基于改进遗传算法的电力系统无功优化

遗传算法是一种模拟生物进化过程的优化算法,可用于求解包含离散化变量的复杂优化问题,本文将遗传算法应用于电力系统无功优化,并对常规遗传算法的编码方式、遗传算子以及终止判据等方面进行了改进,使用该文提出的算法对IEEE 6、IEEE 30节点系统进行了无功优化计算,结果表明该改进遗传算法应用于无功优化是合理可行的。     

改进遗传算法在配电网无功优化中的应用

遗传算法是近年来被广泛应用于无功优化的一种新型的优化算法。结合配电网的特征，对遗传算法的选择操作、交叉和变异算子、终止判据等核心操作进行改进，提出一种适合于配电网无功优化的改进遗传算法。计算实例表明，其优化效果优于传统遗传算法。     

基于小生境遗传算法的电力系统无功优化分析

针对遗传算法(SGA)在电力系统无功优化中存在早熟收敛和后期收敛速度慢的弱点,提出了基于小生境遗传算法的无功补偿优化方法,可在短时间内以极大概率值寻找全网最优补偿配置实现最优补偿.通过对含有14个节点的IEEE 14系统进行模拟分析,结果验证了该算法的有效性,显著提高了无功优化的收敛速度.     

基于逐次优化改进遗传算法的特高压穿越无功规划

与超高压线路相比,特高压线路无功大量富余,会与下级电网形成很大的穿越无功,从而影响无功的分层控制,甚至威胁电力系统的安全稳定运行。常规的优化算法存在维数灾问题,即使是智能算法,也由于解空间维度大而寻优效率低下。对此,提出了一种基于逐次优化改进遗传算法,该方法利用逐次优化的思想,对传统遗传算法的寻优方式进行了改进,并将该算法应用于某实际区域大电网中求解无功规划问题。结果表明,该方法不仅有效降低了解空间的维度,且在保证算法效率的同时使寻优的效果得到较大的改善。     

基于免疫遗传算法的小水电配电网无功优化

针对目前应用于电力系统无功优化的智能算法所存在的问题,提出将免疫遗传算法应用于电力系统无功优化问题的措施。免疫遗传算法是将免疫理论和基本遗传算法各自的优点相结合,不仅具有遗传算法的搜索特性,还具有免疫算法的多机制求解多目标函数最优解的自适应特性,对“早熟”问题有所改善,收敛于全局最优。最后,以安康市某区域电力系统为例对算法进行了性能测试,提出了合理的调压措施,结果表明将免疫遗传算法应用于电力系统无功优化问题可以显著降低系统网损,改善电压质量。     

基于遗传算法的电网无功规划的优化

阐述了在城市电网中长期无功规划中基于遗传算法的软件设计原理,建立了城市中长期电网无功规划优化模型,用遗传算法进行寻优,最后对某一小系统用该程序求解,结果表明该算法在中长期无功规划优化中收敛性、稳定性好,具有一定工程实用价值。     

基于遗传算法的电网无功规划的优化

阐述了在城市电网中长期无功规划中基于遗传算法的软件设计原理,建立了城市中长期电网无功规划优化模型,用遗传算法进行寻优,最后对某一小系统用该程序求解,结果表明该算法在中长期无功规划优化中收敛性、稳定性好,具有一定工程实用价值.     

改进遗传算法的无功优化

电力系统实现无功优化控制是保证系统电压质量、降低网损的重要措施。对于高维、非线性和连续变量与离散变量共存的电力系统无功优化数学模型,对一般遗传算法的无功优化算法在遗传操作过程中,进行了“灾变”,在选择操作中将轮盘赌和竞标赛方法相结合,对交叉变异算子根据每代个体的实际状况进行自适应调整。通过IEEE30节点算例表明了本文方法可有效提高每代种群的多样性,从而提高了一般遗传算法的无功优化的收敛速度和全局优化特性。     

基于遗传算法的电网无功规划的优化

阐述了在城市电网中长期无功规划中基于遗传算法的软件设计原理，建立了城市中长期电网无功规划优化模型，用遗传算法进行寻优，最后对某一小系统用该程序求解，结果表明该算法在中长期无功规划优化中收敛性、稳定性好，具有一定工程实用价值。     

