Optimal clustering of MGs based on droop controller for improving reliability using a hybrid of harmony search and genetic algorithms

This paper proposes a novel method to address reliability and technical problems of microgrids (MGs) based on designing a number of self-adequate autonomous sub-MGs via adopting MGs clustering thinking. In doing so, a multi-objective optimization problem is developed where power losses reduction, voltage profile improvement and reliability enhancement are considered as the objective functions. To solve the optimization problem a hybrid algorithm, named HS-GA, is provided, based on genetic and harmony search algorithms, and a load flow method is given to model different types of DGs as droop controller. The performance of the proposed method is evaluated in two case studies. The results provide support for the performance of the proposed method.     

主动配电网储能优化规划

大量分布式电源的接入使得主动配电网成为现有配电网的发展趋势及方向,可再生能源发电的间歇性将会提高配电网的风险,解决这些问题最有效的方法就是配置适当的储能装置,合理地优化配置储能装置不仅能提升主动配电网对分布式能源的消纳能力还能提高主动配电网运行的稳定性。主动配电网储能长期优化规划以短期优化为基础,短期优化考虑了储能系统的削峰填谷及调节馈线节点电压水平的能力从而决定储能的额定功率,长期优化规划模型以主动配电网经济运行成本最小为目标函数考虑储能投资成本以及主动配电网的运行成本及可靠性成本,通过禁忌搜索-粒子群混合算法求解得到电池储能装置的最优位置、容量及额定功率,算例验证了所提模型及其求解方法的可行性。     

区域微电网群两级能量调度策略优化研究

针对现阶段微电网能量管理技术发展趋势,在满足其内部经济调度的基础上,还需要关注微电网间的能量互补机制。对并网型区域微电网群提出了一种两级能量优化调度模型。引入条件风险指标(CVaR)衡量可再生能源与负荷预测误差对调度方案造成的影响,结合微电网运行收益,作为微电网内部能量调度的优化目标;采用多目标粒子群优化算法(MOPSO)进行求解,研究收益风险比作为优化调度策略的筛选指标,提出微电网内部能量优化调度策略;以区域微电网群公共并网点有功功率梯度变化最小化为前提,获得最佳微电网净功率组合方案,由此平抑微电网群对配电网造成的功率波动;考虑电力传输距离制定了微电网间净功率互补机制,提高功率传输效率。算例仿真结果表明,该模型能够合理实现微电网内与微电网间经济运行与功率平衡,为微电网群日前调度计划提供了有效设计流程。     

冷库用空气冷却器的经济性计算模型

为了优化结霜工况下翅片管式空气冷却器的结构设计,提出经济性分析计算模型。综合考虑换热性能、材料成本及运行费用,以单位制冷量的费用年值为目标函数,以费用年值最小为优化目标求解经济模型得到合理的空气冷却器的结构参数,分析了翅片间距、换热管管径、管排数变化对目标函数的影响,并通过一冷库案例进行实际测试验证。计算及试验结果表明:随着翅片间距的增大,目标函数先减小后增大,翅片间距为10时目标函数取极小值;在相同翅片间距的情况下管径越大,其目标函数值也越大;综合考虑换热管直径、翅片间距和管排数的影响,翅片间距为10,换热管外径为9.52,管排数为6时,目标函数取极小值。     

基于刚粘塑性有限元模拟的汽轮机叶轮模锻的预成形优化

针对汽轮机叶轮模锻的预成形设计,本文建立了以打击能耗最低和模具作用载荷最小为目标函数,以完全填充为约束条件,以坯料初始高径比为优化变量的预成形坯料优化方案。利用刚粘塑性有限元方法模拟汽轮机叶轮等温模锻成形过程,具体分析了不同坯料初始高径比对成形载荷、塑性应变能以及分流面的影响。数值模拟结果表明获得的预成形H/D最佳尺寸可明显减少模锻锤击次数和模具磨损。所提出的有限元模拟预成形优化方法十分有效,克服了以往凭经验设计的弊端。     

