南方电网抽水蓄能电站自动发电控制功能研究与应用

抽水蓄能电站自动发电控制功能是电站的核心控制功能,抽水蓄能电站工况复杂,具备水泵、调相等特殊工况,其自动发电控制逻辑较常规电站复杂。本文以南方电网所辖的各抽水蓄能电站为例,详细介绍了南方电网抽水蓄能电站自动发电控制功能的主要控制算法,包括发电工况成组控制、抽水工况成组启动控制算法等,为抽水蓄能电站自动发电控制功能设计提供参考。     

Stochastic reactive power market with volatility of wind power considering voltage security

While wind power generation is growing rapidly around the globe; its stochastic nature affects the system operation in many different aspects. In this paper, the impact of wind power volatility on the reactive power market is taken into account. The paper presents a novel stochastic method for optimal reactive power market clearing considering voltage security and volatile nature of the wind. The proposed optimization algorithm uses a multiobjective nonlinear programming technique to minimize market payment and simultaneously maximize voltage security margin. Considering a set of probable wind speeds, in the first stage, the proposed algorithm seeks to minimize expected system payment which is summation of reactive power payment and transmission loss cost. The object of the second stage is maximization of expected voltage security margin to increase the system loadability and security. Finally, in the last stage, a multiobjective function is presented to schedule the stochastic reactive power market using results of two previous stages. The proposed algorithm is applied to IEEE 14-bus test system. As a benchmark, Monte Carlo Simulation method is utilized to simulate the actual market of given period of time to evaluate results of the proposed algorithm, and satisfactory results are achieved.     

计及风电条件风险价值的鲁棒柔性超前调度

提出一种考虑大规模风电并网的超前优化调度方法,引入风电条件风险价值来评估风电消纳风险。建立基于鲁棒优化的柔性超前调度模型,以平衡运行成本与风电条件风险价值。根据该模型,对AGC机组的基点功率、参与因子、柔性容量进行协同优化,还可得到各风电场输出功率的可容许区域。提出一种基于大M法和分解法的求解双线性规划模型的高效算法。所提模型及算法结合鲁棒优化与随机优化的优点,在保证计算效率的同时,可避免鲁棒优化的过度保守。仿真结果验证了所提模型及算法的有效性。     

Artificial immune system for fixed head hydrothermal power system

This paper presents artificial immune system for optimal scheduling of thermal plants in coordination with fixed head hydro units. Numerical results of two test systems have been presented to demonstrate the performance of the proposed algorithm. The results obtained from the proposed algorithm are compared with those obtained from differential evolution, particle swarm optimization and evolutionary programming technique. From numerical results, it is found that the proposed artificial immune system based approach is able to provide better solution than differential evolution, particle swarm optimization and evolutionary programming in terms of minimum cost and computation time.     

基于AGC考核指标的PID参数优化研究

在"两个细则"中AGC(自动发电控制)考核标准执行后,针对电厂机组整体优化中PID参数整定的实际问题,提出了基于AGC考核指标的PID参数优化方案。本文以AGC综合考核指标作为目标函数,采用遗传算法进行PID参数优化,并与传统方法(Z-N法和ITAE法)进行控制性能比较与分析,研究其提高机组AGC性能的可行性和有效性。最后以660 MW亚临界单元机组为实验模型进行仿真验证,仿真结果表明,提出的方法能够显著地提高AGC性能指标,对运行操作人员具有指导意义。     

Optimal scheduling of heat-integrated multipurpose plants under fouling conditions

This work presents a systematic mathematical framework for scheduling the operation of multipurpose plants involving heat-integrated unit operations under fouling considerations. A salient characteristic of this problem is that the performance of each heat-integrated unit, which decreases with time due to fouling, can be restored to its initial state by performing cleaning operations. Based on a uniform time discretization, the overall problem is formulated as a mixed integer non-linear programming (MINLP) model. An iterative procedure is proposed for the solution of the resulting non-convex MINLP model, involving the solution of a series of mixed integer linear programming and non-linear programming subproblems. The optimization algorithm determines simultaneously: (i) the production schedule, (ii) the number of cleaning operations required along with their corresponding timings, (iii) the optimal utility utilization profile and (iv) the heat transfer medium flow rate profiles over time. An example problem is presented to illustrate the applicability of the proposed approach. It is proved that fouling considerations can significantly affect the production schedule as well as heat integration opportunities.     

