Genetic algorithm-based method for contingency screening in power systems

This paper proposes an application of genetic algorithm (GA) to contingency screening in power systems. The contingency selected by the GA-based screening method is the double line outage which has the risk of causing transient instability. Generally, the contingency screening problem including multiple outage can be interpreted as the combinatorial optimization one for searching the combination of single outages which makes the system insecure. Therefore, GA which is one of the probabilistic searches for combinatorial problems, is applicable for such contingency screening problems. In the GA-based contingency screening method, a double line outage is represented as a chromosome. The fitness of the chromosome for environment is defined by using the transient energy function of power systems. The new procedure for avoiding the loss of important outage during contingency screening is developed and embedded in the proposed method by using the theorem of schema for GA. The validity of the proposed method is confirmed by applying it to a 6-machine 30-bus 40-line system. The result shows that the GA-based contingency screening has the potential for practical use.     

遗传算法在电力系统无功优化中的应用

遗传算法根据自然界适者生存的原则进行搜索和优化。将遗传算法应用于电力系统无功优化，不仅能避免一般优化算法的局部最优问题，并能解决无功优化中变量的离散问题，避免维数灾难，提供最优及次优方案，使无功优化更切实际。遗传算法的引入，为电力系统无功优化提供了一种新的计算方法，使无功优化方法更加完善和实用。     

基于REI等值的多区域无功优化并行计算(英文)

将REI等值技术应用于求解多区域电力系统的无功优化并行计算问题,对系统各个分区的外部网络进行REI等值化简,并对相角传递、等值网络初值计算、外层协调计算与REI等值网络修正等关键问题提出了具体的解决办法。并以此为基础建立了适合多区域无功优化的并行计算模式,通过采用Matlab并行计算平台实现无功优化并行计算,以IEEE 39节点和某695节点实际系统作为算例,通过与集中优化方法和基于Ward等值的多区域无功优化并行算法进行比较,对所提方法的有效性和优缺点进行了详细分析。     

考虑光储型电热协同系统灵活性的多代理削峰填谷策略

为解决光储型电热协同系统（electric-thermal system, ETS）协作参与电网削峰填谷问题,并减小负荷预测误差和新能源波动对调节效果的影响,提出一种多代理削峰填谷策略。该策略依托由配网代理、区域代理、ETS/光伏发电（PV）代理和执行单元构成的多代理系统实施,包含集中式能量优化和分布式能量管理环节。在集中式能量优化过程中,配网代理可通过求解以自身运行成本最小为优化目标的模型预测控制（model predictive control,MPC）优化模型,为区域代理及其内部的光伏系统提供日内有功功率上限计划。分布式能量管理过程中,区域代理和ETS/PV代理基于多智能体一致性算法获取供暖设备的有功功率修正值,从而减小实际区域代理有功功率与其计划值间的偏差。仿真结果表明：该策略可使系统协同参与削峰填谷且结果更精确。     

多区域并行协同的多目标分布式帕累托最优潮流算法

在开放电力市场的环境下,各区域电网合作与利益博弈共存,区域电网之间的信息保密问题显得愈发重要。现有的帕累托最优潮流求解方法均属于集中式算法,在优化时需要获知全网的信息,无法满足高私密性以及高可靠性的要求。在该背景下,寻求一种去中心化的分布式优化方法以保障系统内各区域电网的信息安全显得尤为重要。基于此,文中提出了一种多区域并行协同的多目标分布式帕累托最优潮流求解算法。该算法以法线边界交叉法为基础,将整个系统的多目标潮流优化问题分解为与多个子区域对应的子优化问题。每个子区域采用独立的优化器完成子问题的优化,区域之间仅交换联络线上的边界变量以及虚拟目标因子进行全局调整,不断逼近原问题的帕累托最优解集。IEEE 118节点算例仿真结果表明:所提算法可在有效实现多目标帕累托最优潮流分布式并行求解的同时,还可提高求解精度和减小计算内存,从而适用于在开放电力市场背景下各区域电网合作与利益博弈共存的运营模式。     

AGC of a multi-area multi-source hydrothermal power system interconnected via AC/DC parallel links under deregulated environment

This paper proposes the automatic generation control (AGC) of an interconnected multi-area multi-source hydrothermal power system under deregulated environment. The two equal control areas with hydro and thermal generating power sources are interconnected via AC/DC parallel links. The optimal proportional integral (PI) regulators are designed for the proposed power system to simulate all power market transactions which are possible in a restructured power system. The concept of DISCO participation matrix (DPM) is harnessed to simulate the transactions. Eigenvalue study is conducted to assess the effect of AC/DC parallel links on system performance. The study is also conducted, considering appropriate generation rate constraints (GRCs) for thermal and hydro generating sources. Further, the dynamic responses of the proposed multi-source hydrothermal power system are compared with single-source thermal–thermal power system and it has been ascertained that the responses of proposed power system are sluggish with large overshoots and settling times. Finally, the study is extended to frame and implement optimal PI regulators for the first time for the AGC of a conventional two-area non-reheat thermal power system with governor dead-band nonlinearity. The superiority of the optimal PI regulators has been established by comparing the results with recently published best claimed craziness based particle swarm optimization (CRAZYPSO) and hybrid bacterial foraging optimization algorithm-particle swarm optimization (hBFOA-PSO) algorithms based PI controller tuned for the same interconnected power system.     