基于小生境遗传算法的风火电节能调度研究

考虑风能为可再生能源的主要方式,建立了以运行成本与污染排放为目标函数的多目标风火发电节能优化调度模型,通过改进的小生境遗传算法对模型求解,并用不同的权重系数表明区域内的多目标决策偏好,以达到发电经济性与环境保护的协调优化。结果表明该模型最小运行成本降低了15％,旋转备用容量增加了700MW左右,达到了优化目标。     

Bi-objective optimization of maintenance scheduling for power systems

Enhance the quality of energy production in power generating stations and reducing its cost have become of paramount importance. One of the methods to reach that goal is by minimizing the maintenance scheduling time. For this purpose, a new competitive mechanism, based on a modified genetic algorithm (MGA), has been proposed to perform the preventive maintenance (PM) scheduling. Firstly, a mono-objective optimization (makespan) has implemented, and the results were quite good. Secondly, and in order to benefit from the waste time, a bi-objective optimization was developed to find a trade-off between makespan and training time of operators. Finally, the MGA-based maintenance scheduling was tested on a hybrid renewable power system (HRPS), that uses photovoltaic modules and a fuel cell (PV/FC) as sources and the telecommunication platform as load, the obtained results have proved the high efficiency of the proposed MGA-based maintenance scheduling.     

Modelling the effects of machine breakdowns in the generalized cell formation problem

Machines are key elements in manufacturing systems and their breakdowns can dramatically affect system performance measures. This paper proposes a new multi-objective pure integer linear programming approach for the cell formation problem with alternative process routings and machine reliability consideration. The model minimizes total cost and maximizes system reliability simultaneously. Traditional reliability evaluation approaches attempt to model the reliability of the manufacturing system as a function of its elements. These approaches have some negative aspects; therefore, instead of modeling the system reliability as an explicit objective function, we use an approach to model the effects of the machine unreliability in terms of cost and time-based effects. Using the ?-constraint method as an optimization tool for multi-objective programming, a numerical example is solved to demonstrate the capability of the proposed model in evaluating various effects of the reliability consideration.     

电站锅炉燃烧系统案例推理自适应寻优方法及应用研究

以提升火电机组调峰调频灵活性,促进可再生能源消纳为目标,针对某火电机组运行过程中燃烧稳定性、经济性等问题展开研究。采用自适应遗传算法优化核函数参数和正规化参数,建立最小二乘支持向量机(LS-SVM)锅炉燃烧过程模型。在建立模型的基础上,采用自适应遗传算法离线建立优化案例库。进而从便于工程应用角度提出一种基于案例推理(CBR)寻优方法,结合主、客观因素利用遗传算法优化案例推理特征权重,提高了检索精度,并自适应地从庞大的案例库中检索出与目标案例相匹配的案例。应用CBR自适应寻优算法,在保证机组稳定燃烧的同时,兼顾锅炉燃烧效率和NO_x排放浓度,合理给出二、三次风门挡板开度指令及氧量定值,实现锅炉稳定经济燃烧。将系统整体运用到某350 MW燃煤发电机组,简化了优化计算的过程,寻优时间短,稳定性高,适合在线实时寻优。     

区域性CT设备质量控制的节能运行优化方法

为减少区域性 CT 设备工作中的能耗,设计一种区域性 CT 设备质量控制节能运行优化方法.首先采用度日法对负荷能量值进行计算,并根据计算结果优化能量参数,在此基础上,设计节能优化目标;然后建立电量与设备质量控制的函数关系,预测消耗的电量;最后采用遗传算法优化节能参数,实现区域性 CT 设备质量控制节能运行优化.实验结果表明,采用此次研究方法优化后,能有效减少设备的运行成本与发生故障的情况,并提高设备工作的实时性.     

