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Recent studies have demonstrated the potential of flexible loads in providing frequency response services. However, uncertainty and variability in various weather-related and end-use behavioral factors often affect the demand-side control performance. This work addresses this problem with the design of a demand-side control to achieve frequency response under load uncertainties. Our approach involves modeling the load uncertainties via stochastic processes that appear as both multiplicative and additive to the system states in closed-loop power system dynamics. Extending the recently developed mean square exponential stability (MSES) results for stochastic systems, we formulate multi-objective linear matrix inequality (LMI)-based optimal control synthesis problems to not only guarantee stochastic stability, but also promote sparsity, enhance closed-loop transient performance, and maximize allowable uncertainties. The fundamental trade-off between the maximum allowable (\textit{critical}) uncertainty levels and the optimal stochastic stabilizing control efforts is established. Moreover, the sparse control synthesis problem is generalized to the realistic power systems scenario in which only partial-state measurements are available. Detailed numerical studies are carried out on IEEE 39-bus system to demonstrate the closed-loop stochastic stabilizing performance of the sparse controllers in enhancing frequency response under load uncertainties; as well as illustrate the fundamental trade-off between the allowable uncertainties and optimal control efforts., accepted for publication at the IEEE Transactions on Power Sysems

Present study was undertaken for assessment of spatial characteristics of road traffic noise at varying intervals viz early morning (8-9 am), late morning (11-12 pm), afternoon (2-3 pm) and evening (6-7 pm) time at ten important locations (near school building) of G. T. Road which is passing through the Burdwan town. Digital noise meter was used for recording the traffic noise and noise contour map was constructed by using Geographical Information System (GIS). The recorded data revealed that the highest and lowest average noise 67.1 dB (A) and 86.9 dB (A), respectively. The results revealed that the performance of the CRTN model in both afternoon and evening time for predicting noise level near school building with a coefficient of determination (R2) are 0.536 and 0.544 and a mean difference of - 1.19 dB (A) and - 0.48 dB (A) between the measured and predicted values respectively. Similarly, Pearson statistics also revealed the strong correlation between measured and predicted noise level at afternoon (r = 0.732, p < 0.016) and evening time (r = 0.744, p < 0.014). However, the predicted traffic noise during early morning and late morning hour are less than 0.5. These low values are due to irregular traffic speed, traffic density and irregular building height are the appropriate reasons for low accuracy in predicting model. Finally, it may be suggested that CRTN model can be a decision tool for predicting equivalent noise level in the city like Burdwan.

Historically, two similar grid-forming droop controls are widely reported in literature—the single-loop and multi-loop droop controls. Although being very similar, the authors find that the dynamic performance and stability characteristics of each control method are very different in a microgrid. Compared with the single-loop droop control, the multi-loop droop control is prone to be less damped and loses stability more easily under some circumstances. This article provides a novel insight into the different dynamic responses of the two basic controls. It points out that the two similar controls adjust the angular frequency and voltage magnitude at different locations within the inverter, resulting in different coupling reactances that impact the dynamic response and stability of microgrids differently. The use of the single-loop droop control results in a larger coupling reactance, which helps improve the dynamic response and stability. This novel insight is verified through full-order small-signal analysis, offline electromagnetic transient simulation, and real-time hardware-in-the-loop simulation experiments. The results show that the microgrid has a larger small-signal stability boundary when using single-loop droop control, and this difference increases as the value of an inverter’s inner filter inductance increases.

The proliferation of inverter-based generation and advanced sensing, controls, and communication infrastructure have facilitated the accelerated deployment of microgrids. A coordinated network of microgrids can maintain reliable power delivery to critical facilities during extreme events. Low inertia offered by the power electronics interfaced energy resources however, can present significant challenges to ensuring stable operation of the microgrids. In this work, distributed small-signal stability conditions for inverter-based microgrids are developed that involve the droop controller parameters and the network parameters such as line impedances, loads, etc. The distributed closed-form parametric stability conditions derived in this paper can be verified in a computationally efficient manner, facilitating the reliable design and operations of networks of microgrids. Dynamic phasor models have been used to capture the effects of electromagnetic transients. Numerical results are presented, along with PSCAD simulations, to validate the analytical stability conditions. Effects of design choices, such as the conductor types, and inverter sizes, on the small-signal stability of inverter-based microgrids are investigated to identify interpretable stable or unstable region estimates., Comment: This paper is accepted in IEEE Transactions on Power Systems

