Multi-timescale robust dispatching for coordinated automatic generation control and energy storage

The increasing penetration of renewable energy into power grids is reducing the regulation capacity of automatic generation control(AGC).Thus,there is an urgent demand to coordinate AGC units with active equipment such as energy storage.Current dispatch decision-making methods often ignore the intermittent effects of renewable energy.This paper proposes a two-stage robust optimization model in which energy storage is used to compensate for the intermittency of renewable energy for the dispatch of AGC units.This model exploits the rapid adjustment capability of energy storage to compensate for the slow response speed of AGC units,improve the adjustment potential,and respond to the problems of intermittent power generation from renewable energy.A column and constraint generation algorithm is used to solve the model.In an example analysis,the proposed model was more robust than a model that did not consider energy storage at eliminating the effects of intermittency while offering clear improvements in economy and efficiency.

Dispatch coordination between high-speed and conventional rail systems

Cross-line trains, as a link between high-speed and conventional rail networks, will increase the complexity of transport organization and lead to significant challenges in dispatch coordination between the two systems. Based on the characteristics of high-speed transport organization, this paper deals with the necessity of dispatch coordination between high-speed and conventional lines from the following two perspectives： the operation of cross-line trains and work coordination in connection stations. An adjustment model for the operation of high-speed trains, taking cross-line trains into account, is established. Finally, the dispatch system is described in terms of construction and process. Methods for organizing dispatch are proposed, and the processes of coordination adjustment under normal and unexpected situations are analyzed. The discussion in this paper may serve as a theoretical basis for the development of high-speed rail dispatch systems.

Short-term train arrival delay prediction:a data-driven approach

Purpose-To optimize train operations,dispatchers currently rely on experience for quick adjustments when delays occur.However,delay predictions often involve imprecise shifts based on known delay times.Real-time and accurate train delay predictions,facilitated by data-driven neural network models,can significantly reduce dispatcher stress and improve adjustment plans.Leveraging current train operation data,these models enable swift and precise predictions,addressing challenges posed by train delays in high-speed rail networks during unforeseen events.Design/methodology/approach-This paper proposes CBLA-net,a neural network architecture for predicting late arrival times.It combines CNN,Bi-LSTM,and attention mechanisms to extract features,handle time series data,and enhance information utilization.Trained on operational data from the Beijing-Tianjin line,it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding trains.Findings-This study evaluates our model’s predictive performance using two data approaches:one considering full data and another focusing only on late arrivals.Results show precise and rapid predictions.Training with full data achieves aMAEof approximately 0.54 minutes and a RMSEof 0.65 minutes,surpassing the model trained solely on delay data(MAE:is about 1.02 min,RMSE:is about 1.52 min).Despite superior overall performance with full data,the model excels at predicting delays exceeding 15 minutes when trained exclusively on late arrivals.For enhanced adaptability to real-world train operations,training with full data is recommended.Originality/value-This paper introduces a novel neural network model,CBLA-net,for predicting train delay times.It innovatively compares and analyzes the model’s performance using both full data and delay data formats.Additionally,the evaluation of the network’s predictive capabilities considers different scenarios,providing a comprehensive demonstration of the model’s predictive performance.

Bus coordination holding control for transit hubs under APTS

To increase the passenger transferring efficiency, the bus coordination holding control for transit hubs, which is as an important dynamic dispatching method for improving the service level of transit hubs, was studied in the framework of bus coordination dispatching mode. Firstly, the bus coordination holding control flow was studied based on Advanced Public Transportation Systems (APTS) environment. Then a control model was presented to optimize the bus vehicle holding time, and a genetic algorithm was designed as the solving method. In the end, an example was given to illustrate the effectiveness of the control strategy and the algorithm.

城市污水处理厂的“水-能-碳”协调调度模型

城市污水处理厂面临能量密集、碳排量大、运行成本高的突出问题。污水厂优化调度是实现“节能降碳增效”的重要途径,但现有调度方法还存在对处理设备控制特性利用不足、对处理过程的温室气体排放缺乏全面管控等局限。为此,该文提出一种面向污水厂“水-能-碳”协调调度的新模型。所提模型利用鼓风机启停/沼气池温度控制来挖掘节能空间、计入碳排放成本来提升低碳水平、引入污水分级处理与回用来促进资源回收利用,在满足污水处理工艺流程和各种设备运行约束及水质处理要求的条件下,实现污水厂的低碳经济运行。以某中小型城市污水厂为算例,验证了所提调度模型的有效性以及各调度要素对污水厂“节能降碳增效”的积极作用。

海上风氢系统与沿海电网能量协同优化调度

为实现海上风电的高效消纳和绿色氢能的制备,提出一种海上风氢系统与沿海电网能量协同优化调度方法。首先,通过评估海上风电场间的风能耦合效应,建立考虑风能异域强化利用的风电场群发电转移模型。其次,量化分析风电场的疲劳载荷与疲劳成本之间的关系,构建考虑检修便利性的风电场群均衡载荷模型。基于此,提出风电场群综合效能调控模型,以协调发电与疲劳寿命之间的关系。然后,考虑海上风流因素对船舶航行时间的影响,建立电氢并举的能量外送模型。最后,以系统运行成本最小为目标,提出考虑主动孤岛、制氢效率特性以及系统旋转备用的海上风氢系统与沿海电网能量协同优化调度模型。算例验证了所提方法的有效性和优越性,为海上风电制氢能量调度提供了一种新的思路。