Adaptive automatic generation control with superconducting magneticenergy storage in power systems

An improved automatic generation control (AGC) system employing self-tuning adaptive control for the main AGC loop and the superconducting magnetic energy storage system (SMES) is presented. Computer simulations on a two-area interconnected power system showed that the proposed adaptive control scheme was very effective in damping, oscillations caused by load disturbances and its performance was quite insensitive to controller gain parameter changes of the SMES system. A performance evaluation of control schemes using adaptive and nonadaptive controllers in the main AGC and in the SMES control loops is presented. The improvement in performance resulting from the adaptive scheme was particularly pronounced for load changes of random magnitude and duration     

Multi-timescale robust dispatching for coordinated automatic generation control and energy storage

The increasing penetration of renewable energy into power grids is reducing the regulation capacity of automatic generation control (AGC). Thus, there is an urgent demand to coordinate AGC units with active equipment such as energy storage. Current dispatch decision-making methods often ignore the intermittent effects of renewable energy. This paper proposes a two-stage robust optimization model in which energy storage is used to compensate for the intermittency of renewable energy for the dispatch of AGC units. This model exploits the rapid adjustment capability of energy storage to compensate for the slow response speed of AGC units, improve the adjustment potential, and respond to the problems of intermittent power generation from renewable energy. A column and constraint generation algorithm is used to solve the model. In an example analysis, the proposed model was more robust than a model that did not consider energy storage at eliminating the effects of intermittency while offering clear improvements in economy and efficiency     

基于SVR增量学习算法的变桨距风力机系统在线辨识

针对变桨距风力机模型非线性很强的特点,采用支持向量回归(SVR)算法进行辨识,数据由BLADED仿真软件提供,经训练检测的结果表明,SVR算法在变桨距风力机非线性模型辨识上具有很高的准确性。考虑到风力机现场工作过程中会出现模型变化,利用增量学习算法实现在线辨识。由于在线SVR辨识计算时间太长,通过改进的序列最小优化(SMO)算法代替原来的凸二次规划(QP)算法。同时提出满足度系数,排除系统无效的突变点,使在线辨识具有鲁棒性,并通过双支持向量机(SVM)算法实现在线辨识的记忆功能,最终辨识结果不仅有很强的精度,而且大大减小了计算时间。     

Practical implementation of the SCADA+AGC/ED system of the Hunanpower pool in the central China power network

To accomplish the dispatching automation of the Hunan Power Pool (HPP), a supervisory control and data acquisition and automatic generation control/economic dispatch (SCADA+AGC/ED) system supplied by Systems Control in the USA was imported by the Hunan Power Dispatch Control Center. In December 1990 the practical implementation acceptance test for the new system was completed. The new system is the first system completed to use closed loop control of AGC in the power pools of China. This paper reviews the background of developing the new system, and describes implementation schemes and operation tests of AGC. At the same time, the Constant Net Interchange (CNI) mode of AGC in the HPP is also discussed in detail, and finally the economic benefits and the improved operation quality after AGC is in operation are described     

Optimum expansion planning of an unconventional generation systemoperating in parallel with a large scale network

A package for the long-term optimal expansion planning of a generation system with renewable energy sources operating in parallel with a large-scale network is presented. The algorithm utilizes meteorological data, load demand data, and estimated economical parameters and determines the candidate plans for the expansion period. A forward dynamic programming algorithm is utilized to determine the optimal plan. Results from the application of the method to a local grid are presented     

基于改进蚁群动态规划的光储微网容量优化配置

为降低并网光储微网的综合发电成本,并提高优化容量配置性能,该文研究基于改进蚁群动态规划算法的光储微网容量优化配置方法。针对传统启发式算法容易陷入局部最优以及早熟收敛的问题,首先将蚁群算法与动态规划算法结合,简化计算过程;进而,将迭代次数有关的衰减参数引入调节因子中,提高其全局搜索能力;最后,将Boltzmann选择机制引进蚁群搜索过程,并在蚁群信息素更新过程中采用偏转角度因子与拐点参数进行修正,从而大幅提高算法的优化性能。通过对海宁某小区实际数据进行仿真分析,验证了该算法的实用性和优越性。     

一种数学规划的分解算法

分析讨论了一种数学规划的分解算法，并在凸多面体方面的基础上，讨论了Ｄａｎｔｚｉｇ－Ｗｏｌｆ算法及其改进。     

一种数学规划的分解算法（续篇）

分析讨论了一种数学规划的分解算法，并在凸体分析的基础上，讨论了Ｄａｎｔｚｉｎｇ－Ｗｏｌｆ算法及其改进。     

虚拟电厂内部资源调度算法的对比分析及应用

分布式发电资源的广泛应用对各类分布式发电机组的协调并网运行及其与传统发电机组的联合调度提出了新的要求。结合微型热电联产机组等分布式发电机组的特性,引入通用虚拟电厂技术,分析虚拟电厂内部发电资源优化调度算法,基于传统调度方法提出三种改进的调度算法,并通过实例分析对比了三种算法的寻优结果及运行时间。结果表明,基于混合整数线性规划算法与启发式算法的启发式线性规划改进算法有利于虚拟电厂业主获得更好的经济与环保收益以及更快的运算时间。     

