Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems:An Uncertainty Handling Perspective

Due to growing concerns regarding climate change and environmental protection,smart power generation has become essential for the economical and safe operation of both conventional thermal power plants and sustainable energy.Traditional first-principle model-based methods are becoming insufficient when faced with the ever-growing system scale and its various uncertainties.The burgeoning era of machine learning(ML)and data-driven control(DDC)techniques promises an improved alternative to these outdated methods.This paper reviews typical applications of ML and DDC at the level of monitoring,control,optimization,and fault detection of power generation systems,with a particular focus on uncovering how these methods can function in evaluating,counteracting,or withstanding the effects of the associated uncertainties.A holistic view is provided on the control techniques of smart power generation,from the regulation level to the planning level.The benefits of ML and DDC techniques are accordingly interpreted in terms of visibility,maneuverability,flexibility,profitability,and safety(abbreviated as the“5-TYs”),respectively.Finally,an outlook on future research and applications is presented.

Power Quality Data Compression Based on Iterative PCA Algorithm in Smart Distribution Systems

To reduce the stress of data transmission and storage for power quality (PQ) in smart distribution systems and help PQ analysis, a multichannel data compression based on iterative PCA (principal component analysis) algorithm is introduced. The proposed method uses PCA to reduce the redundancy of data to achieve the purpose of compressing data. In order to improve the calculating speed, an iterative method is proposed to compute the principal components of the covariance matrix. The correctness and feasibility of the proposed method are verified by field PQ data tests. Compared with discrete wavelet transform (DWT) method, the proposed method has good performance on compression ratio and reconstruction accuracy.

Matlab Code to Assess the Reliability of the Smart Power Distribution System Using Monte Carlo Simulation

Reliability of power systems is a key aspect in modern power system planning, design, and operation. The ascendance of the smart grid concept has provided high hopes of developing an intelligent network that is capable of being a self-healing grid, offering the ability to overcome the interruption problems that face the utility and cost it tens of millions in repair and loss. In this work, we develop a MATLAB code to examine the effect of the smart grid applications in improving the reliability of the power distribution networks via Monte Carlo Simulation approach. The system used in this paper is the IEEE 34 test feeder. The objective is to measure the installations of the Automatic Reclosers (ARs) as well as the Distributed Generators (DGs) on the reliability indices, SAIDI, SAIFI, CAIDI and EUE, and make comparisons with results from a previous study done by the authors using another approach. The MATLAB code should provide close results to the output of the previous research to verify its effectiveness.

Agents for Smart Power Grids

The future of electricity systems will compose of small-scale generation and distribution where end-users will be active participants with localized energy management systems that are able to interact on a free energy market. Software agents will most likely control power assets and interact together to decide the best and safest configuration of the power grid system. This paper presents a design of agents that can be deployed in real-time with capabilities that include optimization of resources, intensive computation, and appropriate decision-making. Jordan 51-bus system has been used for simulation with a total generation capacity of 4050 MW of which 230 MW represents renewable energy. The economic analyses demonstrated the use of smart grid technologies with 2016 generation—load profiles for nominal liquified gas (NLG) prices and ±20% sensitivity analysis. The results have shown variations in the range of 1% in the price of MWh with smart grid technologies. These variations are mainly driven by the fact that agents shift power generation to renewable power plants to produce maximum power at peak hours. As a result, there is a positive economic impact in both NLG ± 20% sensitivity analysis, due to the fact that agents coordinate to better displace expensive thermal generation with renewable generation. It is evident that renewable resources compensate for power at peak times and provide economic benefits and savings.

Application of Smart Power Strategy to Construct a More Effective Strategy to Denuclearize North Korea

The use of sanctions to coerce Pyongyang into abandoning its nuclear weapons and related programs is not working. North Korea continues to survive and expands its nuclear and missile capabilities. It is time for the US to construct a more successful North Korea strategy by applying a smart power strategy before the North Koreans can operationalize their nuclear ICBMs within the next several years. If and when that occurs, the US will inevitably consider the use of force to resolve the North Korean nuclear issue, but that will be too costly for the US and its allies. Washington must do all it can to avoid another war on the Asian mainland. The only practical uses of force are as defensive and enhanced deterrence measures to protect the US and its allies against North Korean nuclear and conventional provocations. Instead, the US should call for an immediate freeze of North Korea＇s nuclear and missile programs and demonstrate its willingness to co-exist with Pyongyang, Only by accepting co-existence with Pyongyang, Washington has an opportunity to persuade Beijing and Moscow to join its efforts with Seoul and Tokyo to denuclearize North Korea. It is time to engage North Korea by accepting it as it is and exercising strategic patience of a different kind to achieve US aims.

