基于改进人工蜂群算法的电力系统无功优化

针对电力系统无功优化存在的问题,提出了一种基于改进人工蜂群算法的无功优化。运用反学习法对人工蜂群算法进行了优化,克服了人工蜂群算法本身容易陷入局部收敛的缺点,并且对IEEE30节点进行仿真计算,结果表明该算法对于求解复杂无功优化问题的可行性和有效性。     

Reactive Power Optimization Using Hybrid CABC-DE Algorithm

Reactive power optimization is closely related to voltage quality and network loss, and it has great significance for the safety, reliability, and economical operation of the power system. Differential evolution (DE) algorithm has been currently applied to reactive power optimization. In order to mitigate the shortcomings of poor local search ability and premature convergence in DE, this paper presents a novel hybrid algorithm–chaotic artificial bee colony differential evolution (CABC-DE) algorithm, which improves the DE algorithm based on artificial bee colony algorithm and ideas of chaotic search. It introduces the observation bees' acceleration operation and the detective bees' chaotic search operation into CABC-DE. The validity of the proposed method is examined using IEEE-14 and IEEE-30 bus system. The experimental results show that CABC-DE algorithm is more effective than regular DE algorithm for reactive power optimization. The algorithm can save the search time greatly and get a better solution for optimization, thus making it suitable for solving reactive power optimization problems.     

基于改进蜂群算法的工业机器人路径规划研究

针对工业机器人在复杂环境中运动的避障及路径优化问题,提出基于改进人工蜂群算法的工业机器人避障路径规划策略。首先针对传统人工蜂群算法搜索能力不足且容易陷入局部最优的问题,将禁忌搜索思想引入到人工蜂群算法最优解搜索过程中,形成了基于禁忌搜索的改进型人工蜂群算法,然后将其应用到工业机器人的路径规划问题中,并进行了仿真实验。结果表明,改进后的方法能够得到最优的路径,且寻优速度快、过程稳定。该方法可用于解决工业机器人路径规划问题。     

基于改进人工蜂群算法的区域电网储能系统能量管理优化策略

为提高新能源消纳水平及系统运行效率,需对储能系统充放电功率进行优化,以平抑功率波动,降低网络损耗,提高经济效益。基于源荷状态判断储能各时段充放电状态,以区域日网损降低收益、日高储低放套利收益及日环境效益最大为目标,综合考虑储能自身约束及网架潮流状态约束等条件,建立了区域电网储能能量管理优化模型。求解过程中提出了一种改进人工蜂群算法（improved artificial bee colony,IABC）,并针对吐鲁番区域网架结构及运行特点进行了建模仿真。结果表明,对储能进行能量管理优化可提升整体经济效益,且改进人工蜂群算法具有很好的全局搜索能力及收敛性。     

基于改进人工蜂群算法的区域电网储能系统能量管理优化策略

为提高新能源消纳水平及系统运行效率,需对储能系统充放电功率进行优化,以平抑功率波动,降低网络损耗,提高经济效益。基于源荷状态判断储能各时段充放电状态,以区域日网损降低收益、日高储低放套利收益及日环境效益最大为目标,综合考虑储能自身约束及网架潮流状态约束等条件,建立了区域电网储能能量管理优化模型。求解过程中提出了一种改进人工蜂群算法（improved artificial bee colony,IABC）,并针对吐鲁番区域网架结构及运行特点进行了建模仿真。结果表明,对储能进行能量管理优化可提升整体经济效益,且改进人工蜂群算法具有很好的全局搜索能力及收敛性。     

An adaptive chaotic artificial bee colony algorithm for short-term hydrothermal generation scheduling

Short-term hydrothermal scheduling (SHS) is a complicated nonlinear optimization problem with a set of constraints, which plays an important role in power system operations. In this paper, we propose to use an adaptive chaotic artificial bee colony (ACABC) algorithm to solve the SHS problem. In the proposed method, chaotic search is applied to help the artificial bee colony (ABC) algorithm to escape from a local optimum effectively. Furthermore, an adaptive coordinating mechanism of modification rate in employed bee phase is introduced to increase the ability of the algorithm to avoid premature convergence. Moreover, a new constraint handling method is combined with the ABC algorithm in order to solve the equality coupling constraints. We used a hydrothermal test system to demonstrate the effectiveness of the proposed method. The numerical results obtained by ACABC are compared with those obtained by the adaptive ABC algorithm (AABC), the chaotic ABC algorithm (CABC) and other methods mentioned in literature. The simulation results indicate that the proposed method outperforms those established optimization algorithms.     

