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人工智能拓展火灾安全研究的进展
From The Sky To The Sea
清华大学材料科学与工程研究院《材料科学论坛》:基于拓扑缺陷理论的轻合金组织设计新...
Quantum information processing based on bosonic modes
报告题目:
Robustly Optimal Operation for Active Distribution Networks
 报告人:
Dr. Cuo Zhang
Cuo Zhang is a research associate with the University of New 
South Wales, Australia and a chief investigator of ARC 
Research Hub for Integrated Energy Storage Solutions.
报告时间:
2019-12-23 10:00
报告地点:
西主楼 3-102
主办单位:
电机系
  简介:

Distributed energy resources (DERs) with nature of flexible allocation bring significant technical and economic benefits to modern power systems. For distribution networks, high penetration of distributed generation with advanced energy storage technologies and demand response strategies helps them evolve from conventional passive systems to active ones, i.e. active distribution networks (ADNs). ADNs control a combination of DERs, providing distribution system operators with possibility of managing electricity flows flexibly and optimally.

Renewable distributed generators such as solar photovoltaics generate eco-friendly, low-cost and sustainable energy to ADNs. However, renewable energy is intermittent, volatile and non-dispatchable. Together with varying loads, the renewable energy brings high uncertainty to the operation for ADNs, impacting power quality and economic benefits. Considering the uncertainty, this lecture focuses on ADN operating robustness and develops a series of approaches to achieve robustly optimal operation which is immune to the uncertainty. 

Firstly, the lecture will introduce the research background of DERs and ADNs as well as challenges to be addressed. Particularly, the development status of Australian renewable energy and future trends will be also involved. Then, the lecture will present the latest research works on robust operation approaches for ADNs such as multi-stage and hierarchically coordinated operation. Last, considering high uncertainty in the networks, the lecture will focus on the robust optimization methods such as two-stage, multi-objective, distributed and probability-weighted robust optimization and their corresponding solution algorithms.

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