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In this paper, we present SoDa, an irradiance-based synthetic Solar Data generation tool to generate realistic sub-minute solar photovoltaic (PV) output power time series, that emulate the weather pattern for a certain geographical location. Our tool relies on the National Solar Radiation Database (NSRDB) to obtain irradiance and weather data patterns for the site. Irradiance is mapped onto a PV model estimate of a solar plant’s 30-min power output, based on the configuration of the panel. The working hypothesis to generate high-resolution (e.g. 1 second) solar data is that the conditional distribution of the time series of solar power output given the cloud density is the same for different locations. We therefore propose a stochastic model with a switching behavior due to different weather regimes as provided by the cloud type label in the NSRDB, and train our stochastic model parameters for the cloudy states on the high-resolution solar power measurements from a Phasor Measurement Unit (PMU). In the paper we introduce the stochastic model, and the methodology used for the training of its parameters. The numerical results show that our tool creates synthetic solar time series at high resolutions that are statistically representative of the measured solar power and illustrate how to make use of the tool to create synthetic data for arbitrary sites in the footprint covered by the NSRDB.

The efficacy of cardiac rehabilitation in heart-failure patients who received a left-ventricular assist device (LVAD) instead of heart transplantation (HTx) is still unclear. This study aims to evaluate whether cardiac rehabilitation is beneficial in LVAD as HTx patients in the short term and whether its effects in LVAD patients persist over time. Twenty-five LVAD patients were evaluated by functional and psychological tests at admission (T0) and discharge (T1) of a 4-week inpatient structured rehabilitation program, and follow-ups 3 (T2), 6 (T3), and 12 months (T4) after discharge. Twenty-five matched HTx patients were also studied from T0 to T1 to compare the improvements in the six-minute walk test (6MWT). The quality-of-life scores substantially improved in LVAD patients and the 6MWT showed the same functional recovery as in HTx patients from T0 to T1. After T1, numerous LVAD patients withdrew from the study. However, the 6MWT outcome increased further from T1 to T3, with a positive trend during the follow-ups. Hemoglobin and the ventilatory performance increased, and the psychological perception of heart-failure symptoms and pain further improved at T2. In conclusion, exercise-based rehabilitation programs provide similar beneficial effects in LVAD and HTx patients, without deterioration in LVAD patients up to 12 months after discharge.

In this paper, a stochastic model is proposed for a joint statistical description of solar photovoltaic (PV) power and outdoor temperature. The underlying correlation emerges from solar irradiance that is responsible in part for both the variability in solar PV power and temperature. The proposed model can be used to capture the uncertainty in solar PV power and its correlation with the electric power consumption of thermostatically controlled loads. First, a model for solar PV power that explicitly incorporates the stochasticity due to clouds via a regime-switching process between the three classes of sunny , overcast and partly cloudy is proposed. Then, the relationship between temperature and solar power is postulated using a second-order Volterra model. This joint modeling is leveraged to develop a joint probabilistic forecasting method for solar PV power and temperature. Real-world datasets that include solar PV power and temperature measurements in California are analyzed and the effectiveness of the joint model in providing probabilistic forecasts is verified. The proposed forecasting methodology outperforms several reference methods thus portraying that the underlying correlation between temperature and solar PV power is well defined and only requires a simple lower-complexity sampling space.

We consider the operation of multiple charging network operators (CNOs), each serving a population of electric vehicle (EV) drivers who subscribe to them for charging services. The CNOs set retail prices for the charging stations they operate, which effectively control the electricity demand and queues formed at their charging stations. The stations’ electricity demand also ties the CNOs’ retail optimization problem to their position in the wholesale market of electricity, creating a coupling between the electric transportation systems and the bulk power grid, which is managed by an independent system operator (ISO). In this paper, we provide retail pricing mechanisms and iterative wholesale market-clearing algorithms that together maximize social welfare in this coupled system. Specifically, our algorithm would enable the CNO to reduce its electricity costs and the customers’ travel delays (including charging station queueing times) and the ISO to reduce societal electricity costs by tapping into the EVs’ flexibility as mobile electric loads. Our theoretical analysis relies on the application of congestion pricing techniques to a capacitated user equilibrium problem on an energy-and-time expanded transportation network model. This expanded network allows us to track the travel costs of individual EVs, including their limited battery charge, the cost of waiting in queues at public charging stations, as well as the temporally variant nature of travel times.

