Adaptive critic design based dynamic optimal power flow controller for a smart grid

Liang, Jiaqi; Harley, R.G.; Venayagamoorthy, G.K.

doi:10.1109/ciasg.2011.5953340

Cited by 12 publications

(4 citation statements)

References 27 publications

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“…These challenges are related to extracting dynamics of the system without knowing the system model, mining and interpreting huge amount of data available from monitoring devices and assessment of the overall dynamics of the system based on wide area information [13]. By employing the type of WAMS presented in this paper in tandem with novel control schemes such as adaptive critics design based control scheme as given in [2] or with conventional controllers we can realize control schemes with system wide information as communication delays will be compensated by predicting for future time instances. As long as duration of n-step ahead prediction is bigger than communication delay, all the required information at time t will be made available to controllers.…”

Section: Resultsmentioning

confidence: 99%

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A scalable wide area monitoring system using cellular neural networks

Balasubramaniam

Luitel

Venayagamoorthy

2012

The 2012 International Joint Conference on Neural Networks (IJCNN)

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Synchrophasor systems make power grids more observable by collecting data from various locations, time-align and process them as a coherent data set. Better observability results in better control actions. A limiting factor to this approach is communication delays. Power system wide area communication delays range from several milliseconds to several seconds depending on the communication media and distance. One way to deal with this is to have an intelligent system which can predict state values for one or more time steps ahead of time. A novel four dimensional scalable multirate cellular neural network (CNN) architecture for use as wide area monitoring system (WAMS) is proposed. Recurrent neural network (RNN) is used as computational engine for each cell as RNNs have dynamic memory. By using information from phasor measurement units (PMUs) that are optimally located in a power system, each layer predicts a state variable for one or more time steps. Data from remote PMUs are replaced by the respective CNN cells' time delayed predicted state values for next time step. This enables local controllers to take real-time control action with system wide information. A 12-bus test power system is used to develop and demonstrate the effectiveness of the proposed CNN framework for WAMS.

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Section: Resultsmentioning

confidence: 99%

“…In power systems, optimal power flow dispatch is updated every five minutes. Variations between dispatches are handled by local controllers with little or no system wide knowledge [2]. A limiting factor in implementing real-time control with system wide information is communication delays.…”

mentioning

confidence: 99%

A scalable wide area monitoring system using cellular neural networks

Balasubramaniam

Luitel

Venayagamoorthy

2012

The 2012 International Joint Conference on Neural Networks (IJCNN)

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“…To provide a coordinating control solution to multiple gridconnected energy systems, dynamic stochastic optimal power flow (DSOPF) control strategies can be used [53] [84] [85]. The DSOPF controller is to replace the traditional automatic generation control and secondary voltage control, while providing nonlinear optimal control to the system-wide AC power flow.…”

Section: Power Generation Controlmentioning

confidence: 99%

Stochastic Information Management in Smart Grid

Liang

Tamang

Zhuang

et al. 2014

IEEE Commun. Surv. Tutorials

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Rising concerns about the efficiency, reliability, economics, and sustainability in electricity production and distribution have been driving an evolution of the traditional electric power grid toward smart grid. A key enabler of the smart grid is the two-way communications throughout the power system, based on which an advanced information system can make optimal decisions on power system operation. Due to the expected deep penetration of renewable energy sources, energy storage devices, demand side management (DSM) tools, and electric vehicles (EVs) in the future smart grid, there exist significant technical challenges on power system planning and operation. Specifically, efficient stochastic information management schemes should be developed to address the randomness in renewable power generation, buffering effect of energy storage devices, consumer behavior patterns in the context of DSM, and high mobility of EVs. In this paper, we provide a comprehensive literature survey on the stochastic information management schemes for the smart grid. We start this survey with an introduction to the smart grid system architecture and the technical challenges in information management. Various component-level modeling techniques are presented to characterize the sources of randomness in the smart grid. Built upon the component-level models, we further explore the system-level stochastic information management schemes for smart grid planning and operation. Future research directions and open research issues are identified.

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“…Equality and inequality constraints on the transmission line connected to the distribution network according to reference [17] and relationships related to the amount and angle of the reference voltage have been determined according to [18]. …”

Section: Introducing the Objective Functionsmentioning

confidence: 99%

Studying the Effect of Existing Technologies in a Smart Grid for Reducing the Distribution Networks Losses

Alizadeh¹

2015

JEEE

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Nowadays, population growth and the electrical load demand on the one hand and rising cost of energy generation on the other hand have led to raises reducing the losses in distribution networks as a serious challenge. According to studies, smart grid is posed as the most important applicable solution to eliminate these problems. The purpose of this paper is to examine the impact of communication and information technologies and new control capabilities in smart grids for reducing distribution network dissipations. In this study, a new formulation and an optimal load flow algorithm are used to reduce losses in the smart distribution network. Optimal load flow for a 69-bus IEEE network as well as the installation of the smart network facilities are implemented in the MATLAB and GAMS software and the results show that distribution network losses are greatly reduced.

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Adaptive critic design based dynamic optimal power flow controller for a smart grid

Cited by 12 publications

References 27 publications

A scalable wide area monitoring system using cellular neural networks

A scalable wide area monitoring system using cellular neural networks

Stochastic Information Management in Smart Grid

Studying the Effect of Existing Technologies in a Smart Grid for Reducing the Distribution Networks Losses

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