大规模水风光互补调度模式探析

光伏发电具有昼发夜停的特点,日内出力过程呈“钟型”;风力发电出力随机性强,并呈现反调峰特点。通过水电调节平抑风电和光伏发电的随机波动,水风光互补利用是支撑“双碳”目标实现的有效途径。在此背景下,针对现有水风光电源并网接入场景,分析了我国典型水风光清洁能源互补开发形式,探析了大规模水风光电源依托电网互补和打捆互补调度模式,对比分析了两种调度模式的优缺点,并对大规模水风光互补调度关键技术进行展望,为将来大规模水风光互补利用提供参考。

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。

考虑天然气柔性供应约束的气-电协同优化调度策略

随着高比例新能源接入和中国“双碳”目标的不断推进,燃气机组运行灵活、启停迅速、碳排放强度低的优势更加引起重视。然而,即使考虑了合同外用气需求和顶峰发电用气需求,相对固定甚至紧张的日供气量仍然很难应对不同气象条件下的天然气需求变化,这将直接影响到电力系统的经济性和可靠性。基于此,文中提出一种考虑天然气柔性供应约束的气-电协同优化调度策略。首先,设计了有限气源供应下多日统筹分配和用气考核的天然气柔性供应机制;其次,提出了考虑风光不确定性和气-电市场价格波动传导的调度风险评估方法;然后,以系统运行成本和风险成本最低为目标构建了系统多日调度优化模型;最后,以修改的某省级含天然气供应的电力系统为例,验证了所构建模型的有效性。算例分析结果表明,结合天然气柔性供应机制的多日调度能够实现气机出力在每日不同负荷需求和风光出力之间的灵活调配。

新型配电系统形态演化与安全高效运行方法综述

配电网是实现能源转型的重要支撑点,“双碳”目标驱动的配电网将发生重大变革,已快速从被动配送网络向主动平衡区域电力供需、支撑能源综合利用的资源配置平台转变,呈现更强的“系统化”特征,即从配电网向配电系统转变。在此背景下,该文首先从形态演化路径、多维度耦合关系以及核心研究手段3个维度梳理总结相关研究,并初步提出新型配电系统未来演化规律与演进方向构想。然后,对新型配电系统演进过程中涉及到的关键技术,即故障保护与自愈技术、振荡分析与抑制技术、系统稳定控制技术与协同调度技术进行了总结与讨论,并进一步通过分析关键技术在典型案例中的应用明确了现阶段技术现状与未来需求的差距。最后,对新型配电系统相关技术未来发展方向提出了展望。

分布式优化在互联电网中的应用分析及展望

在新能源随机性与波动性的影响下,互联电网能量与灵活性的跨区域互济重要性凸显,有效的分布式优化算法是不可或缺的技术支撑。为了在互联电网不同运行层级的重要场景中选择适宜的算法,文中归纳了分布式优化建模和求解的一般原理,并提出将现有分布式算法分为原始可行方向、对偶可行方向、原对偶、切平面、列生成和多参数法6类。针对互联电网的多区域输电网协同调度、输配协同优化调度以及配电网分布式资源协同调度不同运行层级这3个重要场景,阐明了采用分布式优化的必要性,总结了统一的变量/约束耦合问题的典型建模及变换关系,并分析了6类算法在各场景的实施情况和应用优缺点。从物理场景、数学模型以及算法整体角度分析分布式优化在互联电网3个重要场景应用存在的问题,并给出了不同场景的算法选取建议。结合配电网运行新场景、数学建模新问题、算法设计新技术,展望了未来互联电网应用分布式优化的方向。

Initial Exploration of Wind Farm Cluster Hierarchical Coordinated Dispatch Based on Virtual Power Generator Concept

China has made many strides in large-scale development and centralized integration of wind power in recent years.The wind power penetration of some regions has reached a high level,which brings significant challenges for power system dispatch due to the inherent variability and uncertainty of wind resources.To increase the dispatch capabilities of wind power generation,the spatial smoothing effect among adjacent wind farms needs to be fully utilized.This paper presents the concept of hierarchical coordinated dispatch for wind power based on a new concept of a virtual power generator.The spatial smoothing effect of wind power is analyzed first.Next,the virtual power generator method of a wind farm cluster is defined and established.Then,the hierarchical coordinated dispatch mode is compared with an existing wind power dispatch mode for individual wind farms.Finally,the proposed concept is implemented on a simulation case to demonstrate applicability and effectiveness.

