Reinforcement Learning-Based Distributed BESS Management for Mitigating Overvoltage Issues in Systems with High PV Penetration

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F20%3A50016886" target="_blank" >RIV/62690094:18470/20:50016886 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8985179" target="_blank" >https://ieeexplore.ieee.org/document/8985179</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSG.2020.2972208" target="_blank" >10.1109/TSG.2020.2972208</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Reinforcement Learning-Based Distributed BESS Management for Mitigating Overvoltage Issues in Systems with High PV Penetration

Popis výsledku v původním jazyce

High levels of penetration of distributed photovoltaic generators can cause serious overvoltage issues, especially during periods of high power generation and light loads. There have been many solutions proposed to mitigate the voltage problems, some of them using battery energy storage systems (BESS) at the PV generation sites. In addition to their ability to absorb extra power during the light load periods, BESS can also supply additional power under high load conditions. However, their capacity may not be sufficient to allow charging every time when power absorption is desired. Therefore, typical PV/BESS may not fully prevent over-voltage problems in power distribution grids. This work develops a cooperative state of charge control scheme to alleviate the BESS capacity problem through Monte Carlo tree search based reinforcement learning (MCTS-RL). The proposed intelligent method coordinates the distributed batteries from other regions to provide voltage regulation in a distribution network. Furthermore, the energy optimization process during the day hours and the simultaneous state of charge control are achieved using model predictive control (MPC). The proposed approach is demonstrated on two test cases, the IEEE 33 bus system and a practical medium size distribution system in Alberta Canada.

Název v anglickém jazyce

Reinforcement Learning-Based Distributed BESS Management for Mitigating Overvoltage Issues in Systems with High PV Penetration

Popis výsledku anglicky

High levels of penetration of distributed photovoltaic generators can cause serious overvoltage issues, especially during periods of high power generation and light loads. There have been many solutions proposed to mitigate the voltage problems, some of them using battery energy storage systems (BESS) at the PV generation sites. In addition to their ability to absorb extra power during the light load periods, BESS can also supply additional power under high load conditions. However, their capacity may not be sufficient to allow charging every time when power absorption is desired. Therefore, typical PV/BESS may not fully prevent over-voltage problems in power distribution grids. This work develops a cooperative state of charge control scheme to alleviate the BESS capacity problem through Monte Carlo tree search based reinforcement learning (MCTS-RL). The proposed intelligent method coordinates the distributed batteries from other regions to provide voltage regulation in a distribution network. Furthermore, the energy optimization process during the day hours and the simultaneous state of charge control are achieved using model predictive control (MPC). The proposed approach is demonstrated on two test cases, the IEEE 33 bus system and a practical medium size distribution system in Alberta Canada.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Transactions on Smart Grid

ISSN

1949-3053

e-ISSN

—

Svazek periodika

11

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

2980-2994

Kód UT WoS článku

000542571700020

EID výsledku v databázi Scopus

2-s2.0-85087545607