DOI:10.1007/s40565-016-0257-9 |
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Congestion management with demand response consideringuncertainties of distributed generation outputs and market prices |
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Net amount: 1664 |
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Author:
Linna NI1, Fushuan WEN2, Weijia LIU1, Jinling MENG3,
Guoying LIN3, Sanlei DANG3
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Author Affiliation:
1. School of Electrical Engineering, Zhejiang University,
Hangzhou 310027, Zhejiang, China;
2. Department of Electrical and Electronic Engineering,
Universiti Teknologi Brunei, Bandar Seri Begawan BE1410,
Brunei;
3. Electric Power Research Institute of Guangdong Power Grid
Co. Ltd., Guangzhou 510600, Guangdong, China
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Foundation: |
National
Basic Research Program of China (973 Program) (No.
2013CB228202), National Natural Science Foundsation of China
(No. 51477151), Specialized Research Fund for the Doctoral Program
of Higher Education (No. 20120101110112), and a Project by China
Southern Power Grid Company (No. K-GD2014-192). |
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Abstract: |
In recent years, much attention has been devoted
to the development and applications of smart grid technologies,
with special emphasis on flexible resources such as
distributed generations (DGs), energy storages, active loads,
and electric vehicles (EVs). Demand response (DR) is
expected to be an effective means for accommodating the
integration of renewable energy generations and mitigating
their power output fluctuations. Despite their potential
contributions to power system secure and economic operation,
uncoordinated operations of these flexible resources
may result in unexpected congestions in the distribution
system concerned. In addition, the behaviors and impacts of
flexible resources are normally highly uncertain and complex
in deregulated electricity market environments. In this
context, this paper aims to propose a DR based congestion
management strategy for smart distribution systems. The
general framework and procedures for distribution congestion
management is first presented. A bi-level optimization
model for the day-ahead congestion management based on
the proposed framework is established. Subsequently, the
robust optimization approach is introduced to alleviate
negative impacts introduced by the uncertainties of DG
power outputs and market prices. The economic efficiency
and robustness of the proposed congestion management
strategy is demonstrated by an actual 0.4 kV distribution
system in Denmark. |
Keywords: |
Distribution system, Congestion management,
Demand response, Load aggregators, Uncertainty, Robust
optimization, Bi-level linear programming |
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Online Time:2017/01/09 |
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