A Multi-time Scale Tie-line Energy and Reserve Allocation Model Considering Wind Power Uncertainties for Multi-area Systems

Continued expansion of the power grid and the increasing proportion of wind power centralized integration leads to requirements in sharing both energy and reserves among multiple areas under a hierarchical control structure,which successively requires a correction between schedule plans within multi-time scale.In order to address this problem,this paper develops an information integration method integrating complicated relationships among fuel cost,total thermal power output,reserve capacity,owned reserves and expectations of load shedding and wind curtailment,into three types of time-related relationship curves・Furthermore,a multi-time scale tieline energy and reserves allocation model is proposed,which contains two levels in the control structure,two time scales in dispatch sequence and multiple areas integrated within wind farms as scheduling objects・The efficiency of the proposed method is tested in a 9-bus test system and IEEE 118-bus system.The results show that a cross-regional control center is able to approach the optimal scheduling results of the whole system with the integrated uploaded relationship curves.The proposed model not only relieves energy and reserve shortages in partial areas but also allocates them to more urgent need areas in a high effectivity manner in both day-ahead and intraday time scales.

Resilient Fixed-Order Distributed Dynamic Output Feedback Load Frequency Control Design for Interconnected Multi-Area Power Systems

The paper proposes a novel H∞ load frequency control(LFC) design method for multi-area power systems based on an integral-based non-fragile distributed fixed-order dynamic output feedback(DOF) tracking-regulator control scheme. To this end, we consider a nonlinear interconnected model for multiarea power systems which also include uncertainties and timevarying communication delays. The design procedure is formulated using semi-definite programming and linear matrix inequality(LMI) method. The solution of the proposed LMIs returns necessary parameters for the tracking controllers such that the impact of model uncertainty and load disturbances are minimized. The proposed controllers are capable of receiving all or part of subsystems information, whereas the outputs of each controller are local. These controllers are designed such that the resilient stability of the overall closed-loop system is guaranteed. Simulation results are provided to verify the effectiveness of the proposed scheme. Simulation results quantify that the distributed(and decentralized) controlled system behaves well in presence of large parameter perturbations and random disturbances on the power system.

Robust H_∞ Load Frequency Control of Multi-area Power System With Time Delay:A Sliding Mode Control Approach

This paper is devoted to investigate the robust H∞sliding mode load frequency control(SMLFC) of multi-area power system with time delay. By taking into account stochastic disturbances induced by the integration of renewable energies,a new sliding surface function is constructed to guarantee the fast response and robust performance, then the sliding mode control law is designed to guarantee the reach ability of the sliding surface in a finite-time interval. The sufficient robust frequency stabilization result for multi-area power system with time delay is presented in terms of linear matrix inequalities(LMIs). Finally,a two-area power system is provided to illustrate the usefulness and effectiveness of the obtained results.

Solving Multi-Area Environmental/Economic Dispatch by Pareto-Based Chemical-Reaction Optimization Algorithm

In this study, we present a Pareto-based chemicalreaction optimization(PCRO) algorithm for solving the multiarea environmental/economic dispatch optimization problems.Two objectives are minimized simultaneously, i.e., total fuel cost and emission. In the proposed algorithm, each solution is represented by a chemical molecule. A novel encoding mechanism for solving the multi-area environmental/economic dispatch optimization problems is designed to dynamically enhance the performance of the proposed algorithm. Then, an ensemble of effective neighborhood approaches is developed, and a selfadaptive neighborhood structure selection mechanism is also embedded in PCRO to increase the search ability while maintaining population diversity. In addition, a grid-based crowding distance strategy is introduced, which can obviously enable the algorithm to easily converge near the Pareto front. Furthermore,a kinetic-energy-based search procedure is developed to enhance the global search ability. Finally, the proposed algorithm is tested on sets of the instances that are generated based on realistic production. Through the analysis of experimental results, the highly effective performance of the proposed PCRO algorithm is favorably compared with several algorithms, with regards to both solution quality and diversity.

