采用分群优化的电动汽车与电网互动调度措施分析

电动汽车的无序充电会严重影响电网运行的安全性。对此,为了提高电网运行安全性,文章主要采用分群优化的电动汽车与电网互动调度措施进行综合调控,可以综合电池、时间以及充放电等多种因素进行车群的划分,通过控制中心进行调控分析,实现总负荷方差最小化,通过功率分配算法则可以实现综合性的控制。

采用分群优化的电动汽车与电网互动调度策略

大规模电动汽车(EV)无序充电将会威胁电网的安全运行。为此,提出一种采用分群优化的电动汽车与电网互动调度策略。首先,根据EV的电池约束、时间约束及充放电转换次数约束,将各时段的EV动态划分为常规车群和调控车群,常规车群进行无序充电,调控车群包含充电车群和放电车群;然后,在控制中心以车群划分情况和车群负荷信息为约束条件,优化调控车群的可调度负荷使研究时段内的总负荷方差最小化;最后,根据EV的出行约束计算EV的权系数,采用功率分配算法控制充放电功率不高于可调度负荷值。所提方法能保证EV在满足出行需求的情况下,对电网进行削峰填谷。算例结果验证了所提方法的合理性和有效性;所提调度策略方案实施简单,效果明显,有利于进行实际应用。

基于弹性优化机制的社区负荷EV分群优化策略

针对EV无序充电对电网造成的负荷波动,文中提出了基于弹性优化机制的社区负荷EV分群优化策略。该策略以社区EV返程时刻为标准划分社区负荷序列和EV车辆序列,根据策略响应度将EV车辆序列划分策略响应车群和普通车群。电网侧与策略响应车群签订具有弹性限度的合约A与合约B,并制定各车群的充放电计划。合约A侧重考虑用户效益,通过控制策略响应车群各EV序列充放电,分别优化各车辆序列对应的社区负荷序列,尽力获取放电收益。合约B侧重考虑用户用车需求,通过调整EV的充放电计划,在平衡合约执行度和EV可用度的同时最大化降低社区序列负荷波动。文中以某社区居民家庭负荷为算例,以最小化负荷峰谷差和用户支出费用为目标函数,通过MATLAB、Yalmip平台和Gurobi求解器联合建模求解各合约场景。结果表明,策略实施后,各合约场景下的社区负荷峰谷差分别降低了3.74%、2.87%和5.04%,EV序列费用支出分别减少了10.80%、5.23%和10.55%。

采用分群优化的电动汽车与电网互动调度策略

现如今,我国是社会主义经济快速发展的新时期,大规模电动汽车(EV)无序充电将会威胁电网的安全运行。为此,提出一种 采用分群优化的电动汽车与电网互动调度策略。首先,根据 EV 的电池约束、时间约束及充放电转换次数约束,将各时段的 EV 动态划分 为常规车群和调控车群,常规车群进行无序充电,调控车群包含充电车群和放电车群;然后,在控制中心以车群划分情况和车群负荷信息 为约束条件,优化调控车群的可调度负荷使研究时段内的总负荷方差最小化;最后,根据 EV 的出行约束计算 EV 的权系数,采用功率分 配算法控制充放电功率不高于可调度负荷值。所提方法能保证 EV 在满足出行需求的情况下,对电网进行削峰填谷。算例结果验证了所提 方法的合理性和有效性;所提调度策略方案实施简单,效果明显,有利于进行实际应用。

基于分群结构优化的储能配置规划

储能资源与源网荷资源的深度融合是构建高比例可再生能源新型电力系统的关键,随着系统规划和运行环节源网荷储互动程度的不断提高,在储能资源规划过程中考虑其与源网荷资源的时空耦合特性已是储能规划的核心环节。构建储能选址分群结构优化模型,决策储能布点待选站点集合,并以储能投资净收益最大为目标,联合采用遗传算法和二阶锥松弛算法,求解含分群结构优化的储能配置优化规划模型。算例分析表明,面向大规模复杂网络的储能规划,考虑发用电特性及网架空间分布,分群优化布点储能,有利于协调不同送受特性的集群间储能资源的配置与协同增效运行,经济提高可再生能源消纳水平。

