Abstract
Reliability of power distribution systems is of very crucial concern due to cases of mass power outages that occur worldwide. Once an unscheduled outage takes place in power grids, the service restoration is triggered to rapidly return the system to normal conditions and minimize the severity of consequences. This paper proposes a self-healing power distribution grid restoration technique based on decentralized multi-agent systems with reinforcement learning. The system architecture is based on two types of zone agents: Inactive Zone Agent (IZA) and Active Zone Agent (AZA), where the IZA is activated provided that an agent is within the out-of-service area. This study contributes to the advancement of service restoration by endowing agents with learning ability. The reward computation proposed in this paper is based on the load priority factor, and also it ensures preserving the constraints within the limits. Case studies include a comparison of service restoration outcomes with load priority factor and DGs incorporated into the network. All simulations are implemented in the PowerWorld simulator for the medium voltage network of 11kV with 29 buses. The results of the study prove that embedding Q-learning algorithm into service restoration significantly improves the performance metrics and thus, increases the reliability of the distribution grids.
| Original language | English |
|---|---|
| Title of host publication | SEST 2019 - 2nd International Conference on Smart Energy Systems and Technologies |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781728111568 |
| DOIs | |
| Publication status | Published - Sep 1 2019 |
| Event | 2nd International Conference on Smart Energy Systems and Technologies, SEST 2019 - Porto, Portugal Duration: Sep 9 2019 → Sep 11 2019 |
Publication series
| Name | SEST 2019 - 2nd International Conference on Smart Energy Systems and Technologies |
|---|
Conference
| Conference | 2nd International Conference on Smart Energy Systems and Technologies, SEST 2019 |
|---|---|
| Country | Portugal |
| City | Porto |
| Period | 9/9/19 → 9/11/19 |
Fingerprint
Keywords
- DG integration
- multi-agent systems
- Power distribution restoration
- Q-learning
- reinforcement learning
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Energy Engineering and Power Technology
- Renewable Energy, Sustainability and the Environment
- Electrical and Electronic Engineering
- Control and Optimization
Cite this
Machine Learning-based Service Restoration Scheme for Smart Distribution Systems with DGs and High Priority Loads. / Kalysh, I.; Kenzhina, M.; Kaiyrbekov, N.; Kumar Nunna, H. S.V.S.; Dadlani, Aresh; Doolla, S.
SEST 2019 - 2nd International Conference on Smart Energy Systems and Technologies. Institute of Electrical and Electronics Engineers Inc., 2019. 8849002 (SEST 2019 - 2nd International Conference on Smart Energy Systems and Technologies).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Machine Learning-based Service Restoration Scheme for Smart Distribution Systems with DGs and High Priority Loads
AU - Kalysh, I.
AU - Kenzhina, M.
AU - Kaiyrbekov, N.
AU - Kumar Nunna, H. S.V.S.
AU - Dadlani, Aresh
AU - Doolla, S.
PY - 2019/9/1
Y1 - 2019/9/1
N2 - Reliability of power distribution systems is of very crucial concern due to cases of mass power outages that occur worldwide. Once an unscheduled outage takes place in power grids, the service restoration is triggered to rapidly return the system to normal conditions and minimize the severity of consequences. This paper proposes a self-healing power distribution grid restoration technique based on decentralized multi-agent systems with reinforcement learning. The system architecture is based on two types of zone agents: Inactive Zone Agent (IZA) and Active Zone Agent (AZA), where the IZA is activated provided that an agent is within the out-of-service area. This study contributes to the advancement of service restoration by endowing agents with learning ability. The reward computation proposed in this paper is based on the load priority factor, and also it ensures preserving the constraints within the limits. Case studies include a comparison of service restoration outcomes with load priority factor and DGs incorporated into the network. All simulations are implemented in the PowerWorld simulator for the medium voltage network of 11kV with 29 buses. The results of the study prove that embedding Q-learning algorithm into service restoration significantly improves the performance metrics and thus, increases the reliability of the distribution grids.
AB - Reliability of power distribution systems is of very crucial concern due to cases of mass power outages that occur worldwide. Once an unscheduled outage takes place in power grids, the service restoration is triggered to rapidly return the system to normal conditions and minimize the severity of consequences. This paper proposes a self-healing power distribution grid restoration technique based on decentralized multi-agent systems with reinforcement learning. The system architecture is based on two types of zone agents: Inactive Zone Agent (IZA) and Active Zone Agent (AZA), where the IZA is activated provided that an agent is within the out-of-service area. This study contributes to the advancement of service restoration by endowing agents with learning ability. The reward computation proposed in this paper is based on the load priority factor, and also it ensures preserving the constraints within the limits. Case studies include a comparison of service restoration outcomes with load priority factor and DGs incorporated into the network. All simulations are implemented in the PowerWorld simulator for the medium voltage network of 11kV with 29 buses. The results of the study prove that embedding Q-learning algorithm into service restoration significantly improves the performance metrics and thus, increases the reliability of the distribution grids.
KW - DG integration
KW - multi-agent systems
KW - Power distribution restoration
KW - Q-learning
KW - reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85073363547&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073363547&partnerID=8YFLogxK
U2 - 10.1109/SEST.2019.8849002
DO - 10.1109/SEST.2019.8849002
M3 - Conference contribution
AN - SCOPUS:85073363547
T3 - SEST 2019 - 2nd International Conference on Smart Energy Systems and Technologies
BT - SEST 2019 - 2nd International Conference on Smart Energy Systems and Technologies
PB - Institute of Electrical and Electronics Engineers Inc.
ER -