Monitoring, Control, and Protection

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https://hdl.handle.net/10125/63651

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Now showing 1 - 5 of 8
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    An Aggregate Model of the Flexible Energy Demand of Thermostatically Controlled Loads with Explicit Outdoor Temperature Dependency
    ( 2020-01-07) Hreinsson, Kari ; Scaglione, Anna ; Alizadeh, Mahnoosh
    In this paper we describe an aggregate model of Thermostatically Controlled Loads (TCLs) for Demand Response (DR) scheduling that, through a new approximation, makes explicit the dependency between the feasible control region and the time series of outdoor temperatures. In turn, the model can easily account for non-constant, stochastic temperatures during the control period, expressing the feasible load control through a set of linear equations and constraints with stochastic parameters. To highlight this feature we present a stochastic optimization formulation for the management of the DR-TCL and compare it with its deterministic counterpart, and with various equivalent models aimed at reducing the complexity of the constraints in the market optimization.
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    Enhancing the Spatio-Temporal Observability of Residential Loads
    ( 2020-01-07) Lin, Shanny ; Zhu, Hao
    Enhancing the spatio-temporal observability of residential loads is crucial for achieving secure and efficient operations in distribution systems with increasing penetration of distributed energy resources (DERs). This paper presents a joint inference framework for residential loads by leveraging the real-time measurements from distribution-level sensors. Specifically, smart meter data is available for almost every load with unfortunately low temporal resolution, while distribution synchrophasor data is at very fast rates yet available at limited locations. By combining these two types of data with respective strengths, the problem is cast as a matrix recovery one with much less number of observations than unknowns. To improve the recovery performance, we introduce two regularization terms to promote a lowrank plus sparse structure of the load matrix via a difference transformation. Accordingly, the recovery problem can be formulated as a convex optimization one which is efficiently solvable. Numerical tests using real residential load data demonstrate the effectiveness of our proposed approaches in identifying appliance activities and recovering the PV output profiles.
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    Improving End-Use Load Modeling Using Machine Learning and Smart Meter Data
    ( 2020-01-07) Thayer, Brandon ; Engel, Dave ; Chakraborty, Indrasis ; Schneider, Kevin ; Ponder, Leslie ; Fox, Kevin
    An accurate representation of the voltage-dependent, time-varying energy consumption of end-use electric loads is essential for the operation of modern distribution automation (DA) schemes. Volt-var optimization (VVO), a DA scheme which can decrease energy consumption and peak demand, often leverages electric network models and power flow results to inform control decisions, making it sensitive to errors in load models. End-use load modeling can be improved with additional measurements from advanced metering infrastructure (AMI). This paper presents two novel machine learning algorithms for creating data-driven, time-varying load models for use with DA technologies such as VVO. The first algorithm uses AMI data, k-means clustering, and least-squares optimization to create predictive load models for individual electric customers. The second algorithm uses deep learning (via a convolution-based recurrent neural network) to incorporate additional data and increase model accuracy. The improved accuracy of the load models for both algorithms is validated through simulation.
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    Batch Measurement Extremum Seeking Control of Distributed Energy Resources to Account for Communication Delays and Information Loss
    ( 2020-01-07) Sankur, Michael ; Baudette, Maxime ; Macdonald, Jason ; Arnold, Daniel
    Distributed Energy Resources (DER) have great potential to enhance the operation of electric power distribution systems. Previously, we explored the use of 2 Dimensional Extremum Seeking (2D-ES) control algorithms to enable model-free optimal control of DER to provide grid services to both the distribution and transmissions systems. Motivated by preliminary deployments of DER managed by 2D-ES algorithms in hardware-in-the-loop tests and in operational distribution grids, in this work, we extend the control scheme to accommodate communication delays and information loss. We propose a modification to the 2D-ES scheme to allow for the processing of batches of possibly noncontiguous objective function measurements at unknown and possibly uneven intervals. We provide a proof of the convergence of the batch 2D-ES (2D-BES) scheme when optimizing a generic convex objective function, as well as simulation results that demonstrate the suitability of the approach for substation active and reactive power target tracking.
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    Timestamp Error Detection and Estimation for PMU Data based on Linear Correlation between Relative Phase Angle and Frequency
    ( 2020-01-07) Yu, Wenpeng ; Yao, Wenxuan ; Zhao, Yinfeng ; Liu, Yilu
    Time synchronization is essential to synchro-phasor-based applications. However, Timestamp Error (TE) in synchrophasor data can result in application failures. This paper proposes a method for TE detection based on the linear correlation between frequency and relative phase angle. The TE converts the short-term relative phase angle from noise-like signal to one that linear with the frequency. Pearson Correlation Coefficient (PCC) is applied to measure the linear correlation and then detect the timestamp error. The time error is estimated based on the variation of frequency and relative phase angle. Case studies with actual synchrophasor data demonstrate the effectiveness of TE detection and excellent accuracy of TE estimation.