Multi-Area Unit Commitment Using Hybrid Particle Swarm Optimization Technique with Import and Export Constraints

This paper presents a novel approach to solve the Multi-Area unit commitment problem using particle swarm optimization technique. The objective of the multi-area unit commitment problem is to determine the optimal or a near optimal commitment strategy for generating the units. And it is located in multiple areas that are interconnected via tie lines and joint operation of generation resources can result in significant operational cost savings. The dynamic programming method is applied to solve Multi-Area Unit Commitment problem and particle swarm optimization technique is embedded for computing the generation assigned to each area and the power allocated to all committed unit. Particle Swarm Optimization technique is developed to derive its Pareto-optimal solutions. The tie-line transfer limits are considered as a set of constraints during the optimization process to ensure the system security and reliability. Case study of four areas each containing 26 units connected via tie lines has been taken for analysis. Numerical results are shown comparing the cost solutions and computation time obtained by using the Particle Swarm Optimization method is efficient than the conventional Dynamic Programming and Evolutionary Programming Method.

Ant Lion Optimization Approach for Load Frequency Control of Multi-Area Interconnected Power Systems

This work proposes a novel nature-inspired algorithm called Ant Lion Optimizer (ALO). The ALO algorithm mimics the search mechanism of antlions in nature. A time domain based objective function is established to tune the parameters of the PI controller based LFC, which is solved by the proposed ALO algorithm to reach the most convenient solutions. A three-area interconnected power system is investigated as a test system under various loading conditions to confirm the effectiveness of the suggested algorithm. Simulation results are given to show the enhanced performance of the developed ALO algorithm based controllers in comparison with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bat Algorithm (BAT) and conventional PI controller. These results represent that the proposed BAT algorithm tuned PI controller offers better performance over other soft computing algorithms in conditions of settling times and several performance indices.

京津多机场系统容量损失影响分析

全球范围内多机场系统数量和规模都在不断增长。由于终端区内有限资源的限制,邻近机场的运行存在一定的相关性,这导致多机场系统运行效率低下。为了估计多机场运行产生的限制,以北京终端区为例提出一种统计分析框架对多机场系统的容量损失进行整体分析。首先,根据理论容量模型分别计算出北京终端区各机场的理论容量,并进一步确定各机场独立运行下的实际容量;其次,以北京终端区的历史运行数据为基础,估计出3个机场及终端区系统的具有鲁棒性的Pareto容量包络曲线;最后,基于独立运行容量与相关运行容量量化出终端区各机场及多机场系统的实际容量损失。为进一步提高多机场终端区的运行效率提供建议与参考。

“近零碳”目标下港区多能互补标准体系研究

利用多能互补技术推动能源结构调整是实现港口绿色低碳发展的有效手段。本研究以国内主要沿海港口为对象,聚焦港区建设、运营、管理等环节开展多能互补技术标准研究,基于系统工程学方法构建港区多能互补标准体系,利用港区承载的可再生能源自然禀赋与清洁能源应用,通过多能互补技术的能源结构优化减少交通运输行业碳排放,推动双碳目标实现。

面向乡村建筑分时分区用能需求的零碳综合能源系统设计优化

基于传统最大负荷原则设计的综合能源系统容量通常远大于建筑的实际用能需求,导致以户为单位的乡村综合能源系统设计缺乏针对性和适用性。该文通过现场调查和数值模拟的方法精细化乡村建筑的实际用能需求,基于农户用能行为制定4种用能策略。并构建考虑农户经济水平和零碳约束的综合能源系统设计优化模型,采用两阶段带精英策略的快速非支配排序遗传算法(Top-NSGA-II)进行多目标优化问题的求解,获得不同策略下的最佳设计方案。结果表明,与基于建筑全空间连续动态负荷的设计优化方案相比,基于分时分区用能需求的设计方案年总成本降低13.58%,系统购电比例降低7.89个百分点,实现全年净零碳排放。

计及光伏不确定性的多区域综合能源系统多场景分布鲁棒优化调度

为降低光伏出力不确定性对多区域综合能源系统(IES)经济性与安全性的影响以及提升多区域IES系统在多种极端场景下的稳定运行能力,提出一种计及光伏出力不确定性的多区域综合能源系统多场景优化调度策略。针对光伏发电的不确定性,采用拉丁超立方抽样和改进型人工蜂群K-均值聚类算法形成典型光伏场景集。根据供热管道热特性和热能传输动态特性建立热网络模型。融合光伏场景信息,分别将日前阶段的运行成本以及实时阶段最恶劣光伏场景下系统的调整成本作为优化目标,构建两阶段分布鲁棒优化调度模型。采用列与约束生成(C&CG)算法对两阶段模型进行求解。最后,通过算例验证了所提策略的正确性和可行性。

