Multi-agent graph reinforcement learning for decentralized Volt-VAR control in power distribution systems

Hu, Daner; Li, Zichen; Ye, Zhenhui; Peng, Yonggang; Xi, Wei; Cai, Tiantian

doi:10.1016/j.ijepes.2023.109531

Cited by 2 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Paper [37] proposes a new voltage control scheme to cope with DER penetration; the authors exploited a 6.6 kV test grid as a benchmark. Paper [38] proposes an innovative multi-agent graph-based deep reinforcement learning tested on the IEEE 33-bus and 123-bus distribution test feeders. Paper [39] studies a novel physics-informed multi-agent deep reinforcement learning voltage control methods tested on the IEEE 33-bus and IEEE 141-bus systems.…”

Section: Voltage Regulation In Modern Distribution Networkmentioning

confidence: 99%

Decentralised Voltage Regulation through Optimal Reactive Power Flow in Distribution Networks with Dispersed Generation

Daccò,

Falabretti,

Ilea

et al. 2024

Electricity

View full text Add to dashboard Cite

The global capacity for renewable electricity generation has surged, with distributed photovoltaic generation being the primary driver. The increasing penetration of non-programmable renewable Distributed Energy Resources (DERs) presents challenges for properly managing distribution networks, requiring advanced voltage regulation techniques. This paper proposes an innovative decentralised voltage strategy that considers DERs, particularly inverter-based ones, as autonomous regulators in compliance with the state-of-the-art European technical standards and grid codes. The proposed method uses an optimal reactive power flow that minimises voltage deviations along all the medium voltage nodes; to check the algorithm’s performance, it has been applied to a small-scale test network and on a real Italian medium-voltage distribution network, and compared with a fully centralised ORPF. The results show that the proposed decentralised autonomous strategy effectively improves voltage profiles in both case studies, reducing voltage deviation by a few percentage points; these results are further confirmed through an analysis conducted over several days to observe how seasons affect the results.

show abstract

Section: Voltage Regulation In Modern Distribution Networkmentioning

confidence: 99%

Decentralised Voltage Regulation through Optimal Reactive Power Flow in Distribution Networks with Dispersed Generation

Daccò,

Falabretti,

Ilea

et al. 2024

Electricity

View full text Add to dashboard Cite

show abstract

Low-carbon economic dispatch strategy for integrated electrical and gas system with GCCP based on multi-agent deep reinforcement learning

Feng,

Deng,

Zhang

et al. 2024

Front. Energy Res.

View full text Add to dashboard Cite

With the growing concern for the environment, sustainable development centred on a low-carbon economy has become a unifying pursuit for the energy industry. Integrated energy systems (IES) that combine multiple energy sources such as electricity, heat and gas are essential to facilitate the consumption of renewable energy and the reduction of carbon emission. In this paper, gas turbine (GT), carbon capture and storage (CCS) and power-to-gas (P2G) device are introduced to construct a new carbon capture coupling device model, GT-CCS-P2G (GCCP), which is applied to the integrated electrical and gas system (IEGS). Multi-agent soft actor critic (MASAC) applies historical trajectory representations, parameter spatial techniques and deep densification frameworks to reinforcement learning for reducing the detrimental effects of time-series data on the decisional procedure. The energy scheduling problem of IEGS is redefined as a Markov game, which is addressed by adopting a low carbon economic control framework based on MASAC with minimum operating cost and minimum carbon emission as the optimization objectives. To validate the rationality and effectiveness of the proposed low-carbon economy scheduling model of IEGS based on MASAC, this paper simulates and analyses in integrated PJM-5 node system and seven nodes natural gas system.

show abstract

Multi-agent graph reinforcement learning for decentralized Volt-VAR control in power distribution systems

Cited by 2 publications

References 25 publications

Decentralised Voltage Regulation through Optimal Reactive Power Flow in Distribution Networks with Dispersed Generation

Decentralised Voltage Regulation through Optimal Reactive Power Flow in Distribution Networks with Dispersed Generation

Low-carbon economic dispatch strategy for integrated electrical and gas system with GCCP based on multi-agent deep reinforcement learning

Contact Info

Product

Resources

About