Operating Analysis and Data Mining System for Power Grid Dispatching

The dispatching center of power-grid companies is also the data center of the power grid where gathers great amount of operating information. The valuable information contained in these data means a lot for power grid operating management, but at present there is no special method for the management of operating data resource. This paper introduces the operating analysis and data mining system for power grid dispatching. The technique of data warehousing online analytical processing has been used to manage and analysis the great capacity of data. This analysis system is based on the real-time data of the power grid to dig out the potential rule of the power grid operating. This system also provides a research platform for the dispatchers, help to improve the JIT (Just in Time) management of power system.

Framework and Key Technologies of Human-machine Hybrid-augmented Intelligence System for Large-scale Power Grid Dispatching and Control

With integration of large-scale renewable energy,new controllable devices,and required reinforcement of power grids,modern power systems have typical characteristics such as uncertainty,vulnerability and openness,which makes operation and control of power grids face severe security challenges.Application of artificial intelligence(AI)technologies represented by machine learning in power grid regulation is limited by reliability,interpretability and generalization ability of complex modeling.Mode of hybrid-augmented intelligence(HAI)based on human-machine collaboration(HMC)is a pivotal direction for future development of AI technology in this field.Based on characteristics of applications in power grid regulation,this paper discusses system architecture and key technologies of human-machine hybrid-augmented intelligence(HHI)system for large-scale power grid dispatching and control(PGDC).First,theory and application scenarios of HHI are introduced and analyzed;then physical and functional architectures of HHI system and human-machine collaborative regulation process are proposed.Key technologies are discussed to achieve a thorough integration of human/machine intelligence.Finally,state-of-theart and future development of HHI in power grid regulation are summarized,aiming to efficiently improve the intelligent level of power grid regulation in a human-machine interactive and collaborative way.

Automation in Power Grid Dispatch

A project named ＂A New Generation of Energy Management with Three Dimensional Coordination＂ has recently passed technical appraisal from the Ministry of Education. The project has been developed over the past 15 years by a research team led by Professor Zhang Boming from Tsinghua＇s Department of Electrical Engineering.

Forecasting-Based Adaptive Optimized Dispatch in Smart Grid Online

The power grid is a fusion of technologies in energy systems, and how to adjust and control the output power of each generator to balance the load of the grid is a crucial issue. As a platform, the smart grid is for the convenience of the implementation of adaptive control generators using advanced technologies. In this paper, we are introducing a new approach, the Central Lower Configuration Table, which optimizes dispatch of the generating capacity in a smart grid power system. The dispatch strategy of each generator in the grid is presented in the configuration table, and the scenario consists of two-level agents. A central agent optimizes dispatch calculation to get the configuration table, and a lower agent controls generators according to the tasks of the central level and the work states during generation. The central level is major optimization and adjustment. We used machine learning to predict the power load and address the best optimize cost function to deal with a different control strategy. We designed the items of the cost function, such as operations, maintenances and the effects on the environment. Then, according to the total cost, we got a new second-rank-sort table. As a result, we can resolve generator’s task based on the table, which can also be updated on-line based on the environmental situation. The signs of the driving generator’s controller include active power and system’s f. The lower control level agent carries out the generator control to track f along with the best optimized cost function. Our approach makes optimized dispatch algorithm more convenient to realize, and the numerical simulation indicates the strategy of machine learning forecast of optimized power dispatch is effective.

基于GridSim ToolKits的网格仿真环境设计与实现

本文在研究GridSim的基础上,设计并实现一种基于GridSim ToolKits的网格仿真环境MendSim,该网格仿真环境可以对各种高级调度算法进行模拟并实现对各种网格发布规则和调度算法的研究。

基于SimGrid的网格依赖任务的Min-Min算法模拟

网格技术——被称为下一代的Internet的计算机网络技术,是近几年IT领域的一个研究热点,网格的主要目标是实现网络资源的充分共享和提供较高的计算能力。一个好的任务调度算法能够更好地发挥网格资源的作用。在网格任务调度的研究中采用一个实际的网格环境是没有必要的,而往往采用网格模拟器来完成。用SimGrid SG模拟网格环境,应用Min-Min算法思想对依赖任务实现了调度,通过对算法的模拟,验证了算法的可行性,并找出了实现算法高效的适用条件。

Large-Scale Distributed Photovoltaic Power Dispatching and Operation Management Review

Distributed photovoltaic power (PV) is the main development model of distributed generation. It is necessary to research on dispatching and operation management with large-scale distributed PV connected. This paper analyzes development status, technical requirement and dispatching and operation management situation of distributed PV in Germany and China. Then introduce the preparation of distributed PV dispatching and operation management criterion. Through summarizing the experiences and lessons of large-scale distributed PV development in Germany, it gives advice to the development of distributed PV dispatching and operation management in China.