混合策略改进灰狼优化算法的函数优化

为了解决灰狼优化算法在函数优化过程中搜索精度不高的问题,提出了一种分群优化、高斯变异和随机扰动混合策略改进的灰狼优化算法。一方面,通过采用分群优化策略,加强算法局部搜索与全局搜索之间的信息交换;另一方面,采用高斯变异和随机扰动策略维持算法进化过程中的种群多样性,并利用贪婪思想更新种群。通过引入包含单峰、多峰和固定维度多峰的多个基准测试函数,仿真实验验证了所提改进灰狼算法有效性。在与其他几种先进优化算法的综合比较与分析中,改进算法在搜索精度、寻优稳定性和收敛速度上体现出了明显优势。

基于GPSO-DVHop的传感器节点定位方法

为了减少无线传感器网络节点的定位误差,提出一种分群粒子群优化(GPSO)算法修正DV-Hop误差的传感器节点定位方法(GPSO-DVHop)。提出一种节点距离修正值策略,减少未知节点与锚节点间距离的估计误差,采用GPSO算法修正DV-Hop的节点定位误差,最后在Matlab 2012平台上对算法性能仿真分析。相对于对比传感器定位方法,GPSO-DVHop提高了传感器节点定位精度,仿真结果验证了GPSO-DVHop的有效性。

可适性群集变动的微粒算法

针对传统粒子群优化(PSO)算法通过单群优化,存在着精度较低、易陷入局部最优解等缺点,提出一种可适性群集变动的微粒算法(ADCPSO)。此算法将依据收敛公式的数值大小,判断粒子群收敛程度,从而动态地调适粒子群群集大小,以提高种群的多样性,有效地避免提早收敛等问题。通过与其他8种粒子群优化算法在CEC'2010标准函数下的仿真测试结果表明:ADCPSO算法凭借着简明算法结构,在寻优能力和算法精度上表现出明显的优势,体现出了较好的应用前景。

基于GPSO的WSN节点定位技术研究

研究监测空间节点定位精度问题,由于无线传感器网络基于测距节点定位存在误差,造成精确度低。为此采用一种分群式粒子群优化算法(GPSO),对利用TOA的极大似然估计定位法估算出的结果进行优化处理。通过仿真从测距误差、未知节点个数和节点通信半径三方面分析三种算法的平均定位误差。结果表明,分群式粒子群算法能够提升标准粒子群算法跳出局部最优的能力,有效地避免了标准粒子群优化算法易早熟、陷入局部最优的缺点,最大概率地寻找到全局最优解,最终提高了节点定位精度。

θ-PSO: a new strategy of particle swarm optimization

Particle swarm optimization (PSO) is an efficient, robust and simple optimization algorithm. Most studies are mainly concentrated on better understanding of the standard PSO control parameters, such as acceleration coefficients, etc. In this paper, a more simple strategy of PSO algorithm called θ-PSO is proposed. In θ-PSO, an increment of phase angle vector replaces the increment of velocity vector and the positions are decided by the mapping of phase angles. Benchmark testing of nonlinear func- tions is described and the results show that the performance of θ-PSO is much more effective than that of the standard PSO.

Global Optimization for the Synthesis of Integrated Water Systems with Particle Swarm Optimization Algorithm

The problem of optimal synthesis of an integrated water system is addressed in this study, where water using processes and water treatment operations are combined into a single network such that the total cost of fresh water and wastewater treatment is globally minimized. A superstructure that incorporates all feasible design alterna- tives for wastewater treatment, reuse and recycle, is synthesized with a non-linear programming model. An evolutionary approach--an improved particle swarm optimization is proposed for optimizing such systems. Two simple examples are .Presented.to illustrate the global op.timization of inte.grated water networks using the proposed algorithm.

