Electrical vehicle grid integration for demand response in distribution networks using reinforcement learning
Abstract Most utilities across the world already have demand response (DR) programs in place to incentivise consumers to reduce or shift their electricity consumption from peak periods to off‐peak hours usually in response to financial incentives. With the increasing electrification of vehicles, eme...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-12-01
|
| Series: | IET Electrical Systems in Transportation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/els2.12030 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832559661203587072 |
|---|---|
| author | Fayiz Alfaverh Mouloud Denaï Yichuang Sun |
| author_facet | Fayiz Alfaverh Mouloud Denaï Yichuang Sun |
| author_sort | Fayiz Alfaverh |
| collection | DOAJ |
| description | Abstract Most utilities across the world already have demand response (DR) programs in place to incentivise consumers to reduce or shift their electricity consumption from peak periods to off‐peak hours usually in response to financial incentives. With the increasing electrification of vehicles, emerging technologies such as vehicle‐to‐grid (V2G) and vehicle‐to‐home (V2H) have the potential to offer a broad range of benefits and services to achieve more effective management of electricity demand. In this way, electric vehicles (EV) become distributed energy storage resources and can conceivably, in conjunction with other electricity storage solutions, contribute to DR and provide additional capacity to the grid when needed. Here, an effective DR approach for V2G and V2H energy management using Reinforcement Learning (RL) is proposed. Q‐learning, an RL strategy based on a reward mechanism, is used to make optimal decisions to charge or delay the charging of the EV battery pack and/or dispatch the stored electricity back to the grid without compromising the driving needs. Simulations are presented to demonstrate how the proposed DR strategy can effectively manage the charging/discharging schedule of the EV battery and how V2H and V2G can contribute to smooth the household load profile, minimise electricity bills and maximise revenue. |
| format | Article |
| id | doaj-art-e0b00a492a6249038aab0e9e1bf73a3d |
| institution | Kabale University |
| issn | 2042-9738 2042-9746 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Electrical Systems in Transportation |
| spelling | doaj-art-e0b00a492a6249038aab0e9e1bf73a3d2025-02-03T01:29:38ZengWileyIET Electrical Systems in Transportation2042-97382042-97462021-12-0111434836110.1049/els2.12030Electrical vehicle grid integration for demand response in distribution networks using reinforcement learningFayiz Alfaverh0Mouloud Denaï1Yichuang Sun2School of Physics, Engineering and Computer Science University of Hertfordshire Hatfield UKSchool of Physics, Engineering and Computer Science University of Hertfordshire Hatfield UKSchool of Physics, Engineering and Computer Science University of Hertfordshire Hatfield UKAbstract Most utilities across the world already have demand response (DR) programs in place to incentivise consumers to reduce or shift their electricity consumption from peak periods to off‐peak hours usually in response to financial incentives. With the increasing electrification of vehicles, emerging technologies such as vehicle‐to‐grid (V2G) and vehicle‐to‐home (V2H) have the potential to offer a broad range of benefits and services to achieve more effective management of electricity demand. In this way, electric vehicles (EV) become distributed energy storage resources and can conceivably, in conjunction with other electricity storage solutions, contribute to DR and provide additional capacity to the grid when needed. Here, an effective DR approach for V2G and V2H energy management using Reinforcement Learning (RL) is proposed. Q‐learning, an RL strategy based on a reward mechanism, is used to make optimal decisions to charge or delay the charging of the EV battery pack and/or dispatch the stored electricity back to the grid without compromising the driving needs. Simulations are presented to demonstrate how the proposed DR strategy can effectively manage the charging/discharging schedule of the EV battery and how V2H and V2G can contribute to smooth the household load profile, minimise electricity bills and maximise revenue.https://doi.org/10.1049/els2.12030energy storagedemand side managementpower gridssecondary cellsenergy management systemspower consumption |
| spellingShingle | Fayiz Alfaverh Mouloud Denaï Yichuang Sun Electrical vehicle grid integration for demand response in distribution networks using reinforcement learning IET Electrical Systems in Transportation energy storage demand side management power grids secondary cells energy management systems power consumption |
| title | Electrical vehicle grid integration for demand response in distribution networks using reinforcement learning |
| title_full | Electrical vehicle grid integration for demand response in distribution networks using reinforcement learning |
| title_fullStr | Electrical vehicle grid integration for demand response in distribution networks using reinforcement learning |
| title_full_unstemmed | Electrical vehicle grid integration for demand response in distribution networks using reinforcement learning |
| title_short | Electrical vehicle grid integration for demand response in distribution networks using reinforcement learning |
| title_sort | electrical vehicle grid integration for demand response in distribution networks using reinforcement learning |
| topic | energy storage demand side management power grids secondary cells energy management systems power consumption |
| url | https://doi.org/10.1049/els2.12030 |
| work_keys_str_mv | AT fayizalfaverh electricalvehiclegridintegrationfordemandresponseindistributionnetworksusingreinforcementlearning AT moulouddenai electricalvehiclegridintegrationfordemandresponseindistributionnetworksusingreinforcementlearning AT yichuangsun electricalvehiclegridintegrationfordemandresponseindistributionnetworksusingreinforcementlearning |