Optimal Operation of Distributed Generations Considering Demand Response in a Microgrid Using GWO Algorithm

The widespread penetration of distributed energy sources and the use of load response programs, especially in a microgrid, have caused many power system issues, such as control and operation of these networks, to be affected. The control and operation of many small-distributed generation units with different performance characteristics create another challenge for the safe and efficient operation of the microgrid. In this paper, the optimum operation of distributed generation resources and heat and power storage in a microgrid, was performed based on real-time pricing through the proposed gray wolf optimization (GWO) algorithm to reduce the energy supply cost with the microgrid. Distributed generation resources such as solar panels, diesel generators with battery storage, and boiler thermal resources with thermal storage were used in the studied microgrid. Also, a combined heat and power (CHP) unit was used to produce thermal and electrical energy simultaneously. In the simulations, in addition to the gray wolf algorithm, some optimization algorithms have also been used. Then the results of 20 runs for each algorithm confirmed the high accuracy of the proposed GWO algorithm. The results of the simulations indicated that the CHP energy resources must be managed to have a minimum cost of energy supply in the microgrid, considering the demand response program.     

Recurrent neural network for combined economic and emission dispatch

Recently, the combined economic and emission dispatch (CEED) problem, which aims to simultaneously decrease fuel cost and reduce environmental emissions of power systems, has been a widespread concern. To improve the utilization efficiency of primary energy, combined heat and power (CHP) units are likely to play an important role in the future. The goal of this study is to propose an approach to solve the CEED problems in a CHP system which consists of eight power generators (PGs), two CHP units and one heat only unit. Owing to the existence of power loss in power transmission line and the non-convex feasible region of CHP units, the proposed problem is a nonlinear, multi-constraints, non-convex multi-objectives (MO) optimization problem. To deal with it, a recurrent neural network (RNN) combined with a novel technique is developed. It means that the feasible region is separated into two convex regions by using two binary variables to search for different regions. In the frame of the neurodynamic optimization, existence and convergence of the dynamic model are analyzed. It shows that the convergence solution obtained by RNN is the optimal solution of CEED problem. Numerical simulation results show that the proposed algorithm can generate solutions efficiently.     

基于能量约束的自主水下航行器任务规划算法

多水下自主航行器（AUV）任务规划是影响集群智能水平的关键技术。针对现有任务规划模型只考虑同构AUV集群和单潜次任务规划的问题,提出了适用于AUV异构集群的多潜次任务规划模型。首先,该模型考虑了AUV的能量约束、AUV多次往返母船充电的工程代价、异构集群个体间的效能差异、任务多样性等关键因素;然后,为提高问题模型的求解效率,提出了一种基于离散粒子群的优化算法,该算法引入用于描述粒子速度、位置的矩阵编码和用于评估粒子质量的任务损耗模型,改进粒子更新过程,实现了高效的目标寻优。仿真实验表明,该算法不仅解决了异构AUV集群的多潜次任务规划问题,而且与采用遗传算法的任务规划模型相比较,任务损耗降低了11%。     

基于熵权的传感反应网络任务分派算法

针对无线传感反应网络中事件频发区域内执行器节点能耗过大问题,提出一种基于嫡权的任务分派算法。利用拍卖机制,建立基于嫡权的执行代价评估模型,并根据网络的实际情况,将任务元分派建模成平衡或者非平衡整数规划问题,使任务并发执行。仿真实验表明,网络实时性和能耗均衡性都得到了改善。     

Energy management for multi-microgrid system based on model predictive control

To reduce the computation complexity of the optimization algorithm used in energy management of a multi-microgrid system, an energy optimization management method based on model predictive control is presented. The idea of decomposition and coordination is adopted to achieve the balance between power supply and user demand, and the power supply cost is minimized by coordinating surplus energy in the multi-microgrid system. The energy management model and energy optimization problem are established according to the power flow characteristics of microgrids. A dual decomposition approach is imposed to decompose the optimization problem into two parts, and a distributed predictive control algorithm based on global optimization is introduced to achieve the optimal solution by iteration and coordination. The proposed method has been verified by simulation, and simulation results show that the proposed method provides the demanded energy to consumers in real time, and improves renewable energy efficiency. In addition, the proposed algorithm has been compared with the particle swarm optimization (PSO) algorithm. The results show that compared with PSO, the proposed method has better performance, faster convergence, and significantly higher efficiency.     

