Optimal Allocation of Public Transport Hub Based on Load Loss Value and the Economy of Distribution Network

The rapid development of electric buses has brought a surge in the number of bus hubs and their charging and discharging capacities.Therefore,the location and construction scale of bus hubs will greatly affect the operation costs and benefits of an urban distribution network in the future.Through the scientific and reasonable planning of public transport hubs on the premise of meeting the needs of basic public transport services,it can reduce the negative impact of electric bus charging loads upon the power grids.Furthermore,it can use its flexible operation characteristics to provide flexible support for the distribution network.In this paper,taking the impact of public transport hub on the reliability of distribution network as the starting point,a three-level programming optimization model based on the value and economy of distribution network load loss is proposed.Through the upper model,several planning schemes can be generated,which provides boundary conditions for the expansion of middle-level optimization.The normal operation dispatching scheme of public transport hub obtained from the middle-level optimization results provides boundary conditions for the development of lower level optimization.Through the lower level optimization,the expected load loss of the whole distribution system including bus hub under the planning scheme given by the upper level can be obtained.The effectiveness of the model is verified by an IEEE-33 bus example.

Optimal Load Dispatch of Gas Turbine Power Generation Units based on Multiple Population Genetic Algorithm

In this paper, a multiple population genetic algorithm (MPGA) is proposed to solve the problem of optimal load dispatch of gas turbine generation units. By introducing multiple populations on the basis of Standard Genetic Algorithm (SGA), connecting each population through immigrant operator and preserving the best individuals of every generation through elite strategy, MPGA can enhance the efficiency in obtaining the global optimal solution. In this paper, MPGA is applied to optimize the load dispatch of 3×390MW gas turbine units. The results of MPGA calculation are compared with that of equal micro incremental method and AGC instruction. MPGA shows the best performance of optimization under different load conditions. The amount of saved gas consumption in the calculation is up to 2337.45m3N/h, which indicates that the load dispatch optimization of gas turbine units via MPGA approach can be effective.

Optimal Placement of Multi DG Units Including Different Load Models Using PSO

This paper proposes a multi-objective index-based approach to optimally determine the size and location of multi-distributed generators (DG) units in distribution system with different load models. It is shown that load models can significantly affect the optimal location and sizing of DG resources in distribution systems. The proposed multi-objective function to be optimized includes a short circuit level parameter to represent the protective device requirements. The proposed function also considers a wide range of technical issues such as active and reactive power losses of the system, the voltage profile, the line loading and the MVA intake by the grid. The optimization technique based on particle swarm optimization (PSO) is introduced. The analysis of continuation power flow to determine the effect of DG units on the most sensitive buses to voltage collapse is carried out. The proposed algorithm is tested using the 38-bus radial system and the IEEE 30-bus meshed system. The results show the effectiveness of the proposed algorithm.

Optimization of Minimum Power Output for Combined Heat and Power Units Considering Peak Load Regulation Ability

Northern China has rich wind power and photovoltaic renewable resources. Combined Heat and Power (CHP) Units to meet the load demand and limit its peaking capacity in winter, to a certain extent, it results in structural problems of wind-solar power and thermoelectric. To solve these problems, this paper proposes a plurality of units together to ensure supply of heat load on the premise, by building a thermoelectric power peaking considering thermal load unit group dynamic scheduling model, to achieve the potential of different thermoelectric properties peaking units of the excavation. Simulation examples show, if the unit group exists obvious relationship thermoelectric individual differences, the thermal load dynamic scheduling can be more significantly improved overall performance peaking unit group, effectively increase clean energy consumptive.