岩溶区燃气管道灾害系统多因素耦合作用机理研究

基于灾害系统理论,从“孕灾环境-灾害因子-承灾体-灾情”系统性视角出发,结合系统动力学模型,探讨岩溶区燃气管道灾害系统各子系统内部以及子系统间的耦合关系,考虑多因素间的差异化传导路径,深度剖析灾害演化过程中多因素耦合的作用机理和影响规律。研究表明:(1)孕灾环境活跃性与人员、管道、环境不安全状态以及管理漏洞密切相关;灾害因子风险性受到火灾和爆炸危害的概率、强度、范围、持续时间共同作用;承灾体脆弱性受到社会-经济-自然承灾体三者耦合影响;灾情严重性包含人员伤亡程度、经济损失程度及环境破坏程度三个维度。(2)岩溶区燃气管道灾害的孕育和演化是四个子系统共同作用的结果,子系统两两之间以及三者之间的耦合作用表征灾害不同阶段,而四个子系统间的耦合作用是对整个灾害过程的描述。

基于D^(∗)Lite算法的多机器人非凸区域覆盖控制方法研究

针对非凸区域下感知范围有限的多机器人协作区域覆盖问题,重新定义非凸环境下的距离度量函数、控制方法以及未知环境的定义方法,融合D^(∗)Lite算法启发式和增量式的特点,应用于具有动态特性的未知环境的探索覆盖。通过仿真实验,与基于Dijskra算法的非凸覆盖算法相比,在已知环境下,算法覆盖率和冗余率分别达到97.98%和26.53%,未知环境下算法覆盖率和冗余率分别达到98。96%和30。01%,验证了基于D^(∗)Lite的非凸覆盖算法在已知环境下和未知环境下的有效性和优越性。

基于多目标评价体系的老旧小区停车空间优化设计研究——以徐州市主城区为例

随着我国家庭轿车的普及,老旧小区停车成为当前城市交通管理亟待解决的问题。文章以徐州市主城区的老旧小区为研究对象,通过对老旧小区的调研,明确老旧小区停车空间设计的现状,并对其进行多目标体系评价,厘清当前不同类型老旧小区停车空间设计的可优化方向,最终从挖掘内部空间、拓展周边资源、创新管理和智慧化停车等方面针对各类老旧小区停车空间提出优化设计策略,以期为老旧小区停车空间的改造提供参考。

混合攻击下基于容积卡尔曼滤波的多区域互联电力系统的检测与防御

以多区域互联电力系统为研究对象,对混合攻击下多区域互联电力系统的检测与防御进行研究。首先,建立多区域互联电力系统的数学模型,分析多区域互联电力系统遭受网络攻击的位置及其类型,建立虚假数据注入攻击和拒绝服务攻击模型,接着根据是否收到数据包诊断拒绝服务攻击,并用最新接收的数据对丢失的数据进行补偿,实现拒绝服务攻击的防御;然后,基于容积卡尔曼滤波算法检测虚假数据注入攻击,并采用指数平滑法对虚假数据注入攻击进行防御;最后,以两区域互联电力系统为例进行仿真实验。仿真结果表明:所设计的控制算法能有效克服混合攻击对系统造成的不良影响,实现电力系统功率平衡和频率稳定。

基于强化学习的多园区综合能源系统经济调度

多园区综合能源系统中新能源出力与负荷的波动性以及多种能量之间的耦合关系,给多园区综合能源系统的优化调度带来诸多挑战。为此提出一种基于数据驱动的多智能体近端策略优化(multi-agent proximal policy optimization,MAPPO)多园区综合能源系统经济调度方法。文章综合考虑园区间的能量交易与碳市场交易,以园区日运行成本最小为目标,建立多园区综合能源系统实时优化调度模型;将该优化问题建模为马尔科夫决策过程,并设计状态空间、动作空间以及奖励函数,通过大量历史数据的训练得到多园区综合能源系统优化调度神经网络模型,实现多园区分散式实时优化调度。仿真结果表明:在新能源出力与负荷随机性波动影响下,所提方法能够在降低各园区运行成本的同时减少园区间的信息交互,有助于提高各园区隐私信息的安全性。