A novel line drop secondary voltage control algorithm for variable speed wind turbines

This paper addresses the design and implementation of the line drop secondary voltage control (LDSVC) for the doubly fed induction generator‐wind turbine (DFIG‐WT) complemented with reactive power allocation algorithm to achieve more efficient voltage regulation, reactive power compensation and to enhance the transient stability margin of the electric power system. The LDSVC is used to generate the local voltage reference, providing an improvement for overall voltage profile. The paper presents the influence of the integration of variable speed wind turbines‐based doubly fed induction generator (DFIG) while employing LDSVC for increasing the transient stability margin. This paper proposes an improved voltage control scheme, based on a secondary voltage controller complemented with an automatic gain controller (AGC). The scheme is applied to a wind energy system incorporating DFIG‐based wind turbines. The controller structure is developed and the performance of the self‐tuning AGC scheme is developed and analysed. The proposed controller is tested in response to system contingencies for different short circuit ratios. The performance of the secondary voltage control without and with AGC is verified. The influence of the AGC in improving the transient response and damping of voltage oscillations is verified. Copyright © 2010 John Wiley & Sons, Ltd.     

Optimal strategies for wind turbine environmental curtailment

Wind turbine curtailment is oftentimes required as a means to mitigate environmental impacts of a wind energy installation. For example, curtailment may be required to satisfy constraints on the occurrence of shadow flicker on nearby homes or to reduce wildlife fatalities below a certain limit. This paper introduces an optimal curtailment strategy, which seeks to maximize wind plant revenue while meeting imposed environmental constraints by intelligently selecting curtailment times. To formulate the problem, a discrete set of curtailment decision times is defined, and long‐term forecast data are used to predict demand‐weighted power production at each time. Dynamic programming is used in conjunction with in situ meteorological sensors to compute the probabilistically optimal curtailment decision in real time. By leveraging both forecast data and real‐time measurements, the algorithm ensures that the constraint budget is expended strategically, curtailing when revenue is low and saving operating hours for times when revenue is likely to be higher. Through a series of simulation studies involving shadow flicker constraints, algorithm performance is characterized and compared with two simpler approaches: a greedy scheme and a threshold‐based scheme. These studies highlight the benefit of the optimal algorithm as well as the performance tradeoffs inherent in the three different solution approaches. Overall, the dynamic programming algorithm is shown to exhibit significant benefits in cases where sufficient meteorological and demand data are available.     

Simplex ray-object intersection algorithm as ray tracer for Monte Carlo simulations in radiative heat transfer analysis

In the thermal radiation analysis via Monte Carlo method, considerable computational resources are consumed to find the intersection point of an emitted energy bundle with radiant enclosure walls. Therefore, an efficient algorithm for ray-object intersection in complex geometries may cause saving time and computational effort. This paper presents a new ray-object intersection algorithm based on the well-known simplex method from linear programming. This algorithm works by searching a point in the feasible region which is defined by a set of plane equations of enclosure boundaries that maximize the line equation of the emitted energy bundle as the objective function. This algorithm is examined for two benchmark problems, namely two parallel plates with gray specular surfaces and a box with gray diffuse walls both in three-dimensional case. Although the computation time of the new proposed method is a bit higher than the conventional time, it is easy to implement because simplex algorithm is readily available as separate module in most programming languages. By using this algorithm number of objects which must be checked in complex geometries will be reduced considerably.     

多目标综合利用水库实时优化调度模型研究

以河北省洋河水库为例，建立了具有引青调水和水库泄水两个决策变量的综合水库多目标动态规划实时优化调度模型，提出了含变动罚系数的离散微分动态规划方法，经计算七成果分析表明，所提模型与方法进可行和实用的。     

增压压气机的最优设计研究

本文将离心增压压气机的设计问题表述为其空气动力学损失极小化,比面积(质量流率/动叶进口面积)最大化的多目标非线性数学规划问题.求优中考虑了多种气动与机械约束条件.文中介绍了压气机性能估算模型,给出了最大效率、最大比面积以及效率、比面积权衡最优条件下的算例.结果表明,本方法是有效且实用的.