电力系统PMU最优配置问题的混合优化算法

针对电力系统潮流计算方程直接可解的PMU最优配置问题,提出了一种引入小生境技术的遗传禁忌搜索混合算法。混合优化算法以小生境遗传算法为主体,避免传统遗传算法“早熟”和解的多样性不足的问题;结合禁忌搜索思想,使用TSR算子进行交叉操作,解决传统遗传算法局部搜索能力较差和收敛速度有待提高的问题。用该算法与其他两种传统算法进行了对比验证,结果表明该混合算法不仅能寻得全局最优解,而且提供了解的多样性,提高了优化效率,具有广阔的应用前景。     

基于图论的PMU最优配置方法及比较

首先阐述了电力系统状态估计模型和进行PMU最优配置的准则,随后以系统的完全可观测为目标,介绍了基于图论的深度优先搜索法、GTP算法和最小生成树算法。应用上述算法分别对IEEE标准节点系统进行PMU最优配置仿真。仿真结果表明几种方法在解的多样性,计算时间等方面各具特色,适用于不同的配置需要。     

Multi-objective optimal power flow using quasi-oppositional teaching learning based optimization

This paper describes teaching learning based optimization (TLBO) algorithm to solve multi-objective optimal power flow (MOOPF) problems while satisfying various operational constraints. To improve the convergence speed and quality of solution, quasi-oppositional based learning (QOBL) is incorporated in original TLBO algorithm. The proposed quasi-oppositional teaching learning based optimization (QOTLBO) approach is implemented on IEEE 30-bus system, Indian utility 62-bus system and IEEE 118-bus system to solve four different single objectives, namely fuel cost minimization, system power loss minimization and voltage stability index minimization and emission minimization; three bi-objectives optimization namely minimization of fuel cost and transmission loss; minimization of fuel cost and L-index and minimization of fuel cost and emission and one tri-objective optimization namely fuel cost, minimization of transmission losses and improvement of voltage stability simultaneously. In this article, the results obtained using the QOTLBO algorithm, is comparable with those of TLBO and other algorithms reported in the literature. The numerical results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal non-dominated solutions of the multi-objective OPF problem. The simulation results also show that the proposed approach produces better quality of the individual as well as compromising solutions than other algorithms.     

Multi-objective biogeography-based optimization for supply chain network design under uncertainty

This paper proposes a new two-stage optimization method for multi-objective supply chain network design (MO-SCND) problem with uncertain transportation costs and uncertain customer demands. On the basis of risk-neutral and risk-averse criteria, we develop two objectives for our SCND problem. We introduce two solution concepts for the proposed MO-SCND problem, and use them to define the multi-objective value of fuzzy solution (MOVFS). The value of the MOVFS measures the importance of uncertainties included in the model, and helps us to understand the necessity of solving the two-stage multi-objective optimization model. When the uncertain transportation costs and customer demands have joined continuous possibility distributions, we employ an approximation approach (AA) to compute the values of two objective functions. Using the AA, the original optimization problem becomes an approximating mixed-integer multi-objective programming model. To solve the hard approximating optimization problem, we design an improved multi-objective biogeography-based optimization (MO-BBO) algorithm integrated with LINGO software. We also compare the improved MO-BBO algorithm with the multi-objective genetic algorithm (MO-GA). Finally, a realistic dairy company example is provided to demonstrate that the improved MO-BBO algorithm achieves the better performance than MO-GA in terms of solution quality.     

Optimizing an equilibrium supply chain network design problem by an improved hybrid biogeography based optimization algorithm

This paper presents a new equilibrium optimization method for supply chain network design (SCND) problem under uncertainty, where the uncertain transportation costs and customer demands are characterized by both probability and possibility distributions. We introduce cost risk level constraint and joint service level constraint in the proposed optimization model. When the random parameters follow normal distributions, we reduce the risk level constraint and the joint service level constraint into their equivalent credibility constraints. Furthermore, we employ a sequence of discrete possibility distributions to approximate continuous possibility distributions. To enhance solution efficiency, we introduce the dominance set and efficient valid inequalities into deterministic mixed-integer programming (MIP) model, and preprocess the valid inequalities to obtain a simplified nonlinear programming model. After that, a hybrid biogeography based optimization (BBO) algorithm incorporating new solution presentation and local search operations is designed to solve the simplified optimization model. Finally, we conduct some numerical experiments via an application example to demonstrate the effectiveness of the designed hybrid BBO.     

