Three-part auctions versus self-commitment in day-ahead electricity markets

We examine the economic consequences of a bid-based security-constrained centralized unit commitment paradigm based on three-part offers, which is the prevalent day-ahead market-clearing mechanism in restructured electricity markets in the United States. We then compare this approach with an energy-only auction with self-commitment (such as in Australia) addressing efficiency and pricing as well as the tradeoff between coordination losses and incentives to bid truthfully.     

Shape design of arch dams under load uncertainties with robust optimization

Due to an increased need in hydro-electricity, water storage, and flood protection, it is assumed that a series of new dams will be build throughout the world. The focus of this paper is on the non-probabilistic-based design of new arch-type dams by applying means of robust design optimization (RDO). This type of optimization takes into account uncertainties in the loads and in the material properties of the structure. As classical procedures of probabilistic-based optimization under uncertainties, such as RDO and reliability-based design optimization (RBDO), are in general computationally expensive and rely on estimates of the system’s response variance, we will not follow a full-probabilistic approach but work with predefined confidence levels. This leads to a bi-level optimization program where the volume of the dam is optimized under the worst combination of the uncertain parameters. As a result, robust and reliable designs are obtained and the result is independent from any assumptions on stochastic properties of the random variables in the model. The optimization of an arch-type dam is realized here by a robust optimization method under load uncertainty, where hydraulic and thermal loads are considered. The load uncertainty is modeled as an ellipsoidal expression. Comparing with any traditional deterministic optimization method, which only concerns the minimum objective value and offers a solution candidate close to limit-states, the RDO method provides a robust solution against uncertainty. To reduce the computational cost, a ranking strategy and an approximation model are further involved to do a preliminary screening. By this means, the robust design can generate an improved arch dam structure that ensures both safety and serviceability during its lifetime.     

改进粒子群算法的两阶段梁式结构损伤识别

针对实际工程监测时损伤识别误差大的问题,提出一种基于改进粒子群算法的两阶段识别方案。第1阶段利用D-S证据理论融合算法进行损伤定位;第2阶段利用改进的粒子群算法,对定位结果进行修正,同时准确定量损伤。仿真算例和实验分析结果表明:由于第1阶段损伤定位减少了可能损伤单元的数量,第2阶段基于改进粒子群算法的搜索范围减小,能更准确地识别多损伤和小损伤的位置和程度,且抗噪性能良好。     

Condition evaluation for existing reinforced concrete bridge superstructure using fuzzy clustering improved by particle swarm optimisation

Condition evaluations of old bridges are necessary for determining their health states and providing priority levels of maintenance. In this paper, a novel condition evaluation approach for reinforced concrete (RC) bridge superstructure is presented based on fuzzy c-mean clustering optimised by particle swarm optimisation (FCM-PSO) algorithm. It is equipped with the advantages of PSO algorithm in global optimisation and FCM algorithm in convergence acceleration, which greatly improves the effectiveness of clustering. Using this methodology, a reliable evaluation index system and a number of training samples from field-measured data of existing old bridges are prerequisites. In addition, the optimal cluster number for training samples can be determined by Xie–Beni validity evaluation index. Subsequently, condition grades and corresponding cluster centres can be determined based on the calculation of cluster centres and membership matrix for training samples. On the above basis, bridge conditions of testing samples can be evaluated based on the fuzzy membership to the cluster centres of condition grades. A case study was carried out to verify the feasibility and effectiveness of the proposed FCM-PSO method. Evaluation results reveal that the proposed method can effectively reduce the influence of subjective factors and will be favourable for condition evaluation of existing RC bridges.     

A new prediction model of electricity load based on hybrid forecast engine

In this paper, a new prediction model is introduced based on hybrid forecast engine and new feature selection. In this model, the load signal is filtered by feature selection to filter out the best candidates. Then, the proposed forecast engine is predicted the output of feature selection. In this model, the weights of proposed forecast engine are optimised by an intelligent algorithm to increase its accuracy. Effectiveness of the proposed method is applied over real-world engineering test case and compared with other different well-known methods. Obtained results proof the validity of the proposed method.     

