Multi-Agent Deep Reinforcement Learning for Voltage Control With Coordinated Active and Reactive Power Optimization

Hu, Daner; Ye, Zhenhui; Gao, Yuanqi; Ye, Zuzhao; Peng, Yonggang; Yu, Nanpeng

doi:10.1109/tsg.2022.3185975

Cited by 63 publications

(8 citation statements)

References 36 publications

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“…Consequently, [10] and [11] propose the two-stage DRLbased VVC strategy with a day-ahead capacitor bank (CB), on-load tap-changer (OLTC) scheduling, and a real-time PV inverter control. Reference [12] integrates graph reinforcement learning into this two-stage VVC strategy to extract better topological information.…”

Section: Introductionmentioning

confidence: 99%

“…However, [7] - [12] ignore the potential benefits of switch state transitions when designing the DRL-based ADN optimization strategies, highlighting the need for further research. Simultaneously, treating each distributed energy resource (DER) as an agent [9]- [11] poses scalability issues in ADNs that contain a large number of DERs. Reference [13] partitions the entire distribution network into multiple subnetworks and treats each of them as an agent to control internal DERs, thus significantly reducing the number of agents and promoting the scalability of the method.…”

Section: Introductionmentioning

confidence: 99%

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Real-time Operation Optimization in Active Distribution Networks Based on Multi-agent Deep Reinforcement Learning

Xu,

Gao,

Wang

et al. 2024

Journal of Modern Power Systems and Clean Energy

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The increasing integration of intermittent renewable energy sources (RESs) poses great challenges to active distribution networks (ADNs), such as frequent voltage fluctuations. This paper proposes a novel ADN strategy based on multiagent deep reinforcement learning (MADRL), which harnesses the regulating function of switch state transitions for the realtime voltage regulation and loss minimization. After deploying the calculated optimal switch topologies, the distribution network operator will dynamically adjust the distributed energy resources (DERs) to enhance the operation performance of ADNs based on the policies trained by the MADRL algorithm. Owing to the model-free characteristics and the generalization of deep reinforcement learning, the proposed strategy can still achieve optimization objectives even when applied to similar but unseen environments. Additionally, integrating parameter sharing (PS) and prioritized experience replay (PER) mechanisms substantially improves the strategic performance and scalability. This framework has been tested on modified IEEE 33-bus, IEEE 118bus, and three-phase unbalanced 123-bus systems. The results demonstrate the significant real-time regulation capabilities of the proposed strategy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-time Operation Optimization in Active Distribution Networks Based on Multi-agent Deep Reinforcement Learning

Xu,

Gao,

Wang

et al. 2024

Journal of Modern Power Systems and Clean Energy

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“…VVC is used to improve the voltage stability of the distribution network. In traditional VVC practice, voltage-regulating devices, such as on-load tap changers (OLTCs), voltage regulators (VRs), and switchable capacitor banks (SCBs), are leveraged to mitigate voltage violations [ 14 ]. In [ 15 ], a support vector regression based model predictive control (MPC) method was proposed to optimize the voltage of a distribution network.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Charging Scheduling of Electric Vehicles Considering Distribution Network Voltage Stability

Liu

Zeng

Cui

et al. 2023

Sensors

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The rapid development of electric vehicle (EV) technology and the consequent charging demand have brought challenges to the stable operation of distribution networks (DNs). The problem of the collaborative optimization of the charging scheduling of EVs and voltage control of the DN is intractable because the uncertainties of both EVs and the DN need to be considered. In this paper, we propose a deep reinforcement learning (DRL) approach to coordinate EV charging scheduling and distribution network voltage control. The DRL-based strategy contains two layers, the upper layer aims to reduce the operating costs of power generation of distributed generators and power consumption of EVs, and the lower layer controls the Volt/Var devices to maintain the voltage stability of the distribution network. We model the coordinate EV charging scheduling and voltage control problem in the distribution network as a Markov decision process (MDP). The model considers uncertainties of charging process caused by the charging behavior of EV users, as well as the uncertainty of uncontrollable load, system dynamic electricity price and renewable energy generation. Since the model has a dynamic state space and mixed action outputs, a framework of deep deterministic policy gradient (DDPG) is adopted to train the two-layer agent and the policy network is designed to output discrete and continuous control actions. Simulation and numerical results on the IEEE-33 bus test system demonstrate the effectiveness of the proposed method in collaborative EV charging scheduling and distribution network voltage stabilization.

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“…It has superior performance in local sensing and intelligent computation, which can effectively relieve huge communication pressure. However, the limited computing resources and the complex computing tasks at the edge side significantly challenge the collaboration of distribution network regulation and advanced digital technologies (Hu et al, 2022). It is necessary to find out proper methods to utilize advanced digital technology to construct DDNs.…”

Section: Introductionmentioning

confidence: 99%

Editorial: Edge computation and digital distribution networks

Peng¹,

Ji²,

Ding³

et al. 2023

Front. Energy Res.

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Multi-Agent Deep Reinforcement Learning for Voltage Control With Coordinated Active and Reactive Power Optimization

Cited by 63 publications

References 36 publications

Real-time Operation Optimization in Active Distribution Networks Based on Multi-agent Deep Reinforcement Learning

Real-time Operation Optimization in Active Distribution Networks Based on Multi-agent Deep Reinforcement Learning

Deep Reinforcement Learning for Charging Scheduling of Electric Vehicles Considering Distribution Network Voltage Stability

Editorial: Edge computation and digital distribution networks

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