Gradient based centralized optimal Volt/Var control strategy for smart distribution system

Zhang, Shen; Baran, Mesut

doi:10.1109/isgt.2013.6497865

Cited by 28 publications

(8 citation statements)

References 9 publications

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“…Furthermore, implementation of the volt-var control requires communication of all bus voltages to the central controller. The concept of local voltage control that we introduce in this paper is in sharp contrast to the strategy introduced in [6] in that our proposed method does not require centralized computation nor is it applied at every SST. Only a subset of SSTs at particular "pilot" nodes participate in the volt-var control, leaving the remaining SSTs available for other functions.…”

Section: Introductionmentioning

confidence: 81%

“…Furthermore, this method has the additional attribute of operating without communication overhead. Earlier voltvar control methods proposed for SSTs (see [6]) require considerable data exchange to optimize the voltage performance. However, the future smart grid will be inundated with large amounts of communication overhead for power quality and energy management.…”

Section: Introductionmentioning

confidence: 99%

“…SST-based volt-var control was first introduced in [6]. The primary goal of the control strategy in [6] is to minimize system power losses while enforcing voltage limits across the system.…”

Section: Introductionmentioning

confidence: 99%

“…The primary goal of the control strategy in [6] is to minimize system power losses while enforcing voltage limits across the system. The method proposed in [6] is a centralized approach that assumed that all SSTs participate in the volt-var control. Furthermore, implementation of the volt-var control requires communication of all bus voltages to the central controller.…”

Section: Introductionmentioning

confidence: 99%

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Online Volt-Var Control for Distribution Systems With Solid-State Transformers

Shah

Crow

2016

IEEE Trans. Power Delivery

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In distribution power systems, feeder voltages can be very sensitive to changes in load and/or distributed generation. This paper introduces a solid-state-transformer-based local voltage control strategy to reduce variability distribution system bus voltages. An on-line dynamic volt-var control (VVC) algorithm is proposed to regulate bus voltages by injecting or absorbing reactive power through a solid state transformer (SST). The proposed algorithm does not require any communication between the SST and the substation and makes control decisions locally. The main goal of the voltage control algorithm is to enforce strict voltage constraints on the system voltages. The proposed control algorithm is validated in both a radial and meshed distribution system using PSCAD.

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Section: Introductionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Online Volt-Var Control for Distribution Systems With Solid-State Transformers

Shah

Crow

2016

IEEE Trans. Power Delivery

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“…For centralized schemes, the approaches are mixed integer linear programming [2], mixed integer nonlinear programming [10], neural network [27], gradient descent method [13], [14], [28], dynamic programming [7], [21], [26], fuzzy logic [8], [16], particle swarm optimization [11], [24], evolutionary algorithms [1], [23], and others. The optimization problem is solved by the control center with the information collected from the remote terminal units (RTUs) and the control action is sent back to the RTUs.…”

Section: Introductionmentioning

confidence: 99%

Agent-Based Distributed Volt/Var Control With Distributed Power Flow Solver in Smart Grid

Zhang

Flueck

Nguyen³

2016

IEEE Trans. Smart Grid

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In this paper, a new and completely distributed algorithm for integrated volt/var control is presented. The algorithm is based on a multiagent system, which provides distributed intelligence to smart grid. The voltage regulator and shunt capacitor controlled by intelligent agents collaborate to determine the optimal setting for the entire system. The optimization objectives include maintaining the system voltage profile within a specified range, minimizing system loss, and reducing the switching of shunt capacitors. To achieve these objectives, an updated agentbased distributed power flow solver is used. The proposed algorithm is validated through the modified IEEE 34 node test feeder.Index Terms-Distribution automation (DA), multiagent system (MAS), shunt capacitor, smart grid, volt/var control (VVC), voltage regulator.

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