OBJECTIVE Left ventricular assist devices (LVADs) are increasingly used for temporary circulatory support until transplant or as destination therapy for patients with end-stage heart failure. Understanding patients' attitudes and resources is crucial to support them. METHODS Sixty-one LVAD recipients (55 [10] years, 10% women, 15% destination therapy) participating in cardiac rehabilitation (CR) postimplant underwent assessment of coping styles (Coping Orientation for Problem Experiences), quality of life (Medical Outcomes Study Short-Form 36, Minnesota Living with Heart Failure Questionnaire), and mood (Hospital Anxiety and Depression Scale). RESULTS Cluster analysis identified two homogeneous groups: C1 (n = 18) patients had higher anxiety (9.6 [5.0] versus 4.0 [2.7], p < .001), depression (10 [3.0] versus 4 [2.7], p < .001), and avoidant coping (29 [5.2] versus 21.2 [3.8], p < .001) scores and worse quality of life (Short-Form 36 of the Medical Outcomes Study Physical Component Scale 31 [5.3] versus 35.6 [6.9], p < .001; Mental Component Scale 34.3 [6.8] versus 50.3 [8], p < .001; MLHFQ physical 26 [7.2] versus 13.9 [8.2], p < .001; emotional 17.7 [3.4] versus 3.86 [3.2], p < .001) than C2 participants (n = 43). During 31 (15-54) months, 20 patients (33%) died. By Cox multivariable analysis, after adjustment for age, sex, Interagency Registry for Mechanically Assisted Circulatory Support level at surgery, and meters walked during 6 minutes at CR discharge, C1 was associated with mortality (hazard ratio = 2.858; 95% confidence interval = 1.102-7.408, p = .031; model χ = 7.286, df = 5, p = .20). Survival was 44% in C1 and 77% in C2 (log-rank p = .033). CONCLUSIONS Among patients discharged from CR after LVAD implant, one third show a profile characterized by worse perceived quality of life, mood disorders, and dysfunctional coping that is associated with long-term mortality, and these factors are potential targets for post-LVAD intensive support.

The deployment of DER with smart-inverter functionality is increasing the controllable assets on power distribution networks and, consequently, the cyber-physical attack surface. Within this work, we consider the use of reinforcement learning as an online controller that adjusts DER Volt/Var and Volt/Watt control logic to mitigate network voltage unbalance. We specifically focus on the case where a network-aware cyber-physical attack has compromised a subset of single-phase DER, causing a large voltage unbalance. We show how deep reinforcement learning successfully learns a policy minimizing the unbalance, both during normal operation and during a cyber-physical attack. In mitigating the attack, the learned stochastic policy operates alongside legacy equipment on the network, i.e. tap-changing transformers, adjusting optimally predefined DER control-logic.

A framework for natural gas pipelines is developed in a context similar to the theory of electric transmission lines. The system of semi-linear partial differential equations describing the time-dependent flow of natural gas is linearized around the steady-state flow. Additional approximations lead to a constant coefficient linear system that is equivalent to an electrical circuit that is analytically solvable and admits an ABCD matrix representation of input and output. The sinusoidal steady-state operation of natural gas pipelines is analysed including the distortion of waves. It is shown that the timing of the propagation of phases and other events is accurately represented in the approximation. The quantitative accuracy for flux and gas density of the approximation depending on different operating scenarios and depending on the frequency of the disturbances is documented.

This applied research paper introduces a novel framework for integrating hardware security and blockchain functionality with grid-edge devices to establish a distributed cyber-security mechanism that verifies the provenance of messages to and from the devices. Expanding the idea of Two Factor Authentication and Hardware Root of Trust, this work describes the development of a Cryptographic Trust Center(TM) (CTC(TM)) chip integrated into grid-edge devices to create uniform cryptographic key management. Product managers, energy system designers, and security architects can utilize this modular framework as a unified approach to manage distributed devices of various vendors, vintages, and sizes. Results demonstrate the application of CTC(TM) to a blockchain-based Transactive Energy (TE) platform for provisioning of cryptographic keys and improved uniformity of the operational network and data management. This process of configuring, installing, and maintaining keys is described as Eco-Secure Provisioning(TM) (ESP(TM)). Laboratory test results show the approach can resolve several cyber-security gaps in common blockchain frameworks such as Hyperledger Fabric., 2021 North American Power Symposium

Background Patients with advanced heart failure undergoing heart transplant (HTx) or left ventricular assist device (LVAD) implant are at high risk of magnesium deficiency, that may favor development of diabetes. We aimed to comparatively assess prevalence and correlates of hypomagnesemia during cardiac rehabilitation between 51 HTx and 46 LVAD recipients. Methods and results We measured serum magnesium and correlated it to clinical and laboratory findings upon admission (T1 ) and at discharge (T2) from cardiac rehabilitation. Among LVAD, magnesium levels increased from admission to discharge. Among HTx, magnesium concentrations were below normal in 33% and 47% at T1 and T2 , respectively, and decreased from admission to discharge. HTx on tacrolimus showed greater decreases in magnesium and increases in glucose levels than those on cyclosporine. Magnesium levels were inversely associated with >15 mg/dL increased glucose concentrations between T2 and T1 (HR 0.373, 95% CI 0.154-0.903, P = .029) after adjustment for pre-existing diabetes, insulin resistance markers, calcineurin inhibitors (cyclosporine/tacrolimus), prednisone doses, and magnesium supplementation. Conclusion Hypomagnesemia is rare in LVAD recipients, but common within 1 month from HTx, worsens during rehabilitation, despite immunosuppression tapering and magnesium supplements, and is independently associated to increasing glucose levels. Studies evaluating whether correcting hypomagnesemia improves outcome are warranted.

A futuristic infrastructure model is envisioned with distributed modules that can produce solar energy, have a storage system and provide services of lighting, electric-vehicle charging and communications. A stochastic model is formulated for the solar power production and overall consumption of power. Resource allocation in such a system translates to taking decisions in a foresighted manner while maximizing a social surplus with the goal of serving all power demands optimally. A stochastic dynamic programming approach is formulated for the same and a myopic policy is illustrated while discussing the trade-offs between serving competing demands.

