一种混合优化方法在变压器双螺旋式线圈换位优化设计中的应用

怎样最有效地减少电流线圈的环流损耗是变压器设计中一个长期未能解决的难题。双螺旋式线圈的环流损耗与并联导线的换位位置及换位步长密切相关其换位优化设计民磁场开头位置优化总理２。本文针对遗传算法的优缺点,对常规遗传算法作了相应的改进,并将改进的遗传算法与复合形法相结合,应用于双螺旋式线圈的换位 经,并以实际 的电力变压器作为计算实例。     

Stability-constrained optimal power flow

Stability is an important constraint in power system operation. Often trial and error heuristics are used that can be costly and imprecise. A new methodology that eliminates the need for repeated simulation to determine a transiently secure operating point is presented. The theoretical development is straight-forward: dynamic equations are converted to numerically equivalent algebraic equations and then integrated into the standard OPF formulation. Implementation issues and simulation results are discussed in the context of a 162-bus system     

基于最优潮流的含多微网的主动配电网双层优化调度

多个微电网并入主动配电网(Active Distribution Network, ADN)会对ADN系统的经济性和可靠性产生影响。采用传统的多微网与ADN全网统一优化调度方法时,若微网中风/光出力一旦出现波动,难以高效精确地求出系统的最优潮流,甚至造成无解。因此提出一种基于双层规划的多微网并网优化调度模型。上层模型中各微网作为电源并入ADN,以ADN系统潮流平衡为约束,建立最优潮流模型。运用二阶锥松弛技术将非凸非线性的潮流模型转化为凸可行域的二阶锥规划模型,并调用Gurobi求解器求解。下层模型以上层优化出的联络线功率为约束,建立并网微电网内可控电源的调度模型,并采用结合Tent映射混沌和NDX交叉技术的改进遗传算法(GA)求解。以并入多微网的调整IEEE33节点系统为算例,仿真算例表明双层规划的调度模型及算法具有可行性且在此模型下含多微网的ADN系统有更好的经济性。同时当风/光发电出现波动时,下层模型仍然可以进行局部调整优化,从而降低了微电网波动对ADN系统的影响,提高了系统的可靠性和鲁棒性。     

Searching of optimal vaccination schedules

Genetic algorithms (GAs) are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology. These are widely used in different areas of bioinformatics. In immunoinformatics, a common optimization problem is the search of optimal vaccination schedules. The problem of defining optimal schedules is particularly acute in cancer immunopreventive approaches, which requires a sequence of vaccine administrations to keep a high level of protective immunity. This paper presents a formalization of the optimization problem and show how a GA search on a model-based approach can be used to deal with the problem.     

Adaptive multivariable optimal predictive control

A multivariable control strategy that incorporates long-term prediction is presented. The future control policy is calculated so as to minimize a weighted sum of squared input and output deviations from reference values. Constraints on inputs are explicitly incorporated in the minimization to allow optimum operation of the process. The control approach is developed to allow either adaptive operation or operation using a constant model that has been identified on- or off-line. Several simulation examples are presented to demonstrate the robustness of the controller to unknown load dusturbances, process-model mismatch, time-varying dynamics and non-minimum phase characteristics.     

最优潮流的发展

最优潮流是在保证系统安全运行的前提下,实现系统经济运行的问题.由于其安全约束众多、数学模型复杂,而难以实现.此文将回顾近二十年来最优潮流的逐步发展的过程,对主要的优化方法列出几篇具有代表性的文章,列出几种简单的数学模型并对各种方法的优化效果做出比较.并对最优潮流的进一步发展做出深入的探讨.     

最优潮流的发展

最优潮流是在保证系统安全运行的前提下 ,实现系统经济运行的问题。由于其安全约束众多、数学模型复杂 ,而难以实现。此文将回顾近二十年来最优潮流的逐步发展的过程 ,对主要的优化方法列出几篇具有代表性的文章 ,列出几种简单的数学模型并对各种方法的优化效果做出比较。并对最优潮流的进一步发展做出深入的探讨。     

Real-time coordinated optimal facts controllers

A coordinated optimal (CDO) controller is designed to implement multiple Thyristor Controlled Series Compensation (TCSC) devices in a transmission network of interconnected power systems. The proposed controller is utilized to damp inter-area oscillations and to enhance power system damping during large disturbances. The effectiveness of the CDO controller to properly control such devices is demonstrated for a two-machine power system through real-time digital simulation studies using a PSCAD/RTDS.     

On-line adaptive optimal load flow

This paper presents a new on-line approach for solving the optimal power flow problem without sacrificing the accuracy and constraints of the system components. A procedure which merges non-linear programming (NLP) and linear programming (LP) techniques is utilized to make use of the merits of both algorithms. The NLP and LP form a closed loop with a decision maker (DM) which directs the solution algorithm either to NLP or LP according to the analysis of the permissible errors during the calculation process. The input to the closed loop is fed either from the telemetered data or the state estimator.

To adapt the LP algorithm, the objective function and the system constraints are linearized, using Taylor expansion series, around the exact solution obtained as a result of employing the NLP technique. A new linearization algorithm is presented.

The proposed approach has been applied to a 19-bus system and the results are judged satisfactory. Its use as an on-line computational tool for power system operation and control seems very promising.     