Multi-source coordinated stochastic restoration for SOP in distribution networks with a two-stage algorithm

After suffering from a grid blackout, distributed energy resources(DERs), such as local renewable energy and controllable distributed generators and energy storage can be used to restore loads enhancing the system’s resilience. In this study, a multi-source coordinated load restoration strategy was investigated for a distribution network with soft open points(SOPs). Here, the flexible regulation ability of the SOPs is fully utilized to improve the load restoration level while mitigating voltage deviations. Owing to the uncertainty, a scenario-based stochastic optimization approach was employed,and the load restoration problem was formulated as a mixed-integer nonlinear programming model. A computationally efficient solution algorithm was developed for the model using convex relaxation and linearization methods. The algorithm is organized into a two-stage structure, in which the energy storage system is dispatched in the first stage by solving a relaxed convex problem. In the second stage, an integer programming problem is calculated to acquire the outputs of both SOPs and power resources. A numerical test was conducted on both IEEE 33-bus and IEEE 123-bus systems to validate the effectiveness of the proposed strategy.

Multi-level Evaluation of Load Supply Adequacy for Transmission Network

Considering sharp increase of central integration scale of intermittent energy generation, power system couldn’t rely on the determined model based operation schedule to assure its reliable load supply. Then, this article presents a new load supply adequacy evaluation method, which based on continuous security constrained generation optimization, and provides the functional architecture design of online multi-level short term load supply adequacy evaluation system.

Binary Gravitational Search based Algorithm for Optimum Siting and Sizing of DG and Shunt Capacitors in Radial Distribution Systems

This paper presents a binary gravitational search algorithm (BGSA) is applied to solve the problem of optimal allotment of DG sets and Shunt capacitors in radial distribution systems. The problem is formulated as a nonlinear constrained single-objective optimization problem where the total line loss (TLL) and the total voltage deviations (TVD) are to be minimized separately by incorporating optimal placement of DG units and shunt capacitors with constraints which include limits on voltage, sizes of installed capacitors and DG. This BGSA is applied on the balanced IEEE 10 Bus distribution network and the results are compared with conventional binary particle swarm optimization.

An Improved Differential Evolution Whale Algorithm for Economic Load Distribution

An improved optimization algorithm combining the differential evolution algorithm and the whale algorithm is proposed for the problem of not being able to get rid of the local optimum in the economic load distribution algorithm. The algorithm adopts a nonlinear convergence strategy, a crossover strategy of differential evolution and the introduction of an elimination mechanism, which balances the global search and local exploitation ability of the algorithm and improves the accuracy of the solved optimal solution. The 13-unit and 40-unit systems are selected for economic load distribution calculation, and the experimental results show that the proposed improved algorithm is superior in distributing the economic load of the power system and can effectively reduce the economic cost.

考虑净负荷不确定性的配电网负荷恢复仿射可调鲁棒优化方法

为应对负荷恢复过程中源荷双重不确定性问题,提出了基于仿射可调鲁棒优化的配电网负荷恢复调整策略。第1阶段基于净负荷预测场景,建立一个以最大化加权负荷恢复量为目标函数的优化模型,决策最优的负荷恢复量;第2阶段采用仿射策略,利用预算不确定集来描述净负荷的不确定性,构建考虑净负荷误差的负荷恢复调整方案。在求解过程中,对双线性变量进行对偶变换得到可快速求解的线性规划模型。最后,在改进33节点和123节点配电系统进行仿真验证,结果表明:相比于传统的鲁棒优化方法,该方法降低了优化结果的保守性,有助于加快负荷恢复,减小停电损失。

考虑空载损失的非集中式共同配送订单分派及路径优化研究

物流活动的空载率居高不下源于路径规划不合理及企业间缺少合作,共同配送是降低空载损失的有效模式,但非集中式共同配送下物流企业可能基于被分派的订单选择自身成本最小的配送路径,从而导致共同配送联盟的空载损失变大。本文研究考虑空载损失的非集中式共同配送订单分派及路径优化,首先提出空载损失定义,权衡整个配送过程的成本最小和空载损失最小两个目标,基于非集中式共同配送的特征设计订单分派策略,进而建立订单分派及路径优化模型。设计了基于ε约束法的精确算法、改进的MOPSO(multiple objective particle swarm optimization)算法、多项式时间快速算法进行求解,并结合算例验证算法的有效性。数值分析结果表明,即使物流企业均追求自身成本最小化,提出的订单分派策略也可得到与全局优化相近的结果。