For effective integration of building operations into the evolving demand response programs of the power grid, real-time decisions concerning the use of building appliances for grid services must excel on multiple criteria, ranging from the added value to occupants' comfort to the quality of the grid services. In this paper, we present a data-driven decision-support framework to dynamically rank load control alternatives in a commercial building, addressing the needs of multiple decision criteria (e.g. occupant comfort, grid service quality) under uncertainties in occupancy patterns. We adopt a stochastic multi-criteria decision algorithm recently applied to prioritize residential on/off loads, and extend it to i) complex load control decisions (e.g. dimming of lights, changing zone temperature set-points) in a commercial building; and ii) systematic integration of zonal occupancy patterns to accurately identify short-term grid service opportunities. We evaluate the performance of the framework for curtailment of air-conditioning, lighting, and plug-loads in a multi-zone office building for a range of design choices. With the help of a prototype system that integrates an interactive \textit{Data Analytics and Visualization} frontend, we demonstrate a way for the building operators to monitor the flexibility in energy consumption and to develop trust in the decision recommendations by interpreting the rationale behind the ranking., Comment: accepted in the 2021 American Control Conference

Increasing deployment of advanced sensing, controls, and communication infrastructure enables buildings to provide services to the power grid, leading to the concept of grid-interactive efficient buildings. Since occupant activities and preferences primarily drive the availability and operational flexibility of building devices, there is a critical need to develop occupant-centric approaches that prioritize devices for providing grid services, while maintaining the desired end-use quality of service. In this paper, we present a decision-making framework that facilitates a building owner/operator to effectively prioritize loads for curtailment service under uncertainties, while minimizing any adverse impact on the occupants. The proposed framework uses a stochastic (Markov) model to represent the probabilistic behavior of device usage from power consumption data, and a load prioritization algorithm that dynamically ranks building loads using a stochastic multi-criteria decision-making algorithm. The proposed load prioritization framework is illustrated via numerical simulations in a residential building use-case, including plug-loads, air-conditioners, and plug-in electric vehicle chargers, in the context of load curtailment as a grid service. Suitable metrics are proposed to evaluate the closed-loop performance of the proposed prioritization algorithm under various scenarios and design choices. Scalability of the proposed algorithm is established via computational analysis, while time-series plots are used for intuitive explanation of the ranking choices.

Renewable portfolio standards are targeting high levels of variable solar photovoltaics (PV) in electric distribution systems, which makes reliability more challenging to maintain for distribution system operators (DSOs). Distributed energy resources (DERs), including smart, connected appliances and PV inverters, represent responsive grid resources that can provide flexibility to support the DSO in actively managing their networks to facilitate reliability under extreme levels of solar PV. This flexibility can also be used to optimize system operations with respect to economic signals from wholesale energy and ancillary service markets. Here, we present a novel hierarchical scheme that actively controls behind-the-meter DERs to reliably manage each unbalanced distribution feeder and exploits the available flexibility to ensure reliable operation and economically optimizes the entire distribution network. Each layer of the scheme employs advanced optimization methods at different timescales to ensure that the system operates within both grid and device limits. The hierarchy is validated in a large-scale realistic simulation based on data from the industry. Simulation results show that coordination of flexibility improves both system reliability and economics, and enables greater penetration of solar PV. Discussion is also provided on the practical viability of the required communications and controls to implement the presented scheme within a large DSO.

The promise of data-driven predictive modeling is being increasingly realized in various science and engineering disciplines, where experts are used to the more conventional, simulation-driven modeling practices. However, trust remains a bottleneck for greater adoption of machine learning-based models for domain experts, who might not be necessarily trained in data science. In this paper, we focus on the building energy domain, where physics-based simulations are being complemented or replaced by machine learning-based methods for forecasting energy supply and demand at various spatio-temporal scales. We study the trust problem in close collaboration with energy scientists and engineers and describe how visual analytics can be leveraged for alleviating this trust bottleneck for stakeholders with varying degrees of expertise and analytical goals in this domain.

