综合智慧能源管理系统架构分析与研究

能源互联网是利用"互联网+"技术手段实现能源和互联网深度融合的新型能源利用模式,综合能源管理是能源互联网的重要表现形式,区域能源管理和智能优化调度系统是建设与运行能源互联网的重要且关键一环。未来的综合智慧能源管理系统是以大数据、物联网、移动互联网技术等为支撑的大型开放式能源管理服务平台,采用分层分布式结构,借助云数据中心,对电、热(冷)、气等多种能源的生产、输送、消费等各类信息进行智能处理,对整个能源系统进行监控和管理。其主要特点包括创新能源生产模式,创新需求侧消费模式,实现能源供需互动。综合能源管理系统由能源管理平台、通信系统、终端三部分组成,管理模式可分为总管、协管以及总管与协管相结合的复合模式等三种,可实现能源最优调度、市场主体之间的互动、综合监视、用户用能分析等多种功能。     

关于智能电网发展的几点思考

  [目的]  为了理清智能电网作为新一代能源体系核心平台的相关概念和发展思路。  [方法]  利用文献综述法梳理了微电网、泛能网和能源互联网的概念及其与智能电网的关系。结合我国的能源发展现状和战略需求,探讨了我国智能电网的建设思路和重点。  [结果]  智能电网是传统电力系统和现代信息技术的深度融合,而能源互联网是智能电网和互联网思维模式、技术的深度融合。智能电网是能源互联网的基础平台,微电网、泛能网、智能电网和能源互联网都以实现更加清洁、高效、灵活的用能为目标。  [结论]  智能电网建设应在强调高压侧智能化控制的同时,同步建设智能配电网,鼓励以多能互补为核心的泛能网的发展。     

泛能网:推动能源革命的产业实践

<正>泛能网是利用能源和信息技术,将能源网、物联网和互联网进行高效集成,形成能源生产者和消费者信息对称、平等参与、自由多边的"互联网能源"。"泛能网是利用能源和信息技术,将能源网、物联网和互联网进行高效集成,形成能源生产者和消费者信息对称、平等参与、自由多边的‘互联网能源’。依托泛能网的三层网络最终形成可再生能源优先化石能源补充、分布式为主集中式为辅、供需互动智能用能的现     

综合能源系统的发展历程、典型形态及未来趋势

基于能源互联网"三横四纵"的概念框架,该文按能量利用特征将综合能源系统的发展历程划分为孕育、概念、起航和升华4个阶段,并梳理了各阶段对应的关键技术及典型形态,即:1)孕育阶段,以基于能的梯级利用的天然气冷热电联产系统为主;2)概念阶段,以基于能的因地制宜的分布式能源系统为主;3)起航阶段,以基于能的多能互补的综合能源系统为主;4)升华阶段,以基于能的互联互济的能源互联网为主。可看出,综合能源系统是在能源转型驱动下逐渐形成的"冷热电气"多能源供应态势,并在互联网技术下逐渐体现出能的"商品化"共享特征,相关形态的规划、标准以及示范工程也已先行先试。最后,从熵的角度阐释综合能源系统能量流和信息流高度融合的机制,印证泛在能源网成为综合能源系统的未来趋势。     

能源互联网技术实现路径及实践

能源互联网是在新时代背景下互联网与能源系统相结合的产物。本文总结了能源互联网以能源微网、储能技术、综合能源系统为代表的三种技术实现路径,提出了适合我国能源资源禀赋的能源互联网架构,介绍了神华集团在能源互联网领域以分布式光伏发电构建的能源微网、以氢能与电池储能耦合、以传统燃煤与生物质能、分布式光伏发电构建的综合能源系统等3个实践案例,可为我国未来能源互联网的建设提供有益的借鉴。     