高强度片区类地面连接效能量化指标研究

高强度片区立体开发及连接缺乏管控指标,对过往建筑设计与城市设计领域关于类地面、立体城市、多层地表等内容进行梳理总结,进而提出“高强度片区类地面”的概念,并提出能够表征类地面连接效能的三个指标,即面积系数、楼层连通率和类地连接工效比,为总设计师在城市设计阶段的立体连接导控提供更全面、精准、高效的量化评估方法。

低压配电台区柔性互联系统多目标DDPG优化调度

针对配电台区柔性互联系统(DSAFIS)中源、荷、设备强不确定性,及台区间功率互济特性,提出基于深度确定性策略梯度(DDPG)的台区运行成本、新能源消纳、负载均衡目标协调优化调度方法。构建系统模型与物理系统自动联动的深度强化学习日前优化调度决策框架,设计考虑多目标奖励和运行约束奖励的优化调度DDPG模型;DDPG采用“在线学习”模式,算法收敛后输出日前调度计划给实际DSAFIS。算例验证了所提方法能自动适应系统的强不确定性,且在降低运行成本的同时兼顾了新能源消纳和台区负载均衡。

多波束测深仪在内陆水域测绘中的实践与思考

根据国务院批复的《全国基础测绘中长期规划纲要(2015—2030年)》的要求,抓紧“启动内陆水体水下地形测绘”[1]。针对内陆水域水下地形测绘的特点、难点,文章提出相应的思考建议及补充方法,并选取贵州省沿河县画廊天街周边区域作为项目试点,使用多波束测深仪开展水下测量试点工作,同时,使用机载Lidar测量系统,作为内陆水域测绘的重要补充,形成水域测绘数字产品,并对数字产品精度进行验证。

煤矿火灾智能预警系统研发与应用

目前煤矿火灾监测系统实现了对矿井煤自燃标志性气体、温度、烟雾、火焰等部分指标的单独监测,未对煤矿火灾相关因素进行有效、全面、统一的监测。针对该问题,从内因、外因2个方面分析了煤矿火灾潜在危险因素,提出一种分源分区监测火情态势的方法。内因火灾方面,主要针对较易发生火灾的工作面采空区、密闭采空区及人工自然发火观测点等进行监测;外因火灾方面,主要针对机电硐室及其配电点、带式输送机系统、电缆等方面进行监测。建立了煤矿火灾分源分区监测指标体系,采用人工监测或在线监测的方式定期采集或更新火灾特征参量数据,按数据采集方式及影响程度,将火灾监测指标分为动态指标、静态指标和关联指标。设计了火灾智能预警系统的总体架构和业务流程,采用基于多指标联合逻辑推理的预警方法实现内因火灾预警,采用基于D-S证据理论的多参量融合预警方法实现外因火灾预警。现场试验结果表明,火灾智能预警系统实现了对矿井火灾的有效监测预警,具有煤矿火灾风险预警“一张图”可视化展示功能,同时具备火灾智能模拟演示功能及避灾路线动态规划功能。

港区多能源融合综合管控系统

在“碳达峰”、“碳中和”背景下,港区节能减排已迫在眉睫。若能充分利用港区本地的风、光等可再生能源为港区提供能源,可以从源头降低碳排放,助力港区“碳达峰”“碳中和”目标的实现。但新能源出力的不确定性为系统运行的经济性、安全性带来挑战,故需要对多种能源进行综合管控。文中主要针对某港区风力发电系统、光伏发电系统、储能系统、港区氢气的制、注、储、供一体化系统所构成的多能源融合系统,进行能量综合管控系统的设计。该系统整体分为5层架构,硬件上分控制区和管理区,可满足安全防护要求;功能上可协同调控风光发电、储能、制氢、氢燃料电池发电、港区用电、船岸用电等多个子系统,在保持多能融合系统稳定运行的同时最大化经济效益。