We consider a large-scale optimization problem with private utilities over a network where servers are connected through a graph and each server has their own edge devices connected to it. This setup finds applications in many network architectures from wireless communications to power networks and to supply chain management. Over this architecture, we provide a decentralized federated optimization algorithm that uses decomposition methods along with a single round of consensus exchange on the global dual variables. The convergence performance of the method is illustrated with numerical examples.

Conventional cyber-security intrusion detection systems monitor network traffic for malicious activity and indications that an adversary has gained access to the system. The approach discussed here expands the idea of a traditional intrusion detection system within electrical power systems, specifically power distribution networks, by monitoring the physical behavior of the grid. This is achieved through the use of high-rate distribution Phasor Measurement Units (PMUs), alongside SCADA packets analysis, for the purpose of monitoring the behavior of discrete control devices. In this work we present a set of algorithms for passively learning the control logic of voltage regulators and switched capacitor banks. Upon detection of an abnormal operation, the operator is alerted and further action can be taken. The proposed learning algorithms are validated on both simulated data and on measured PMU data from a utility pilot deployment site.

As the distribution grid moves toward a tightly monitored network, it is important to automate the analysis of the enormous amount of data produced by the sensors to increase the operators situational awareness about the system. In this paper, focusing on microphasor measurement unit ( $\mu \text{PMU}$ ) data, we propose a hierarchical architecture for monitoring the grid and establish a set of analytics and sensor fusion primitives for the detection of abnormal behavior in the control perimeter. Due to the key role of the $\mu \text{PMU}$ devices in our architecture, an optimal $\mu \text{PMU}$ placement with limited number of sensors is also described that finds the best location of the devices with respect to our rules. The effectiveness of the proposed methods are tested through the synthetic and real $\mu \text{PMU}$ data.

Existing radio access networks (RANs) allow only for very limited sharing of the communication and computation resources among wireless operators and heterogeneous wireless technologies. We introduce the LayBack architecture to facilitate communication and computation resource sharing among different wireless operators and technologies. LayBack organizes the RAN communication and multi-access edge computing (MEC) resources into layers, including a devices layer, a radio node (enhanced Node B and access point) layer, and a gateway layer. LayBack positions the coordination point between the different operators and technologies just behind the gateways and thus consistently decouples the fronthaul from the backhaul. The coordination point is implemented through a software defined networking (SDN) switching layer that connects the gateways to the backhaul (core) network layer. A unifying SDN orchestrator implements an SDN-based management framework that centrally manages the fronthaul and backhaul communication and computation resources and coordinates the cooperation between different wireless operators and technologies. We illustrate the capabilities of the introduced LayBack architecture and SDN-based management framework through a case study on a novel fluid cloud RAN (CRAN) function split. The fluid CRAN function split partitions the RAN functions into function blocks that are flexibly assigned to MEC nodes, effectively implementing the RAN functions through network function virtualization. We find that for non-uniform call arrivals, the computation of the function blocks with resource sharing among operators increases a revenue rate measure by more than 25% compared to the conventional CRAN where each operator utilizes only its own resources.

To introduce more automation in the distribution level of the power system, increasingly more data are needed to serve as input to control algorithms. To examine the complex interaction between the various layers of the system and verify the effectiveness of automation, difficult to obtain, realistic test systems are necessary. An algorithm for automatically synthesizing realistic medium voltage distribution feeders, and thus circumvent the data access problem, is presented. The algorithm treats distribution system feeders as graphs with nodes and edges, each with various properties, and leverages this structure to search for emerging statistical patterns. Using a large data set from a DSO in The Netherlands, clear statistical distributions are identified linking properties, such as load, node degree, or cable length, to the feeder structure. Specifically, many properties are linked to a node's or edge's distance, in hops, from the primary substation. With consideration for standard engineering practices, the statistical trends are exploited in the synthesis process, to generate feeders, which display similar characteristics to the real samples. The KL-divergence is used in the evaluation of analysis and synthesis results. Beyond solving the data access problem, the use of automatically generated, synthetic, distribution systems will enable testing and validation techniques, such as Monte Carlo simulations, which are currently not possible in this field, where single test cases are the norm.

Power systems often rely on natural gas pipeline networks to supply fuel for gas-fired generation. Market inefficiencies and a lack of formal coordination between the wholesale power and gas delivery infrastructures may magnify the broader impact of a cyber-attack on a natural gas pipeline. In this study we present a model that can be used to quantify the impact of cyber-attacks on electricity and gas delivery operations. We model activation of cyber-attack vectors that attempt to gain access to pipeline gas compressor controls using a continuous-time Markov chain over a state space based on the gas operator Industrial Control System firewall zone partition. Our approach evaluates the operating states and decision-making in the networks using physically realistic and operationally representative models. We summarize these models, the sequence of analyses used to quantify the impacts of a cyber-incident, and propose a Monte Carlo simulation approach to quantify the resulting effect on the reliability of the bulk power system by the increase in operational cost. The methodology is applied to a case study of interacting power, gas, and cyber test networks.