含低负荷场景低碳多源协调调度

温室效应导致全球持续升温,巨大碳排放量导致地球已不堪重负,如何降低碳排放成为目前亟待解决的问题。当风电出力与火电机组最小出力之和大于负荷量,只能通过储能、储热装置或弃风来达到功率平衡,此时可定义为低负荷运行状态。在低负荷时段,负荷消纳风电难、储能成本运行高。首先,考虑火电机组深调对发电成本和碳排放量的影响,建立了火电机组分阶段出力模型,将碳交易机制引入系统的调度模型中,构建阶梯型碳交易成本的计算模型;其次,以碳排放量和发电成本最小为优化目标,综合考虑系统的各种约束条件,建立了基于多目标的含低负荷场景低碳多源协调调度模型;然后,采用改进萤火虫算法,得到最优调度方案;最后,以带10个风电场的系统为算例,采用3种不同对比实验证明,所提方法可有效降低碳排放量,提高了系统运行经济性。所提方法分析了风电并网渗透率对系统运行方式的影响,表明含低负荷场景低碳多源协调调度与风电并网渗透率密切相关。     

Optimal load distribution between units in a power plant

This paper presents a strategy for load distribution between the generating units in hydro power plants. The objective is to reach the maximum energy conversion efficiency for a given dispatched power. The developed tool employs a heuristic-based combinatorial optimization technique in conjunction with a set of system variables measurement allowing real-time load sharing. The developed equipment is used to give online energy conversion efficiency from each unit of the power plant. No specific previous information about the efficiency of system components is required. Simulation results of the proposed optimization technique when applied to typical hydro power plant data are presented.     

兼顾用户满意度和发电侧收益的微电网系统量子粒子群优化

兼顾微电网系统发电侧与用户侧的综合利益,从能量管理的角度出发,建立了以用户满意度和发电侧收益为目标的优化模型。首先,采用多目标局部变异-自适应量子粒子群算法(Multi-objective local mutation adaptive quantum particle swarm optimization,MO-LM-AQPSO)获得用户满意度及发电侧收益的Pareto前沿。然后,引入缺电损失,以发电侧收益最大为目标,选取了非支配解中的最优解,并通过算例仿真验证其有效性。进而引入可平移负荷及分时电价激励机制,通过合理的峰谷电价比以引导用户积极参与需求侧响应。仿真结果表明,合理的激励措施,可提高微电网收益和用户满意度实现可再生能源的最大化利用及蓄电池运行损耗的有效减少。     

Multiobjective trajectory optimization of intelligent electro-hydraulic shovel

Multiobjective trajectory planning is still face challenges due to certain practical requirements and multiple contradicting objectives optimized simultaneously. In this paper, a multiobjective trajectory optimization approach that sets energy consumption, execution time, and excavation volume as the objective functions is presented for the electro-hydraulic shovel (EHS). The proposed cubic polynomial S-curve is employed to plan the crowd and hoist speed of EHS. Then, a novel hybrid constrained multiobjective evolutionary algorithm based on decomposition is proposed to deal with this constrained multiobjective optimization problem. The normalization of objectives is introduced to minimize the unfavorable effect of orders of magnitude. A novel hybrid constraint handling approach based on ε-constraint and the adaptive penalty function method is utilized to discover infeasible solution information and improve population diversity. Finally, the entropy weight technique for order preference by similarity to an ideal solution method is used to select the most satisfied solution from the Pareto optimal set. The performance of the proposed strategy is validated and analyzed by a series of simulation and experimental studies. Results show that the proposed approach can provide the high-quality Pareto optimal solutions and outperforms other trajectory optimization schemes investigated in this article.     

A distributed model predictive control based load frequency control scheme for multi-area interconnected power system using discrete-time Laguerre functions

This paper proposes a distributed model predictive control based load frequency control (MPC-LFC) scheme to improve control performances in the frequency regulation of power system. In order to reduce the computational burden in the rolling optimization with a sufficiently large prediction horizon, the orthonormal Laguerre functions are utilized to approximate the predicted control trajectory. The closed-loop stability of the proposed MPC scheme is achieved by adding a terminal equality constraint to the online quadratic optimization and taking the cost function as the Lyapunov function. Furthermore, the treatments of some typical constraints in load frequency control have been studied based on the specific Laguerre-based formulations. Simulations have been conducted in two different interconnected power systems to validate the effectiveness of the proposed distributed MPC-LFC as well as its superiority over the comparative methods.     