换热站并联水泵分布式优化控制

针对现有换热站并联水泵优化算法在集中式架构下控制适应性不足的问题,本文提出了一种改进的分布式并联水泵优化算法.首先,建立了并联水泵的分布式控制系统,并对该优化问题的数学模型进行描述,在目标函数中引入自适应非线性因子;然后,设计了改进的分布式果蝇优化算法,在该算法中每台水泵的控制器仅通过与邻居控制器交互信息即可完成并联水泵的优化;并且,在嗅觉搜索阶段,使用正弦余弦策略替代赋予个体距离与方向的随机策略;最后,以两个实际换热站中不同并联水泵系统为例对算法进行仿真验证,并基于仿真结果进行性能分析.结果表明,相较于传统算法,改进的分布式果蝇优化算法能得到更优的控制策略,有着收敛速度快、稳定性好和鲁棒性强的特点;并且该算法适用于不同系统的并联水泵优化问题,具有可扩展性.在实际工程验证中相较于集中式算法,该算法在总功率和计算时间上分别平均降低了5.47%和29.90%,因此,能够满足实际换热站中对并联水泵热负荷优化分配的需求.

基于联络馈线负载率松弛约束的中压配电网供电单元划分

当前中压配电网供电单元划分时未能充分考虑相互联络馈线间所供负荷的互补特性,易造成供电单元数量偏多、整体线路利用效率偏低等问题,为此提出了一种基于负载率松弛约束的供电单元划分方法。构建了以供电单元数量最少、各供电单元内N-1校验后转供馈线负载率均衡为目标的中压配电网供电单元划分数学模型;解析了网格中站内供电单元与站间供电单元之间的划分次序问题,并提出了基于馈线负载率松弛约束的站间供电单元划分方法及以此为基础的站内供电单元划分方法。通过实例分析验证所提模型和方法可以对供电单元进行合理划分,节约变电站出线条数与供电单元数量,有效提高配电线路的资产利用效率。

基于BO-BiGRU-Attention短期电力负荷预测

电力系统的可靠供应对于工业、商业和居民的生活至关重要。为了满足电力需求并维持电力系统的稳定运行,提高短期电力负荷预测的准确性和可靠性尤为关键;针对负荷数据存在复杂的非线性特性,该文提出一种基于贝叶斯优化算法的双向门控循环单元和注意力机制(BO-BiGRU-Attention)的混合预测模型对短期电力负荷进行精准预测。首先,使用Min-Max Normalization方法对负荷数据进行归一化处理。其次,利用BiGRU网络捕获序列中的长期依赖关系和上下文信息,结合注意力机制,通过在输入序列的不同部分给予不同的权重,从而突出关键特征。最后,针对BiGRU-Attention模型的超参数难以选取最优解的问题,引入贝叶斯优化算法对BiGRU-Attention模型的超参数进行寻优,完成短期电力负荷的预测。采用印度北部某地区的电力负荷数据进行预测分析,仿真结果表明,BO-BiGRU-Attention网络表现优于其他模型,各误差评价指标最小,其中MAE、RMSE和MAPE分别为56.67,73.49和1.16%,预测精度达到了99.47%。

面向分布式数据库的算子并行优化策略

随着网络技术的不断发展,数据规模呈现爆发式增长,使得传统的单机数据库逐步被分布式数据库所取代。分布式数据库采用节点协同工作方式解决了大规模数据存储问题,但由于增加了节点间通信开销,查询效率却不如单机数据库。分布式架构下,存储节点的数据仅用作多备份的冗余,为系统故障时提供数据恢复,并未被利用起来改善查询效率。针对上述问题,提出了一种面向分布式数据库的算子并行优化策略,通过对关键物理算子进行拆分,将拆分后的子请求均匀分配到存储层多个节点,由多个节点并行处理,从而减少查询响应时间。上述策略已经在分布式数据库CBase上进行了应用,实验表明,提出的并行优化策略可显著缩短SQL请求查询时间,并提高系统资源利用率。