With the emergence of small cell networks and fifth-generation (5G) wireless networks, the backhaul becomes increasingly complex. This study addresses the problem of how a central SDN orchestrator can flexibly share the total backhaul capacity of the various wireless operators among their gateways and radio nodes (e.g., LTE enhanced Node Bs or Wi-Fi access points). In order to address this backhaul resource allocation problem, we introduce a novel backhaul optimization methodology in the context of the recently proposed LayBack SDN backhaul architecture. In particular, we explore the decomposition of the central optimization problem into a layered dual decomposition model that matches the architectural layers of the LayBack backhaul architecture. In order to promote scalability and responsiveness, we employ different timescales, i.e., fast timescales at the radio nodes and slower timescales in the higher LayBack layers that are closer to the central SDN orchestrator. We numerically evaluate the scalable layered optimization for a specific case of the LayBack backhaul architecture with four layers, namely a radio node (eNB) layer, a gateway layer, an operator layer, and central coordination in an SDN orchestrator layer. The coordinated sharing of the total backhaul capacity among multiple operators lowers the queue lengths compared to the conventional backhaul without sharing among operators.

In this paper, we present a distributed array processing algorithm to analyze the power output of solar photo-voltaic (PV) installations, leveraging the low-rank structure inherent in the data to estimate possible faults. Our multi-agent algorithm requires near-neighbor communications only and is also capable of jointly estimating the common low rank cloud profile and local shading of panels. To illustrate the workings of our algorithm, we perform experiments to detect shading faults in solar PV installations within a single ZIP code. Additionally, we also derive a Bayesian lower bound on the shading parameter’s mean squared estimation error. The results are promising and show that we can successfully estimate the fraction of partial shading in solar installations that can usually go unnoticed.

The pulse-coupled synchronization and scheduling (PulseSS) protocol is proposed in this paper for simultaneous synchronization and scheduling of communication activities in clustered wireless sensor networks (WSNs), by emulating the emergent behavior of pulse-coupled oscillator (PCO) networks in mathematical biology. Different from existing works that address synchronization and scheduling (i.e., desynchronization) separately, PulseSS provides a coordination signaling mechanism that achieves decentralized network synchronization and time division multiple access scheduling simultaneously at different time scales for clustered WSNs. Here, we assume that the nodes are connected only locally via their respective cluster heads. Moreover, PulseSS addresses the issue of propagation delays, that may plague the accuracy of PCO synchronization in practice, by providing ways to estimate and precompensate for these values locally at the sensors (i.e., PCOs). At the same time the protocol retains the adaptivity and light-weight nature of PCO protocols both in terms of signaling and computations. Simulations of both physical and medium access control layers show a synchronization accuracy of factions of microseconds above 15 dB of signal to interference and noise ratio for a five cluster network. A hardware implementation of PulseSS using TinyOS is also provided to corroborate the real world applicability of our protocol.

This paper develops an active sensing method to estimate the relative weight (or trust) agents place on their neighbors' information in a social network. The model used for the regression is based on the steady state equation in the linear DeGroot model under the influence of stubborn agents, i.e., agents whose opinions are not influenced by their neighbors. This method can be viewed as a \emph{social RADAR}, where the stubborn agents excite the system and the latter can be estimated through the reverberation observed from the analysis of the agents' opinions. The social network sensing problem can be interpreted as a blind compressed sensing problem with a sparse measurement matrix. We prove that the network structure will be revealed when a sufficient number of stubborn agents independently influence a number of ordinary (non-stubborn) agents. We investigate the scenario with a deterministic or randomized DeGroot model and propose a consistent estimator of the steady states for the latter scenario. Simulation results on synthetic and real world networks support our findings., Comment: Final version appeared in IEEE Transactions on Signal and Information Processing over Networks ( Volume: 2, Issue: 3, Sept. 2016 )

In this paper, we explore the application of system identification techniques to the inference of a model that characterizes crowd dynamics, inspired by the social force model proposed by Helbing and Molnar. We focus then on sensor observations of pedestrians' actions considering that wearables, smart mobile phones and other IoT devices embedded in the environment give significant insights on their expected mobility patterns. Previous work using IoT sensors to uncover social interactions is not based on mathematical models, while most models used for tracking mobility ignore the strong coupling between the model-agents as well as their surroundings. Our aim is to bridge these approaches, by capturing in the data model the swarming behavior of the network, including social interactions

The inter-dependency between the gas and electric grid is growing as the reliance on the gas-fired generators increases. Strong coupling between two large infrastructures requires a coordinated planning and operational framework. In fact, the constraints of both grids must be respected, while their objective functions are co-optimized. In this paper, we present a new formulation for Gas and Electric Grid Coordinated Unit Commitment (GECUC). We apply the standard DC power flow approximation and propose a new formulation to relax the nonlinear gas grid constraints so that the GECUC can be mapped into a Mixed-Integer Linear Program (MILP). In addition, the N −1 generator contingency security constraints are extended to include the gas grid constraints and find the required reserve from gas wells and generators after the loss of a generating unit.