基于非线性原-对偶内点法的OPF算法及其校正策略

给出了电力系统最优潮流（OPF）的数学模型以及求解该模型的非线性原-对偶内点法，在阐述OPF数学模型的基础上，对基于非线性原-对偶内点法的OPF算法进行了详细的数学描述.并就改善内点法的2种高阶校正策略进行了论述，通过对3个不同规模电力系统的数值计算，验证了2种策略对于不同规模系统均优于原-对偶内点法，同时指出2种策略对不同规模系统有着不同的适应性.     

A new approach to fault section estimation in power systems using Ant system

In this paper, a new method to fault section estimation problem in power systems is developed using Ant System. Based on the information from the operated protective relays and tripped circuit breakers, the fault section estimation is first formulated as a combinatorial optimization problem. Then, a new approach known as the Ant System is applied to solve this optimization problem. The Ant System is suitable for solving problems of combinatorial optimization by means of agents that work in parallel, among a population and without a supervisor in a cooperative manner. Finally, a sample power system is used to demonstrate the efficiency of the Ant System based method. From these test results, it appears that the Ant System is of promise for the fault-section estimation problem.     

Improving high-voltage transmission system adequacy under contingency by genetic algorithms

The paper presents a methodology which can be used to improve the static adequacy of high-voltage (HV) transmission systems under contingency. In this case the most suitable actions to be taken for bringing the power system back to acceptable operation conditions are identified by means of a power system management software. The proposed software combines a micro-genetic algorithm (μGA) optimization procedure with a load-flow program based on the fringing current correction (FCC) method. The foreseen control actions consist in transformer tap setting, insertion and/or regulation (if variable) of shunt reactor and capacitor banks, change of network configuration, power re-dispatching and load shedding. The performance of the proposed procedure is tested with respect to the main parameters both of electrical power systems and of genetic algorithms (GAs). An application to existing HV transmission systems is presented and discussed in order to evaluate its possible use in a System Control Center.     

含暂态能量裕度约束多故障最优潮流计算

借助暂态能量裕度对发电机有功和无功出力的解析灵敏度，将暂态能量裕度约束直接加入最优潮流模型中，建立含暂态能量裕度约束多故障最优潮流逐次线性规划模型，采用单纯形法求解，取得了较好的效果。此外，还提出了根据大步长单故障最优潮流近似计算获得的暂态能量裕度进行故障扫描方法，并寻找到同一失稳模式下影响系统稳定的关键故障，验证了满足关键故障稳定性要求的最优潮流解可以同时满足同失稳模式下的其它故障的稳定约束。新英格兰10机39节点系统的最优潮流及暂态稳定计算验证了所提方法的有效性。所有优化结果均用一个在电力系统中广泛使用的暂态稳定仿真程序进行了验证。     

Preventive and corrective control applications in power systems via Big Bang–Big Crunch optimization

This paper proposes a new method of optimization, the Big Bang–Big Crunch (BB–BC) method, for the optimization of preventive and corrective control actions to enhance the dynamic security of a power system against transient instabilities. The control actions, generation rescheduling and load shedding, are considered as constrained optimization problems with static and dynamic security constraints. These optimization problems are solved through the BB–BC method to minimize some operational costs related to the control actions. To reduce the size of the search spaces and the computational burden, decision trees and correlation coefficients are used as feature selection tools, which determine the most effective generators and loads for shaping the system’s transient stability. The proposed method is applied to a test system and compared with genetic algorithms, particle swarm optimization, differential evolution and active set method. The BB–BC method is promising since it gives comparable results with the other population based optimization methods.     

基于模糊隶属度及区间算术的电网电压优化新算法

传统无功优化由于寻优策略复杂、计算机时较长,目前尚较难应用于电网实时优化控制。基于最优电压模糊隶属度及区间潮流算法,提出了一种简单快速的寻优新思路。先由电网能提供的控制变量区间求解对应的电压区间,与电网安全约束电压区间求交后便得到既可行又能满足安全约束的电压区间。在此区间中找出隶属度最大的电压值,以此电压值为已知量求解反潮流,所得的控制变量解便为要求的最优控制变量组合。该方法与传统的智能算法比较,结果说明该方法的确优化速度快,适合作为省级电网的AVC系统电压优化的快速寻优方法。     

广域后备保护多代理系统的仿真建模与实现

针对广域后备保护多代理系统分布式协作的特点,为广域后备保护代理构造了环境感知、情景感知、综合决策等智能模块和接口,设计了多代理系统的容错性处理措施。EPOCHS是将电力系统电磁暂态仿真软件EMTDC/ PSCAD、机电动态仿真软件PSLF、网络通信仿真软件NS2、Agent集成的电力和通信同步仿真环境,将保护代理映射到EPOCHS予以仿真设计和实现,构造了电网的EMDTC/ PSCAD模型和SDH通信网络的NS2仿真模型。通过仿真系统可观察保护代理的内部活动和多代理间的交互过程,从而评价广域保护算法及策略。通过3个仿真实例证明广域后备保护多代理仿真系统的代理设计是正确的。仿真试验能够有效地检验在电力系统故障、保护代理失效、断路器失灵等多种情景下多代理推演带来的整体性能。     

