Shunt capacitor placement in radial distribution networks considering switching transients decision making approach

This paper provides a new approach in decision making process for shunt capacitor placement in distribution networks. The main core of the evaluation process is a multi-objective framework to allocate the capacitor banks. The power loss and the total harmonic distortion (THD) are the objective functions of the system under study in a long-term planning horizon. In order to select the executive plan introduced by using a multi-objective model, transient switching overvoltages have been considered. As the size and location of shunt capacitors may result in unacceptable overvoltages, the proposed technical decision making framework can be applied to avoid corresponding damages. In this paper, an iterative conventional power flow technique is introduced. This technique can be applied to evaluate THD for distribution networks as well as other power flow based objectives, such as power losses calculation and voltage stability assessment. The presented framework is a two stage one where at the first stage, a non-dominated sorting genetic algorithm (NSGA-II) augmented with a local search technique is used in order to solve the addressed multi-objective optimization problem. Then, at the second stage, a decision making support technique is applied to determine the best solution from the obtained Pareto front. In order to evaluate the effectiveness of the proposed method, two benchmarks are addressed in this paper. The first test system is a 9-bus distribution network and the second one is an 85-bus large scale distribution network. The simulation results show that the presented method is satisfactory and consistent with the expectation.     

基于万有引力搜索算法的微电网风光储容量优化配置

针对规模化风光储发电单元运行可靠性差、经济效益低等问题,通过采用多目标万有引力搜索算法,实现对风光储发电单元的优化配置。建立发电单元的输出功率模型,包括风力发电机输出功率模型、光伏电池输出功率模型和储能电池输出功率模型;采用某地区2015年1月至12月的风光数据,得出风机、光伏电池一年的输出功率。以风机、光伏电池及蓄电池的容量为决策变量,以成本、能源浪费率及缺电率最小为优化目标,得到最优风光储容量配置组合。     

基于改进DFT和时域准同步的间谐波检测算法

快速傅里叶变换(FFT)在非同步采样时存在频谱泄漏和栅栏效应,由此产生的谐波与间谐波之间的频谱干扰会严重影响间谐波参数测量的准确度。为减小谐波与间谐波之间的频谱干扰,提出一种基于改进离散傅里叶变换(DFT)和时域准同步的间谐波检测算法,采用改进DFT算法精确估计基波频率,利用三次样条插值重构准同步采样序列,用FFT算法对单个周期重构序列进行处理,得到基波和谐波的参数,并将基波和谐波成分从重构序列中减去,再次用FFT算法和最大谱峰搜索法对剩余序列进行处理,确定每一个间谐波成分的参数。仿真结果表明,该算法不仅能提高频率分辨率,还可以有效排除谐波和间谐波的频谱干扰,且间谐波检测的准确度高、稳定性好、运算量小。     

采用改进最优模糊C均值聚类和改进和声搜索算法的配电网动态重构

针对配电网络动态重构中的开关操作次数约束难以处理与和声搜索算法寻优效率低的问题,分别提出了改进的最优模糊C均值聚类法和改进的和声搜索算法,利用前者对研究时间区间的负荷进行聚类从而将配电网动态重构转为以聚类中心代表各分段负荷的多个静态重构子问题,利用后者对各静态重构问题进行求解。每个时间点的优化网络结构由相应的聚类中心的重构结果决定,由此得到配电网络重构的操作时间点和操作开关。在IEEE 33节点配电系统负荷数据的基础上构建1天的负荷数据并对该系统进行动态重构。研究结果表明,该方法能够控制开关操作次数,提高静态重构算法的寻优效率。     

球面并联机构的结构降耦及其位置正解求解

基于一般结构的S+3-SPS球面机构的耦合度分析,通过动平台二球副重合的方法,使之结构降耦演变为0耦合度球面机构,并得到其位置正解的解析解;而对于k=1的一般结构的S+3-SPS,虚设一条支链使机构包含k=0的零耦合度机构,再在约束度为-1的支链上建立一个一元运动相容性方程,并采用一维搜索法求得全部正解数值解。这种基于耦合度分析的求解并联机构位置正解的程式化通用方法,易于实现计算机自动生成,并适用于其它含SPS支链并联机构位置正解的快速求解。     