基于MOABC 算法的冷热电联供系统优化调度研究

综合能源系统包含多种分布式能源,各能源相互补充,能有效提高能源的利用率,在经济、环保等方面具有显著优势。冷热电联供系统作为综合能源系统的重要补充,具有灵活可靠、高效清洁等优点,现已得到广泛的发展和重视。综合考虑各微源的发电特性和冷热电负荷需求,建立了包含燃料电池、微燃机、余热锅炉、吸收式制冷机和蓄能装置的多目标冷热电联供系统模型。该模型考虑分时电价对冷热电联供系统的影响,以经济成本和环境成本作为目标函数,提出基于Pareto理论的多目标蜂群算法作为模型的求解算法。以实际冷热电联供系统为算例进行仿真,验证了所提模型的有效性,并与多目标粒子群算法进行对比,结果表明,采用基于Pareto理论的多目标蜂群算法能更有效地降低经济成本和环境成本。     

基于量子人工蜂群算法的风电场多目标无功优化

为了分析风机的不确定性出力对电网运行的影响,建立了风电场的概率模型,利用两点估计法(2PEM)进行概率潮流计算。然后,建立了综合考虑有功网损、电压偏移量和静态电压稳定裕度的多目标无功优化模型,并通过层次分析法(AHP)确定各个目标函数的权重,避免了人为主观臆断性。提出了量子人工蜂群算法,并将该算法和前述的概率潮流计算相结合应用到风电场无功优化当中。最后,以IEEE 14节点系统为例,将风电场接入该系统进行无功优化,并和传统的人工蜂群算法(ABC)进行比较,结果表明量子人工蜂群算法优化效果更好,具有更高的收敛精度,有效地避免了早熟现象。     

Review on application and comparison of metaheuristic techniques to multi-area economic dispatch problem

This paper presents both application and comparison of the metaheuristic techniques to multi-area economic dispatch (MAED) problem with tie line constraints considering transmission losses, multiple fuels, valve-point loading and prohibited operating zones. The metaheuristic techniques such as differential evolution, evolutionary programming, genetic algorithm and simulated annealing are applied to solve MAED problem. These metaheuristic techniques for MAED problem are evaluated on three different test systems, both small and large, involving varying degree of complexity and the results are compared against each other     

基于改进遗传算法的电力系统多目标无功优化

针对遗传算法在求解多目标无功优化方面存在的缺陷,本文提出了基于蜜蜂双种群进化型云自适应遗传算法(double bee population evolutionary cloud adaptive genetic algorithm,BEPE-CAGA)。该算法根据蜜蜂双种群进化思想,引入了雄峰通过竞争参与交叉及雄峰与决定双峰群优秀遗传基因的蜂后交叉的策略,并结合正态云模型云滴的随机性和稳定倾向性特点对其进行改进,改善了算法陷入早熟的问题,提高了算法的收敛速度。建立了以有功网损最小、电压偏差最小及电压稳定裕度最大为目标的无功优化数学模型,并以BEPE-CAGA算法求解该模型。最后通过对IEEE14和IEEE30节点系统进行算例仿真,仿真结果验证了本文所提算法的有效性,同时也证明了该算法在收敛速度和优化效果上具有比基本GA算法和CAGA算法更佳的性能。     

基于反向学习的人工蜂群算法的电力系统最优潮流

以发电机大气污染物排放量最小为目标函数,建立一种电力系统最优潮流模型,并提出一种基于反向学习的人工蜂群算法进行求解。IEEE一30节点系统仿真分析结果表明,与其它算法进行相比,提出的算法能够有效降低发电机大气污染物排放量,算法简单,具有更好的寻优能力和收敛特性。     

Artificial bee colony algorithm for solving multi-objective optimal power flow problem

This paper presents a new and efficient method for solving optimal power flow (OPF) problem in electric power systems. In the proposed approach, artificial bee colony (ABC) algorithm is employed as the main optimizer for optimal adjustments of the power system control variables of the OPF problem. The control variables involve both continuous and discrete variables. Different objective functions such as convex and non-convex fuel costs, total active power loss, voltage profile improvement, voltage stability enhancement and total emission cost are chosen for this highly constrained nonlinear non-convex optimization problem. The validity and effectiveness of the proposed method is tested with the IEEE 9-bus system, IEEE 30-bus system and IEEE 57-bus system, and the test results are compared with the results found by other heuristic methods reported in the literature recently. The simulation results obtained show that the proposed ABC algorithm provides accurate solutions for any type of the objective functions.     

改进自适应蜂群优化算法在变压器故障诊断中的应用

为增强神经网络对变压器故障诊断的能力,同时避免蜂群算法出现局部最优和易早熟,提出一种改进自适应搜索策略蜂群优化算法。该方法通过自适应调整种群更新步长来协调蜂群算法的全局和局部搜索能力,避免出现局部最优状况,同时引入Levy变异因子提高局部搜索的性能。利用改进的蜂群算法优化BP神经网络权值和阈值,通过反复迭代算法,直到达到目标精度要求。该方法以变压器数据为依据进行测试。仿真结果表明,改进后的方法收敛速度更快、判别故障准确率更高。     

Evolutionary Programming-Based Multiarea Economic Dispatch with Tie Line Constraints

This paper presents a new approach to include tie line constraints in multiarea economic dispatch problems by using evolutionary programming (EP). The proposed method always finds the global or near global optimum for small and reasonable-sized multiarea economic dispatch (MAED) problems. The inclusion of tie line constraints to MAED does not introduce any complexity in the approach. The applicability and validity of the proposed method is shown by implementing it on three example systems - 2, 4, and 14 areas - and their results are compared with those obtained by classical economic dispatch, network flow programming, and dynamic programming methods, respectively. The results show that the proposed method can serve as a potential tool for solving MAED problems.     

