Distribution Characteristics and Optimization Strategies of Spatial Attractiveness of the Roof Garden in Meilecheng Commercial Complex of Handan City

With the rapid development of urban commercial complex in China,its roof garden becomes an important container for outdoor consumption and activities as the link between itself and the city,and more and more attention has been paid to its attractiveness.First,roof garden of Handan Meilecheng is taken as research object,and quantitative method of spatial attractiveness is established based on the number of spontaneous and chain activities and their intensity score.On this basis,data are collected via field investigation,and spatial attractiveness of roof garden of Meilecheng is quantified,and its distribution characteristics are summarized.Second,the cause of the characteristics formation is explained from the perspective of built environment elements.Finally,corresponding optimization strategies are proposed from four aspects:setting of landscape platform,increase of interest in sculpture sketches,design of space boundary,and improvement of spatial accessibility,which could provide reference for planning,design and transformation of roof garden of commercial complex in the future.

Joint Optimization Strategy for Video Transmission over Distributed Cognitive Radio Networks

A novel joint optimization strategy for the secondary user( SU) was proposed to consider the short-term and long-term video transmissions over distributed cognitive radio networks( DCRNs).Since the long-term video transmission consisted of a series of shortterm transmissions, the optimization problem in the video transmission was a composite optimization process. Firstly,considering some factors like primary user's( PU's) collision limitations,non-synchronization between SU and PU,and SU's limited buffer size, the short-term optimization problem was formulated as a mixed integer non-linear program( MINLP) to minimize the block probability of video packets. Secondly,combining the minimum packet block probability obtained in shortterm optimization and SU's constraint on hardware complexity,the partially observable Markov decision process( POMDP) framework was proposed to learn PU's statistic information over DCRNs.Moreover,based on the proposed framework,joint optimization strategy was designed to obtain the minimum packet loss rate in long-term video transmission. Numerical simulation results were provided to demonstrate validity of our strategies.

A Novel Two-Level Optimization Strategy for Multi-Debris Active Removal Mission in LEO

Recent studies of the space debris environment in Low Earth Orbit(LEO)have shown that the critical density of space debris has been reached in certain regions.The Active Debris Removal(ADR)mission,to mitigate the space debris density and stabilize the space debris environment,has been considered as a most effective method.In this paper,a novel two-level optimization strategy for multi-debris removal mission in LEO is proposed,which includes the low-level and high-level optimization process.To improve the overall performance of the multi-debris active removal mission and obtain multiple Pareto-optimal solutions,the ADR mission is seen as a Time-Dependant Traveling Salesman Problem(TDTSP)with two objective functions to minimize the total mission duration and the total propellant consumption.The problem includes the sequence optimization to determine the sequence of removal of space debris and the transferring optimization to define the orbital maneuvers.Two optimization models for the two-level optimization strategy are built in solving the multi-debris removal mission,and the optimal Pareto solution is successfully obtained by using the non-dominated sorting genetic algorithm II(NSGA-II).Two test cases are presented,which show that the low level optimization strategy can successfully obtain the optimal sequences and the initial solution of the ADR mission and the high level optimization strategy can efficiently and robustly find the feasible optimal solution for long duration perturbed rendezvous problem.

Study on optimal state estimation strategy with dual distributed controllers based on Kalman filtering

Considering dual distributed controllers, a design of optimal state estimation strategy is studied for the wireless sensor and actuator network(WSAN). In particular, the optimal linear quadratic(LQ) control strategy with estimated plant state is formulated as a non-cooperative game with network-induced delays. Then, using the Kalman filter approach, an optimal estimation of the plant state is obtained based on the information fusion of the distributed controllers. Finally, an optimal state estimation strategy is derived as a linear function of the current estimated plant state and the last control strategy of multiple controllers. The effectiveness of the proposed closed-loop control strategy is verified by the simulation experiments.

A Maintenance Optimzation Policy for an Electric Power Distribution System: Case of the HV/MV Substations