基于区间建模的新能源电网无功优化策略

  目的  新能源发电具有间歇性和随机性,其功率为不确定性数据,会造成电网电压和频率的变化,对电力系统安全运行构成威胁。为保证大规模新能源并网后电网电压的安全,考虑新能源发电波动不确定性,提出一种基于区间建模的新能源电网无功优化策略。  方法  该策略采用区间数描述无功优化模型中的不确定参数,进而建立区间无功优化模型,采用基于优化场景的区间潮流算法求解区间潮流方程,获取状态变量区间,确定控制变量的可行性,在此基础上采用改进的粒子群优化算法求解区间无功优化模型,在粒子群算法中加入局部搜索环节和离散变量交叉处理操作以提高算法寻优能力。为了验证所提方法的有效性和优越性,分别采用IEEE 14节点和IEEE 30节点算例进行仿真计算,与自适应遗传算法和普通粒子群算法进行对比分析。  结果  仿真结果表明:与自适应遗传算法和普通粒子群算法相比,采用改进粒子群的区间无功优化策略具有更快的收敛速度,更强的寻优能力,并且可有效处理模型中离散变量。  结论  所提策略可有效解决区间无功优化问题,能保障大规模新能源并网后电网电压的运行安全。     

Differentiation of multiple maximum power points of partially shaded photovoltaic power generators

Partial shading conditions have a major effect on the electrical characteristics of photovoltaic (PV) power generators. In this paper, the effects of partial shading on maximum power points (MPPs) of a PV power generator have been systematically studied by using Simulink simulation model of a PV power generator composed of 18 series-connected PV modules. It is shown that the local MPPs can be classified into MPPs at low and high voltages based on the MPP operating point of the PV generator. The results also show that based on the MPP current and voltage it is possible to directly know if the MPP at high voltages is a local or a global MPP. The differentiation between local and global MPPs at high voltages is based on the voltage difference between the actual MPP voltage at high voltages and the theoretical MPP voltage under corresponding uniform conditions. This differentiation method was also tested to work correctly by utilizing experimental measurements of the Tampere University of Technology Solar PV Power Station Research Plant. By using this method, it can be identified if the system is operating at a local or a global MPP. This method can further be utilized to develop global MPP tracking algorithms.     

Reliability of power stations: Stochastic versus derated power approach

Consideration of the eventual forced outage of individual power stations leads to a large number of possible states of the power generating system, all with their own probability. It is possible to design a stochastic method to properly take into account all of these possibilities and to weigh them accordingly. In broader energy models, instead of these stochastic techniques that require a considerable amount of calculation time, mostly approximative static simplified methods are applied. Up till now, these simplified techniques have not been validated. The scope of this paper is to check their validity. Therefore, two approaches are compared: a complete stochastic approach and a method based on the derated power (which is the nominal power multiplied with the average availability) of the individual plants. The conclusion of this comparison is that derated power may be used in energy modelling instead of the complicated stochastic approach. The error made is very small and the correlation between the unserved load probability functions obtained by both methods is excellent. Copyright © 2004 John Wiley & Sons, Ltd.     

Personal power systems

The lack of compact, efficient, human compatible, lightweight power sources impedes the realization of machine-enhanced human endeavor. Electronic and communication devices, as well as mobile robotic devices, need new power sources that will allow them to operate autonomously for periods of hours. In this work, a personal power system implies an application of interest to an individual person. The human-compatible gravimetric energy density spans the range from 500 to 5000 Wh/kg, with gravimetric power density requirements from 10 to 1000 W/kg. These requirements are the primary goals for the systems presented here. The review examines the interesting and promising concepts in electrochemical, thermochemical, and biochemical approaches to small-scale power, as well as their technological and physical challenges and limitations. Often it is the limitations that dominate, so that while the technology to create personal autonomy for communications, information processing and mobility has accelerated, similar breakthroughs for the systems powering these devices have not yet occurred.

