Abstract
Controlled charging and discharge of electric vehicles (EVs) in distributed power systems can reduce the peak demand on the grid. This paper presents an energy management strategy (EMS) using an artificial neural network to shave the domestic peak grid load by the coordinated response of distributed energy resource (DER) units including photovoltaic (PV) systems, V2G (vehicle to grid)-capable EVs, and battery energy storage systems (BESS). The developed EMS is implemented on a test system including 15 houses and the realistic load pattern of California, U.S. From the simulation results for this network it is found that an artificial-neural-network controller can effectively coordinate the system to reshape the load curve and shave the domestic peak loading by up to 77%.
Original language | English |
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Title of host publication | 2017 IEEE International Conference on Industrial Technology (ICIT) |
Subtitle of host publication | proceedings |
Place of Publication | Piscataway, NJ |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 586-591 |
Number of pages | 6 |
ISBN (Electronic) | 9781509053209, 9781509053193 |
ISBN (Print) | 9781509053216 |
DOIs | |
Publication status | Published - 2017 |
Event | 2017 IEEE International Conference on Industrial Technology, ICIT 2017 - Toronto, Canada Duration: 23 Mar 2017 → 25 Mar 2017 |
Other
Other | 2017 IEEE International Conference on Industrial Technology, ICIT 2017 |
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Country/Territory | Canada |
City | Toronto |
Period | 23/03/17 → 25/03/17 |
Keywords
- artificial neural network
- demand management
- peak-load management
- peak shave