Horizontal gene transfer is one of the most important mechanisms for microbial evolution and adaptation. It is well known that horizontal gene transfer can be either additive or replacing depending on whether the transferred gene adds itself as a new gene in the recipient genome or replaces an existing homologous gene. Yet, all existing phylogenetic techniques for the inference of horizontal gene transfer assume either that all transfers are additive or that all transfers are replacing. This limitation not only affects the applicability and accuracy of these methods but also makes it difficult to distinguish between additive and replacing transfers.Here, we address this important problem by formalizing a phylogenetic reconciliation framework that simultaneously models both additive and replacing transfer events. Specifically, we (1) introduce the DTRL reconciliation framework that explicitly models both additive and replacing transfer events, along with gene duplications and losses, (2) prove that the underlying computational problem is NP-hard, (3) perform the first experimental study to assess the impact of replacing transfer events on the accuracy of the traditional DTL reconciliation model (which assumes that all transfers are additive) and demonstrate that traditional DTL reconciliation remains highly robust to the presence of replacing transfers, (4) propose a simple heuristic algorithm for DTRL reconciliation based on classifying transfer events inferred through DTL reconciliation as being replacing or additive, and (5) evaluate the classification accuracy of the heuristic under a range of evolutionary conditions. Thus, this work lays the methodological and algorithmic foundations for estimating DTRL reconciliations and distinguishing between additive and replacing transfers.An implementation of our heuristic for DTRL reconciliation is freely available open-source as part of the RANGER-DTL software package from https://compbio.engr.uconn.edu/software/ranger-dtl/.

Unlike conventional generators, inverter-based generation do not possess any rotational inertia. While grid-forming inverters can synthesize small (virtual) inertia via advanced feedback control loops, additional control mechanisms are needed to ensure safety and security of the power grid during transients. In this paper, we propose novel real-time safety-constrained feedback controllers ("safety filters") for droop-based (grid-forming) inverters to ensure transient security of the grid. The safety filter acts as a buffer between the network operational layer and the inverter-control layer, and only lets those dispatch control signals pass to the inverter droop-controller, which are guaranteed to not violate the safety specifications (frequency, voltage, current limits). Using a distributed barrier certificates method, we construct state-inclusive bounds on the allowable control inputs, which guarantee the satisfaction of transient safety specifications. Sum-of-square programming is used to synthesize the safety filters. Numerical simulation results are provided to illustrate the performance of the proposed filter in inverter-based microgrids., accepted for presentation at the 2020 American Control Conference. arXiv admin note: text overlap with arXiv:1903.09743

Effective utilization of flexible loads for grid services, while satisfying end-user preferences and constraints, requires an accurate estimation of the aggregated predictive flexibility offered by the electrical loads. Virtual battery (VB) models are often used to quantify the predictive flexibility in thermostatic loads (e.g. residential air-conditioners, electric water-heaters), which model the temporal evolution of a (virtual) energy state via a first order dynamics including self-dissipation rate, and power and energy capacities as parameters. Uncertainties and lack of information regarding end-usage and equipment models render deterministic VB models impractical. In this paper, we introduce the notion of stochastic VB models, and propose a \textit{variational autoencoder}-based deep learning algorithm to identify the probability distribution of the VB model parameters. Using available sensors and meters data, the proposed algorithm generates not only point estimates of the VB parameters, but also confidence intervals around those values. Effectiveness of the proposed frameworks is demonstrated on a collection of electric water-heater loads, whose operation is driven by uncertain water usage profiles., accepted for presentation at the 2020 American Control Conference

Duplication-Transfer-Loss (DTL) reconciliation is a powerful and increasingly popular technique for studying the evolution of microbial gene families. DTL reconciliation requires the use of rooted gene trees to perform the reconciliation with the species tree, and the standard technique for rooting gene trees is to assign a root that results in the minimum reconciliation cost across all rootings of that gene tree. However, even though it is well understood that many gene trees have multiple optimal roots, only a single optimal root is randomly chosen to create the rooted gene tree and perform the reconciliation. This remains an important overlooked and unaddressed problem in DTL reconciliation, leading to incorrect evolutionary inferences. In this work, we perform an in-depth analysis of the impact of uncertain gene tree rooting on the computed DTL reconciliation and provide the first computational tools to quantify and negate the impact of gene tree rooting uncertainty on DTL reconciliation. Our analysis of a large data set of over 4500 gene families from 100 species shows that a large fraction of gene trees have multiple optimal rootings, that these multiple roots often, but not always, appear closely clustered together in the same region of the gene tree, that many aspects of the reconciliation remain conserved across the multiple rootings, that gene tree error has a profound impact on the prevalence and structure of multiple optimal rootings, and that there are specific interesting patterns in the reconciliation of those gene trees that have multiple optimal roots. Our results show that unrooted gene trees can be meaningfully reconciled and high-quality evolutionary information can be obtained from them even after accounting for multiple optimal rootings. In addition, the techniques and tools introduced in this paper make it possible to systematically avoid incorrect evolutionary inferences caused by incorrect or uncertain gene tree rooting. These tools have been implemented in the phylogenetic reconciliation software package RANGER-DTL 2.0, freely available from http://compbio.engr.uconn.edu/software/RANGER-DTL/ .