基于SSD数据库负载的SQL能耗感知模型

面对大数据带来的能耗及环境方面的严峻问题，构建节能的绿色数据库系统已成为关键需求和重要挑战。针对现有数据库系统主要以性能优化为目标，缺少对能耗的感知及优化的问题，提出基于数据库负载的能耗感知模型，并将模型应用于基于固态硬盘（SSD）的数据库系统中。首先，将数据库负载执行过程中对主要系统资源（CPU、固态硬盘）的消耗解析为时间开销和功耗开销，并基于SSD数据库负载的基本I/O类型构建时间开销模型和功耗开销模型，实现为数据库构建资源开销单位统一的能耗感知模型；然后，利用多元线性回归实现对模型的求解，并分别在独占环境和竞争环境下，验证模型对不同I/O类型的数据库负载能耗估算的准确性；最后，分析实验结果，并讨论了影响模型准确性的因素。经实验验证模型准确度较高，在DBMS独占系统资源情况下的平均误差为5.15%，绝对误差不超过9.8%；竞争环境下的准确率相对下降，但平均误差也低于12.21%，可有效构建能耗感知的绿色数据库系统。     

基于概率优化的水下通道感知能量优化路由

针对水下传感器网络能量损耗较大,延迟较严重的问题,提出一种基于概率优化的水下通道感知能量优化路由（PPUN）。在能量优化上,针对水下节点随机覆盖存在的多余感测覆盖范围所造成的额外能量损耗问题,采用传感器节点数量的概率优化方法,在保证覆盖率和节点连通率的情况下推导出网络所需要的最小节点数目,从减少传感器数目的问题上来优化总体能量。而针对路由的能量损耗问题,在节点的链路规划上采用了通道感知路由算法,考虑了在一定能量损耗阈值条件下的最短节点路径,避免水下节点盲目选择能量损耗较大的最短路径而导致数据转发失败,消耗更多能量。延迟问题抓住主要的解码延迟问题进行了分析并利用HARQ-III方案对延迟时间加以控制。实验对比分析表明,算法采取控制传感器数目和链路规划的方法,在实现能量优化上具有一定优势,延迟控制方案也得到了较好的效果。     

考虑客户聚类与产品回收的两级闭环物流网络选址-路径优化

针对目前不合理的废旧产品回收以及物流活动产生的碳排放污染，提出了一种考虑客户聚类与产品回收的两级闭环物流网络选址-路径优化模型。首先，结合实际物流网络的动态性假设客户需求量和回收率的不确定性特征，以最小运营成本和最小环境影响为目标建立选址-路径优化模型；其次，对多目标进化算法进行改进，提出了考虑客户聚类结果的两级物流设施选址-路径问题求解算法；最后，对该优化算法进行算法性能分析，并以重庆市某企业为例进行了模型和算法验证。结果表明，所建立的模型和算法能有效降低决策难度并提高物流系统的运作效率，所求出的优化方案能减少物流运作成本和降低物流运输过程对环境的影响。     

基于变分不等式的多商品物流网络优化设计

为优化设计多级多商品流的物流网络,按网络状态把物流网络划分为静态网络和动态网络,分析了静态网络的基础设施建设和动态网络的物流活动问题,构建了可描述不同网络阶段的运营成本和建设成本函数,并且考虑了运营过程带来的环境污染问题,构建了治理费用函数。基于以上函数,建立以供给能力为约束条件,以总成本最小为目标的网络设计和重新设计模型,然后将模型转换为变分不等式问题,证明了所设计模型与变分不等式等价。最后通过算例,运用修正投影算法对模型进行数值演算和验证,得到了最优成本下的设施建设方案和物流组织方案。     

