有功、无功预测及优化理论研究
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摘要
如今,可耗竭资源日益枯竭,生态环境不断恶化,节能减排势在必行。电网如何应对这一形势显得十分关键。因此,伴随大规模可再生资源发电并网,以及主动负荷的不断融入,未来电网必然是骨干电网、分布式发电以及微网共同发展。
在这一背景下,如何在电网安全运行前提下,充分挖掘调度和控制的主动性,以应对被动性的波动,达到精细化的优化决策显得尤为重要。由此,在前人研究的基础上,如何提升预测的精度?如何深入研究电压支撑的规律?如何在交流潮流环境下研究机组的启停问题?对未来电网运行效率的提高有重要的理论意义和现实价值。
对此,本文从电网短期运行优化决策角度出发,将研究焦点放在：电网节点有功、无功变化规律的预测,电网自动电压控制(AVC)中的无功优化,以及交流潮流约束下的机组组合等问题的研究上,其主要研究工作和成果如下：
(1)针对当前电网中节点负荷自身单独、孤立预测所显现的弱点,提出了节点负荷的立体化预测体系与方法,主要包括：电网能量流按层、区的拓扑结构划分；在节点与负荷总量相关性分析的基础上,选择电网中各节点的预测方式；结合相应预测方法实现任一节点的预测。文中的预测方法分别基于最小二乘支持向量机、卡尔曼滤波以及加权递推最小二乘等技术。立体化预测方法,实现了节点负荷自身变化规律和与负荷总量以及其他节点的牵制规律之间的有机结合,对现有的预测方法具有包容性,能够满足灵活多变的电网安全分析与决策的需求。
(2)对应三级控制模式,提出在AVC实施中,一定有功模式下,第三级无功优化问题的两阶段处理思路,即动态无功优化体现在未来周期(24小时)内考虑连续量作用的离散量的定位,在此基础上的仅考虑连续量决策的超前无功优化,从而为二级控制确定有效的基点。针对其中的动态无功优化,由于离散调节设备动作次数的限制带来的时间上的耦合,加之连续量对其影响,提出了遗传算法与内点法有机结合的算法。其核心在于：将优化过程分为内层和外层的交替处理,在外层,以融入模拟退火思想的改进遗传算法求解离散变量的最优配置,在内层,则采用非线性原对偶内点法求解离散量确定下的连续优化问题,充分发挥两种算法各自的优势,实现动态无功优化问题的有效求解。
(3)针对电网电压、无功特性对机组组合的影响,构建考虑交流潮流约束的机组组合模型,并将发电机组的安全运行极限引入其中,以计及有功、无功输出间的耦合。对于该模型依据Benders分解的基本原理,将其分解为主、子两层优化问题,其中主问题是无网络约束的机组组合,用以决策机组的启停及有功输出,子问题是以节点电压约束及支路载流约束所对应的松弛量最小为目标的优化潮流问题,用以实现交流潮流网络约束的校验。以子问题目标函数非零判定主问题决策有功方式不可行,并产生Benders割反馈给主问题,通过主、子问题的迭代,实现对原问题的求解。文中还就电压、无功特性制约机组启停的机理进行了分析。
(4)提出了计及系统校正再调度能力的安全约束机组组合(SCUC)模型以及求解方法,将机组的启停状态纳入到计及预想事件的调度当中,有利于提高电网运行的安全性。文中基于Benders分解的基本思想,构造主、子问题的两层迭代模式,其中主问题为无网络约束的机组组合,子问题分为正常状态下和预想事件状态下的网络安全校验。SCUC中计及系统的校正控制能力,既能减少预想事件对于正常状态下机组运行点的影响,提高经济性,又能扩大优化调度的空间,提高运行决策的适应能力。同时,该模型采用全交流潮流约束,因此能够计及无功、电压特性的制约,避免了由于直流潮流约束条件不满足而导致的决策结果不可行的情况。
According to the depletion of exhaustible resources and deterioration of the ecological environment, it is imperative to save energy and reduce emission. And it's extremely crucial to deal with this situation for the power grid. So the grid interconnection of large-scale renewable resources generation and the increasing responsive load prompt the coordinated development of backbone power grid, Distributed Generation(DG) and Microgrid in future power grid.
Against this background and under the precondition of safe operation of power grid, it is very important to realize the refinement decision-making to utilize the initiative of the power grid scheduling and control for dealing with the passive variation. Based on the previous studies, it has important theoretical and practical value for improving the operation efficiency of the future power grid for how to improve the forecast accuracy, analysis the regularity of voltage support and solve the unit commitment problem with AC power flow.