Summary SaGePhy is a software package for improved phylogenetic simulation of gene and subgene evolution. SaGePhy can be used to generate species trees, gene trees and subgene or (protein) domain trees using a probabilistic birth–death process that allows for gene and subgene duplication, horizontal gene and subgene transfer and gene and subgene loss. SaGePhy implements a range of important features not found in other phylogenetic simulation frameworks/software. These include (i) simulation of subgene or domain level evolution inside one or more gene trees, (ii) simultaneous simulation of both additive and replacing horizontal gene/subgene transfers and (iii) probabilistic sampling of species tree and gene tree nodes, respectively, for gene- and domain-family birth. SaGePhy is open-source, platform independent and written in Java and Python. Availability and implementation Executables, source code (open-source under the revised BSD license) and a detailed manual are freely available from http://compbio.engr.uconn.edu/software/sagephy/. Supplementary information Supplementary data are available at Bioinformatics online.

The potential of distributed energy resources (including, flexible electrical loads) in providing grid services can be maximized with the recent advancements in demand side control. Effective coordination of the flexible loads for grid services, while satisfying end-user preferences and constraints, requires the knowledge of aggregated predictive flexibility of the distributed energy resources (DERs). Recent works have shown that the aggregated predictive flexibility of DERs can be modeled as a virtual battery (VB) whose state evolution is governed by a first-order dynamics including self-dissipation rate and energy capacity. Identifying the VB model parameters for a collection of DERs, however, is challenging primarily due to the following reasons: (1) the composition of DERs is time-varying and uncertain, with the device availability determined by uncertain end-user behavior, (2) the underlying device models and parameters are mostly unknown and uncertain, and (3) lack of available behind-the-meter sensing and measurements (partly due privacy concerns). As such, data-driven deep learning based frameworks have been proposed in this work to identify aggregated predictive flexibility models of a collection of DERs, using front-of-the-meter data (such as net power consumption, etc.). The effectiveness of the proposed frameworks is demonstrated on an ensemble of residential air conditioners and electric water heaters.

Malicious activities on measurements from sensors like Phasor Measurement Units (PMUs) can mislead the control center operator into taking wrong control actions resulting in disruption of operation, financial losses, and equipment damage. In particular, false data attacks initiated during power systems transients caused due to abrupt changes in load and generation can fool the conventional model-based detection methods relying on thresholds comparison to trigger an anomaly. In this paper, we propose a Koopman mode decomposition (KMD) based algorithm to detect and identify false data attacks in real-time. The Koopman modes (KMs) are capable of capturing the nonlinear modes of oscillation in the transient dynamics of the power networks and reveal the spatial embedding of both natural and anomalous modes of oscillations in the sensor measurements. The Koopman-based spatio-temporal nonlinear modal analysis is used to filter out the false data injected by an attacker. The performance of the algorithm is illustrated on the IEEE 68-bus test system using synthetic attack scenarios generated on GridSTAGE, a recently developed multivariate spatio-temporal data generation framework for simulation of adversarial scenarios in cyber-physical power systems., Comment: This work has been accepted in the 2020 IEEE SmartGridComm

This paper investigates the use of Infrastructure-To-Vehicle (I2V) communication to generate routing suggestions for drivers in transportation systems, with the goal of optimizing a measure of overall network congestion. We define link-wise levels of trust to tolerate the non-cooperative behavior of part of the driver population, and we propose a real-time optimization mechanism that adapts to the instantaneous network conditions and to sudden changes in the levels of trust. Our framework allows us to quantify the improvement in travel time in relation to the degree at which drivers follow the routing suggestions. We then study the resilience of the system, measured as the smallest change in routing choices that results in roads reaching their maximum capacity. Interestingly, our findings suggest that fluctuations in the extent to which drivers follow the provided routing suggestions can cause failures of certain links. These results imply that the benefits of using Infrastructure-To-Vehicle communication come at the cost of new fragilities, that should be appropriately addressed in order to guarantee the reliable operation of the infrastructure., Accepted for presentation at the IEEE 2019 American Control Conference