Today, in the modern era, and under the pressure of rapid development around the kingdom regions, electric utilities are confronted with a myriad of challenges that include aging infrastructure, enhanced expectation of reliability, reduced cost, and coping effectively with uncertainties and changing regulation requirements. Indeed, for distribution systems, HV/MV substations represent a complex and critical physical asset that requires a careful evaluation of their maintenance practices aiming at more cost-effective tasks. The first concern of the electric power distribution company in Morocco is the optimization of the distribution network maintenance scheduling, to minimize system operating costs and ensure that the system is running most economically. There is significant pressure on power providers for greater system reliability and improvement of customer satisfaction, while similar emphasis is placed on cost reduction. These cost reductions focus on reducing operating and maintenance expenses, and minimizing investments in new plants and equipment. However, the present of the electricity distribution, influenced by a number of technical restrictions and strict regulatory requirements to meet, leads distribution utilities to search new ways of optimizing the maintenance management, make an effort to maximize profit by reducing their electricity supply and operation costs while maintaining their reliability, developing more effective activities, with lower costs. Furthermore, maximal HV/MV substation asset value and minimal maintenance cost are typical economic objectives of the electric utilities. The optimization of maintenance is one possible technique to reduce maintenance costs while improving reliability and utilities need to implement new strategies for more effective maintenance techniques and Asset Management (AM) methods programs to manage inspections and maintenance activities in order to control HV/MV substation equipment conditions. However, development of strategies to make sound decisions in order to effectively improve equipment and system reliability while meeting constraints such as a maintenance budget is a challenge. Finally, this paper describes the application of a new approach based Reliability Centered Maintenance (RCM) methodology to optimize maintenance resources and to the development of maintenance plan for HV/MV distribution substations. For this purpose, a case study was performed on the equipment of power distribution systems for the benefit of a holding company (ONEE Electricity Distribution) in Morocco. The present study was conducted in Oujda Exploitation Maintenance Service (OEXMS), in charge of operation and maintenance of distribution networks.

Initiative Optimization Operation Strategy and Multi-objective Energy Management Method for Combined Cooling Heating and Power

This paper proposed an initiative optimization operation strategy and multi-objective energy management method for combined cooling heating and power U+0028 CCHP U+0029 with storage systems. Initially, the initiative optimization operation strategy of CCHP system in the cooling season, the heating season and the transition season was formulated. The energy management of CCHP system was optimized by the multi-objective optimization model with maximum daily energy efficiency, minimum daily carbon emissions and minimum daily operation cost based on the proposed initiative optimization operation strategy. Furthermore, the pareto optimal solution set was solved by using the niche particle swarm multi-objective optimization algorithm. Ultimately, the most satisfactory energy management scheme was obtained by using the technique for order preference by similarity to ideal solution U+0028 TOPSIS U+0029 method. A case study of CCHP system used in a hospital in the north of China validated the effectiveness of this method. The results showed that the satisfactory energy management scheme of CCHP system was obtained based on this initiative optimization operation strategy and multi-objective energy management method. The CCHP system has achieved better energy efficiency, environmental protection and economic benefits. © 2014 Chinese Association of Automation.

基于数字孪生技术的云储能点对点交易双层优化策略

为解决云储能(Cloud energy storage, CES)日前交易模型预测精度低、信息传输不及时、交易机制可靠性差等问题,提出一种基于数字孪生技术的云储能点对点交易双层优化策略。首先,针对环境因素的时变性,采用数字孪生(Digital Twins, DT)技术实现新能源电厂出力与负荷的超短期预测;其次,考虑到用户规模不断增大,构建以云储能供应商-社区-用户为体系的改进分布式点对点(Peer-to-peer, P2P)交易策略,实现交易的去中心化;再次,为同时满足CES供应商与用户两大主体的利益需求,建立基于日内滚动优化的双层优化配置模型,上层求解储能电站的优化配置,下层求解用户用电策略,并采用Karush-Kuhn-Tucher(KKT)条件与Big-M法将多目标双层非线性优化问题转化为单层线性优化问题;最后,通过两次使用shapley值计算社区与用户成本分摊。算例仿真结果表明,所提策略能够实现新能源电厂出力与负荷的高精度预测,降低用户群与CES供应商交易的复杂度,在用户购电成本最小的基础上最大化CES供应商的收益,保证交易市场的长期稳定运行。

IC卡数据驱动下的公交客流分布及线路优化策略研究

为提升公交车的运营效率,研究以某市公交IC数据为基础,对公交客流进行分析,并提出基于遗传算法改进的公交线路优化模型。研究发现,该市全天公交客流出现早晚两个峰值段。工作日到节假日,客流集中地从市中心附近转变为旅游景点、公园和购物中心等地区。公交线路优化模型将某公交线路的利用率提高了15%,将乘客的平均等待时间降低了21%,有效提高了公交系统的运营效率和乘客满意度。此次研究为提升公交运营效率、改善乘客体验、优化城市交通结构、促进城市可持续发展提供重要依据和方法。