基于MATLAB的商用车平顺性优化与分析

为解决传统商用车平顺性优化三要素之间隐性表达式给优化带来不便的问题、分析乘员舒适性与货物安全性对悬架参数改变的响应,提出半显性优化方法。以驾驶室振动和货箱振动加速度加权均方根值为改进目标函数、悬架动行程和车轮动载荷为约束条件,调用振动仿真模型获得目标函数评价,运用均匀设计法得出约束条件与优化变量之间的关系。应用遗传算法实现商用车平顺性优化,获取最优参数匹配,并根据驾驶室与货箱振动时域图与功率谱密度图进行对比分析。结果表明:优化后整车平顺性有明显改善,优化方法可行;货物安全性改变较乘员舒适性改变将近两倍,优化时考虑货箱振动十分必要。     

基于动态分时电价的电动汽车有序充放电研究

大规模电动汽车随机无序充电将对电网安全运行带来巨大挑战,诸如增大负荷峰谷差、加大运营成本、增加谐波污染等。该文在考虑电动汽车充放电功率约束、电池容量约束的前提下,基于动态分时电价制度,建立电动汽车多目标优化调度模型,以降低电网负荷峰谷差率和用户充电成本,并采用改进学习因子与惯性权重的粒子群优化算法对模型进行求解。仿真结果表明,基于动态分时电价的调度策略比固定电价下优化效果更优,能够更好地减小系统负荷峰谷差率,提高负荷率,增加电力设备的利用率,降低电动汽车充电成本。     

车削加工中切削用量的程序优化求解

本文基于切削用量选择的基本原则,直接以切削深度ap、进给量f、和切削速度v为设计变量,以加工时间为目标函数,以刀具耐用度、机床功率、轴向载荷和表面粗糙度等限定条件为约束函数,建立切削用量选择的优化问题。应用随机优化方法,编制优化程序求解了具体实例。该方法为工程应用提供条件。     

主动配电网实时经济分配的一致性协同控制策略研究

Aiming at solving the economic operation problem when large scale of distributed energy resources (such as photovoltaic,wind power and energy storage) integrated into distribution network and to improve the real-time ability,robustness and flexible scalability of the control strategy,a distributed consensus-based collaborative control strategy was proposed. The power sources were controlled in a distributed way. The optimal operation point was realized in the condition that each power source could only collect information from its own and its neighbors. The results indicate that the proposed strategy is effect in real-time economic dispatch in cases of peak-valley price,intermittency of the renewable energy and power fluctuation of the load. [ABSTRACT FROM AUTHOR]     

Multi-objective supply chain sourcing strategy design under risk using PSO and simulation

Sourcing strategy design in a supply chain is vital to gain competitive advantage. In recent years, supply chain risks are growing significantly and supplier failure is identified as one of the top supply chain risks. Researchers attempt to mitigate the negative impacts of supplier failure by applying strategies such as local versus global sourcing, single versus dual/multiple-sourcing, performance-based supply contracts, and optimizing the order allocation among suppliers. Global sourcing is a widely recognized strategy among firms, and it involves a trade-off between reliable, high-cost local suppliers and unreliable, low-cost offshore suppliers. The global sourcing is associated with the risks of exchange rate volatility, trade restrictions, longer lead time, and problems with supplier reliability. Sourcing strategy design considering price, exchange rate risks, and supplier delivery reliability is an important research topic and needs attention. In this work, a hybrid optimization and simulation approach is proposed to design the supply chain sourcing strategy. In the optimization approach, a multi-objective binary particle swarm algorithm is developed for minimizing the total cost and maximizing the supplier delivery reliability. Selected scenarios from the optimization results are modeled using Witness simulation software to evaluate the robustness of sourcing strategies under price, exchange rate and demand risks. The proposed approach is exemplified using a real-life case study of a plastic product manufacture in India.