计及备用风险性的新型电力系统源网协同安全经济调度

为合理利用源侧备用资源,提出基于随机潮流和半不变量法的备用风险评估指标,以准确量化因备用不足而造成的潮流越限风险;为了实现源侧备用资源和网侧可控资源的协同优化,将网侧有载调压变压器和电容器组无功补偿装置纳入决策变量,以系统运行成本最低和系统运行越限风险指标值最小为优化目标,构建计及备用风险性的新型电力系统源网协同安全经济调度模型;将布朗运动和莱维飞行随机游走机制融入多目标微分进化算法,设计新型的布朗-莱维多目标微分进化算法,对模型进行高效求解。算例分析结果验证了所提方法的有效性和优越性。

重庆轨道交通一体化运营调度系统研究

在轨道交通“多网融合”与多主体协同运营的新形势下,为实现不同交通网络、不同线路之间的高效协同与衔接,有必要构建面向一体化运营背景下的多层级轨道交通运营调度系统。以重庆轨道交通一体化运营调度系统构建为例,在总结重庆轨道交通运营调度现状的基础上,通过分析重庆轨道交通运营调度需求与目标,从总体架构、功能定位、协调机制、协同模式等方面,构建重庆轨道交通一体化运营调度指挥系统方案,提出面向重庆远期交通规划的多主体协同运营调度中心设置方案,并基于大数据平台和云计算技术,设计重庆轨道交通一体化运营调度指挥平台技术方案,以实现不同运营主体之间运营调度指挥的协调,为多层级轨道交通多主体之间线路互联互通运营奠定基础。

Coordinated dispatching strategy of multiple energy sources for wind power consumption

Heat storage systems with multiple heat sources play an important role in consuming extra wind power.A reasonable scheduling strategy for a hybrid system with multiple heat and electric sources could provide greater economic benefits.However,the present scheduling methods primarily focus on extra wind power consumption alone.This paper aims to develop a coordinated dispatching method that targets the maximum extra wind power consumed and highest economic benefit of the hybrid energy system as the optimization objective.A two-step coordinated dispatching method is proposed,where the first step focuses on optimizing the extra wind power consumed by coordinating the consumption quota for different types of energy sources at the system level and distributes the consumption share for every unit within each type of energy source,thereby maximizing fuel savings and economic benefits in the second step.The effectiveness of the approach is demonstrated using simulation results for an electric-heat hybrid system.Compared with two existing dispatching methods,the scheduling strategy presented in this paper could consume more extra wind power and provide higher fuel savings and economic benefits.

电网调度AVC系统自动优化协调控制方法

由于电网调度过程中电压变化量较大,电网调度AVC系统难以精准控制电压变化,所以提出电网调度AVC系统自动优化协调控制方法。确定总-中-地调三个层次协同的电力系统架构,搭建协调调度控制模型,确定母线、机组、无功裕度、功率方面的协调约束规则。在协调约束规则下确定电网AVC系统分层分区结构,计算电压无功控制空间中任意两个待分区节点之间的距离,划分电压无功控制区域,结合灵敏度确定优化协调控制矩阵,实现AVC系统自动优化协调控制。实验结果表明,该方法控制下的电压与实际数值之间误差小,能够实现电压平衡和降低网损的目标。

基于语音识别技术的配电网多任务协调优化调度

针对多任务调度受到不稳定耦合影响,造成数据不完全拟合,从而影响调度效果的问题,提出了基于语音识别技术的配电网多任务协调优化调度方法。根据不同任务间的类别映射关系,计算配电网多任务协调概率,确定多任务适合的调度语音场景。根据该场景设计基于语音识别的调度控制流程。结合禁忌搜索算法获取最优位置数据,避免出现不稳态解耦情况。构建PID控制器,通过导数定义迭代步骤,协调调度步长。通过设计语音识别智能控制平台,引入极小正值,实现配电网多任务协调优化调度。由实验结果可知,该调度技术数据拟合曲线与标准拟合曲线吻合,且送电时间与实际控制时间的偏差仅为2 min,调度效果精准。

基于混合粒子群优化的智能电网负荷协同调度

由于智能电网负荷频率和负荷电压调节向量不确定,难以实现智能电网负荷协同调度,因此,提出基于混合粒子群优化的智能电网负荷协同调度方法。根据混合粒子群优化条件,求解电网负荷协同调度中的优化参数,定义目标负荷函数。分别计算智能电网负荷频率与负荷电压调节向量,结合电量信号确定协同调度决策指标的取值范围,实现智能电网负荷协同调度。实验结果表明,所提方法应用下,电量信号传输功率极值之差不超过100 dBm,满足智能电网负荷协同调度的实际应用需求。

基于粒子群优化算法的工业可调负荷协同优化调控方法研究

针对工业负荷调度中的负荷类型多、总量大且调度空间大的特点,基于需求响应的负荷调度是削峰填谷、提升电网可靠性、降低企业与电网成本的重要方法。为了对不同类型负载进行全面的建模,且兼顾电网与企业的利益问题,提出一种工业负荷调度方法,对于工业场景中的可调负荷、恒功率负荷、从属负荷、储能负荷进行建模,同时考虑降低峰值负荷与生产成本,在实时电价的背景下,通过改进的粒子群优化算法进行求解。对于所提出的工业负荷调度方法进行仿真分析,验证其在工业场景下的可靠性与实用性。