Low-to-medium voltage distribution networks are experiencing rising levels of distributed energy resources, including renewable generation, along with improved sensing, communication, and automation infrastructure. As such, state estimation methods for distribution systems are becoming increasingly relevant as a means to enable better control strategies that can both leverage the benefits and mitigate the risks associated with high penetration of variable and uncertain distributed generation resources. The primary challenges of this problem include modeling complexities (nonlinear, non-convex power-flow equations), limited availability of sensor measurements, and high penetration of uncertain renewable generation. This paper formulates the distribution system state estimation as a nonlinear, weighted, least squares problem, based on sensor measurements as well as forecast data (both load and generation). We investigate the sensitivity of state estimator accuracy to (load/generation) forecast uncertainties, sensor accuracy, and sensor coverage levels., accepted for presentation at the IEEE 2019 American Control Conference

Integration of distributed energy resources (DERs) in grid planning and control requires estimating and aggregating the active and reactive power flexibility offered by an ensemble of DERs. The flexibility of DERs to provide grid services is, however, often uncertain due to insufficient end-use measurements (e.g. power consumption) and inaccurate forecast (e.g. solar irradiance). In this short note, we propose a numerical simulation based framework for modeling the flexibility in uncertain individual continuous DERs.

Protection equipment is used to prevent damage to induction motor loads by isolating them from power systems in the event of severe faults. Modeling the response of induction motor loads and their protection is vital for power system planning and operation, especially in understanding system's dynamic performance and stability after a fault occurs. Induction motors are usually equipped with several types of protection with different operation mechanisms, making it challenging to develop adequate yet not overly complex protection models and determine their parameters for aggregate induction motor models. This paper proposes a novel cross-categorical transfer learning based framework to determine protection model parameters for aggregate induction motor loads in commercial buildings. Introducing a mathematical abstraction, the task of determining a suitable set of parameters for the protection model in composite load models is formulated as a combination of feature extraction using transferable layers, followed by affine operations through non-transferable layers applied on the previous extracted features. Numerical results are provided to illustrate the application of the framework.

Inverter-interfaced microgrids differ from the traditional power systems due to their lack of inertia. Vanishing timescale separation between voltage and frequency dynamics makes it critical that faster-timescale stabilizing control laws also guarantee by-construction the satisfaction of voltage limits during transients. In this article, we apply a barrier functions method to compute distributed active and reactive power setpoint control laws that certify satisfaction of voltage limits during transients. Using sum-of-squares optimization tools, we propose an algorithmic construction of these control laws. Numerical simulations are provided to illustrate the proposed method., accepted at the IEEE 2019 American Control Conference

As modern engineering systems grow in complexity, attitudes toward a modular design approach become increasingly more favorable. A key challenge to a modular design approach is the certification of robust stability under uncertainties in the rest of the network. In this paper, we consider the problem of identifying the parametric region, which guarantees stability of the connected module in the robust sense under uncertainties. We derive the conditions under which the robust stability of the connected module is guaranteed for some values of the design parameters, and present a sum-of-squares (SOS) optimization-based algorithm to identify such a parametric region for polynomial systems. Using the example of an inverter-based microgrid, we show how this parametric region changes with variations in the level of uncertainties in the network., accepted for presentation at the IEEE 2019 American Control Conference

The potential of distributed energy resources in providing grid services can be maximized with the recent advancements in demand side control. Effective utilization of this control strategy requires the knowledge of aggregate flexibility of the distributed energy resources (DERs). Recent works have shown that the aggregate flexibility of DERs can be modeled as a virtual battery (VB) whose state evolution is governed by a first order system including self-dissipation. The VB parameters (self-dissipation rate, energy capacity) are obtained by solving an optimization problem which minimizes the tracking performance of the ensemble and the proposed first-order model. For the identified first order model, time-varying power limits are calculated using binary search algorithms. Finally, this proposed framework is demonstrated for different homogeneous and heterogeneous ensembles consisting of air conditioners (ACs) and electric water heaters (EWHs)., This is a preprint version of PESGM 2019 (accepted) paper