This work proposes a new method for securing the gossip algorithm for average consensus on communication networks. The gossip algorithm is appealing for its ability to harness distributed computational resources while adapting to arbitrarily connected networks without coordination overhead, however it is inherently vulnerable to the insider attack by adversarial node since each node locally updates its local states and passes information to its neighbors without supervision. In light of this, this work proposes new methods for detecting and localizing adversarial nodes using a neural network system. We show that our neural network-based method delivers a significantly improved detection and localization performance, compared to the state of the art.

BACKGROUND Although cardiac rehabilitation (CR) is cost- effective in improving the health of patients with coronary heart disease (CHD), less than half of eligible CHD patients attend a CR program. Innovative web-based technologies might improve CR delivery and utilization. We assessed the feasibility and impact on functional capacity and secondary prevention targets of a long-term web-monitored exercise-based CR maintenance program. METHODS Low- to moderate-risk CHD patients were recruited at discharge from inpatient CR after a coronary event or revascularization. We developed an interactive web-based platform for secure home individual access control, monitoring, and validation of exercise training. Of 86 eligible patients, 26 consented to participate in the study intervention (IG). Using a quasi-experimental design, we recruited in parallel 27 eligible patients, unavailable for regular web monitoring, who consented to a follow-up visit as usual care (UC). RESULTS Among IG, active daily data transmission was 100% during month 1, 88% at month 3, and 81% at 6 months, with sustained improvement in self-reported physical activity beginning with the first week after discharge from inpatient CR (2467 [1854-3554] MET-min/wk) to month 3 (3411 [1981-5347] MET-min/wk, P = .019). Both groups showed favorable changes over time in lipid profile, ventricular function, distance walked in 6 min, and quality of life. At 6 mo, IG achieved a significantly higher proportion of cardiovascular risk factor targets than UC (75 ± 20% vs 59 ± 30%, P = .029). CONCLUSIONS Our web-based home CR maintenance program was feasible, well-accepted, and effective in improving physical activity during 6 mo and achieved higher overall adherence to cardiovascular risk targets than UC.

This paper describes a unifying optimization framework to share backhaul network resources across different operators and wireless platforms. The architecture we consider, named LayBack, requires introducing a unifying Software Defined Network (SDN) orchestrator, sited where their respective traffic streams meet: at the wireless network backhaul. The work we present proposes a scalable decomposition of the resource allocation problem across different layers and time-scales.

We provide a compressive-measurement based method to detect susceptible agents who may receive misinformation through their contact with ‘stubborn agents’ whose goal is to influence the opinions of agents in the network. We consider a DeGroot-type opinion dynamics model where regular agents revise their opinions by linearly combining their neighbors' opinions, but stubborn agents, while influencing others, do not change their opinions. Our proposed method hinges on estimating the temporal difference vector of network-wide opinions, computed at time instances when the stubborn agents interact. We show that this temporal difference vector has approximately the same support as the locations of the susceptible agents. Moreover, both the interaction instances and the temporal difference vector can be estimated from a small number of aggregated opinions. The performance of our method is studied both analytically and empirically. We show that the detection error decreases when the social network is better connected, or when the stubborn agents are ‘less talkative’.

Learning the underlying structure of a social network and the influence of various agents is a fundamental problem for designing optimal actions, such as targeted advertisement. In many studies the structure of the network is assumed to be known based on the existence of an interaction or because the agents have professed a friendship. However, it is more reasonable to assume that the underlying opinion dynamics are shaped by a weighted directed graph that is latent. Therefore, it is crucial to estimate the strength of the agents’ influence on each other to single out which interactions have little effect on the dynamics versus those that truly matter. The goal of this chapter is to explore how polarized opinions can be used to identify the weighted directed network graph that captures the agents’ mutual influence, based on either passive or active observations. Opinion dynamics models provide the mathematical underpinning for the low-rank structure exhibited in rating and opinion polling data, especially when the data are drawn from a social network, supporting the observation that real-world opinions are often polarized. We begin with an overview of social dynamics models. Then, we concentrate on the well-known DeGroot dynamics and demonstrate that sparsity and low-rank conditions provide superior performance over other conventional techniques and provide examples of applications. We also show how inserting active agents into the network can significantly enhance our ability to sense the network. We conclude with a number of interesting research problems and extensions.

Decentralized signal processing methods offer an attractive solution to perform big data analytics by harnessing distributed databases, servers, and network resources in a flexible and adaptive fashion. Often, machine learning algorithms on big data are instances of high-dimensional constrained optimization problems, where the projection step comes at a significant computational cost. In this context, the goal of this chapter is to review recent advances in the solution and analysis of decentralized projection-free algorithms, focusing on both convex and nonconvex problems and comparing them with decentralized projective gradient methods. The popular machine learning problem of robust low rank matrix completion is considered to validate our theoretical claims numerically.

To respond to volatility and congestion in the power grid, demand response (DR) mechanisms allow for shaping the load compared to a base load profile. When tapping on a large population of heterogeneous appliances as a DR resource, the challenge is in modeling the dimensions available for control. Such models need to strike the right balance between accuracy of the model and tractability. The goal of this paper is to provide a medium-grained stochastic hybrid model to represent a population of appliances that belong to two classes: deferrable or thermostatically controlled loads. We preserve quantized information regarding individual load constraints, while discarding information about the identity of appliance owners. The advantages of our proposed population model are 1) it allows us to model and control load in a scalable fashion, useful for ex-ante planning by an aggregator or for real-time load control; 2) it allows for the preservation of the privacy of end-use customers that own submetered or directly controlled appliances.

