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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2300438


Multiagent evaluation for energy management by Practically scaling α-rank


Author(s):  Yiyun SUN, Senlin ZHANG, Meiqin LIU, Ronghao ZHENG, Shanling DONG, Xuguang LAN

Affiliation(s):  National Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   12110066@zju.edu.cn, slzhang@zju.edu.cn, liumeiqin@zju.edu.cn

Key Words:  Energy management, Multiagent deep reinforcement learning, Strategy evaluation, Power grid system


Yiyun SUN, Senlin ZHANG, Meiqin LIU, Ronghao ZHENG,Shanling DONG, Xuguang LAN. Multiagent evaluation for energy management by Practically scaling α-rank[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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author="Yiyun SUN, Senlin ZHANG, Meiqin LIU, Ronghao ZHENG,Shanling DONG, Xuguang LAN",
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number="-1",
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year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300438"
}

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%T Multiagent evaluation for energy management by Practically scaling α-rank
%A Yiyun SUN
%A Senlin ZHANG
%A Meiqin LIU
%A Ronghao ZHENG
%A Shanling DONG
%A Xuguang LAN
%J Journal of Zhejiang University SCIENCE C
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%D 1998
%I Zhejiang University Press & Springer
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A1 - Xuguang LAN
J0 - Journal of Zhejiang University Science C
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Abstract: 
Currently, decarbonization has become an emerging trend in the power system arena. However, the increasing amounts of PV units distributed into a distribution network may result in voltage issues, providing challenges for voltage regulation across a large-scale power grid network. RL-based intelligent control of smart inverters and other smart building energy management (EM) systems can be leveraged to alleviate these issues. To achieve the best EM strategy for building microgrids in a power system, this paper presents two large-scale multiagent strategy evaluation methods to preserve building owner comfort while pursuing system-level objectives. The EM problem is formulated as a general-sum game to optimize the benefits at both the system and building levels. The α-rank algorithm can solve the general-sum game and guarantee the ranking theoretically, but it is limited by the interaction complexity and hardly applies to the practical power system. A new evaluation algorithm (TcEval) is proposed by practically scaling the α-rank algorithm through a tensor complement to reduce interaction complexity. Then, considering the noise prevalent in practice, a noise processing model with domain knowledge is built to calculate the strategy payoffs, and, thus, the TcEval-AS algorithm is proposed when noise exists. Both the evaluation algorithms developed in this paper greatly reduce the interaction complexity compared with existing approaches, including ResponseGraphUCB (RG-UCB) and αInformationGain (α-IG). Finally, the effectiveness of the proposed algorithms is verified in the EM case with realistic data.

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