An optimal coordination of residential motor protection has been considered a potential remedy for many sustained low voltage problems caused by faults in the transmission system. This paper presents the modeling of residential end-use motor loads equipped with dedicated protection devices in a dynamic-phasor-based simulation program. Common types of mechanical torque are implemented for different functional motors in typical single-family and multifamily homes. The point-on-wave study is performed to examine the responses of air-conditioner (A/C) compressor to the instant of voltage depression. Voltage depressions are imposed at the head of the feeder supplying multiple residential homes to investigate the behaviors of A/C compressor stalling, actions of motor tripping and reconnection, and system-level responses to motor stalling and motor protection.

Protection equipment is used to prevent damage to induction motor loads by isolating them from power systems in the event of severe faults. Modeling the response of induction motor loads and their protection is vital for power system planning and operation, especially in understanding system's dynamic performance and stability after a fault occurs. Induction motors are usually equipped with several types of protection with different operation mechanisms, making it challenging to develop adequate yet not overly complex protection models and determine their parameters for aggregate induction motor models. This paper proposes an optimization-based nonlinear regression framework to determine protection model parameters for aggregate induction motor loads in commercial buildings. Using a mathematical abstraction, the task of determining a suitable set of parameters for the protection model in composite load models is formulated as a nonlinear regression problem. Numerical examples are provided to illustrate the application of the framework. Sensitivity studies are presented to demonstrate the impact of lack of available motor load information on the accuracy of the protection models., Comment: accepted for presentation at The Eleventh Conference on Innovative Smart Grid Technologies (ISGT 2020)

Recent studies have shown that the aggregated dynamic flexibility of an ensemble of thermostatic loads can be modeled in the form of a virtual battery. The existing methods for computing the virtual battery parameters require the knowledge of the first-principle models and parameter values of the loads in the ensemble. In real-world applications, however, it is likely that the only available information are end-use measurements such as power consumption, room temperature, device on/off status, etc., while very little about the individual load models and parameters are known. We propose a transfer learning based deep network framework for calculating virtual battery state of a given ensemble of flexible thermostatic loads, from the available end-use measurements. This proposed framework extracts first order virtual battery model parameters for the given ensemble. We illustrate the effectiveness of this novel framework on different ensembles of ACs and WHs., 8 pages, 6 figures, accepted to IEEE ICMLA 2018

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., accepted for presentation at the 52nd Hawaii International Conference on System Sciences

Protection strategies for transmission and distribution systems have been extensively investigated to facilitate better coordination of physical protection devices. A diverse range of functional motors with dedicated protection schemes are being used more and more in commercial, residential, and industrial buildings. This paper focuses on simulating several of the most popular protection schemes using the ElectroMagnetic Transient Program (EMTP) model for three-phase and single-phase induction motors in existing commercial buildings connected to typical distribution feeders. To investigate the behaviors of single-phase motors stalling, the actions of motor protection and reconnections, and the impacts of device-level protection on system-level dynamics, we imposed voltage depressions at the head of a feeder fully loaded with functional induction motors. Several distribution feeders are represented in a standard IEEE 39-bus transmission system to simulate fault-induced delayed voltage recovery (FIDVR) and explore mitigation strategies by optimally configuring the building-level motor protection settings., 6 figures, 2 tables, 5 pages, IEEE PES General Meeting 2018

International audience; Transient stability is an important issue in power systems but difficult to quantify analytically. Most of the approaches in this case lie on a very simplified model of the generators, usually reduced to the swing equation. In this work, a more detailed model which includes voltage dynamics and both voltage and frequency regulators is considered to get more realistic results. The used methodology for algorithmic construction of Lyapunov functions is based on recent advances in the field of positive polynomials. This analysis framework uses an algebraic reformulation technique that recasts the systems dynamics into a set of polynomial differential algebraic equations in conjunction with a sum of Squares method to search for a Lyapunov function. Next, linear matrix inequalities are used to expand the region of attraction of the considered equilibrium point. The results are checked against an experimental (by extensive numerical simulations) evaluation of the region of attraction. Index Terms-Lyapunov theory, nonlinear systems, power systems analysis, region of attraction, sum of squares.