基于改进粒子群优化支持向量机的水利闸门卡阻故障诊断

针对水利闸门卡阻故障诊断主要依赖专家经验、故障样本少且难以自动识别的特点,提出了一种基于改进粒子群算法优化最小二乘支持向量机的水利闸门卡阻故障诊断方法。该方法以闸门的开度值、左右两侧荷重值及上下游水位差作为输入向量,通过最小二乘支持向量机进行故障分类,通过改进粒子群算法对支持向量机的参数进行优化。并将建立的诊断模型制作成Labview子VI程序模块,嵌入到闸门调度监控软件中,实现了闸门卡阻故障的快速自动诊断。通过在淮洪新河某泄洪闸实地实验及应用表明,该诊断程序具有较高的诊断准确率和诊断效率。     

Assessment of total suspended solids (TSS) event load and its uncertainties in combined sewer system from continuous turbidity measurements

This paper presents a methodology for assessing TSS wet weather event load along with the associated uncertainties from continuous turbidity measurements. The proposed method is applied to dozens of rainfall events sampled on various urban catchments in the cities of Paris and Nantes. This method takes into account the dependency between the residual errors made by converting the turbidity values into TSS concentrations using an average TSS-turbidity relationship. Results obtained indicate that the inter-event variations of TSS-turbidity relationships induce significant systematic errors on TSS event loads which can reach ±30% of the average TSS event load.     

基于粒子群算法的水文地质参数求解研究

为了提高求解割离井公式中水文地质参数的速度和精度,研究粒子群算法的适用性。本文将观测出水量与理论出水量的偏差平方和作为目标函数,利用粒子群算法反求割离井公式中水文地质参数给水度μ和渗透系数K,并将其计算结果与直线图解法、试算法和配线法计算结果进行对比分析。结果表明,粒子群算法的精度最高且在计算过程中无须人工干预,不受主观影响。粒子群算法在水文地质参数求解计算中具有较好的适用性和应用价值。     

基于微粒群优化的智能位移反分析研究

优化技术是影响反分析精度和效率的重要因素,将微粒群优化技术与支持向量机技术结合,提出了一种新的智能位移反分析方法。该方法利用了支持向量表达非线性关系方面的优良特性,可以避免大量的数值计算,同时充分利用微粒群的全局优化、收敛速度快的优点。将提出的方法应用到具体的算例中,比较表明,本方法是一种科学、可行、收敛快、精度高的优秀算法。     

基于荷载效应的结构抗风设计方法研究

对等效静风荷载的基本理论进行分析,指出GB 50009-2001《建筑结构荷载规范》的风振系数法给出的等效静风荷载在只考虑1阶振型的前提下,能够实现各种荷载效应的等效;需考虑多阶振型的结构,只有在各阶振型完全相关的条件下,才能得到可满足全部荷载效应等效的静风荷载;一般情况下,包括阵风荷载因子法在内的计算方法,给出的等效静风荷载只能保证单个或多个响应目标的等效,其他荷载效应存在较大不确定性,可能造成据此进行的抗风设计偏于保守或不安全。为克服这些传统抗风设计方法的局限,提出了直接以荷载效应包络值进行结构抗风设计的基本思路：结合风洞试验得到各种荷载效应在不同风向角下可能出现的上、下限值,再将其与其他荷载作用下的效应直接组合进行结构设计。通过工程实例分析表明,采用阵风荷载因子法得出的等效静风荷载值可能导致其他荷载效应被大大高估,而直接采用荷载效应包络值用于结构设计,不但其值准确,且易于操作。     

基于微粒群算法和BP网络的结构损伤识别

提出了基于BP网络的结构损伤识别方法,同时引入了微粒群算法对BP网络权值和阀值进行优化,建立了完整模型和损伤模型,进行了仿真,经检测样本证明,微粒群算法优化后的BP网络能很好的识别出结构损伤的位置和程度,是一种快速、有效的结构损伤识别方法。     

基于负荷预测的中央空调节能控制

结合对空调冷负荷进行预测的必要性,通过具体工程实例介绍了基于负荷预测的中央空调节能控制,阐述了冰蓄冷空调系统优化控制的一般过程,给出了进行负荷预测的方法及基于负荷预测的中央空调优化控制方案。     

一种基于遗传算法的动载荷识别方法

提出了一种基于遗传算法的时域内载荷识别方法,并通过工程实例加以分析,将遗传算法应用到载荷识别过程中,将结构动力学的反问题转化为正计算,避免了传统方法的不足,进一步证明了该算法的有效性。     

