Visibility-enhanced model-free deep reinforcement learning algorithm for voltage control in realistic distribution systems using smart inverters

Increasing integration of distributed solar photovoltaic (PV) into distribution networks could result in adverse effects on grid operation. Traditional model-based control algorithms require accurate model information that is difficult to acquire and thus are challenging to implement in practice. Th...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Applied energy Ročník 372; číslo C; s. 123758
Hlavní autori: Pei, Yansong, Ye, Ketian, Zhao, Junbo, Yao, Yiyun, Su, Tong, Ding, Fei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United Kingdom Elsevier Ltd 15.10.2024
Elsevier
Predmet:
Active distribution systems
algorithms
electric potential difference
Grid visibility
PV inverter
Reinforcement learning
Surrogate model
uncertainty
Grid visibility
Surrogate model
Active distribution systems
PV inverter
Reinforcement learning
ISSN:0306-2619, 1872-9118
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Increasing integration of distributed solar photovoltaic (PV) into distribution networks could result in adverse effects on grid operation. Traditional model-based control algorithms require accurate model information that is difficult to acquire and thus are challenging to implement in practice. This paper proposes a surrogate model-enabled grid visibility scheme to empower deep reinforcement learning (DRL) approach for distribution network voltage regulation using PV inverters with minimal system knowledge. In contrast to existing DRL methods, this paper presents and corroborates the adverse impact of missing load information on DRL performance and, based on this finding, proposes a surrogate model methodology to impute load information utilizing observable data. Additionally, a multi-fidelity neural network is utilized to construct the DRL training environment, chosen for its efficient data utilization and enhanced robustness to data uncertainty. The feasibility and effectiveness of the proposed algorithm are assessed by considering DRL testing across varying degrees of observable load information and diverse training environments on a realistic power system. •A visibility-enhanced surrogate has been introduced, empirically demonstrating its effectiveness in enhancing deep reinforcement learning (DRL) performances.•A multi-fidelity neural network is employed as a surrogate model within the training environment, improving data utilization efficiency while ensuring the effectiveness of DRL training.•Realistic test feeders have been used for testing, further validating the feasibility of the proposed method in practical scenarios.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
37770
USDOE
ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2024.123758