Research and Practice of Smart Power Consumption

As a vital part of a smart grid, smart power consumption enables real-time interaction between consumers and the grid. With improved management of the demand side and energy efficiency, smart power consumption plays an important role in emission reduction as well as the economic operation of the power grid. Meanwhile, it enhances the power grid to a larger scale infrastructure by the two-way transmission of energy and information. This paper introduces the research, practice and vision of smart power consumption in China.

Towards Implementation of Smart Grid: An Updated Review on Electrical Energy Storage Systems

A smart grid will require, to greater or lesser degrees, advanced tools for planning and operation, broadly accepted communications platforms, smart sensors and controls, and real-time pricing. The smart grid has been described as something of an ecosystem with constantly communication, proactive, and virtually self-aware. The use of smart grid has a lot of economical and environmental advantages;however it has a downside of instability and unpredictability introduced by distributed generation (DG) from renewable energy into the public electric systems. Variable energies such as solar and wind power have a lack of stability and to avoid short-term fluctuations in power supplied to the grid, a local storage subsystem could be used to provide higher quality and stability in the fed energy. Energy storage systems (ESSs) would be a facilitator of smart grid deployment and a “small amount” of storage would have a “great impact” on the future power grid. The smart grid, with its various superior communications and control features, would make it possible to integrate the potential application of widely dispersed battery storage systems as well other ESSs. This work deals with a detailed updated review on available ESSs applications in future smart power grids. It also highlights latest projects carried out on different ESSs throughout all around the world.

‘Smart Power’ for ‘Sharing’ As an attitude of life

The Interst off Asia Essential Spring will be held from March 17th to 19th,2010,in Hong Kong Convention & Exhibition Centre. During this exhibition,the Interst off Asia Directions trend committee will present its apparel fabric trend forecast for next season's wardrobes,unfolding a story of ‘Smart Power’ of ‘Sharing’.

Distribution System Reliability Analysis for Smart Grid Applications

Reliability of power systems is a key aspect in modern power system planning, design, and operation. The ascendance of the smart grid concept has provided high hopes of developing an intelligent network that is capable of being a self-healing grid, offering the ability to overcome the interruption problems that face the utility and cost it tens of millions in repair and loss. In this work, we will examine the effect of the smart grid applications in improving the reliability of the power distribution networks. The test system used in this paper is the IEEE 34 node test feeder, released in 2003 by the Distribution System Analysis Subcommittee of the IEEE Power Engineering Society. The objective is to analyze the feeder for the optimal placement of the automatic switching devices and quantify their proper installation based on the performance of the distribution system. The measures will be the changes in the reliability system indices including SAIDI, SAIFI, and EUE. In addition, the goal is to design and simulate the effect of the installation of the Distributed Generators (DGs) on the utility’s distribution system and measure the potential improvement of its reliability.

智能电厂安全防范系统联动研究

发电厂中的安防子系统较多,但数据资源共享和关联程度不够,信息孤岛现象显著。本文通过安防子系统联动的研究,实现各安全功能模块的协调和配合,保证各功能模块间的互联互享互通。通过三维可视化平台,利用灵活的联动配置方式,实现对现场复杂环境、重要设备及人员安全的联合防护,有效提升发电厂安全管理效率和应急响应能力。

极简基站智慧能源管理系统研究与应用

极简基站通过BBU集中和电源室外化改造可有效节省基站空调电费和降低基站机房租赁费用。针对极简基站建设模式,本文提出了一种极简基站电源监控系统的解决方案,实现了对室外一体化电源的信息化管控,提升了移动通信网的运维效能。

高速铁路牵引变电所智能化建设方案研究

智能牵引变电所是智能牵引供电系统的核心,由智能化一次系统、广域保护测控系统和智能辅助系统构成。为提高目前在建高铁牵引供电系统智能化水平,探讨并提出了针对其牵引变电所的智能化改造方案。

含电动汽车和5G基站的综合能源系统优化调度

综合能源系统中的风光发电受环境因素影响较大,日间波动剧烈。同时,多种能源之间的相互关系复杂,这使系统的优化调度存在困难。为此,将电动汽车与5G基站作为柔性负荷纳入综合能源系统中,并提出一种基于模糊C均值–多元宇宙优化算法的优化调度方法。在原有电–冷–热能源系统的基础上,引入电动汽车与5G基站的数学模型,分析了两者的可调节潜力,同时考虑经济性与环保性指标,建立了多目标优化调度模型。针对风光发电的不确定性问题,通过模糊C均值算法将风光出力聚类为4种典型场景,并运用多元宇宙优化算法对模型进行仿真验证。结果表明,所提方法可有效缓解综合能源系统的用能压力,使不同场景下的系统总体运行成本平均下降11.0%,污染物排放量平均下降16.4%,系统的整体运行品质有效提高。