Fuel cells, model airplane engines, and hummingbird metabolism, are three promising examples, respectively, of electrochemical, thermochemical, and biochemical power production strategies that are close to achieving personal power systems' power demands. Fuel cells show great promise as an energy source when relatively low power density is demanded, but they cannot yet deliver high peak powers nor respond quickly to variable loads. Current small-scale engines, while achieving extraordinary power densities, are too inefficient to achieve the energy density needed for long-duration autonomous operation. Metabolic processes of flying insects and hummingbirds are remarkable biological energy converters, but duplicating, accelerating, and harnessing such power for mobility applications is virtually unexplored. These challenges are significant, and they provide a fertile environment for research and development.     

Agent-based power sharing scheme for active hybrid power sources

The active hybridization technique provides an effective approach to combining the best properties of a heterogeneous set of power sources to achieve higher energy density, power density and fuel efficiency. Active hybrid power sources can be used to power hybrid electric vehicles with selected combinations of internal combustion engines, fuel cells, batteries, and/or supercapacitors. They can be deployed in all-electric ships to build a distributed electric power system. They can also be used in a bulk power system to construct an autonomous distributed energy system. An important aspect in designing an active hybrid power source is to find a suitable control strategy that can manage the active power sharing and take advantage of the inherent scalability and robustness benefits of the hybrid system. This paper presents an agent-based power sharing scheme for active hybrid power sources. To demonstrate the effectiveness of the proposed agent-based power sharing scheme, simulation studies are performed for a hybrid power source that can be used in a solar car as the main propulsion power module. Simulation results clearly indicate that the agent-based control framework is effective to coordinate the various energy sources and manage the power/voltage profiles.     

风-水-火电互补系统中风电穿越功率的研究

针对风电容量与风-水-火电互补系统稳定性的关系问题,研究了风电机组的数学模型,搭建了风-水-火电互补系统的仿真模型.将遗传算法(GA)应用于风电穿越功率的研究,建立了基于遗传算法的风电容量的优化数学模型,构造了适值函数.对实际电网中的风电容量进行了优化编程计算和仿真,优化和仿真结果虽有差异,但很近似,验证了所建优化数学模型的合理性及遗传算法应用于风电穿越功率计算的可行性.     

风电系统的无功功率与电网电压稳定

论述了风电容量在占局部电网相当比例时,风电机组的无功功率调整与电网电压之间的关系,对于定速和变速风电机组的运行特性做了分析,提出了在需要做无功功率调整时风电机组应能满足的特殊要求。     

基于分布式供能技术的能源系统

由于2003年美国和加拿大大面积停电事故的发生,人们对电网安全的要求越来越高,分布式供能技术引起了世界能源界的广泛关注。能源工业亟待解决的四大问题：合理调整能源结构,进一步提高能源利用效率,改善能源产业的安全性,解决环境污染。而分布式能源系统恰好在这些方面能给以补充,因此大电网与分布式能源系统的合理结合,被认为是21世纪电力工业的发展方向。详细介绍了基于分布式能源系统的概念、特点及发展状况和前景展望。还通过对几种主要的分布式供能技术的技术特点、国内外发展状况及前景展望的介绍,进一步说明了分布式供能技术的发展状况,阐释了分布式供能技术的优势。     

Estimating future balancing power requirements in wind–PV power system

This paper presents a general model—based on the Monte Carlo simulation—for the estimation of power system uncertainties and associated reserve and balancing power requirements. The proposed model comprises wind, PV and load uncertainty, together with wind and PV production simulation. In the first stage of the model, wind speed and solar irradiation are simulated, based on the plant disposition and relevant data. The second stage of the model consists of wind speed, PV power and load forecast error simulation, based on the associated statistical parameters. Finally, both wind and PV forecast error are combined with the load forecast error, resulting in the net uncertainty. This net uncertainty, aggregated on a yearly level, presents a dominant component in balancing power requirements. Proposed model presents an efficient solution in planning phase when the actual data on wind and PV production is unavailable.     

低压无功功率补偿装置的选择

无功功率补偿装置在电力系统中的作用是提高电网的功率因数,降低供电变压器及输送线路的损耗,提高供电效率,改善供电环境。所以无功功率补偿装置在电力供电系统中非常重要。合理的选择补偿装置,可以最大限度的减少网络的损耗,提高电网质量。