面向用户端的供能管网建模与仿真研究

三联供用户端供能管网因其地块结构不同、运行工况多变等原因,使得调试周期长、调试时间成本高,并且由于供能系统的调试受季节影响,导致调试灵活性较差。针对该问题,本文提出面向用户端的供能管网仿真模型,用以模拟实际供能系统的运行工况、研究供能管网受控下的工作性能。该模型主要研究对象包括管道、换热器、阀门等器件,通过对其建立数学模型和分析系统结构,而后在MATLAB仿真平台上搭建系统仿真模型。基于该模型,研究管网阀门的控制以调节系统传输介质的流量,达到控制供能系统二次侧供水温度、一次侧回水温度和总管压差等目的。仿真结果验证了本系统建模的可行性与有效性,且该模型对供能系统的调试运行、控制调节具有一定的指导意义。     

面向可重构系统的负载均衡低能耗调度算法

主要研究了基于多FPGAs部件的可重构系统高能耗问题。首先,对多FPGAs部件可重构系统的特征进行了建模,包括重构端口受限、资源受限及通信开销等建立了问题模型;接着,基于概率论与统计学的离散方差理论,采用负载均衡思想设计和实现了一种低能耗调度算法MLB。它的原理是通过计算各个FPGA部件的总能耗方差来引导负载的均衡分配。最后,通过模拟仿真实验,将提出的MLB算法分别与贪心算法和最新研究MFIT算法进行了比较,结果表明提出的算法复杂度低、运行速度快,不仅多节约了15%的能量,而且缩短了最大完成时间。     

宽通道换热板片结构的多目标头脑风暴优化设计

换热板片作为构成宽通道板式换热器的核心部件,对其换热效果具有直接影响.为有效提升换热器性能,减少能量损耗,提出一种宽通道换热板片结构的多目标头脑风暴优化设计方法.首先,根据换热板片的形状和布局特点,提取梯形凸台尺寸及其分布间隔构成结构参数,采用正交法,基于Fluent数值模拟软件获得25组换热板片结构样本;然后,采用回归构建换热努塞尔数和压力损失的代理模型,以最大换热效果和最小能量损耗作为优化目标,采用基于网格的多目标头脑风暴优化算法,寻优获得最佳换热板片的结构设计方案.统计实验结果表明,换热性能代理模型可以有效降低评价代价,所提出优化设计方法可以更加高效地获得具有最佳换热效果和能量损耗的换热板片结构.     

萤火虫算法的电动汽车综合成本运行优化研究

为实现节能减排,文章以增程式电动汽车为研究对象,提出了一种基于动态综合成本的增程器运行优化方法。首先以增程器发动机外特性为研究基础,根据实际工作状况分别建立了发动机燃油消耗率及CO排放率模型,再通过归一化后将多个目标加权求和的方法建立电动汽车综合成本运行优化模型。模型建立后,在全局优化及特定功率优化这两种常见模式下以萤火虫算法进行寻优,最后在不同的权重条件下得出最佳综合成本运行曲线。实验结果表明,文章提出的方法能够在不同的运行环境下通过动态调整权重值,实现基于燃油消耗率及CO排放的综合成本运行优化。     

基于分布式神经动态优化的综合能源系统多目标优化调度EI北大核心CSCD

研究了基于神经动态优化的综合能源系统(Integrated energy systems,IES)分布式多目标优化调度问题.首先,将IES元件单元(包含负荷)作为独立的决策主体,联合考量其运行成本和排放成本,并计及多能源设备间的传输损耗,提出了IES多目标优化调度模型,该模型可描述为一类非凸多目标优化问题.其次,针对此类问题的求解,提出了一种基于神经动力学系统的分布式多目标优化算法,该算法基于动态权重的神经网络模型,可以解决不可分离的不等式约束问题.该算法计算负担小,收敛速度快,并且易于硬件实现.仿真结果表明,所提算法能同时协调综合能源系统的经济性和环境性这两个冲突的目标,且获得了整个帕累托前沿,有效降低了综合能源系统的污染物排放量和综合运行成本.     