卫星网络混合负载均衡策略下的多径流量分配算法

为均衡卫星网络流量分配,满足用户QoS需求,提出一种全局和局部结合的混合负载均衡策略下的多径流量分配算法(HLB-MTD)。根据QoS业务需求进行部分路径筛选,基于表的散列算法进行流量的初次分配;在初次分配的基础上针对突发拥塞进行局部调整,建立重映射目标函数,通过改进交叉和变异概率的遗传算法求出最优解;求得局部优化的,流到路径的映射策略。仿真结果表明,该算法可有效缓解卫星网络拥塞,实现流量均衡分配,在丢包率、平均排队时延、等指标上有更好的提升。

海上液压修井机承载模式优化及应用

液压修井机自从在国内海上平台用于修井以来,已经进行了多次作业。在作业过程中,如何提高液压修井机的抗风能力、安全快速高效地实现液压修井机在海上平台槽口间的转换,一直是一项重要的课题。通过对承载模式的创新,优化设计出了适用于渤海各采油平台的滑移导轨,承载模式由传统的井口承载优化成为滑移底座承载,增加了液压修井机的稳定性,提高了修井作业效率,降低了修井作业成本,是对液压修井作业模式的再一次创新。

基于时空图注意力网络的超短期区域负荷预测

目前,空间负荷预测研究对复杂时空关系的考虑不足。为此,文中提出一种基于多维、多源特征的区域级负荷超短期时空预测模型。首先,根据已有的区域级负荷进行元胞划分,构建考虑元胞相关性的图拓扑。其次,分别通过图注意力网络、一维卷积神经网络和门控循环单元,从空间、特征和时间维度提取有效特征,连接全连接层输出结果。最后,基于美国新英格兰地区的真实电力负荷数据进行仿真验证,并提取模型注意力权重,分析元胞之间的空间依赖性。结果表明,所提模型相比传统模型在不同预测步长上均具有更高的预测精度和稳定性,有效挖掘了区域级负荷的空间依赖性。

泵站-水库原水调水系统经济运行方式优化

为提高泵站-水库原水调水系统中泵站的运行效益,最大限度降低输水系统的运行成本.通过分析该类输水系统特性以及供水方式,基于遗传算法构建泵站机组流量分配模型,优化分配泵站运行流量.在此基础上,考虑泵站时间维度上的运行方式,结合亳州加压泵站的工程状况和区域分时电价,以单位提水费用最小为优化目标,构建经济运行模型.将该方法应用于亳州供水工程,结果表明模型效果显著,并对历史运行区间内周期输水量进行离散计算,拟合得出周期内单位提水费用最低工况,进而对拟合结果进行分析.结果表明:所构建经济运行模型具有较好的适用性,可降低工程运行费用,为工程优化调度提供指导;受到泵站运行效率和分时电价的影响,泵站单位提水费用随调水量增加先降低后升高.

曹娥江流域水环境容量与污染负荷分配研究

以曹娥江流域为研究对象,计算水环境容量,将其分配至各控制单元并细化到污染源类型,提出各控制单元污染物的削减量,以期提升曹娥江水环境质量。结果表明:1)2016—2021年,曹娥江流域内的屠家埠、汤曹汇合口和曹娥江大闸闸前3个监测断面的COD、氨氮、TP均达到《地表水环境质量标准》(GB 3838—2002)Ⅲ类要求。2)COD、氨氮、TN、TP入河量分别为16013.20、959.10、3658.53、416.05 t/a,且主要以非点源为主。3)基于满足曹娥江流域水体监测断面和入海断面水质达标的约束条件下,COD、氨氮、TN、TP水环境容量分别为47945.18、2435.81、6863.19、474.98 t/a。流域内COD还有剩余水环境容量,不需削减,氨氮、TN、TP分别需削减6.91、501.52、128.34 t/a。