合作共融 加快能源互联网创新发展

正6月28日,"2018首届长三角区域能源互联网创新发展论坛"上,中国工程院翁史烈院士就能源互联网有关问题作了讲话。他指出,当前,能源互联网较正规、权威性的描述有三种。第一种是智能电网与新能源的融合;第二种是大电网、分布式能源网、热网、冷网、天然气网等与信息互联网的深度融合,形成智慧能源系统;第三种是运用信息互联网的思维与理念,在对能源构架进行改造和改革的基础上,形成能源互联网,使能源     

能源互联网技术的现状及发展趋势研究

摘要： 以能源互联网技术为代表的第三次工业革命蓬勃发展,其核心研究领域从智能电网、能源互联网进而拓展到全球能源互联网。首先对能源互联网技术的国内外研究现状进行了分析,在此基础预测了能源互联网技术的发展趋势,探讨了该领域的发展重点,以期为推动全球能源互联网的发展提供参考。     

探析多能协同下的综合能源系统协调调度策略

随着新能源和化石能源互补的"混合能源时代"的来临,传统的化石能源时代难以满足经济发展的需求,由此也引发人们对综合能源系统的关注和重视,在互联网支撑的智能电网前提下,衍生出以供电、供热、供气等系统相集成的综合能源系统,要重点研究多能互补和能量梯级利用的IES优化调度模型和策略,探析其与上级电网互动的IES综合需求响应模型和策略,更好地实现多能互补及能量的梯级利用,促进各能量管理系统之间的协调与互动。     

区块链在能源互联网的应用和上海发展建议

当今,在能源互联网的研究和应用中,区块链技术受到日益重视。本文基于能源互联网当前发展瓶颈的分析,研究提出上海能源区块链技术发展的三个阶段路线图,构建新一代智能综合能源管理和服务系统,以期项目具有经济效益,兼顾用户体验,实现经济社会的可持续发展,推动我国加速达成碳中和的目标。     

国际能源互联网推进历程与启示

能源互联网是一种互联网与能源生产、传输、存储、消费以及能源市场深度融合的能源产业发展新形态,有助于建设安全、清洁、高效、可持续的能源系统。本文梳理了国内外对能源互联网内涵的理解,美国与德国政府对能源互联网及智能电网的推进历程,并在此基础上提出我国应抓住能源互联网这一产业发展新形态,鼓励各类主体探索创新,实现能源和互联网的融合发展。     

Electric vehicle charging in stochastic smart microgrid operation with fuel cell and RES units

Plug-in electric vehicles increasingly augment their share in the global market as they appear to be an economic and emission-free alternative to modern means of transportation. As their presence strengthens, ways that will ensure economic charge along with uninterrupted grid operation are necessary to be found. This paper aims to approach the economic optimization problem that includes several Electric Vehicles (EVs) within a Low Voltage (LV) network comprising various Distributed Energy Resources (DER) as fuel cell, Renewable Energy Sources (RES), (photovoltaics, wind turbine) etc. via a scenario based simulation. The purpose is to investigate the main variables of the grid, such as its operating cost, charging patterns, power injection from the upstream network, resulting from the coordinated control of DER in Smart Microgrid operation in conjunction to the flexible load the controlled EV charging introduces. The base case study is that of absence of EVs, and therefore the demand is met only by the upstream network and the DER units. Subsequently, EVs are introduced as controllable loads and finally as dispatchable storage units incorporating a Vehicle to Grid (V2G) capability to the Smart Microgrid. Furthermore, the problem is not tackled deterministically and although forecasts for all network parameters are assumed to be known, forecasting errors and stochastic driver patterns cannot be ignored. Thus, for each imposed policy, a scenario based approach is implemented to determine operating cost in various cases along to DER utilization and the effect EVs bear on these results.     

