Integrating Distributed or Renewable Resources

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    Impact of Large Distributed Solar PV Generation on Distribution Voltage Control
    ( 2019-01-08) Begovic, Miroslav ; Peerzada, Aaqib ; Mohan, Shipra ; Balog, Robert ; Rohouma, Wesam
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    The Value of Distributed Energy Resources (DER) to the Grid: Introductionto the concepts of Marginal Capital Cost and Locational Marginal Value
    ( 2019-01-08) Tabors, Richard ; Masiello, Ralph ; Caramanis, Michael C. ; Andrianesis, Panagiotis
    Distributed Energy Resources (DERs) are argued to be a significant benefit to the electric utility grid. While DERs generate significant benefits to their owners and as well as society, the compensation and operating structure of the distribution system of most utilities is such that DERs result in minimal benefits to the distribution system. As we show, the benefits correctly attributed to the distribution company (the wires company) are a function of what service (real, reactive power) the DER is able to provide, when and where, and at what level of certainty the DER is able to provide the service. We introduce the concepts of Marginal Cost of Capacity (MCC) and Locational Marginal Value (LMV) in the calculation of the value of DERs to the distribution system.
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    Autonomous Multi-Stage Flexible OPF for Active Distribution Systems with DERs
    ( 2019-01-08) Meliopoulos, Sakis ; Zhong, Chiyang ; Cokkinides, George ; Xie, Boqi ; Dalton, Catherine ; Myrda, Paul ; Farantatos, Evangelos
    The variability of renewable resources creates challenges in the operation and control of power systems. One way to cope with this issue is to use the flexibility of customer resources in addition to utility resources to mitigate this variability. We present an approach that autonomously optimizes the available distributed energy resources (DERs) of the system to optimally balance generation and load and/or levelize the voltage profile. The method uses a dynamic state estimator which is continuously running on the system providing the real-time dynamic model of the system and operating condition. At user selected time intervals, the real-time model and operating condition is used to autonomously assemble a multi-stage optimal power flow in which customer energy resources are represented with their controls, allowing the use of customer flexibility to be part of the solution. Customer DERs may include photovoltaic rooftops with controllable inverters, batteries, thermostatically controlled loads, smart appliances, etc. The paper describes the autonomous formation of the Multi-Stage Flexible Optimal Power Flow and the solution of the problem, and presents sample results.
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    Grid-Aware versus Grid-Agnostic Distribution System Control: A Method for Certifying Engineering Constraint Satisfaction
    ( 2019-01-08) Molzahn, Daniel ; Roald, Line A
    Growing penetrations of distributed energy resources (DERs) in distribution systems have motivated the design of controllers that leverage DER capabilities to achieve system-wide objectives. These controllers may be either grid-agnostic or grid-aware, depending on whether distribution network constraints are considered. Grid-agnostic controllers have the benefit of not requiring network models or system measurements, but may cause dangerous constraint violations. Rather than develop a specific controller, this paper considers the potential impacts of DER controllers with respect to network constraint violations. Specifically, this paper develops an optimization-based method to rigorously certify when any grid-agnostic controller can be applied without concern regarding network constraint violations, or, conversely, when grid-aware control may be needed to maintain distribution grid security. The proposed method uses convex optimization techniques to bound the impacts of load variability, given a subset of buses with voltage measurements and control. The method either provides a certificate for secure operation or identifies potentially critical constraints and the need for additional controllability. Numerical tests illustrate the ability to certify secure operation for different ranges of variability.
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    Physics-informed Machine Learning Method for Forecasting and Uncertainty Quantification of Partially Observed and Unobserved States in Power Grids
    ( 2019-01-08) Tartakovsky, Alexandre ; Tipireddy, Ramakrishna
    We present a physics-informed Gaussian Process Regression (GPR) model to predict the phase angle, angular speed, and wind mechanical power from a limited number of measurements. In the traditional data-driven GPR method, the form of the Gaussian Process auto- and cross-covariance functions is assumed and its parameters are found from measurements. In the physics-informed GPR, we treat unknown variables (including wind speed and mechanical power) as a random process and compute the auto and cross-covariance functions from the resulting stochastic power grid equations. We demonstrate that the physics-informed GPR method is significantly more accurate than the standard data-driven one for immediate forecasting of generators' angular velocity and phase angle. We also show that the physics-informed GPR provides accurate predictions of the unobserved wind mechanical power, phase angle, or angular velocity when measurements from only one of these variables are available. The immediate forecast of observed variables and predictions of unobserved variables can be used for effectively managing power grids (electricity market clearing, regulation actions) and early detection of abnormal behavior and faults. The physics-based GPR forecast time horizon depends on the combination of input (wind power, load, etc.) correlation time and characteristic (relaxation) time of the power grid and can be extended to short and medium-range times.