From the perspective of short-term operation optimization, this dissertation focuses on the load forecasting of the nodal active and reactive power, the research of reactive power optimization in AVC and the unit commitment considering AC power flow constraints. The main works and achievements of the dissertation are as follows:
1. Considering the weakness of separated nodal load forecasting and based on the correlation analysis, this paper proposes a multi-dimensional nodal load forecast system and corresponding method for effective prediction of any nodal load of the grid. This system includes topology partitioning of the grid energy flow according to layers and regions, basic forecasting unit composed of each layer's total amount of load and its nodal loads, and combination forecasting to any node. The forecasting method is based on techniques including the least square support vector machine, Kalman filtering and weighted recursive least squares. Multi-dimensional load forecasting can realize any nodal load forecasting with the help of energy flow topology, which achieve the organic combination of nodal load variation regularity and association relationship between nodes. The proposed method can meet the demand of security analysis and decision-making in power grid.
2. Corresponding to the hierachical voltage control mode of AVC, the reactive power optimization in the tertiary voltage control adopts two-stage processing method. The dynamic reactive power optimization aims to determine the discrete variables in advance(the next24hours) with the full use of continuous variables. And based on this, the advanced reactive power optimization only considers the continuous variables, which will help to decide the base point of the secondary voltage control. A new method that combination of genetic algorithm and the interior point method is proposed to solve the coupling of time intervals caused by the action number constraint of the discrete variables:In the outer circle, the improved genetic algorithm is employed to solve the optimal arrangement of discrete variables. In the inner part, the nonlinear primal-dual interior point method is adopted to solve the continuous variables optimization. And this method can solve the dynamic reactive power optimization effectively.
3. Considering the effects on unit commitment made by voltage and reactive power constraints, the model of unit commitment considering ac power flow constraint is established, which includes the limits for safe operation of the generator. According to the Benders decomposition, the primal problem is decomposed into a master problem and a subproblem. The master problem solves unit commitment without AC constraints, and the subproblem checks the AC constraints for the result of the master problem. Benders cuts which formed by subproblem are introduced to the master problem to reschedule unit commitment. The mechanism of voltage and reactive power characteristic affecting the combination states of the units is analyzed, and the essence of the decision is the compromise and coordination of the economic and security operation of the power grid.
4. A security-constrained unit commitment method considering corrective control is proposed. It helps to improve the security of power grid operation for introducing the unit states to the pre-contingency dispatch. Base on the Benders decomposition, the optimization problem is decomposed into a master problem and a subproblem. Unit commitment considering corrective ability of the power system can not only reduce the impact of the pre-contingency on normal condition and improve the operation efficiency, but also improve the adaptability of the decision. Meanwhile, ac power flow constraint is introduced to avoid the infeasible solution of the unit commitment problem.
在这一背景下,如何在电网安全运行前提下,充分挖掘调度和控制的主动性,以应对被动性的波动,达到精细化的优化决策显得尤为重要。由此,在前人研究的基础上,如何提升预测的精度?如何深入研究电压支撑的规律?如何在交流潮流环境下研究机组的启停问题?对未来电网运行效率的提高有重要的理论意义和现实价值。
对此,本文从电网短期运行优化决策角度出发,将研究焦点放在：电网节点有功、无功变化规律的预测,电网自动电压控制(AVC)中的无功优化,以及交流潮流约束下的机组组合等问题的研究上,其主要研究工作和成果如下：
(1)针对当前电网中节点负荷自身单独、孤立预测所显现的弱点,提出了节点负荷的立体化预测体系与方法,主要包括：电网能量流按层、区的拓扑结构划分；在节点与负荷总量相关性分析的基础上,选择电网中各节点的预测方式；结合相应预测方法实现任一节点的预测。文中的预测方法分别基于最小二乘支持向量机、卡尔曼滤波以及加权递推最小二乘等技术。立体化预测方法,实现了节点负荷自身变化规律和与负荷总量以及其他节点的牵制规律之间的有机结合,对现有的预测方法具有包容性,能够满足灵活多变的电网安全分析与决策的需求。
(2)对应三级控制模式,提出在AVC实施中,一定有功模式下,第三级无功优化问题的两阶段处理思路,即动态无功优化体现在未来周期(24小时)内考虑连续量作用的离散量的定位,在此基础上的仅考虑连续量决策的超前无功优化,从而为二级控制确定有效的基点。针对其中的动态无功优化,由于离散调节设备动作次数的限制带来的时间上的耦合,加之连续量对其影响,提出了遗传算法与内点法有机结合的算法。其核心在于：将优化过程分为内层和外层的交替处理,在外层,以融入模拟退火思想的改进遗传算法求解离散变量的最优配置,在内层,则采用非线性原对偶内点法求解离散量确定下的连续优化问题,充分发挥两种算法各自的优势,实现动态无功优化问题的有效求解。
(3)针对电网电压、无功特性对机组组合的影响,构建考虑交流潮流约束的机组组合模型,并将发电机组的安全运行极限引入其中,以计及有功、无功输出间的耦合。对于该模型依据Benders分解的基本原理,将其分解为主、子两层优化问题,其中主问题是无网络约束的机组组合,用以决策机组的启停及有功输出,子问题是以节点电压约束及支路载流约束所对应的松弛量最小为目标的优化潮流问题,用以实现交流潮流网络约束的校验。以子问题目标函数非零判定主问题决策有功方式不可行,并产生Benders割反馈给主问题,通过主、子问题的迭代,实现对原问题的求解。文中还就电压、无功特性制约机组启停的机理进行了分析。