Size optimization for hybrid photovoltaic–wind energy system using ant colony optimization for continuous domains based integer programming

In this paper, ant colony optimization for continuous domains (ACO_R) based integer programming is employed for size optimization in a hybrid photovoltaic (PV)–wind energy system. ACO_R is a direct extension of ant colony optimization (ACO). Also, it is the significant ant-based algorithm for continuous optimization. In this setting, the variables are first considered as real then rounded in each step of iteration. The number of solar panels, wind turbines and batteries are selected as decision variables of integer programming problem. The objective function of the PV–wind system design is the total design cost which is the sum of total capital cost and total maintenance cost that should be minimized. The optimization is separately performed for three renewable energy systems including hybrid systems, solar stand alone and wind stand alone. A complete data set, a regular optimization formulation and ACO_R based integer programming are the main features of this paper. The optimization results showed that this method gives the best results just in few seconds. Also, the results are compared with other artificial intelligent (AI) approaches and a conventional optimization method. Moreover, the results are very promising and prove that the authors’ proposed approach outperforms them in terms of reaching an optimal solution and speed.     

无线网状网容量分析与优化理论研究

首先对网状网容量估计与优化理论的技术难点进行分析,总结了其中的研究意义.根据国内外的研究现状,对干扰模型和调度模型进行总结与归纳,并对典型的优化模型进行了介绍.对目前容量优化算法常用的数学模型——规划模型、信息论模型、组合优化和随机过程模型进行了总结,提出了算法评价准则,对现有模型进行了点评.最后对未来的发展趋势提出了自己的观点.     

基于能量效率的认知无线网络联合优化算法

为提高认知无线网络能量有效性,提出一种基于能量效率的联合优化算法。在考虑主用户干扰容限的基础上构建了能量有效性模型,将优化目标分解为接入策略求解和功率优化问题,采用粒子群算法反复迭代,得到接入概率与功率分配的联合最优解。仿真结果表明,相对于不考虑功率优化或接入概率的传统优化方法,所提算法可使系统能量效率得到显著提升。     

多级压缩空气储能系统变工况的优化运行控制

多级压缩空气储能系统以压缩空气储能及热存储技术为基础,具备冷、热、电多种能量存储及供给能力.针对多变复杂工况下系统热能合理分配问题,提出了一种多级压缩空气储能系统变工况优化运行控制策略.首先基于热力学方法分析,建立压缩空气储能系统模块化数学模型.其次根据系统负荷需求,在"以电定热"及"以热定电"两种工作模式下,分别以储热热量消耗量最小及输出电功最大为优化目标建立优化模型,求解得到不同工况下系统各部件运行参数,并以1 MW多级压缩空气储能系统为例,进行优化求解.该控制策略有效地解决了多级压缩空气储能系统在变工况下的内部能量调配、系统内部运行参数选取的问题,为实现系统高效运行提供有效途径.     

Performance and energy consumption estimation for commercial off-the-shelf component system design

Nowadays, component-based embedded real- time systems have been used to improve the system development as well as to keep cost down through the reuse of embedded software applications. Besides, the use of semi-formal models has been widely adopted in the embedded real-time system component and system life cycle due to their friendly and intuitive notations. However, the ever more complex systems of today require modeling methods that allow early detection of potential problems in the initial phases of development. This paper presents the mapping process of UML state machine diagram into a time Petri net with energy constraints so as to estimate execution time and energy consumption in early phases of the embedded real-time component development life cycle. The estimates obtained from the model show that the proposed approach is indeed a good approximation to the respective measures obtained from the real hardware platform.