台风灾害下提升电网韧性的实时优化调度策略

针对在台风灾害下输电线路停运可能引发的电网大停电事故,构建了提升电网韧性的实时优化调度策略。运用脆弱性曲线刻画风速与线路故障率关系,并考虑线路负载率对线路故障影响,获得台风灾害下的输电线路故障概率;通过蒙特卡罗抽样生成故障场景集,采用置信集法进行场景削减;在此基础上构建了综合考虑潮流均匀度、受灾区线路负载率和发电成本的多目标实时优化调度模型,将输电线路开断(Optimal Transmission Switching, OTS)引入到模型中以降低受台风灾害影响的线路数量。为提高优化调度模型求解的计算速度,采用Benders分解法进行求解。以IEEE 118节点系统为例进行仿真,验证了所提方法的有效性。

Cost Optimization Modeling of Renewable Energy Sources in Smart-Grid Using SCADA

In recent times, renewable energy production from renewable energy sources is an alternative way to fulfill the increased energy demands. However, the increasing energy demand rate places more pressure, leading to the termination of conventional energy resources. However, the cost of power generation from coal-fired plants is higher than the power generation’s price from renewable energy sources. This experiment is focused on cost optimization during power generation through pumped storage power plant and wind power plant. The entire modeling of cost optimization has been conducted in two parts. The mathematical modeling was done using MATLAB simulation while the hydro and wind power plant’s emulation was performed using SCADA (Supervisory control and data acquisition) designer implementation. The experiment was conducted using ranges of generated power from both power sources. The optimum combination of output power and cost from both generators is determined via MATLAB simulation within the assumed generated output power range. Secondly, the hydro-generator and wind generator’s emulation were executed individually through synchronizing the grid to determine each generator’s specification using SCADA designer, which provided the optimum power generation from both generators with the specific speed, aligning with results generated through MATLAB. Finally, the operational power cost (with no losses consideration) from MATLAB was compared with the local energy provider to determine the cost-efficiency. This experiment has provided the operational cost optimization of the hydro-wind combined power system with stable wind power generation using SCADA, which will ultimately assist in operations of large-scale power systems, remotely minimizing multi-area dynamic issues while maximizing the system efficiency.

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.

基于人工蜂群算法的配电网无功优化调度研究

为了改善现有无功功率调度时算法执行效率,解决模型易陷入局部极值问题,提出一种基于改进人工蜂群算法模型。在分析等式和不等式约束的情况下,建立最小化实际功率损耗、电压偏差和电压稳定增强的无功功率调度目标函数。提出一种基于邻域半径选择的改进人工蜂群算法,设计一种新的邻域选择机制和改进的搜索策略。实验阶段,以IEEE39总线电力系统为例,对所提算法有效性和执行效率进行验证。与线性规划(LP)、粒子群优化(PSO)算法和基础人工蜂群(ABC)算法相比,改进ABC算法比其他算法执行效率更优,且解的质量更高。该模型为配电网电力调度及稳定性运行的发展提供了一定借鉴作用。

基于层次分析法的电网调度操作效率评价

基于层次分析法的电网调度操作效率评价能够从多维度评价线路操作效率、主变操作效率、母线操作效率;基于停电、复电、检修工作、许可的准时率,能够给调度操作提供辅助决策分析功能,以实现电网精益化管理。

基于电网调度系统的网络安全态势感知方法研究

由于网络外界病毒入侵的隐蔽性极高、入侵类型较多,病毒难以被全面地被感知,导致电网调度自动化系统运行安全受到威胁。为提升电网调度自动化系统网络的安全性,提出基于随机矩阵理论(random matrix theory,RMT)的电网调度自动化系统网络安全态势感知技术。采集电网调度自动化系统网络安全信息数据,构建病毒入侵环境下的安全状态分布模型,并利用该模型完成电网调度系统网络安全态势特征信息的提取。采用RMT中的M-P模型及单环定律对网络安全态势特征值在随机矩阵内的分布情况进行谱分析,根据特征值分布状态获取网络安全态势在稳定情况及异常情况下的变换规律,实现电网调度系统网络安全态势感知。实验结果表明,通过对该方法开展安全态势感知测试,验证了该方法应用在电网调度系统中组件安全态势感知及网络安全态势感知的精准度均较高,体现了该方法的有效性。