A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

The new generation of artificial intelligence (AI), called AI 2.0, has recently become a research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and electric power system (Smart EEPS). In AI 2.0, machine learning (ML) forms a typical representative algorithm category used to achieve predictions and judgments by analyzing and learning from massive amounts of historical and synthetic data to help people make optimal decisions. ML has preliminarily been applied to the Smart Grid (SG) and Energy Internet (EI) fields, which are important Smart EEPS representatives. AI 2.0, especially ML, is undergoing a critical period of rapid development worldwide and will play an essential role in Smart EEPS. In this context, this study, combined with the emerging SG and EI technologies, takes the typical representative of AI 2.0—ML—as the research objective and reviews its research status in the operation, optimization, control, dispatching, and management of SG and EI. The paper focuses on introducing and summarizing the mainstream uses of seven representative ML methods, including reinforcement learning, deep learning, transfer learning, parallel learning, hybrid learning, adversarial learning, and ensemble learning, in the SG and EI fields. In this survey, we begin with an introduction to these seven types of ML methods and then systematically review their applications in Smart EEPS. Finally, we discuss ML development under the big data thinking and offer a prospect for the future development of AI 2.0 and ML in Smart EEPS. We conduct this survey intended to arouse the interest and excitement of experts and scholars in the EEPS industry and to look ahead to efforts that jointly promote the rapid development of AI 2.0 in the Smart EEPS field.     

分布式供能系统协同电网调峰是历史的必然

基于工程项目的经济分析提出的天然气分布式冷热电联供能源系统DES/CCHP昼开夜停协同电网调峰、实现互利双赢的集成创新,展望了可再生能源为主的互联网＋智慧能源时代能源系统的新架构和电力供需时变性的新挑战,论证了作为能源互联网基础单元的16 h/d运行的DES/CCHP和带储电的光伏为主的DES将成分别成为电网昼夜调峰和随机调峰的主力,并指出电力交易的市场化改革、与DES同步的微电网建设和配电网改造,以及建立在互联网、大数据、云计算和人工智能的基础上SG和IEN系统的建设是实现电力供需平衡的决定因素。     

Prospect and key techniques of Global Energy Interconnection Zhangjiakou Innovation Demonstration Zone

In the Background of implementing innovation-driven development strategy and building Global Energy Interconnection, the necessity of building Global Energy Interconnection Zhangjiakou Innovation Demonstration Zone for stimulating economic growth, promoting social development and supporting 2022 Winter Olympics are discussed by analyzing the location advantages of Zhangjiakou and the characteristics of renewable energy in Zhangjiakou. Solutions are put forward in aspects of renewable energy integration in National Wind/Solar/Storage/Transmission Pilot Project, VSC-HVDC transmission of renewable energy and demonstration utilization of renewable energy in the Olympics zone, which could be a reference for problems of large-scale renewable energy in secure integration, outgoing transmission and flexible consumption. And replicable experience for building Global Energy Interconnection will be provided by conducting ± 500kV VSC-HVDC Power Grid Demonstration Project, Virtual Synchronous Generator Demonstration Project, Flexible Substation and AC/DC Power Distribution Network Demonstration Project, EV Battery Secondary Utilization Energy Storage Demonstration Project, Smart Grid Demonstration Project of Low Carbon Winter Olympics and other demonstration projects.     

能源互联网内多类设备协同运行的能量管理系统软件设计

针对能源互联网中的海量分布式设备和电动汽车的需求,利用C#和Java编程语言设计并开发面向能源互联网的能量管理系统。该系统采用典型B/S 架构,以Asp.Net动态网页开发技术和数据库技术为核心,由云端后台算法、云端服务器的MySQL数据库、前端的网页三大部分构成。详细介绍了软件平台的系统框架,分为上下两层。其中,上层为日前-实时协同的多时间尺度能量管理系统模块,下层包含分布式设备协调控制子系统和电动汽车协调控制子系统。最后,通过仿真算例分析,能量管理系统能对海量的分布式设备和电动汽车进行优化调度,验证了该软件的可移植性、有效性和实用性。     