The Velocity Measurement of Two-phase Flow Based on Particle Swarm Optimization Algorithm and Nonlinear Blind Source Separation

In order to overcome the disturbance of noise,this paper presented a method to measure two-phase flow velocity using particle swarm optimization algorithm,nonlinear blind source separation and cross correlation method.Because of the nonlinear relationship between the output signals of capacitance sensors and fluid in pipeline,nonlinear blind source separation is applied.In nonlinear blind source separation,the odd polynomials of higher order are used to fit the nonlinear transformation function,and the mutual information of separation signals is used as the evaluation function.Then the parameters of polynomial and linear separation matrix can be estimated by mutual information of separation signals and particle swarm optimization algorithm,thus the source signals can be separated from the mixed signals.The two-phase flow signals with noise which are obtained from upstream and downstream sensors are respectively processed by nonlinear blind source separation method so that the noise can be effectively removed.Therefore,based on these noise-suppressed signals,the distinct curves of cross correlation function and the transit times are obtained,and then the velocities of two-phase flow can be accurately calculated.Finally,the simulation experimental results are given.The results have proved that this method can meet the measurement requirements of two-phase flow velocity.

Component based ant routing protocols analysis over mobile ad hoc networks

To deeply exploit the mechanisms of ant colony optimization （ACO） applied to develop routing in mobile ad hoe networks （MANETS）,some existing representative ant colony routing protocols were analyzed and compared.The analysis results show that every routing protocol has its own characteristics and competitive environment.No routing protocol is better than others in all aspects.Therefore,based on no free lunch theory,ant routing protocols were decomposed into three key components：route discovery,route maintenance （including route refreshing and route failure handling） and data forwarding.Moreover,component based ant routing protocol （CBAR） was proposed.For purpose of analysis,it only maintained basic ant routing process,and it was simple and efficient with a low overhead.Subsequently,different mechanisms used in every component and their effect on performance were analyzed and tested by simulations.Finally,future research strategies and trends were also summarized.

Hybrid particle swarm optimization with differential evolution and chaotic local search to solve reliability-redundancy allocation problems

In order to solve reliability-redundancy allocation problems more effectively, a new hybrid algorithm named CDEPSO is proposed in this work, which combines particle swarm optimization （PSO） with differential evolution （DE） and a new chaotic local search. In the CDEPSO algorithm, DE provides its best solution to PSO if the best solution obtained by DE is better than that by PSO, while the best solution in the PSO is performed by chaotic local search. To investigate the performance of CDEPSO, four typical reliability-redundancy allocation problems were solved and the results indicate that the convergence speed and robustness of CDEPSO is better than those of PSO and CPSO （a hybrid algorithm which only combines PSO with chaotic local search）. And, compared with the other six improved meta-heuristics, CDEPSO also exhibits more robust performance. In addition, a new performance was proposed to more fairly compare CDEPSO with the same six improved recta-heuristics, and CDEPSO algorithm is the best in solving these problems.

Run-to-run Optimization for Fed-batch Fermentation Process with Swarm Energy Conservation Particle Swarm Optimization Algorithm

An iterative optimization strategy for fed-batch fermentation process is presented by combining a run-to-run optimization with swarm energy conservation particle swarm optimization (SEC-PSO). SEC-PSO, which is designed with the concept of energy conservation, can solve the problem of premature convergence frequently appeared in standard PSO algorithm by partitioning its population into several sub-swarms according to the energy of the swarm and is used in the optimization strategy for parameter identification and operation condition optimization. The run-to-run optimization exploits the repetitive nature of fed-batch processes in order to deal with the optimal problems of fed-batch fermentation process with inaccurate process model and unsteady process state. The kinetic model parameters, used in the operation condition optimization of the next run, are adjusted by calculating time-series data obtained from real fed-batch process in the run-to-run optimization. The simulation results show that the strategy can adjust its kinetic model dynamically and overcome the instability of fed-batch process effectively. Run-to-run strategy with SEC-PSO provides an effective method for optimization of fed-batch fermentation process.