基于分布式新能源集群的微能源网优化调度综述

为促进符合本地园区/社区资源禀赋的分布式新能源集群消纳,现有研究通过微能源网实现园区/社区级多能转换、混合储能及综合需求侧响应方式。该文对日趋丰富、渐成体系的国内外微能源网优化调度研究进行综述。首先,概述微能源网的背景及特点。然后,回顾微能源网优化调度研究,涉及微能源网系统运行建模、优化调度问题及最优化方法。最后,展望微能源网优化调度发展方向,面向多种能源类型的大规模、集群化分布式新能源消纳,拓展“电-碳”市场协同下的新型应用场景及能量共享与交易机制,构建感知-推理-决策一体化的微能源网优化调度体系并实现规模化互联微能源网在线鲁棒决策。

大电网调控人机混合增强智能:概念内涵、应用框架、关键技术以及系统验证

伴随深度学习等人工智能技术的飞速发展,通过数字化赋能电力来实现电网的信息化、数字化和智能化成为未来新型电力系统发展的必然趋势。人工智能技术在电网调控应用研究广泛,但是实际应用过程中,由于电网不确定性、开放性和脆弱性造成的模型可信性和可解释性等问题,使得单纯的人工智能技术应用于电网调控领域难以满足安全可靠应用的需求。为了解决上述问题,需要引入人类的监督与互动,融合调控人员的智能与机器智能,实现电网调控人机智能的混合增强。该文首先介绍混合增强智能技术的概念和内涵以及在不同领域的研究现状。随后,针对电网调控业务特点,从人工智能和调控应用两个维度,提出电网调控人机混合增强智能的应用框架以及电网调控人机交互协同的目标和基本原则。在此基础上,分析和讨论电网调控人机混合增强智能所涉及的关键技术,并研发大电网调控人机混合紧急控制决策支持验证系统,用于省级规模电网的热稳调整、频率控制等多种电网调控场景应用验证,为人机混合增强智能技术在电网调控领域的应用提供参考和借鉴。

基于网络交互与智能规则的调度操作指挥模块

针对传统的手打拟票、手工记录以及电话下令的业务开展方式已成为操作效率提升的瓶颈,提出在调度指挥控制系统(dispatch command control system,DCCS)上设计调度操作指挥模块。将电话下令转变为网络交互;构建多种基于智能规则的自动成票手段,取代传统的手打出票方式;操作完成后系统可自动记录设备状态信息并通知相关单位,实现调度操作全流程的网络化、信息化与智能化。模块上线运行结果表明,数据显示对调度操作效率的提升作用显著。

基于深度强化学习的微电网在线优化

针对微电网的随机优化调度问题,提出了一种基于深度强化学习的微电网在线优化算法。利用深度神经网络近似状态-动作值函数,把蓄电池的动作离散化作为神经网络输出,然后利用非线性规划求解剩余决策变量并计算立即回报,通过Q学习算法,获取最优策略。为使得神经网络适应风光负荷的随机性,根据风电、光伏和负荷功率预测曲线及其预测误差,利用蒙特卡洛抽样生成多组训练曲线来训练神经网络;训练完成后,保存权重,根据微电网实时输入状态,神经网络能实时输出蓄电池的动作,实现微电网的在线优化调度。在风电、光伏和负荷功率发生波动的情况下与日前优化结果进行对比,验证了该算法相比于日前优化在微电网在线优化中的有效性和优越性。

碳排放限制的混杂最优闭环电网经济调度

针对电网经济调度效率低下等问题,提出了一种碳排放限制的混杂最优闭环电网调度算法。算法在考虑火力发电机组和多分布式发电聚合体组合调度的同时,以电网实际需求响应和碳排放限制作为约束条件,建立电网经济调度模型;并将经济调度问题转化为混杂系统的最优控制,从而建立电网经济调度与混杂系统最优控制序列的等价一致性;推导混杂系统获取满足贝尔曼方程的最优代价函数,借助神经网络逼近最优值获得最优闭环调度序列。仿真实验验证了本调度算法的闭环有效性,能在不同初始条件和外部扰动下依然获得最优调度序列,同时仿真结果证实降低碳排放限额或提高碳排放交易价格都有助于减少电网系统的总碳排放量。

面向多虚拟电厂的分层分区多层互补动态聚合调控策略

虚拟电厂(virtual power plant,VPP)是一种新型运行模式,通过有效聚合电网中大量需求侧资源并制定有效的动态聚合调控策略,实现电网不同时空的功率互补,提高电网调控的灵活性和系统的经济性。从电网调度角度分析了典型电网需求响应行为特性,提出了需求响应能力指标和虚拟电厂分类聚合方法,构建了多源虚拟电厂调控模型,以其结果支撑虚拟电厂响应资源的分层分区互补调控。最后,以某园区为案例,分析了虚拟电厂调控策略的合理性和多源虚拟电厂调控的科学性。结果表明,整体动态调控策略可以引导虚拟电厂科学合理地发挥需求响应价值,促进电网负荷平稳和系统安全稳定运行。