Protection plays a critical role in determining the dynamic response of the induction motor loads and the overall performance of composite load models when subjected to voltage sags or disturbances. Up to date, due to lack of understanding of the aggregate performance of the protection of motors, protection modeling of the motors becomes one of the least accurate parts in dynamic (composite) load models. This paper fills this gap by providing detailed analysis of the motor loads and their associated protection in commercial buildings and deriving the aggregate protection response based on integrated transmission and distribution dynamic co-simulation. The results showed that the aggregate protection response of motor loads varies significantly among different classes, which indicates the protection modeling that has been developed based on the assumption of similar or even identical responses and used in some composite load models need to be updated.

With increasing availability of communication and control infrastructure at the distribution systems, it is expected that the distributed energy resources (DERs) will take an active part in future power systems operations. One of the main challenges associated with integration of DERs in grid planning and control is in estimating the available flexibility in a collection of (heterogeneous) DERs, each of which may have local constraints that vary over time. In this work, we present a geometric approach for approximating the flexibility of a DER in modulating its active and reactive power consumption. The proposed method is agnostic about the type and model of the DERs, thereby facilitating a plug-and-play approach, and allows scalable aggregation of the flexibility of a collection of (heterogeneous) DERs at the distributed system level. Simulation results are presented to demonstrate the performance of the proposed method., accepted for presentation at the Power Systems Computations Conference 2018

Motor loads are equipped with protective mechanisms to prevent any damage to the loads in the event of a fault. Accurate representation of dynamic motor loads and their protection schemes is vital for power system planning and operation, especially in understanding system's response moments after a fault has occurred. Existing load models are inadequate to capture the behavior of protection mechanisms which can vary widely between different end-use appliances. This article proposes a methodology to generate composite protection profiles for commercial building motor loads. Combining knowledge of various protection schemes available in various end-use appliances, with the commercial buildings survey data (from U.S. Energy Information Administration) and typical end-use profiles generated using EnergyPlusTM), composite load protection profiles were generated at one-hour interval over a year, for different commercial buildings in representative cities from different climate zones.

Higher penetration of renewable generation will increase the demand for adequate (and cost-effective) controllable resources on the grid that can mitigate and contain the contingencies locally before it can cause a network-wide collapse. However, end-use constraints can potentially lead to load unavailability when an event occurs, leading to unreliable demand response services. Sensors measurements and knowledge of the local load dynamics could be leveraged to improve the performance of load control algorithms. In the context of hierarchical frequency response using ensemble of switching loads, we present a metric to evaluate the fitness of each device in successfully providing the ancillary service. Furthermore a fitness-based assignment of control set-points is formulated which achieves reliable performance under different operating conditions. Monte Carlo simulations of ensembles of electric water heaters and residential air-conditioners are performed to evaluate the proposed control algorithm., Comment: accepted at the 2018 IEEE Conference on Control Theory and Applications

We provide a distributed coordinated approach to the stability analysis and control design of large-scale nonlinear dynamical systems by using a vector Lyapunov functions approach. In this formulation the large-scale system is decomposed into a network of interacting subsystems and the stability of the system is analyzed through a comparison system. However finding such comparison system is not trivial. In this work, we propose a sum-of-squares based completely decentralized approach for computing the comparison systems for networks of nonlinear systems. Moreover, based on the comparison systems, we introduce a distributed optimal control strategy in which the individual subsystems (agents) coordinate with their immediate neighbors to design local control policies that can exponentially stabilize the full system under initial disturbances. We illustrate the control algorithm on a network of interacting Van der Pol systems.

The Koopman operator has recently garnered much attention for its value in dynamical systems analysis and data-driven model discovery. However, its application has been hindered by the computational complexity of extended dynamic mode decomposition; this requires a combinatorially large basis set to adequately describe many nonlinear systems of interest, e.g. cyber-physical infrastructure systems, biological networks, social systems, and fluid dynamics. Often the dictionaries generated for these problems are manually curated, requiring domain-specific knowledge and painstaking tuning. In this paper we introduce a deep learning framework for learning Koopman operators of nonlinear dynamical systems. We show that this novel method automatically selects efficient deep dictionaries, outperforming state-of-the-art methods. We benchmark this method on partially observed nonlinear systems, including the glycolytic oscillator and show it is able to predict quantitatively 100 steps into the future, using only a single timepoint, and qualitative oscillatory behavior 400 steps into the future., 16 pages, 5 figures