随机工时下柔性加工车间的鲁棒优化调度方法

为了提高柔性加工车间调度方案的可行性、保障生产过程的稳定性,提出一种鲁棒优化调度方法。引入两个不确定参数来描述随机工时的波动程度和约束条件的允许违背程度,提出随机变量服从概率分布时一般线性规划问题的鲁棒优化方法。采用该方法将含随机工时而难以求解的随机型柔性加工车间调度模型转化为确定型鲁棒对等模型。基于该模型,将随机工时融入适应度函数中,结合遗传进化的全局优化和邻域搜索的空间拓展能力研制出鲁棒调度算法,同步实现工件排序和机器分配的双重决策。案例测试表明,所提方法可以在较短计算时间内、以较小性能损失、将近95%的置信度获得当前最优解。     

IP2DM: integrated privacy‐preserving data management architecture for smart grid V2G networks

With the development of battery vehicles, vehicle‐to‐grid (V2G) networks are becoming more and more important in smart grid. Although battery vehicles are environmentally friendly and flexible to use two‐way communication and two‐way electricity flow, they also raise privacy‐preservation challenges, such as location and movement privacy. On the one hand, utility companies have to monitor the grid and analyze user data to control the power production, distribution, scheduling, and billing process, while typical users need to access their data later online. On the other hand, users are not willing to provide their personal data because they do not trust the system security of the utility companies where their data stored, and it may potentially expose their privacy. Therefore, in this paper, we study data management of V2G networks in smart grid with privacy‐preservation to benefit both the customers and the utility companies. Both data aggregation and data publication of V2G networks are protected in the proposed architecture. To check its security, we analyze this architecture in several typical V2G networks attacks. We conduct several experiments to show that the proposed architecture is effective and efficient, and it can enhance user privacy protection while providing enough information for utility companies to analyze and monitor the grid. Copyright © 2016 John Wiley & Sons, Ltd.     

考虑电池寿命和运行控制策略影响的风电场储能容量优化配置

为了满足电网对风电场功率变化率的要求,提出了一种改进的风电场储能容量优化配置方法.该方法计及电池使用寿命以及由此带来的电池更换成本,能够更加精确地反映风电场寿命周期内的储能系统经济性.以风电场寿命周期内储能系统初始购置及更换总成本最低为优化目标,建立了储能容量优化配置模型;将电池寿命和运行控制策略纳入优化模型;并将计及风储联合长期运行的优化模型视为黑盒函数,采用网格自适应直接搜索算法对该黑盒函数进行搜索求解,得到满足风电并网相关技术规定的最佳配置容量.在算例分析中采用实际风电场1a发电数据进行仿真计算,对比了改进前后优化方法对优化结果的影响,采用改进后的优化方法,风电场寿命周期内储能投资总成本可以降低15％～20％,验证了所提模型及其求解方法的有效性.     

Long-term Sector-wise Electrical Energy Forecasting Using Artificial Neural Network and Biogeography-based Optimization

This article presents a hybrid model involving artificial neural networks and biogeography-based optimization for long-term forecasting of India's sector-wise electrical energy demand. It involves socio-economic indicators, such as population and per capita gross domestic product, and uses two artificial neural networks, which are trained through a biogeography-based optimization algorithm with a goal of perfect mapping of the input–output data in the non-linear space through obtaining the global best weight parameters. The biogeography-based optimization based training of the artificial neural network improves the forecasting accuracy and avoids trapping in local optima besides enhancing the convergence to the lowest mean squared error at the minimum number of iterations than existing approaches. The model requires an input and the year of the forecast and predicts the sector-wise energy demand. Forecasts up to the year 2025 are compared with those of the regression model, the artificial neural network model trained by back-propagation, and the artificial neural network model trained by harmony search algorithm to exhibit its effectiveness.     

Online Droop Tuning of a Multi-DG Microgrid Using Cuckoo Search Algorithm

This article presents an intelligent strategy to achieve appropriate real power sharing among distributed generators in a microgrid. The presented strategy employs two droop-based control methods and automatically adjusts their parameters. The first method is unit power control, which has specifications similar to the conventional droop method, and the second is feeder flow control, showing significant characteristics in both grid-connected and islanded modes operation of a microgrid. A combination of unit power control and feeder flow control methods is used for a multi-distributed generator microgrid. The microgrid operation mode passes from the grid-connected to the islanded through a transition. A new evolutionary algorithm called cuckoo search is employed to coordinate the power management of distributed generators within an on-line droop tuning. In comparison to the predecessor evolutionary algorithms, the cuckoo search algorithm represents more effective random processes with fewer parameters. Using the proposed control strategy, while the distributed generators contribute to load demand provision based on their rated powers, their powers are optimized in terms of overshoot and settling time. Digital time-domain simulation studies are carried out in the MATLAB/SIMULINK (The MathWorks, Natick, Massachusetts, USA) environment to verify the performance of the proposed control system.