimal economic operation of wind farms and allocation for optimal reactive compensation of wind farms, research on different types of WGs are conducted setting voltage limitation violation probability at integration point as constraints. Besides, the optimal allocation scheme and compensation scheme of three objective functions under different correlations between all the wind farms. The optimization has demonstrated relatively remarkable changes in optimal scheme due to the difference of WG and wind velocity correlations.
4. Complementarity analysis of the power output of intermittent DG on time domain and regional domain has given birth to its optimal allocation model. Given the probabilistic distribution of power output of DG and PV in each period of a typical day and the distribution level of system load in different periods, a typical day is divided into many parts. Then, an objective function aimed at comprehensive optimization of electricity sale, investment, line loss, gas emission and system voltage in an entire period is established with opportunity-constrained planning method applied in each part. The results have shown the great impact that difference and complementarity of the probabilistic distribution of system load and intermittent DGs' power output in the whole time-and-space area have on the optimal allocation scheme of DG and system operation.
5. Intermittent DG and reactive compensating capacitor can both help to improve system voltage and reduce network loss, nevertheless, separate independent allocations of them have hardly turned out to be satisfying. In order to solve this problem, the comprehensive optimization of DG and reactive compensating capacitor has been conducted through opportunity-constrained planning method, which has taken full advantage of compensating capacitor to improve system probabilistic power flow, raise expectation of node voltage and increase its probability of remaining in the normal scope. Results have favored this method for reducing overall investment cost of intermittent DG and compensating capacitor and achieving a comprehensive optimization of economy, environment and voltage quality in operation.
引文
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