低碳高效安全可靠的智能电网

21世纪电力供应将面临越来越多的挑战,未来电力技术急需进一步的发展。由于环境压力、能源短缺,人类社会向低碳经济发展,欧美提出了智能电网的方案。本文通过讨论智能电网的产生背景、概念及其物理构成,指出低碳高效安全可靠的智能电网是解决当前电网面临挑战的一个有效方法,智能电网的发展将满足未来供电的竞争性、可持续发展性、安全性等方面的要求,最后总结提出了智能电网的发展应上升为国家能源战略。     

Smart Grid as advanced technology enabler of demand response

Numerous papers and articles presented worldwide at different conferences and meetings have already covered the goals, objectives, architecture, and business plans of Smart Grid. The number of electric utilities worldwide has followed up with demonstration and deployment efforts. Our initial assumptions and expectations of Smart Grid functionality have been confirmed. We have indicated that Smart Grid will fulfill the following goals: enhance customer service, improve operational efficiency, enhance demand response and load control, transform customer energy use behavior, and support new utility business models. For the purpose of this paper, we shall focus on which of those abovementioned Smart Grid functionalities are going to facilitate the ever-growing need for enhanced demand response and load control.     

中国特色的低碳经济、能源、电力之路初探

中国作为发展中大国,探索一条适合我国国情的低碳经济-能源-电力的发展路径是至关重要的。在过去30年中,我国在节能方面取得了巨大成就,可以概括为具有中国特色的能源使用管理,它是低碳能源的重要组成部分。综合资源战略规划与智能电网构成低碳电力。它们将在中国的低碳经济中发挥重要作用。本文结合中国国情,在低碳经济(低碳能源与低碳电力)的模型基础上,展望了未来20年我国经济发展前景、能源的需求、能源消费结构、非化石能源的比重、能耗-电耗水平、CO2排放强度,以及经济发展对电力的需求、电源的结构、清洁能源的发电比重,能效电厂及其发电量(终端电力用户节约的电量)等,分析了今后发展应关注的问题。     

“能源转型”意味着什么

许多专家呼吁中国“能源转型”,由以煤炭为主的能源结构向多元化能源结构转变;由能源粗放型利用向能源高效利用转变;由直接大量燃用煤炭向煤炭清洁利用转变;由直接管理企业向市场引导企业转变。为此,必须建立综合能源战略,转变职能,完善能源法规政策和市场监督机制。     

Evaluation of energy efficiency of various biogas production and utilization pathways

The energy efficiency of different biogas systems, including single and co-digestion of multiple feedstock, different biogas utilization pathways, and waste-stream management strategies was evaluated. The input data were derived from assessment of existing biogas systems, present knowledge on anaerobic digestion process management and technologies for biogas system operating conditions in Germany. The energy balance was evaluated as Primary Energy Input to Output (PEIO) ratio, to assess the process energy efficiency, hence, the potential sustainability. Results indicate that the PEIO correspond to 10.5–64.0% and 34.1–55.0% for single feedstock digestion and feedstock co-digestion, respectively. Energy balance was assessed to be negative for feedstock transportation distances in excess of 22 km and 425 km for cattle manure and for Municipal Solid Waste, respectively, which defines the operational limits for respective feedstock transportation. Energy input was highly influenced by the characteristics of feedstock used. For example, agricultural waste, in most part, did not require pre-treatment. Energy crop feedstock required the respect cultivation energy inputs, and processing of industrial waste streams included energy-demanding pre-treatment processes to meet stipulated hygiene standards. Energy balance depended on biogas yield, the utilization efficiency, and energy value of intended fossil fuel substitution. For example, obtained results suggests that, whereas the upgrading of biogas to biomethane for injection into natural gas network potentially increased the primary energy input for biogas utilization by up to 100%; the energy efficiency of the biogas system improved by up to 65% when natural gas was substituted instead of electricity. It was also found that, system energy efficiency could be further enhanced by 5.1–6.1% through recovery of residual biogas from enclosed digestate storage units. Overall, this study provides bases for more detailed assessment of environmental compatibility of energy efficiency pathways in biogas production and utilization, including management of spent digestate.