DocumentCode :
88710
Title :
Smart Home in Smart Microgrid: A Cost-Effective Energy Ecosystem With Intelligent Hierarchical Agents
Author :
Bingnan Jiang ; Yunsi Fei
Author_Institution :
Dept. of Electr. & Comput. Eng., Northeastern Univ., Boston, MA, USA
Volume :
6
Issue :
1
fYear :
2015
fDate :
Jan. 2015
Firstpage :
3
Lastpage :
13
Abstract :
Smart grid is advancing power grids significantly, with higher power generation efficiency, lower energy consumption cost, and better user experience. Microgrid utilizes distributed renewable energy generation to reduce the burden on utility grids. This paper proposes an energy ecosystem; a cost-effective smart microgrid based on intelligent hierarchical agents with dynamic demand response (DR) and distributed energy resource (DER) management. With a dynamic update mechanism, DR automatically adapts to users´ preference and varying external information. The DER management coordinates operations of micro combined heat and power systems ($boldsymbol {mu }$ CHPs), and vanadium redox battery (VRB) according to DR decisions. A two-level shared cost-led $boldsymbol {mu }$ CHPs management strategy is proposed to reduce energy consumption cost further. VRB discharging is managed to be environment-adaptive. Simulations and numerical results show the proposed system is very effective in reducing the energy consumption cost while satisfying user´s preference.
Keywords :
cogeneration; cost reduction; distributed power generation; ecology; flow batteries; home automation; power consumption; power generation economics; power system management; smart power grids; μCHP; DER management; DR; VRB; cost-effective energy ecosystem; cost-effective smart microgrid; distributed energy resource management; distributed renewable energy generation; dynamic demand response; dynamic update mechanism; energy consumption cost reduction; intelligent hierarchical agent; micro combined heat and power system; power generation efficiency; smart home; vanadium redox battery; Cogeneration; Communities; Fuels; Microgrids; Optimization; Resistance heating; Wind power generation; Demand response (DR); Q-learning; distributed energy resources (DER); microgrid; particle swarm optimization (PSO); smart grid;
fLanguage :
English
Journal_Title :
Smart Grid, IEEE Transactions on
Publisher :
ieee
ISSN :
1949-3053
Type :
jour
DOI :
10.1109/TSG.2014.2347043
Filename :
6912013
Link To Document :
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