中国电力 ›› 2018, Vol. 51 ›› Issue (10): 26-31.DOI: 10.11930/j.issn.1004-9649.201808046

• 智能发电关键技术专栏 • 上一篇    下一篇

平行智能与智慧能源:全面融合人因的社会能源技术

张俊1,2,3, 王飞跃1,3, 林洁瑜2   

  1. 1. 中国科学院自动化研究所复杂系统管理与控制国家重点实验室, 北京 100190;
    2. 武汉大学 电气工程学院, 湖北 武汉 430072;
    3. 青岛智能产业技术研究院, 山东 青岛 266071
  • 收稿日期:2018-08-09 出版日期:2018-10-05 发布日期:2018-10-12
  • 通讯作者: 王飞跃(1961-),男,通信作者,博士,教授,从事为智能系统和复杂系统的建模、分析与控制研究,E-mail:feiyue.wang@ia.ac.cn
  • 作者简介:张俊(1981-),男,博士,教授,从事智能系统、人工智能知识自动化及其在智能电力和能源系统中的应用研究,E-mail:jun.zhang@qaii.ac.cn
  • 基金资助:
    国家电网公司科技项目(基于人工智能的调控操作决策辅助分析技术研究及示范应用)(SGTJDK00DWJS1700060)。

Parallel Intelligence and Smart Energy: Social Energy Technology with Human Factors Incorporated

ZHANG Jun1,2,3, WANG Feiyue1,3, LIN Jieyu2   

  1. 1. The State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;
    2. School of Electrical Engineering, Wuhan University, Wuhan 430072, China;
    3. Qingdao Academy of Intelligent Industries, Qingdao 266071, China
  • Received:2018-08-09 Online:2018-10-05 Published:2018-10-12
  • Supported by:
    This work is supported by Science and Technology Project of State Grid Corporation of China (No.SGTJDK00DWJS1700060).

摘要: 中国能源发展转型,风电、光伏等可再生能源渗透率提高,使电力系统日益复杂并带来一系列挑战。基于能源5.0的思想设计了融合人因的电力系统全管控理念,结合电力生产、传输和供应3个环节的特点,运用平行感知与虚拟人工系统技术来实现发电环节的智能协调,运用语音识别和语义解析技术实现对输电环节的自动调配和辅助决策,运用社会能源和平行智能技术实现了对配电环节的优化与交互。通过对于上述几个环节的探讨和研究,最终实现电力系统的人机交互、自学习,实现对电网日常操作和故障处理的辅助决策和判断。

关键词: 人机交互, 平行智能, 虚拟人工系统, 辅助决策, 发输配电, 智能发电, 智能电网

Abstract: With the integration of new energy resources and the increased power demands for both quantity and quality, Chinese power grid faces a series of new challenges. Within the framework of Energy 5.0, this article provides our perspective on the social energy technology with human factors fully incorporated. This article discusses the technical framework and application examples in the power supply side, transmission side, distribution side and demand side, specifically, including the intelligent power generation coordination using parallel perception and virtual system technology, the intelligent auxiliary decision-making for power dispatch using speech recognition and semantic analysis, and the demand side management using social energy and parallel intelligence technology. We aim to equip power systems with the capabilities of human-system interaction, artificial intelligence aided auxiliary decision making, self-learning, and demand side management with self-adaptation to volatile renewable energy generation.

Key words: human-system interaction, parallel intelligence, virtual artificial system, auxiliary decision-making, power transmission and distribution, smart power generation, smart power grid

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