蚁群优化算法及其在电力系统中的应用

蚁群优化算法是意大利学者M Dorigo受蚂蚁觅食行为的启发 ,提出的一种新型组合优化算法 ,具有正反馈、分布式计算等特点 ,已在许多问题中得到成功的应用。本文对蚁群优化算法的研究现状作一综述 ,并指出在电力系统中可能的应用方向     

应用图形处理器实现无功优化并行计算

以求解无功优化问题的内嵌离散惩罚非线性原对偶内点法为基础,利用高性能图形处理器实现了线性修正方程的并行求解。将计算密集部分在图形处理器上实现,其余部分在CPU上执行,并且采用单精度和双精度两种模式进行对照。该算法充分利用了图形处理器强大的并行处理能力和极高的存储器带宽,可获得显著的加速效果。在IEEE 118节点系统和实际538、1133和2212节点系统的计算表明,采用单精度浮点运算的无功优化计算速度最快,加速效果最好,在2212节点系统上的加速比达到近30倍。     

SA-PSO在水火电混合电力系统电源规划中的应用

电源规划是电力系统电源布局的战略决策,在电力系统规划中处于十分重要的地位。其核心问题是要确定在规划期内随着负荷的增长,系统应在何时、何地、建什么类型、多大容量的电厂。由于其本身的的复杂性,用传统的优化方法求解需采取简化措施,寻求一个满足各种约束条件和可靠性指标及环保要求的最优电源建设方案,以满足系统负荷发展的需要。为此,提出一种粒子群算法与模拟退火算法结合的模拟退火粒子群算法,并将其用于求解复杂的、非线性的水火电混合电力系统(包含核电)电源规划问题。该组合算法在粒子群算法中引入了模拟退火算法成功的提高了基本粒子群算法的全局搜索能力。算例结果表明:该算法能可靠、快速的收敛到全局最优解,特别适合于大型电力系统的中长期电源规划。     

Representation of FACTS devices in power system economic dispatch

The main idea behind FACTS (flexible AC transmission system) is to use network parameters as controls to direct flow, thus eliminating problems caused by unwanted loop or parallel flows. The authors present a methodology for the representation of FACTS device such as phase shifters and series compensations in optimal power flow models. The proposed approach is based on, mathematical decomposition and network compensation, techniques. It is possible to incorporate existing optimal power flow algorithms, thus taking advantage of accumulated experience in optimization methods and reducing software development costs. A case study with a Brazilian-derived system is presented     

径流式小水电群随机环境经济调度方法

针对径流式小水电群的电力系统,建立了以污染气体排放和购电费用最小为目标的随机动态环境经济调度模型。借助场景法将该模型转化为大规模两目标确定性动态调度模型。采用法线边界交叉法将这个两目标优化问题转换为一系列单目标优化问题,并采用非线性原对偶内点法求解。在迭代过程中,按照场景顺序将简化修正方程的系数矩阵排列为对角加边形式,方便对其实施解耦,并运用异步块迭代法求解,从而将一组高维修正方程组的求解转化为若干个分别与预测场景和误差场景相对应的低维修正方程组的求解。采用某省级电网的真实数据进行计算,在高性能集群上建立了并行计算框架以缓解计算占用内存并提高计算速度。通过这个计算架构,可以获得一组日前调度计划,且使得更加全面的折中优化结果可以应用于电力系统的调度。     

Large-Scale Economic Dispatch by Artificial Ant Colony Search Algorithms

This paper presents a new cooperative agent search approach for solving power economic dispatch problems. One of the main objectives of this paper is to investigate the applicability of an alternative agent search method in power system optimization. The proposed Artificial Ant Colony Search Algorithm (ACSA) is based on the results of real ant trail formation and foraging obtainedfrom theoretical biology science. A new encoding technique is proposed to overcome the difficulties of applying ACSA in a continuous search space, such as economic dispatch problems. An object-oriented ACSA system is developed and programmed. The effectiveness of the proposed technique has been demonstrated on a number of systems, which include an actual utility system of up to 40 units. Comparison with conventional genetic algorithms is presented. The outcome of the study shows that an emergent collective search ability resulted from the massive parallel fashion and positive feedback of ant colony is particularly attractive in addressing some difficult engineering problems; however, it should be observed that current research of Ant Colony Search Algorithm in power system is relatively new and still at a feasibility stage. More potentially beneficial work remains to be done.     

Parallel simulated annealing for power system decomposition

This paper proposes a parallel optimization approach to power system decomposition for voltage control. In this paper, a parallel simulated annealing (PSA) technique is developed to decompose power systems so that subsystems are equally separated in terms of the number of nodes and control variables. System decomposition is one of difficult discrete number combinatorial problems. The PSA technique provides better solutions than the conventional SA because of searching a solution near a global minimum over a wide range. The proposed method is tested in IEEE 30, 57, and 118 node systems