基于变密度法的飞行器升力面结构多目标拓扑优化设计

为研究权衡结构刚度与低阶振动频率的飞行器升力面最优结构设计,提出两种多目标拓扑优化方案（约束法、结合约束法与评价函数法）.基于变密度方法,在约束法方案中将多目标优化转化为设定参考点位移约束和低阶振动频率约束下,求解结构质量最小化的优化问题.在结合约束法与评价函数法方案中,定义组合柔度指数为评价函数（结构柔度与振动频率的函数）,将多目标优化转化为设定低阶振动频率约束和体积分数约束下,求解结构最小组合柔度指数的优化问题.结果表明两种方案的优化结果具有一定的相似性,各有所长.优化设计不仅减轻了升力面结构重量,而且提高了结构的一、二阶振动频率.     

目标空间映射策略的高维多目标粒子群优化算法

为了平衡优化算法在高维多目标优化问题中收敛性和多样性之间的关系,增加算法的选择压力,本文提出了一种基于目标空间映射策略的高维多目标粒子群优化算法(many-objective particle swarm optimization al-gorithm based on objective space mapping ...     

Dynamic optimal reactive power dispatch based on parallel particle swarm optimization algorithm

In this paper, Message Passing Interface (MPI) based parallel computation and particle swarm optimization (PSO) algorithm are combined to form the parallel particle swarm optimization (PPSO) method for solving the dynamic optimal reactive power dispatch (DORPD) problem in power systems. In the proposed algorithm, the DORPD problem is divided into smaller ones, which can be carried out concurrently by multi-processors. This method is evaluated on a group of IEEE power systems test cases with time-varying loads in which the control of the generator terminal voltages, tap position of transformers and reactive power sources are involved to minimize the transmission power loss and the costs of adjusting the control devices. The simulation results demonstrate the accuracy of the PPSO algorithm and its capability of greatly reducing the runtimes of the DORPD programs.     

面向集装箱码头物流系统的仿真优化研究

针对集装箱码头物流系统(containerterminallogisticssystems,CTLS)的自身特点和运营需求,根据当前国内外已有的相关研究及其发展趋势,提出了面向CTLS的仿真优化完整方法体系,并给出利用哈佛体系结构、基于Agent的计算、混合智能优化和企业服务总线的参考设计实现模型,以期为CTLS的设备配置、资源分配、生产计划和调度控制获得高效敏捷鲁棒的解决方案。通过对码头前沿泊位-岸桥联合调度生产实例的仿真、优化和分析,验证了所提思想的可行性与可信性。     

Particle swarm optimization with an aging leader and challengers algorithm for the solution of optimal power flow problem

Solution of optimal power flow (OPF) problem aims to optimize a selected objective function such as fuel cost, active power loss, total voltage deviation (TVD) etc. via optimal adjustment of the power system control variables while at the same time satisfying various equality and inequality constraints. In the present work, a particle swarm optimization with an aging leader and challengers (ALC-PSO) is applied for the solution of the OPF problem of power systems. The proposed approach is examined and tested on modified IEEE 30-bus and IEEE 118-bus test power system with different objectives that reflect minimization of fuel cost or active power loss or TVD. The simulation results demonstrate the effectiveness of the proposed approach compared with other evolutionary optimization techniques surfaced in recent state-of-the-art literature. Statistical analysis, presented in this paper, indicates the robustness of the proposed ALC-PSO algorithm.     

A computational algorithm based on biogeography-based optimization method for computing power system security constrains with multi FACTS devices

A major role is played by the analysis of power system security in heightening system security and in system collapse condition avoidance. This article presents a cutting edge mechanism which is devised applying transmission line loadings as well as variance in bus voltage magnitude. The use of flexible alternating current transmission systems devices improves the objectives of generation fuel charges in addition to the severity index proposed which were investigated considering the contingency circumstances of generator(s) or/and transmission channel(s). To boost system security in spite of contingency circumstances in the existence of unified power flow controller or UPFC, it would be most appropriate to pinpoint a most advantageous position to install aforementioned device. We propose a model of UPFC where power insertion is done by using voltage source. Also a procedure to incorporate the same and a strategy to find optimum position has been proposed which uses line overload sensitivity indices. This work mainly focused on establishment of available transfer capability on the heavily congested line. The proposed congestion management scheme alleviates the heavy stress in transmission line and provides an ample corridor for the power to flow. Biogeography-based optimization or BBO in short, is a technique which is a growing recognized optimization method which has been lucratively engaged in solving intricate optimization problem in dissimilar fields. The BBO provides better results than the metaheuristic counter parts such as Genetic Algorithm and Particle Swarm Optimization. The effectiveness of proposed BBO has been tested on standard IEEE 30 bus system and the results are compared with classic methods and other metaheuristic methods. This is established through the MATLAB package. Improved bus voltage profile was also attained and it can be inferred from the outcome that the prospective approach can drastically enhance security of power system when comparing with other optimization methods.