柔性网架结构下考虑双边交易的多微网与配网协同规划

高比例风光接入下,多微网与配网间的双边交易对配电网的调控能力提出了更高要求。考虑柔性配电装置快速灵活调节网络潮流的优点,提出一种柔性网架结构下考虑多微网与配网双边交易的协同规划方法。首先,在考虑智能软开关规划的基础上构建协同规划的广义纳什议价模型。其次,采用交替优化策略将议价模型转化为易于求解的双层迭代过程,上层各主体各自优化规划方案,下层完成双边交易的结算问题。然后,将下层问题等效转化为电量结算和费用结算两个子问题,并分别采用交替方向乘子法求解。最后,在IEEE33节点配电系统中的算例表明所提规划方法可有效缩减社会总成本和各主体年综合费用。

分布式机器学习网络通信优化技术

Ring all-reduce算法被广泛应用在分布式机器学习之中,其同步过程会受到慢节点的影响进而降低整个系统的效率。对Ring all-reduce中的Reduce_Scatter和Allgather 2个阶段进行分析,针对Reduce_Scatter数据汇总过程提出优化策略,其主要思想是将慢节点多出的计算时间与通信时间进行重叠。使用OMNet++对Ring all-reduce和优化策略进行对比仿真,仿真结果与理论分析相一致,该策略相比Ring all-reduce算法最高能缩短25.3%的训练时间。

计及区间预测信息的含风电电力系统有功多时间尺度协调调度优化方法

间歇性、集群式的风电并入大电网将会增加输电断面功率越限风险,将点预测信息纳入含风电的电力系统调度将不可避免出现“过调度”和“欠调度”。为此,该文提出计及区间预测信息的含风电电力系统有功多时间尺度协调调度优化方法。首先,利用点模型及其预测误差分布构建风电功率区间预测模型;其次,建立具有区间预测信息的风电场有功功率动态分群策略;再次,构建风电集群和自动发电控制(automatic generation control,AGC)机组与非AGC机组等多时间尺度协调的调度策略,通过滚动优化和反馈校正环节协调日前-日内以及实时调度;最后,采用实际风电集群有功功率数据和改进的IEEE-118节点标准测试系统验证所提方法的有效性。研究表明,所提方法不仅可以合理协调风电集群内各个风电场输出功率,还可以协调风电集群功率和AGC机组与非AGC之间的输出功率,有效解决了调度指令与实际输出功率不协调问题。

基于扎根理论的公立医院薪酬分配机制优化策略分析

目的为更好地调动广大医务人员的工作积极性,探索公立医院薪酬分配机制的优化策略。方法采用目的抽样法于2024年3月对河南省某大型公立医院职工进行半结构式访谈。根据扎根理论对访谈资料进行梳理、编码和整合。结果根据40份访谈资料归纳出44个初始概念和14个基本范畴、6个主范畴、3个核心范畴。构建了一个基于薪酬分配原则、薪酬分配结构、薪酬管理制度的公立医院薪酬分配机制模型。结论建议医院保证薪酬分配的公平性和竞争性,实施多样化的薪酬政策,不断完善薪酬分配结构和监督管理制度。

基于价格机制的微网群三层主体交易策略

为更好地研究微网群多利益相关者的交易行为并为当地能源市场提供指导,该研究提出一种分布式求解方法,用以求解微网群三层主体间复杂的交易策略。该研究建立由配电网、微网群运营商、微网群及用户组成的优化模型,包括微网群运营商与各微网的供需互动模型和微网与用户的主从博弈模型,并采用分布式方法求解三层主体间的优化均衡策略。结果表明该方法既可实现微网群运营商最大的社会福利,也可提升微网及用户的经济效益,最终实现整个微网群能源互济、协作共赢。

一种多策略改进鲸鱼优化算法的混沌系统参数辨识

针对混沌系统参数辨识精度不高的问题,以鲸鱼优化算法(whale optimization algorithm,WOA)为基础,提出一种多策略改进鲸鱼优化算法(multi-strategy improved whale optimization algorithm,MIWOA)。采用Chebyshev混沌映射选取高质量初始种群,采用非线性收敛因子和自适应权重,提高算法收敛速度,为了避免算法陷入局部最优,动态选择自适应t分布或蚁狮优化算法更新后期位置,提高处理局部极值的能力。通过对10个基准函数和高维测试函数进行仿真试验,表明MIWOA具有良好的稳定性和收敛精度。将MIWOA应用于辨识Rossler和Lu混沌系统参数,仿真结果优于现有成果,表明本文MIWOA辨识混沌系统参数的高效性和实用性。

配电网络电能质量综合治理设备优化配置策略

针对配电网络电能质量治理设备缺乏全局统筹配置的现状,提出了一种基于多目标粒子群优化算法对谐波、无功及三相不平衡治理设备的综合优化配置策略。分别采用有源电力滤波器抑制谐波、智能电容器补偿无功、换相开关治理三相不平衡,并以每类电能质量问题的治理效果和投入成本为优化对象,以满足相关电能质量标准为约束条件,通过多目标粒子群算法确定治理设备配置节点和相应投入容量的优化配置方案。建立了电能质量评估模型,搭建了基于IEEE-18节点的配电系统仿真模型,并分散接入谐波、无功和三相不平衡负载,模拟电能质量问题,通过仿真验证了所提出的治理设备综合优化配置策略的可行性和相较于传统电能质量治理方案在电能质量治理上的优越性。

