Proactive energy management for smart city with edge computing using meta-reinforcement learning scheme
draft-hongcs-t2trg-pem-00

Document Type Active Internet-Draft (individual)
Authors Choong Hong  , Md. Munir  , Kitae Kim  , Seok Kang 
Last updated 2020-10-15
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T2TRG                                               Hong, Choong Seon
Internet-Draft                                   Kyung Hee University
Intended status: Standards Track                    Munir, Md. Shirajum 
Expires: August 15, 2022                         Kyung Hee University
                                                      Kitae Kim
                                                      Kyung Hee University
                                       Seok Won Kang
                                         Kyung Hee University

Proactive energy management for smart city with edge 
computing using meta-reinforcement learning scheme
                        draft-hongcs-t2trg-pem-00

Abstract

Renewable energy enabled sustainable energy management ensures 
a high degree of reliability in order to fulfill the energy 
demand of a smart city. In such case, renewable energy 
generation is random over time and also energy consumption of 
smart city users’ is nondeterministic in nature. Therefore, to   
ensure sustainable energy management for smart city, 
a proactive energy management scheme should be integrated into 
smart city network. In which, edge node should be considered as 
local computational unit for each energy user and microgrid 
controller should be played the role of energy management decision
aggregator. As a result, proactive energy management scheme
not only overcomes the challenges of renewable energy-aware 
demand scheduling but also establishes a strong relationship 
for both energy generation and consumption over time. 
Therefore, a distributed mechanism is considered, where the edge 
node for executing local agent to determine an individual 
users’ policy with respect to energy consumption and renewable 
energy generation (users’ own sources). On the other hand, 
microgrid controller determines meta-policy through a meta-agent 
with Recurrent Neural Network (RNN). Since a meta-agent accepts 
local policy as an input with historical observations, which 
ensures fast and efficient execution of proactive energy management
for the smart city.

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Internet-Draft  Proactive energy management for smart city  October 2020

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Table of Contents

 1.  Introduction . . . . . . . . . . . . . . . . . . . .. . . . . .  2
      1.1.  Terminology and Requirements Language  . . . . . . . . .  2
 2.  Energy Data Flow Management . . . . . . . . . . . . . . . . . .  3
      2.1.  Energy Data Flow . . . . . . . . . . . . . .. . . .  . .  4
      2.2.  Energy Data Format . . . . . . . . . . . . . . . . . . .  5
 3.  Proactive Energy Management for Smart City . . . . . . . .  . .  6
      3.1   Meta-Reinforcement Learning with Edge Computing . .  . .  7
      3.2.  Process flow of proactive energy management. . . . . . . .8
 4.  IANA Considerations  . . . . . . .. . . . .  . . . . . . . . . . 8
 5.  Security Considerations  . . . . . . . . . . . .  . . .  . . . . 9
 6.  References . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
 6.1.  Normative References . . . . . . . . . . . . . . . . . . . . . 9
 6.2.  Informative References . . . . . .. . . . .  . . . . . . . . . 9
 Authors' Addresses . . . . . . . . . . . . . . . . . . . . .. . . . 10

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1.  Introduction

In the modern development arena, smart city, and renewable energy 
are indispensable toward the ecological growth of urban technology 
to enable sustainable smart services [a,b]. A microgrid is 
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