dc.contributor.author |
Latani, T |
|
dc.contributor.author |
Parameswaran, G |
|
dc.contributor.author |
Priyanthan, G |
|
dc.contributor.author |
Hemapala, KTMU |
|
dc.contributor.editor |
Abeysooriya, R |
|
dc.contributor.editor |
Adikariwattage, V |
|
dc.contributor.editor |
Hemachandra, K |
|
dc.date.accessioned |
2024-03-22T05:18:05Z |
|
dc.date.available |
2024-03-22T05:18:05Z |
|
dc.date.issued |
2023-12-09 |
|
dc.identifier.citation |
T. Latani, G. Parameswaran, G. Priyanthan and K. T. M. U. Hemapala, "Coordination of PV Smart Inverters for Grid Voltage Regulation," 2023 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka, 2023, pp. 84-89, doi: 10.1109/MERCon60487.2023.10355465. |
en_US |
dc.identifier.uri |
http://dl.lib.uom.lk/handle/123/22372 |
|
dc.description.abstract |
In the contemporary energy market, the utilization
of photovoltaic (PV) is increasing considerably. This change
brings new challenges to the power grid because of its variable
and intermittent nature. One of the main issues is voltage
violations and PV curtailment. A smart inverter (SI) provides
a fast response method to regulate the voltage by varying real or
reactive power at the point of common coupling (PCC). When
multiple SIs operate under an autonomous control scheme, the
reactive power level exceeds the threshold level. This creates
an undesirable situation in the system. This paper mainly
considers the coordination of the SI using a deep reinforcement
learning algorithm (DRL). The DRL agent learns the policy
through interaction with the IEEE-37 test feeder in the OpenDSS
simulation to find out the optimal action. By defining the rewards
scheme of the action carefully, the reactive power of SI can
be utilized optimally, and the PV voltage will be maintained
within the normal operating zone. Validation of the DRL agent’s
performance is done with the local autonomous control scheme.
The results assure that a well-trained DRL agent can coordinate
multiple SIs for voltage regulation and PV curtailment reduction. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IEEE |
en_US |
dc.relation.uri |
https://ieeexplore.ieee.org/document/10355465 |
en_US |
dc.subject |
Deep Reinforcement learning |
en_US |
dc.subject |
Smart inverters |
en_US |
dc.subject |
Deep deterministic policy gradient |
en_US |
dc.subject |
Photovoltaic |
en_US |
dc.subject |
Voltage regulation and PV curtailment |
en_US |
dc.title |
Coordination of pv smart inverters for grid voltage regulation |
en_US |
dc.type |
Conference-Full-text |
en_US |
dc.identifier.faculty |
Engineering |
en_US |
dc.identifier.department |
Engineering Research Unit, University of Moratuwa |
en_US |
dc.identifier.year |
2023 |
en_US |
dc.identifier.conference |
Moratuwa Engineering Research Conference 2023 |
en_US |
dc.identifier.place |
Katubedda |
en_US |
dc.identifier.pgnos |
pp. 84-89 |
en_US |
dc.identifier.proceeding |
Proceedings of Moratuwa Engineering Research Conference 2023 |
en_US |
dc.identifier.email |
tlatani18@gmail.com |
en_US |
dc.identifier.email |
gayani.parameswaran@gmail.com |
en_US |
dc.identifier.email |
govindarajpriyanthan@gmail.com |
en_US |
dc.identifier.email |
ktmudayanga@gmail.com |
en_US |