Mitigating Voltage Unbalance Using Distributed Solar Photovoltaic Inverters

Yao, Mengqi; Hiskens, Ian A.; Mathieu, Johanna L.

doi:10.1109/tpwrs.2020.3039405

Cited by 34 publications

(23 citation statements)

References 23 publications

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“…Moreover, OpenDSS offers a Python COM interface that enables users to script customized simulations adopting Python's reinforcement learning package PyTorch, which provides NN and training for agents [28]. To demonstrate the effectiveness of the system, a simulation was conducted on the modified IEEE 13 bus system from [8] and the modified IEEE 34 bus system from [29], as shown in Figure 8. In this study, we did not consider the size and the connected position of PV systems.…”

Section: Case Studymentioning

confidence: 99%

“…By using the Karush-Kuhn-Tucker optimization method, the negative and zero phase components of the voltage can be selectively reduced. In another study [8], the voltage unbalance in the DN was solved using the reactive power compensation capability of the inverter through centralized and decentralized methods. The centralized method solves the voltage unbalance by presenting a mathematically calculated value using a Steinmetz design.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Volt–Var Control in Smart PV Inverter for Mitigating Voltage Unbalance at PCC Using Multiagent Deep Reinforcement Learning

et al. 2021

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Modern distribution networks face an increasing number of challenges in maintaining balanced grid voltages because of the rapid increase in single-phase distributed generators. Because of the proliferation of inverter-based resources, such as photovoltaic (PV) resources, in distribution networks, a novel method is proposed for mitigating voltage unbalance at the point of common coupling by tuning the volt–var curve of each PV inverter through a day-ahead deep reinforcement learning training platform with forecast data in a digital twin grid. The proposed strategy uses proximal policy optimization, which can effectively search for a global optimal solution. Deep reinforcement learning has a major advantage in that the calculation time required to derive an optimal action in the smart inverter can be significantly reduced. In the proposed framework, multiple agents with multiple inverters require information on the load consumption and active power output of each PV inverter. The results demonstrate the effectiveness of the proposed control strategy on the modified IEEE 13 standard bus systems with time-varying load and PV profiles. A comparison of the effect on voltage unbalance mitigation shows that the proposed inverter can address voltage unbalance issues more efficiently than a fixed droop inverter.

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Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Volt–Var Control in Smart PV Inverter for Mitigating Voltage Unbalance at PCC Using Multiagent Deep Reinforcement Learning

et al. 2021

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“…However, different network parameters, e.g., negative and zero sequence components of line impedance can affect the system stability and additional switching devices are required for the compensation. In [14], a distributed control method of a single-phase H-bridge PV-VSI was proposed for compensating the voltage unbalance factor (VUF) at a critical bus. Steinmetz design was employed for calculating required reactive power injections at different PV and load connections.…”

Section: Unbalance Compensation By Power Electronic Convertersmentioning

confidence: 99%

Distributed Control Strategy of Single-Phase Battery Systems for Compensation of Unbalanced Active Powers in a Three-Phase Four-Wire Microgrid

Pinthurat

Hredzak

2021

Energies

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Unbalanced active powers can affect power quality and system reliability due to high penetration and uneven allocation of single-phase photovoltaic (PV) rooftop systems and load demands in a three-phase four-wire microgrid. This paper proposes a distributed control strategy to alleviate the unbalanced active powers using distributed single-phase battery storage systems. In order to balance the unbalanced active powers at the point of common coupling (PCC) in a distributed manner, the agents (households’ single-phase battery storage systems) must have information on the active powers and phases. Inspired by supervised learning, a clustering approach was developed to use labels in order to match the three-phase active powers at the PCC with the agents’ phases. This enables the agent to select the correct active power data from the three-phase active powers. Then, a distributed power balancing control strategy is applied by all agents to compensate the unbalanced active powers. Each agent calculates the average grid power based on information received from its neighbours so that all agents can then cooperatively operate in either charging or discharging modes to achieve the compensation. As an advantage, the proposed distributed control strategy offers the battery owners flexibility to participate in the strategy. Case studies comparing performance of local, centralized, and the proposed distributed strategy on a modified IEEE-13-bus test system with real household PV powers and load demands are provided.

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“…In addition, we constrain the total compensating reactive power injections to be zero in order to avoid significant changes in the feeder's voltage profile. Reference [7] details the Steinmetz formulations developed by the project team for the decentralized, distributed, and grouped controllers.…”

Section: Steinmetz Formulationmentioning

confidence: 99%

“…Table 4 then provides a quantitative summary of the results for this test case. Moreover, reference [7] provides additional details regarding comparisons of the Steinmetz-based controllers to model-free controllers.…”

Section: Application To Nreca Test Casesmentioning

confidence: 99%

Mitigating Phase Unbalance for Distribution Systems with High Penetrations of Solar PV (Final Technical Report)

Molzahn¹,

Roald²,

Mathieu³

et al. 2021

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Mitigating Voltage Unbalance Using Distributed Solar Photovoltaic Inverters

Cited by 34 publications

References 23 publications

Adaptive Volt–Var Control in Smart PV Inverter for Mitigating Voltage Unbalance at PCC Using Multiagent Deep Reinforcement Learning

Adaptive Volt–Var Control in Smart PV Inverter for Mitigating Voltage Unbalance at PCC Using Multiagent Deep Reinforcement Learning

Distributed Control Strategy of Single-Phase Battery Systems for Compensation of Unbalanced Active Powers in a Three-Phase Four-Wire Microgrid

Mitigating Phase Unbalance for Distribution Systems with High Penetrations of Solar PV (Final Technical Report)

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