Design and Implementation of Energy Management System for Energy Hub in Smart Grid

2013 ◽  
Vol 12 (18) ◽  
pp. 4797-4804 ◽  
Author(s):  
Wei Pei ◽  
Wei Deng ◽  
Ziqi Shen ◽  
Zhenxing Zhao ◽  
Jiarui Wang
Keyword(s):  
Smart Grid ◽  
Energy Management ◽  
Management System ◽  
Energy Management System ◽  
Energy Hub ◽  
Design And Implementation

scholarly journals Energy management system for residential buildings based on fuzzy logic: design and implementation in smart‐meter

2020 ◽  
Vol 3 (2) ◽  
pp. 254-266
Author(s):  
Adrian Chojecki ◽  
Michał Rodak ◽  
Arkadiusz Ambroziak ◽  
Piotr Borkowski
Keyword(s):  
Fuzzy Logic ◽  
Energy Management ◽  
Management System ◽  
Residential Buildings ◽  
Energy Management System ◽  
Logic Design ◽  
Smart Meter ◽  
Design And Implementation

scholarly journals Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2010 ◽  
Author(s):  
Sunyong Kim ◽  
Hyuk Lim
Keyword(s):  
Reinforcement Learning ◽  
Smart Grid ◽  
Energy Management ◽  
Management System ◽  
Transition Probability ◽  
Storage System ◽  
Energy Storage System ◽  
Energy Costs ◽  
Energy Management System ◽  
Management Algorithm

A smart grid facilitates more effective energy management of an electrical grid system. Because both energy consumption and associated building operation costs are increasing rapidly around the world, the need for flexible and cost-effective management of the energy used by buildings in a smart grid environment is increasing. In this paper, we consider an energy management system for a smart energy building connected to an external grid (utility) as well as distributed energy resources including a renewable energy source, energy storage system, and vehicle-to-grid station. First, the energy management system is modeled using a Markov decision process that completely describes the state, action, transition probability, and rewards of the system. Subsequently, a reinforcement-learning-based energy management algorithm is proposed to reduce the operation energy costs of the target smart energy building under unknown future information. The results of numerical simulation based on the data measured in real environments show that the proposed energy management algorithm gradually reduces energy costs via learning processes compared to other random and non-learning-based algorithms.

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