Application of SVM and PCA-CS algorithms for prediction of strip crown in hot strip rolling

To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown,an optimized model based on support vector machine(SVM)is put forward firstly to enhance the quality of product in hot strip rolling.Meanwhile,for enriching data information and ensuring data quality,experimental data were collected from a hot-rolled plant to set up prediction models,as well as the prediction performance of models was evaluated by calculating multiple indicators.Furthermore,the traditional SVM model and the combined prediction models with particle swarm optimization(PSO)algorithm and the principal component analysis combined with cuckoo search(PCA-CS)optimization strategies are presented to make a comparison.Besides,the prediction performance comparisons of the three models are discussed.Finally,the experimental results revealed that the PCA-CS-SVM model has the highest prediction accuracy and the fastest convergence speed.Furthermore,the root mean squared error(RMSE)of PCA-CS-SVM model is 2.04μm,and 98.15%of prediction data have an absolute error of less than 4.5μm.Especially,the results also proved that PCA-CS-SVM model not only satisfies precision requirement but also has certain guiding significance for the actual production of hot strip rolling.

An algorithm for earthwork allocation considering non-linear factors

For solving the optimization model of earthwork allocation considering non-linear factors,a hybrid algorithm combined with the ant algorithm(AA)and particle swarm optimization(PSO)is proposed in this paper.Then the proposed method and the LP method are used respectively in solving a linear allocation model of a high rockfill dam project.Results obtained by these two methods are compared each other.It can be concluded that the solution got by the proposed method is extremely approximate to the analytic solution of LP method.The superiority of the proposed method over the LP method in solving a non-linear allocation model is illustrated by a non-linear case.Moreover,further researches on improvement of the algorithm and the allocation model are addressed.

Ant Colony Optimization for Task Allocation in Multi-Agent Systems

Task allocation is a key issue of agent cooperation mechanism in Multi-Agent Systems. The important features of an agent system such as the latency of the network infrastructure, dynamic topology, and node heterogeneity impose new challenges on the task allocation in Multi-Agent environments. Based on the traditional parallel computing task allocation method and Ant Colony Optimization (ACO), a novel task allocation method named Collection Path Ant Colony Optimization (CPACO) is proposed to achieve global optimization and reduce processing time. The existing problems of ACO are analyzed; CPACO overcomes such problems by modifying the heuristic function and the update strategy in the Ant-Cycle Model and establishing a threedimensional path pheromone storage space. The experimental results show that CPACO consumed only 10.3% of the time taken by the Global Search Algorithm and exhibited better performance than the Forward Optimal Heuristic Algorithm.

Dependent task assignment algorithm based on particle swarm optimization and simulated annealing in ad-hoc mobile cloud

In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on particle swarm optimization and simulated annealing( PSO-SA) transforms the dependencies between tasks into a directed acyclic graph( DAG) model. The number in each node represents the computation workload of each task and the number on each edge represents the workload produced by the transmission. In order to simulate the environment of task assignment in AMC,mathematical models are developed to describe the dependencies between tasks and the costs of each task are defined. PSO-SA is used to make the decision for task assignment and for minimizing the cost of all devices,which includes the energy consumption and time delay of all devices.PSO-SA also takes the advantage of both particle swarm optimization and simulated annealing by selecting an optimal solution with a certain probability to avoid falling into local optimal solution and to guarantee the convergence speed. The simulation results show that compared with other existing algorithms,the PSO-SA has a smaller cost and the result of PSO-SA can be very close to the optimal solution.

Reliability analysis of earth slopes using hybrid chaotic particle swarm optimization

A numerical procedure for reliability analysis of earth slope based on advanced first-order second-moment method is presented,while soil properties and pore water pressure may be considered as random variables.The factor of safety and performance function is formulated utilizing a new approach of the Morgenstern and Price method.To evaluate the minimum reliability index defined by Hasofer and Lind and corresponding critical probabilistic slip surface,a hybrid algorithm combining chaotic particle swarm optimization and harmony search algorithm called CPSOHS is presented.The comparison of the results of the presented method,standard particle swarm optimization,and selected other methods employed in previous studies demonstrates the superior successful functioning of the new method by evaluating lower values of reliability index and factor of safety.Moreover,the presented procedure is applied for sensitivity analysis and the obtained results show the influence of soil strength parameters and probability distribution types of random variables on the reliability index of slopes.