Experimental studies on impedance basedfault location for long transmission lines

Current Transformer (CT) modeling, by which CT’s characteristics can be studied has a significant importance in CT selection and design. In spite of numerous studies about closed-core CT model, only a few works have been conducted on air-gapped ones with the following problems: models of which required data is easily-accessible, have poor simulations of magnetization process; on the contrary, models which have satisfactory simulations, are hard to be established because of the hard-to-get required data. Therefore, based on Preisach Theory, a novel air-gapped CT model is deduced from the closed core CT model. The proposed model is accurate and can be established easily. The saturation and remanence properties of closed-core CT and air-gapped CT are simulated and compared.     

基于改进人工蜂群算法的配电网多点故障应急抢修优化调度

为提高配电网故障应急抢修调度在电网应急管理的辅助决策作用,建立了综合考虑抢修资源分配、多小组协作、抢修顺序的配电网多点故障应急抢修优化模型。引入多种群协同进化机制对传统人工蜂群算法进行改进,通过多蜂群智能体增强算法解决高维度复杂优化问题的能力。结合改进人工蜂群算法提出了应急抢修优化模型的统一调度方法。PGE69节点算例仿真研究表明,所提方法可在配电网发生多处故障后快速给出应急抢修预案,减少了停电经济损失。     

Congestion Management Considering Wind Energy Sources Using Evolutionary Algorithm

It is necessary to maintain power system security within a reasonable limit without harming the electric power supply in an emerging electricity market. Power systems are said to be congested when power transmission reaches beyond its limits. Therefore, in a deregulated power system environment, the system operator must look after the power transactions made by market parties. In this article, a novel congestion management method considering wind energy sources is introduced. The proposed congestion management technique is constructed considering the calculation of bus sensitivity factor and generator sensitivity factor. The bus sensitivity factor is used for finding the optimal location of a wind farm. The most sensitive generators are identified to reschedule their output by using the generator sensitivity factor. The artificial bee colony algorithm and particle swarm optimization techniques are applied to compare the generator rescheduling with earlier literature for congestion management. The proposed method has been tested on the 39-bus New-England test system to investigate the effectiveness of the proposed approach of wind farm integration for congestion management.     

基于改进人工蜂群算法的家庭储能容量优化配置*

家庭能源系统中的储能设备初始投资成本高,限制其实际应用。针对此问题,文章对混合储能的容量配置进行了研究。分别构建了刚性负荷、柔性负荷和储能类设备负荷模型;在此基础上搭建以用户每天用电费用最低为目标的家庭能源管理系统模型;提出一种改进的人工蜂群算法对模型求解。实验结果表明,通过和单储能的系统相比,在满足用户用电需求的同时,配置混合储能的家庭能源系统能有效减小用户每天用电费用。对文中算法与人工蜂群算法和粒子群算法优化结果进行比对,证实所提算法优化时长短、收敛速度快且不易于陷入局部最优。     

基于改进人工蜂群算法的直流配电网典型应用场景拓扑结构研究

为了解决直流配电网规划中场景针对性不强的问题,给出了不同场景下直流配电网的网络拓扑结构规划方案。将直流配电网应用场景分为居民住宅区、工业园区和新能源集结区,考虑不同应用场景的特点,利用层次分析法提出一种新的可靠性指标计算方法。结合变权的思想,综合考虑电网的经济性和可靠性,给出了直流配电网的规划模型。利用由最大最小积改进的人工蜂群算法,以IEEE标准14节点电路为对象,对三种场景下的规划模型进行寻优,并利用griewank函数对比了改进前后算法的性能。仿真结果表明,所得线路规划方案满足各场景要求,改进后的蜂群算法收敛速度和精度均有提升。所提方法可为直流配电网规划提供参考。     

Multi-area power systems economic dispatch using nonlinear convex network flow programming

This paper presents a new nonlinear convex network flow programming (NLCNFP) model and algorithm for solving the security-constrained multi-area economic dispatch (MAED) problem. The tie-line security and transfer constraints in each area are considered in the MAED model. A simple analysis of a buying and selling contract in a multi-area environment is also made. The NLCNFP model of security-constrained multi-area economic dispatch was set up and solved by using a combined method of quadratic programming (QP) and network flow programming (NFP). The concept of maximum basis in the network flow graph was introduced to change the constrained model into an unconstrained QP model, which was easily solved by the reduced gradient method. The proposed approach is tested on a system of four interconnected areas with satisfactory results.