We study the system-level effects of the introduction of large populations of Electric Vehicles on the power and transportation networks. We assume that each EV owner solves a decision problem to pick a cost-minimizing charge and travel plan. This individual decision takes into account traffic congestion in the transportation network, affecting travel times, as well as as congestion in the power grid, resulting in spatial variations in electricity prices for battery charging. We show that this decision problem is equivalent to finding the shortest path on an "extended" transportation graph, with virtual arcs that represent charging options. Using this extended graph, we study the collective effects of a large number of EV owners individually solving this path planning problem. We propose a scheme in which independent power and transportation system operators can collaborate to manage each network towards a socially optimum operating point while keeping the operational data of each system private. We further study the optimal reserve capacity requirements for pricing in the absence of such collaboration. We showcase numerically that a lack of attention to interdependencies between the two infrastructures can have adverse operational effects., Comment: Submitted to IEEE Transactions on Control of Network Systems on June 1st 2015

We consider the coupling introduced between power and transportation systems through Electric Vehicles (EVs). We envision that a number of charging network operators track the mobility of EVs to better estimate their battery charging needs. They use this information to make better-informed decisions on how to participate in the wholesale market of electricity and also to design marginal retail prices that reduce wait times and electricity costs at charging stations. To formulate these decision-making and price design problems, we introduce a network flow model that can capture the temporally-variable travel and charge patterns of an EV population through expanding the transportation network with virtual arcs.

We propose a Least Squares Estimation procedure for estimating the admittance matrix of multi-bus DC MicroGrids (MGs). In the proposed solution, the generators simultaneously inject training signals in the form of small deterministic perturbations of the primary droop control parameters and measure the related steady state deviations of the bus voltage. When the training signals meet sufficient excitation conditions seen in the number of injected perturbations, the admittance matrix can be uniquely identified. The measurements are collected and processed by a topology identification and monitoring system that produces unbiased estimate of the admittance matrix. The numerical evaluations show that the estimator can recover the admittance matrix of the MG with very high precision, proving its practical viability.

Automated Anomaly Detection in Distribution Grids Using µ PMU Measurements Mahdi Jamei ∗ , Anna Scaglione ∗ , Ciaran Roberts † , Emma Stewart † , Sean Peisert † , Chuck McParland † , Alex McEachern ‡ , ∗ School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ, USA † Lawrence Berkeley National Laboratory, Berkeley, CA, USA ‡ Power Standards Laboratory, Alameda, CA, USA Abstract—The impact of Phasor Measurement Units (PMUs) for providing situational awareness to transmission system op- erators has been widely documented. Micro-PMUs (µPMUs) are an emerging sensing technology that can provide similar benefits to Distribution System Operators (DSOs), enabling a level of visibility into the distribution grid that was previously unattainable. In order to support the deployment of these high resolution sensors, the automation of data analysis and prioritizing communication to the DSO becomes crucial. In this paper, we explore the use of µPMUs to detect anomalies on the distribution grid. Our methodology is motivated by growing concern about failures and attacks to distribution automation equipment. The effectiveness of our approach is demonstrated through both real and simulated data. Index Terms—Intrusion Detection, Anomaly Detection, Micro- Phasor Measurement Unit, Distribution Grid I. I NTRODUCTION The state vectors of the transmission grid are closely monitored and their physical behavior is well-understood [1]. In contrast, Distribution System Operators (DSOs) have historically lacked detailed real-time actionable information about their system. This, however, is set to change. As the distribution grid shifts from a demand serving network towards an interactive grid, there is a growing interest in gaining situational awareness via advanced sensors such as Micro- Phasor Measurement Units (µPMUs) [2]. The deployment of the µPMUs in isolation without addi- tional data driven applications and analytics is insufficient. It is critical to equip DSOs with complimentary software tools that are capable of automatically mining these large data sets in search of useful, actionable information. There has been a lot of work focused on using PMU data at the transmission level to improve Wide-Area Monitoring, Protection and Control (WAMPC) [3], [4]. The distribution grid, however, is lagging in this respect. Due to inherent differences between operational behavior, such as imbalances and increased variability on the distribution and transmission grid, the algorithms derived for WAMPC at the transmission level are generally not directly applicable at the distribution level. Our work is aimed at addressing this issue. We focus on an important application of µPMU data in the distribution system: anomaly detection, i.e., behavior that differs significantly from normal operation of the grid during (quasi) steady-state. An anomaly can take a number of forms, including faults, misoperations of devices or switching transients, among others, and its root cause can be either a natural occurrence, error or attack. The risk of cyber-physical attacks via an IP network has recently gained significant interest due to the increase in automation of our power gird via two-way communication. This communication is typically carried out on breachable networks that can be manipulated by attackers [5]. Even if an anomaly naturally occurs, it is important to notify the DSO to ensure proper remedial action is taken. A. Related Work The majority of published work in anomaly detection using sensor data, primarily SCADA and PMU data, has focused on the transmission grid. The proposed methods are typically data-driven approaches, whereby the measurements are in- spected for abnormality irrespective of the underlying physical model. One such example, the common path data mining approach implemented on PMU data and audit logs at a central server, is proposed in [6] to classify between a disturbance, an attack via IP computer networks and normal operation. Chen et al., [7] derive a linear basis expansion for the PMU data to reduce the dimensionality of the measurements. Through this linear basis expansion, it is shown how an anomaly, which changes the grid operating point, can be spotted by comparing the error of the projected data onto the subspace spanned by the basis and the actual values. Valenzuela et al., [8] used Principal Component Analysis (PCA) to classify the power flow results into regular and irregular subspaces. Through analyzing the data residing in the irregular subspace, their method determines whether the irregularity is caused by a network attack or not. Jamei et al., [9] propose an intrusion detection architecture that leverages µPMU data and SCADA communication over IP networks to detect potentially damag- ing activities in the grid. These aforementioned algorithms are all part of the suite of machine learning techniques that the security monitoring architecture will rely on. B. Our Contribution µPMUs, due to their high sampling frequency, are a much richer data source in comparison to traditional Distribution Supervisory Control and Data Acquisition (DSCADA). In this