基于多源融合的船岸供电系统协调运行优化策略

针对岸电系统供电模式由单一电网侧供电变成由风、光、网、储等多源供电,所带来的新能源出力波动,消纳率降低等问题。通过构建基于移动储能装置的多源融合岸电协调运行模型,将岸电子系统通过公共母线互联实现闭环运行。提出了岸电分层分布式运行优化策略,通过边缘计算技术实现对各分布式子系统的实时就地协调,减小因预测误差,出力波动性带来的弃风弃光问题。基于电网的分时电价机制,通过移动储能的“谷充峰放”和“转移消纳”的工作模式实现岸电能源的优化调配技术,达到平抑分布式能源出力波动,提高岸电系统的经济性与可再生能源消纳率的效果。同时也为建立港口岸电系统互联运营的优化平台打下良好的基础。

基于需求响应的虚拟电厂多时间尺度优化调度

随着我国不断加快新型电力系统建设,风电、光伏等越来越多的分布式资源大规模并网,但其随机性、波动性及分散性的特点给电力系统带来了多重不确定性。提出了一种考虑价格型需求响应的虚拟电厂多时间尺度优化调度方法,在日内调度模型中引入用户价格型需求响应,对用户负荷进行精准调控;采用基于蒙特卡罗与曼哈顿概率距离的场景生成与削减法处理风光出力、电价不确定性,减少日前日内出力偏差,再结合日前调度构成了多时间尺度优化调度模型,并采用滚动优化得到日内优化调度结果。以湖南某小型虚拟电厂系统进行算例仿真,结果表明,所提多时间尺度优化调度策略能够精确预测风光出力及用户负荷,在有效抑制功率波动的同时保证了系统的经济性。

计及源荷储的多台区负载均衡协同调控方法

分布式可再生能源(光伏等)发电的不确定性以及柔性负荷的“即插即用”,易引发台区负载不均衡、三相不平衡等问题。为了解决上述问题,文章提出计及源荷储的多台区负载均衡协同调控方法,其由上层台区之间负载均衡协同控制策略与下层台区内部源荷储协调优化调度策略组成。在上层基于台区之间环型电力网架以及与之对应的通信网架,并考虑台区之间通信传输延时和网架拓扑的可变性,设计多台区之间协同一致性控制,实现台区负载均衡;在下层基于台区融合终端与台区内部源荷储之间的纵向辐射型通信网架,设计台区内部分布式发电和柔性负荷等可控资源协调优化调度算法,以低碳环保为目标来响应上层台区负载均衡的功率调节指令。最后通过仿真算例验证了所提方法的有效性,实现了多台区负载的均衡调控。

基于最优经济效益的虚拟电厂平台优化调度模型

为了解决多种分布式资源的协调调度并提高负荷聚合商的经济效益,提出了一种考虑分布式储能、风电、及需求侧响应的虚拟电厂调度模型。基于对不同种类分布式资源的日前出力基础上,对虚拟电厂调度策略进行实时优化,并通过协调多种需求侧资源的出力预测情况,降低分布式资源的实际出力与预测值之间的偏差,使得虚拟电厂偏离日前计划安排最小,实现虚拟电厂经济效益最优。仿真实验结果表明,含有多种不同种类分布式资源的虚拟电厂经济效益高于只含有单一分布式资源的虚拟电厂,并且当分布式资源的预测出力越接近于实际出力时所构建的虚拟电厂调度模型经济效益最优。

基于改进二进制蛇优化算法的配电网故障定位

分布式电源(distributed generation,DG)大规模接入给配电系统带来更多不确定性、随机性,系统运行方式更复杂,传统故障定位方法难以适应新型电力系统构建。提出了一种基于改进二进制蛇优化算法(improved binary snake optimization,IBSO)的新型故障区段定位方法。利用SPM混沌映射生成高质量的随机数序列,以提高算法种群中个体的随机性,并引入了遗传算法的动态变异策略,根据不同的搜索状态和进化阶段来调整变异率和变异方式,提高算法的灵活性和准确性。通过仿真证明,该方法适用于在含有分布式电源的配电网中定位单一和多重故障区段,相比蛇优化算法、传统二进制粒子群算法以及遗传算法在收敛性、快速性和准确性方面更优。