Potential of electrical loads in providing grid ancillary services is often limited due to the uncertainties associated with the load behavior. A knowledge of the expected uncertainties with a load control program would invariably yield to better informed control policies, opening up the possibility of extracting the maximal load control potential without affecting grid operations. In the context of frequency responsive load control, a probabilistic uncertainty analysis framework is presented to quantify the expected error between the target and actual load response, under uncertainties in the load dynamics. A closed-form expression of an optimal demand flexibility, minimizing the expected error in actual and committed flexibility, is provided. Analytical results are validated through Monte Carlo simulations of ensembles of electric water heaters., IEEE International Conference on Smart Grid Communications

Summary RANGER-DTL 2.0 is a software program for inferring gene family evolution using Duplication-Transfer-Loss reconciliation. This new software is highly scalable and easy to use, and offers many new features not currently available in any other reconciliation program. RANGER-DTL 2.0 has a particular focus on reconciliation accuracy and can account for many sources of reconciliation uncertainty including uncertain gene tree rooting, gene tree topological uncertainty, multiple optimal reconciliations and alternative event cost assignments. RANGER-DTL 2.0 is open-source and written in C++ and Python. Availability and implementation Pre-compiled executables, source code (open-source under GNU GPL) and a detailed manual are freely available from http://compbio.engr.uconn.edu/software/RANGER-DTL/. Supplementary information Supplementary data are available at Bioinformatics online.

Stability analysis tools are essential to understanding and controlling any engineering system. Recently sum-of-squares (SOS) based methods have been used to compute Lyapunov based estimates for the region-of-attraction (ROA) of polynomial dynamical systems. But for a real-life large scale dynamical system this method becomes inapplicable because of growing computational burden. In such a case, it is important to develop a subsystem based stability analysis approach which is the focus of the work presented here. A parallel and scalable algorithm is used to infer stability of an interconnected system, with the help of the subsystem Lyapunov functions. Locally computable control laws are proposed to guarantee asymptotic stability under a given disturbance., to be published in 2015 American Control Conference, Chicago, USA

Sum-of-squares (SOS) methods have been shown to be very useful in computing polynomial Lyapunov functions for systems of reasonably small size. However for large scale systems it is necessary to use a scalable alternative using vector Lyapunov functions. Earlier works have shown that under certain conditions the stability of an interconnected system can be studied through suitable comparison equations. However finding such comparison equations can be non-trivial. In this work we propose an SOS based systematic procedure to directly compute the comparison equations for interconnected system with polynomial dynamics. With an example of interacting Van der Pol systems, we illustrate how this facilitates a scalable and parallel approach to stability analysis., Comment: accepted for the 54th IEEE Conference on Decision and Control

We explore methods to use thermostatically controlled loads (TCLs), such as water heaters and air conditioners, to provide ancillary services by assisting in balancing generation and load. We show that by adding simple imbedded instructions and a small amount of memory to temperature controllers of TCLs, it is possible to design open-loop control algorithms capable of creating short-term pulses of demand response without unwanted power oscillations associated with temporary synchronization of the TCL dynamics. By moving a small amount of intelligence to each of the end point TCL devices, we are able to leverage our knowledge of the time dynamics of TCLs to shape the demand response pulses for different power system applications. A significant benefit of our open-loop method is the reduction from two-way to one-way broadcast communication which also eliminates many basic consumer privacy issues. In this work, we focus on developing the algorithms to generate a set of fundamental pulse shapes that can subsequently be used to create demand response with arbitrary profiles. Demand response control methods, such as the one developed here, open the door to fast, nonperturbative control of large aggregations of TCLs., 36 pages, 14 figures

Recent studies on control of aggregate power of an ensemble of thermostatically-controlled-loads (TCLs) have been concentrated on shifting the temperature set points of each TCL in the population. A sudden shift in the set point, however, is known to be associated with undesirable power oscillations which require closed-loop control strategies to regulate the aggregate power consumption of the population. In this article, we propose a new approach which we term as a "safe protocol" to implement the shift in temperature set point. It is shown analytically and verified numerically that by shifting the set point "safely" the aggregate power consumption can be changed to a different value within a time frame of the order of a TCL's cycle duration and avoid the undesired oscillations seen otherwise in a "sudden" shift. We discuss how the excess aggregate energy transferred under a safe shift in the set point could potentially mitigate the burden due to abnormal energy generation within a short time span., 6 pages; American Control Conference, Montreal, 2012