Enabling low power wireless devices to adopt Nyquist sampling at high carriers is prohibitive. In spectrum sensing, this limit calls for an analog front-end that can sweep different bands quickly, in order to use the available spectrum opportunistically. In this paper we propose a new framework that allows to optimize a sub-Nyquist sampling front-end to combine the benefits of optimum sequential sensing with those of Compressive Spectrum Sensing (CSS). The sensing strategy we propose is formulated as a sequential utility optimization problem. The utility function decreases linearly with the number of measurements (accrued to make a decision) and increases as a function of the signal components binary states. The optimization selects the best linear combinations of sub-bands to mix, in order to accrue maximum utility. The structure of the utility represents the trade-off between exploration, exploitation and risk of making an error, that is characteristic of the spectrum sensing problem. We first present the analog front-end architecture, and then map the measurement model into the abstract optimization problem proposed, and analyzed, in the remainder of the paper. We characterize the optimal policy under constraints on the sensing matrix and derive the approximation factor of the greedy approach. Numerical simulations showcase the benefits of combining opportunistic spectrum sensing with sub-Nyquist sampling., Comment: submitted to IEEE Transactions on Cognitive Communications and Networking

In this paper we propose a stochastic model, based on queueing theory, for electric vehicle (EV) and plug-in hybrid electric vehicle (PHEV) charging demand. Compared to previous studies, our model can provide 1) more accurate forecasts of the load using real-time sub-metering data, along with the level of uncertainty that accompanies these forecasts; 2) a mathematical description of load, along with the level of demand flexibility that accompanies this load, at the wholesale level. This can be useful when designing demand response and dynamic pricing schemes. Our numerical experiments tune the proposed statistics on real PHEV charging data and demonstrate that the forecasting method we propose is more accurate than standard load prediction techniques.

We study binary opinion dynamics in a social network with stubborn agents who influence others but do not change their opinions. We focus on a generalization of the classical voter model by introducing nodes (stubborn agents) that have a fixed state. We show that the presence of stubborn agents with opposing opinions precludes convergence to consensus; instead, opinions converge in distribution with disagreement and fluctuations. In addition to the first moment of this distribution typically studied in the literature, we study the behavior of the second moment in terms of network properties and the opinions and locations of stubborn agents. We also study the problem of optimal placement of stubborn agents where the location of a fixed number of stubborn agents is chosen to have the maximum impact on the long-run expected opinions of agents.

We consider a variation of the Deffuant-Weisbuch model introduced by Deffuant et al. in 2000, to provide new analytical insights on the opinion dynamics in a social group. We model the trust that may exist between like-minded agents through a trust function, which is a discontinuous (hard-interaction) non-increasing function of the opinion distance. In this model, agents exchange their opinions with their neighbors and move their opinions closer to each other if they are like-minded (that is, the distance between opinions is smaller than a threshold). We first study the dynamics of opinion formation under random interactions with a fixed rate of communication between pairs of agents. Our goal is to analyze the convergence properties of the opinion dynamics and explore the underlying characteristics that mark the phase transition from opinion polarization to consensus. Furthermore, we extend the hard-interaction model to a strategic interaction model by considering a time-varying rate of interaction. In this model, social agents themselves decide the time and energy that should be expended on interacting each of their neighbors, based on their utility functions. The aim is to understand how and under what conditions clustering patterns emerge in opinion space. Extensive simulations are provided to validate the analytical results of both the hard-interaction model and the strategic interaction model. We also offer evidence that suggests the validity of the proposed model, using the location and monthly survey data collected in the Social Evolution experiment over a period of nine months.

In this paper we propose a new stochastic model for solar Photo-Voltaic (PV) power that explicitly models the effect of cloud coverage as an attenuation of the two components that make up the deterministic solar irradiation pattern. Relying on compressive sensing methods we are able to fit a set of solar PV power data from California with the components of this stochastic model and extract the parameters of such a process thus effectively capturing the variability of solar power production. One can leverage the rich information coming from this parametric model for stochastic optimization and decision making.

