Abstract
In this paper, multi-agent architecture was used to provide control for standalone microgrid with distributed generations. Therefore, to achieve a faster control compared to the centralized controller, each agent incorporated with a local prediction or forecasting model to provide anticipatory responses. To accomplish their common goals successfully, the agents cooperated based on facilitator architecture with game-theory. Initially, the agents estimate its own parameters and dynamically adjust them by playing non-cooperative game with other agents. The predictive algorithm is based on autoregressive model where each agent will predict the load demand alongside renewable energy resources in order to dynamically regulate the control parameters. This will provide a faster response where the agents will anticipate future load demand and available renewable resources and adjust their parameters beforehand. Hence, this will minimize the fluctuations of voltage and frequency in the microgrid leading to more efficient power dispatch and lower power losses.
| Original language | English |
|---|---|
| Title of host publication | PECON 2016 - 2016 IEEE 6th International Conference on Power and Energy, Conference Proceeding |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 686-691 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509025473 |
| DOIs | |
| Publication status | Published - 16 Jun 2017 |
| Event | 6th IEEE International Conference on Power and Energy, PECON 2016 - Melaka, Malaysia Duration: 28 Nov 2016 → 29 Nov 2016 |
Publication series
| Name | PECON 2016 - 2016 IEEE 6th International Conference on Power and Energy, Conference Proceeding |
|---|
Other
| Other | 6th IEEE International Conference on Power and Energy, PECON 2016 |
|---|---|
| Country | Malaysia |
| City | Melaka |
| Period | 28/11/16 → 29/11/16 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering
- Fuel Technology
Cite this
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Anticipatory response model for multi-agent based energy management system in a standalone microgrid. / Khan, M. Reyasudin Basir; Jagadeesh, Pasupuleti; Jidin, Razali.
PECON 2016 - 2016 IEEE 6th International Conference on Power and Energy, Conference Proceeding. Institute of Electrical and Electronics Engineers Inc., 2017. p. 686-691 7951647 (PECON 2016 - 2016 IEEE 6th International Conference on Power and Energy, Conference Proceeding).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Anticipatory response model for multi-agent based energy management system in a standalone microgrid
AU - Khan, M. Reyasudin Basir
AU - Jagadeesh, Pasupuleti
AU - Jidin, Razali
PY - 2017/6/16
Y1 - 2017/6/16
N2 - In this paper, multi-agent architecture was used to provide control for standalone microgrid with distributed generations. Therefore, to achieve a faster control compared to the centralized controller, each agent incorporated with a local prediction or forecasting model to provide anticipatory responses. To accomplish their common goals successfully, the agents cooperated based on facilitator architecture with game-theory. Initially, the agents estimate its own parameters and dynamically adjust them by playing non-cooperative game with other agents. The predictive algorithm is based on autoregressive model where each agent will predict the load demand alongside renewable energy resources in order to dynamically regulate the control parameters. This will provide a faster response where the agents will anticipate future load demand and available renewable resources and adjust their parameters beforehand. Hence, this will minimize the fluctuations of voltage and frequency in the microgrid leading to more efficient power dispatch and lower power losses.
AB - In this paper, multi-agent architecture was used to provide control for standalone microgrid with distributed generations. Therefore, to achieve a faster control compared to the centralized controller, each agent incorporated with a local prediction or forecasting model to provide anticipatory responses. To accomplish their common goals successfully, the agents cooperated based on facilitator architecture with game-theory. Initially, the agents estimate its own parameters and dynamically adjust them by playing non-cooperative game with other agents. The predictive algorithm is based on autoregressive model where each agent will predict the load demand alongside renewable energy resources in order to dynamically regulate the control parameters. This will provide a faster response where the agents will anticipate future load demand and available renewable resources and adjust their parameters beforehand. Hence, this will minimize the fluctuations of voltage and frequency in the microgrid leading to more efficient power dispatch and lower power losses.
UR - http://www.scopus.com/inward/record.url?scp=85024400881&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85024400881&partnerID=8YFLogxK
U2 - 10.1109/PECON.2016.7951647
DO - 10.1109/PECON.2016.7951647
M3 - Conference contribution
T3 - PECON 2016 - 2016 IEEE 6th International Conference on Power and Energy, Conference Proceeding
SP - 686
EP - 691
BT - PECON 2016 - 2016 IEEE 6th International Conference on Power and Energy, Conference Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
ER -