Multiobjective optimal thermal power dispatch

A multiobjective thermal power dispatch problem is formulated using noncommensurable objectives such as operating costs and minimal emission. A sensitivity measure is chosen whereby the effects of variations in the nominal conditions describing a given multiobjective problem can be measured and incorporated as a performance index to be minimized. A nonlinear programming problem provides the framework for examining the objective constraint level in an ε-constant form of the multiobjective optimization problem. The dispersion index is chosen as the sensitivity measure for the investigation of the effects of random variations in the model parameters of the optimal solution. A sensitivity trade-off is exploited for the multiobjective problem that represents the trade-off between sensitivity and objective level. Validity of the method has been demonstrated by analysing a three-generator sample system.     

The optimal basics for GAs

Genetic algorithms were introduced by John Holland in early 1970s as a special technique for function optimization. They are quite different from other more conventional optimization methods that are mainly stochastic in nature. A typical GA will have three phases; i.e., initialization, evaluation and genetic operation. In each phase, various parameters of GA need to be selected based on the nature of the optimization problem. A genetic algorithm is also classified based on the various combinations of parameters and strategies employed. However, the designer is free to develop a hybrid genetic algorithm. The main goal is to deliver the most enhanced performance possible to the optimization problem     

基于最优覆盖法的变电站无功补偿容量优化配置研究

为有效寻找电网无功补偿配置的薄弱点,提出了一种新的无功补偿设备优化配置的方法.该方法根据110 kV变电站全年的无功负荷情况统计出无功负荷概率分布曲线,采用最优覆盖法建立无功补偿配置优化模型,并通过全局搜索寻求优化配置方案.通过对优化配置方案与实际补偿容量配置的比较,给出了无功补偿配置相对薄弱的变电站,为电力系统规划人员提供决策依据.     

Dynamic equivalence by an optimal strategy

Due to the curse of dimensionality, dynamic equivalence remains a computational tool that helps to analyze large amount of power systems’ information. In this paper, a robust dynamic equivalence is proposed to reduce the computational burden and time consuming that the transient stability studies of large power systems represent. The technique is based on a multi-objective optimal formulation solved by a genetic algorithm. A simplification of the Mexican interconnected power system is tested. An index is used to assess the proximity between simulations carried out using the full and the reduced model. Likewise, it is assumed the use of information stemming from power measurements units (PMUs), which gives certainty to such information, and gives rise to better estimates.     

优化潮流的数值计算

通过对电力系统运行状态的分析，就潮流计算的数学模型进行了合理的简化并针对其解的稳定性提出了延拓法确定初值。利用此优化潮流的计算方法，可以迅速且有效地对系统潮流进行适当的调整和控制，以满足安全可靠或经济性的要求。     

混合最优/H2设计

提出了系统性设计最优控制的新方法．先是利用对称根轨迹确定加权阵Q和R的各种适当的组合，然后计算这些组合下系统的H2范数。取H2范数最小的设计。用这种方法设计的系统，其性能可以认为是用状态反馈所能达到的最好的性能。     

牛顿法最优潮流的改进

对牛顿法最优潮流提出了改进措施,将发电节点有功功率、无功功率以及补偿节点无功功率作为状态量处理,通过拉格朗日乘子求取Pgi和Qgi简化了不等式约束的处理,改进后的方法不再需要罚函数法.改进后的算法程序实现简捷,计算速度快,收敛性能好,计算结果准确可靠.     

最优潮流算法综述

最优潮流是一个典型的非线性优化问题,且由于约束的复杂性使得其计算复杂,难度较大。虽然人们已经提出了许多种方法,并且在部分场合有所应用,但是要大规模实用化,满足电力系统的运行要求还有不少问题要解决。此文总结了现今有关最优潮流的几个方面,从优化方法和所遇到的新问题出发,对主要的优化方进行了介绍和简要的分析,以供从事无功优化的人员参考,同时还对最优潮流的进一步发展做了一些探讨。     

Quasi-Newton method for optimal power flows

A prototype procedure for solving the optimal power flow problem with a quasi-Newton (variable metric) method is described. The method was developed by Powell and later extended by Berna, Locke and Westerberg. It is attractive for three reasons. First, it can accommodate optimal power flow constraints in a straightforward manner. Second, it is robust and will home in on a solution even from infeasible starting points. Third, it promises to be very fast. The adaptation of the method to the optimal power flow is discussed and illustrated with the results from tests on two small power systems.     

Security-constrained optimal generation scheduling for GENCOs

This paper presents an approach for maximizing a GENCO's profit in a constrained power market. The proposed approach considers the Interior Point Method (IPM) and Benders decomposition for solving the security-constrained optimal generation scheduling (SC-GS) problem. The master problem represents the economic dispatch problem for a GENCO which intends to optimize its profit. The formulation of the master problem does not bear any transmission network constraints. The subproblem will be used by the same GENCO to check the viability of its proposed bidding strategy in the presence of transmission network constraints. In this case if the subproblem does not yield a certain level of financial return for the GENCO or if the subproblem results in an infeasible solution of the GENCO's proposed bidding strategy, the GENCO will modify its proposed solution according to the Benders cuts that stem out of the subproblem. The study shows a more flexible scheduling paradigm for a GENCO in a competitive arena. The proposed approach proves practical for modeling the impact of transmission congestion on a GENCO's expected profit in a competitive environment.     

微网优化运行研究进展与展望

微网将多种分布式电源、负荷、储能装置集成在一起,可以灵活地并网或孤岛运行,在节能降耗、提高供电可靠性等方面具有巨大潜力。如何实现微网优化运行是微网研究的关键问题之一。本文对微网优化运行研究进展进行综述,概括和分析相关数学模型和优化算法,讨论多目标优化处理方法,探讨模型和算法的应用前景。最后,结合研究现状中存在的不足,对进一步的研究动向进行展望。