基于改进粒子群算法的智能微电网多目标调度

微电网优化调度是一个复杂问题,具有多个目标和非线性特征,仅以运行成本为目标的优化方法可能导致解决方案单一且运行环境受到影响.本文提出了一种经济、环保和创新的微电网调度模型,并对微电网中的微型燃气轮机、风力发电机、光伏发电、柴油发电机和电池等进行了详细的优化研究.构建了一种将运行成本和环境保护成本结合在一起的多目标模型.采用多目标粒子群优化算法(MOPSO)求解,以满足各种系统约束条件.该方法旨在确保微电网的运行在经济和环境方面都能取得最佳效果.这一模型提供了一个更全面和实用的框架,为微电网实现经济性和环境友好性的运行提供了重要指导.

基于边缘计算的电力系统智能终端配置优化方法

针对电力系统大规模智能终端配置中存在的问题,研究基于边缘计算的电力系统智能终端优化配置方法。构建面向边缘计算的智能终端配置模型,设计通信网络模型、计算资源评估与分配机制及配置优化算法,并通过仿真实验验证所提出方法的有效性。结果表明,边缘计算可以发挥终端协同效应,优化终端在电力系统中的配置,大幅降低网络时延,提高资源利用率,为电力系统智能终端的规范化、协同化、优化配置提供新思路。

智能电网中的电力系统规划与优化分析

阐述智能电网技术在发电系统、智能固态表针、电力调度、高速实时通信中的应用。探讨基于智能电网的电力系统优化策略,包括传输网优化、光缆优化,提升电力系统科学化与智能化规划。

我国智能电网产业政策量化分析研究

首先,运用文献计量法,对我国智能电网产业政策的主要发文维度进行了统计分析;其次,构建政策工具与产业链的二维分类框架,量化分析了我国智能电网产业政策分布,探讨了我国智能电网产业政策的主要关注点及其主题分类.研究结果表明:中国智能电网产业整体处于加速转型阶段;产业政策类型以环境型和供给型为主,面向产业链各个环节;政策关注点主要分布在电网的智能化转型、新型电力系统的建设、新能源发电、电力市场等4个方面.

基于智慧电厂的智能寻优系统与工况判别技术研究

传统火电厂机组运行管理存在一些弊端,运行工况判定技术与工况监督技术有待革新,随着自动化技术与智能化技术在电力企业的广泛运用,构建智能寻优系统有助于提升电厂的智能管理效果,进而逐步建立智慧电厂,达到国家要求的寻优管理水平,保证电力机组的稳定运行。智能寻优系统通过高级统计分析、大数据和人工智能等技术提升电力产生效率,进而促进火电厂管理系统的升级。本文对智能寻优系统关键技术进行了阐述,交代了功能框架,对典型电厂应用的效果进行了分析,旨在为智慧电厂寻优系统的建设提供科学的依据。

智能电网技术在电力系统调度中的实践

智能电网技术逐渐成为解决当前电力系统调度问题和挑战的有效工具。该技术为提高电网的稳定性、可靠性和效率提供了创新性的解决方案。先对智能电网技术进行概述,并详细描述了其在电力系统调度中的具体应用方法。结合实际案例分析,实证了此类技术在降低运营成本、提升供电质量和增强系统稳定性等方面所达成的优化目标。对于电力系统调度而言,智能电网技术的应用不仅仅是一种技术创新,更是一种理念的变革,预示着电力行业朝向更加高效、更加可持续的发展模式迈进。

智能配电系统的通信网络建模与性能分析

由于我国的综合能源系统(IES)迅速发展,智能配电系统(SPDS)中集成了越来越多的先进设备和组件,伴随而来的是多样化的信息交互、海量的复杂数据、先进的智能设备使用所带来的技术挑战,因而如何可以在有限的通信网络资源内满足各种应用需求成为一个关键的问题。首先分析了在智能配电系统中的各类应用对于系统通信网络的性能要求,然后介绍了一种基于物联网技术的通信网络架构,并描述了其在智能配电系统中的应用和服务质量QoS要求。进一步地,基于排队论的带宽模型提出了一种考虑QoS的数据采集应用的预测带宽方法。最后,考虑三种典型智能配电站应用场景对网络性能进行分析,通过OPNET仿真,证明所提方法具有合理性与有效性。