Monitoring, Control and Protection

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    Experimental Investigation of a Capacity-Based Demand Response Mechanism for District-Scale Applications
    ( 2019-01-08) de Chalendar, Jacques ; Glynn, Peter ; Benson, Sally
    District heating and cooling systems incorporating heat recovery and large-scale thermal storage dramatically reduce energy waste and greenhouse gas emissions. Electrifying district energy systems also has the effect of introducing city-scale controllable loads at the level of the electrical substation. Here we explore the opportunity for these systems to provide energy services to the grid through capacity-based demand response mechanisms. We present both a planning approach to estimate available demand-side capacity and a control framework to guide real-time scheduling when the program is active. These tools are used to assess the technical feasibility and the economic viability of participating in capacity-based demand response in the context of a real-world, megawatt-scale pilot during the summer of 2018 on the Stanford University campus.
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    Extremum Seeking Control of Distributed Energy Resources with Decaying Dither and Equilibrium-based Switching
    ( 2019-01-08) Sankur, Michael ; Arnold, Daniel
    Optimal control of Distributed Energy Resources (DER) may be a critical component for proper operation of the electric distribution grid in the near future. However, many optimization-based approaches for managing DER require knowledge of the underlying distribution system topology, network impedances, and access to feeder-wide real time load information. In order to ameliorate these requirements, we propose a 2-dimensional Extremum Seeking (2D-ES) control scheme to manage DER active and reactive power contributions. We augment the 2D-ES scheme with an exponentially decaying probing (dither) signal that activates based on an equilibrium-based switching criteria. Our simulation results show that the approach can enable substation real and reactive power target tracking with dither signals that exponentially decay once the individual ES controllers have each reached their optimum values.
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    Scalable Computation of 2D-Minkowski Sum of Arbitrary Non-Convex Domains: Modeling Flexibility in Energy Resources
    ( 2019-01-08) Kundu, Soumya ; Chandan, Vikas ; Kalsi, Karan
    The flexibility of active ($p$) and reactive power ($q$) consumption in distributed energy resources (DERs) can be represented as a (potentially non-convex) set of points in the $p$-$q$ plane. Modeling of the aggregated flexibility in a heterogeneous ensemble of DERs as a Minkowski sum (M-sum) is computationally intractable even for moderately sized populations. In this article, we propose a scalable method of computing the M-sum of the flexibility domains of a heterogeneous ensemble of DERs, which are allowed to be non-convex, non-compact. In particular, the proposed algorithm computes a guaranteed superset of the true M-sum, with desired accuracy. The worst-case complexity of the algorithm is computed. Special cases are considered, and it is shown that under certain scenarios, it is possible to achieve a complexity that is linear with the size of the ensemble. Numerical examples are provided by computing the aggregated flexibility of different mix of DERs under varying scenarios.
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    Heterogeneous Electric Vehicle Charging Coordination: A Variable Charging Speed Approach
    ( 2019-01-08) Valogianni, Konstantina ; Ketter, Wolfgang ; Collins, John ; Adomavicius, Gediminas
    We present a coordination mechanism that reduces peak demand coming from EV charging, supports grid stability and environmental sustainability. The proposed mechanism accounts for individual commuting preferences, as well as desired states of charge by certain deadlines, which can serve as a proxy for range anxiety. It can shape EV charging toward a desired profile, without violating individual preferences. Our mechanism mitigates herding, which is typical in populations where all agents receive the same price signals and make similar charging decisions. Furthermore, it assumes no prior knowledge about EV customers and therefore learns preferences and reactions to prices dynamically. We show through simulations that our mechanism induces a less volatile demand and lower peaks compared to currently used benchmarks.
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    Iterative Matrix Pencil Method for Power System Modal Analysis
    ( 2019-01-08) Trinh, Wei ; Shetye, Komal ; Idehen, Ikponmwosa ; Overbye, Thomas
    This paper introduces a modal analysis approach termed as the Iterative Matrix Pencil method. It uses the Matrix Pencil Method as the primary tool for mode identification, and adds to it by utilizing the concept of a cost function in order to reduce the number of signals needed to identify the modes for a large system. The method is tested for a variety of large synthetic power grids in this paper, with the cost function being reported to measure accuracy. A sensitivity analysis is also considered, showing how this new method behaves when adjusting the two primary user-based inputs; the number of iterations, and the SVD threshold.