(4)提出了计及系统校正再调度能力的安全约束机组组合(SCUC)模型以及求解方法,将机组的启停状态纳入到计及预想事件的调度当中,有利于提高电网运行的安全性。文中基于Benders分解的基本思想,构造主、子问题的两层迭代模式,其中主问题为无网络约束的机组组合,子问题分为正常状态下和预想事件状态下的网络安全校验。SCUC中计及系统的校正控制能力,既能减少预想事件对于正常状态下机组运行点的影响,提高经济性,又能扩大优化调度的空间,提高运行决策的适应能力。同时,该模型采用全交流潮流约束,因此能够计及无功、电压特性的制约,避免了由于直流潮流约束条件不满足而导致的决策结果不可行的情况。
According to the depletion of exhaustible resources and deterioration of the ecological environment, it is imperative to save energy and reduce emission. And it's extremely crucial to deal with this situation for the power grid. So the grid interconnection of large-scale renewable resources generation and the increasing responsive load prompt the coordinated development of backbone power grid, Distributed Generation(DG) and Microgrid in future power grid.
Against this background and under the precondition of safe operation of power grid, it is very important to realize the refinement decision-making to utilize the initiative of the power grid scheduling and control for dealing with the passive variation. Based on the previous studies, it has important theoretical and practical value for improving the operation efficiency of the future power grid for how to improve the forecast accuracy, analysis the regularity of voltage support and solve the unit commitment problem with AC power flow.
From the perspective of short-term operation optimization, this dissertation focuses on the load forecasting of the nodal active and reactive power, the research of reactive power optimization in AVC and the unit commitment considering AC power flow constraints. The main works and achievements of the dissertation are as follows:
1. Considering the weakness of separated nodal load forecasting and based on the correlation analysis, this paper proposes a multi-dimensional nodal load forecast system and corresponding method for effective prediction of any nodal load of the grid. This system includes topology partitioning of the grid energy flow according to layers and regions, basic forecasting unit composed of each layer's total amount of load and its nodal loads, and combination forecasting to any node. The forecasting method is based on techniques including the least square support vector machine, Kalman filtering and weighted recursive least squares. Multi-dimensional load forecasting can realize any nodal load forecasting with the help of energy flow topology, which achieve the organic combination of nodal load variation regularity and association relationship between nodes. The proposed method can meet the demand of security analysis and decision-making in power grid.
2. Corresponding to the hierachical voltage control mode of AVC, the reactive power optimization in the tertiary voltage control adopts two-stage processing method. The dynamic reactive power optimization aims to determine the discrete variables in advance(the next24hours) with the full use of continuous variables. And based on this, the advanced reactive power optimization only considers the continuous variables, which will help to decide the base point of the secondary voltage control. A new method that combination of genetic algorithm and the interior point method is proposed to solve the coupling of time intervals caused by the action number constraint of the discrete variables:In the outer circle, the improved genetic algorithm is employed to solve the optimal arrangement of discrete variables. In the inner part, the nonlinear primal-dual interior point method is adopted to solve the continuous variables optimization. And this method can solve the dynamic reactive power optimization effectively.
3. Considering the effects on unit commitment made by voltage and reactive power constraints, the model of unit commitment considering ac power flow constraint is established, which includes the limits for safe operation of the generator. According to the Benders decomposition, the primal problem is decomposed into a master problem and a subproblem. The master problem solves unit commitment without AC constraints, and the subproblem checks the AC constraints for the result of the master problem. Benders cuts which formed by subproblem are introduced to the master problem to reschedule unit commitment. The mechanism of voltage and reactive power characteristic affecting the combination states of the units is analyzed, and the essence of the decision is the compromise and coordination of the economic and security operation of the power grid.
4. A security-constrained unit commitment method considering corrective control is proposed. It helps to improve the security of power grid operation for introducing the unit states to the pre-contingency dispatch. Base on the Benders decomposition, the optimization problem is decomposed into a master problem and a subproblem. Unit commitment considering corrective ability of the power system can not only reduce the impact of the pre-contingency on normal condition and improve the operation efficiency, but also improve the adaptability of the decision. Meanwhile, ac power flow constraint is introduced to avoid the infeasible solution of the unit commitment problem.
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