基于全年动态负荷分析的冷热源方案设计

以北京某酒店为例,对该建筑进行全年逐时动态负荷计算分析。根据计算分析结果,确定制冷季梯级利用离心式冷水机组、螺杆机组和空气源热泵机组作为空调系统冷源,供热季利用板式换热器和空气源热泵机组作为空调系统热源,以达到系统节能的目的。     

基于负荷密度模型的长沙城区负荷预测

建立了负荷密度模型，分析了使用负荷密度模型进行负荷预测需要考虑的问题，介绍了负荷密度模型参数的取定方法，并以长沙城区为例进行分区负荷预测。     

Resource-constrained critical path analysis based on discrete event simulation and particle swarm optimization

The absence of a valid resource-constrained critical path method (CPM) not only hampers the widespread use of mainstream project scheduling software in construction management practice, but also destabilizes the very foundation of any sophisticated, CPM-based time or cost analysis in construction scheduling research. This has motivated us into developing an innovative, fully-automated solution to resource-constrained CPM called the Simplified Simulation-based Scheduling system (short as S3). S3 takes advantage of the simplified discrete event simulation approach (SDESA) and the evolutionary optimization technique called particle swarm optimizer (PSO) to automate the formulation of a resource-constrained schedule with the shortest total project duration. We clarify basic issues of resource scheduling, elaborate on the formation of a CPM simulation model by SDESA, present PSO algorithms, and discuss the PSO solution formulation and simulation–optimization interaction in relation to the development of S3 software. In order to introduce S3 to construction schedulers, we also reference the relevant functionalities and features of Primavera Project Planner (P3) and Microsoft Project, which are applied alongside S3 in two case studies. The first case is a classic textbook example while the second case is based on a real drainage project in Hong Kong. In both cases, S3 eclipses the current CPM software with respect of (1) shortening the total project duration; (2) optimizing provisions of resources of various types; and (3) producing valid total float values to guide schedule implementation.     

基于微粒群算法和模糊神经网络的火灾探测研究

针对火灾探测的特点,将模糊系统和神经网络有机结合,实现模糊系统设计参数的自动调整。采用符合国家标准明火、阴燃火以及厨房环境下的干扰火等作为模糊神经网络的训练样本和测试样本,依据模糊神经网络算法要求,完成了网络结构的设计,并给出相应的计算模型,利用微粒群算法对网络的权值进行学习与训练。结果表明,该算法在探测国家标准火的火灾状态方面具有有效性和可行性。     

基于建筑全年动态冷负荷的冷水机组优化配置方案

提出一种在设计阶段对冷水机组方案进行优化配置的方法.首先,冷水机组的能耗计算简化为制冷机的实际制冷量和冷却水进口温度两个独立变量的函数.进一步,通过建筑动态负荷计算获得全年冷负荷频率特性以及相应的室外湿球温度分布,其中湿球温度决定了冷却水最低进口温度.最终,计算出各种冷水机组配置方案的全年以及不同冷负荷需求工况下的运行电耗,并得出最优化的节能方案.     

Traffic load modelling based on structural health monitoring data

Live and fatigue load models are foundations for the life-cycle design of highway bridges. Many highway bridges are now equipped with structural health monitoring (SHM) systems, which provide valuable data to establish load models. In this paper, traffic load models of the Binzhou Yellow River Highway Bridge are developed based on the field measurement of vehicles by an existing SHM system. The probabilistic distribution model and extreme value distribution of gross vehicle weight are statistically analysed using the monitoring data. The results indicate that they follow the bimodal lognormal and Gumbel distributions, respectively. The fatigue load spectrum is also studied. The logistic model is employed to predict the long-term traffic volume, and its parameters of the logistic model are updated using the monitored traffic volume. The combination of the fatigue load spectrum and the traffic volume forecast using the updated logistic model provides a load model for estimation of fatigue damage evolution of bridges.     

基于动静载试验的某市政桥梁性能评价

针对某市政桥梁的成桥状态和性能进行了检测和评价,通过利用重车对桥梁进行静载和动载试验,测量了桥梁跨中截面的竖向挠度和应力(应变)分布、构件的开裂情况、控制截面的冲击系数、结构的动力特性等指标,进而对该桥梁的性能进行评估,研究结果表明该桥承载能力及工作状况符合设计要求和满足正常的使用要求。