In this paper, we propose an opportunistic amplify-and-forward relaying scheme for a cognitive radio network, which is aimed at allowing a secondary user (SU) to transmit over the same time-frequency slot of a primary user (PU). In our scheme, the SU amplifies and transmits the PU signal it receives, by using as relaying gain the information symbols that the SU wishes to transmit towards its own secondary receiver. The information theoretic limits of the proposed protocol are investigated by showing that, in some operative conditions of practical interest, the SU can embed its information symbols in the PU signal, without violating the cognitive radio principle of protecting the PU transmission and, at the same time, by attaining low transmission rates

In power system operations, the success of stochastic optimization methods in helping to cope with increasing penetration of wind and solar power, rests on the effective construction of scenario trees that efficiently and accurately approximate the true probability space of the renewable power stochastic processes. In this work, by analyzing the statistical properties of wind turbines' power outputs which are recorded in Washington state (WA) for years 2012–2014, we identify the existing gaps in traditional modeling approaches and propose a possible solutions. The key idea we propose is to view scenario tree generation as a compression scheme of the wind power trajectories, sidestepping completely the model selection approach. We argue that to retain key features of the high order statistics, one can directly quantize realizations over the optimization horizon. We propose two approaches: one based on directly using scenarios in the time domain and an alternative one based on performing the quantization in the finite subspace of Morlet-wavelets. We compare the accuracy of the scenario trees with the prevalent approach based on an ARMA model selection step and show that the direct construction outperforms this approach when the complexity of the scenario tree is fixed.

This paper considers an online optimization algorithm for actively learning trusts on social networks. We first introduce a DeGroot model for opinion dynamics under the influence of stubborn agents and demonstrate how an observer with estimates of the individuals opinions can actively learn the relative trusts among different agents, by fitting the opinions to the steady state equations of the social system equations. The main contribution of this article is an online algorithm for extracting the trust parameters from streaming data of randomly sampled, noisy opinion estimates. The algorithm is based on the stochastic proximal gradient method and it is proven to converge almost surely. Finally, numerical results are presented to corroborate our findings.

Background: ST elevation myocardial infarction (STEMI) in old and old-old patients presents several peculiarities in natural history, delay of hospitalization and response to treatment. Aim of this retrospective case control study was to determine presentation, complications and management of elderly patients with STEMI compared to a younger population. Methods: 462 patients (205 M and 257 F) aged ≥75 years, hospitalized in CCU between 1999 and 2003 for STEMI, were valuated. The control group consisted of 490 consecutive patients (268 M and 222 F) aged 50-70 years. Attention was focused on clinical presentation, complications, management and outcome in elderly compared with younger patients. Results: The mean interval between the onset of symptoms and the arrive in CCU was of 9 hour in the elderly compared to 4,5 hour in the control. Chest pain was less frequent (50% vs 90%) in the elderly; the prevalence of dyspnoea and neurological symptoms was higher in patients >75 years (30% vs 15% and 25% vs 10%). In the elderly, previous angina and AMI, cerebral and peripheral vascular diseases, peripheral and renal failure were frequent. Early severe complications prevailed in the elderly. Thrombolysis was performed only in 39% of the elderly compared to 65% of the control. Significantly higher was cerebral haemorrhage after thrombolysis (4,9% vs 1,8%). Comparable were the mayor extra cranial bleedings. Primary or facilitated PTCA was performed in few patients in the last year. Two weeks mortality was 20%, compared to 6,5% in the control group. Conclusion: The patientes >75 years with STEMI were hospitalized later, had atypical presentation with less chest pain and more cardiac failure, were less likely to receive thrombolysis, had more complications and more cerebral bleedings. Elderly had more associated diseases and in-hospital mortality was higher.

Background: The quantity and intensity of physical activity required for the primary prevention of coronary heart disease remain unclear. Therefore, we examined the association between physical activity and coronary risk. Methods: We studied 100 patients with chest pain, 78 men and 22 women, not older than 65 years, admitted to a coronary care unit. Patients were subdivided in 3 groups: the first group included patients with acute myocardial infarction, the second group included patients with chronic heart disease, the third included patients with non-ischemic chest-pain. A questionnaire on daily physical activity was filled by each patient. Results: A significantly higher percentage of patients with myocardial infarction and coronary heart disease had a sedentary life style compared to patients of the third group. Compared with subjects without heart disease, a significantly higher percentage of patients of the first and second group covered a daily average distance shorter than 500 meters, while a significantly inferior percentage covered a distance longer than 1 Km every day. A significantly lower percentage of patients with coronary heart disease practised sport compared with the third group. At the time of hospitalization a very small percentage of coronary heart disease patients still practised sport. Conclusions: The association between physical activity and reduced coronary risk is clear; in order to obtain benefits it is sufficient just walking every day. Regarding physical activity, continuity is important: patients, who practised sport only in juvenile age, breaking off when older, may lose the obtained advantages.

In this paper we formulate of the Economic Dispatch (ED) problem in Power Systems in continuous time and include in it ramping constraints to derive an expression of the price that reflects some important inter-temporal constraints of the power units. The motivation for looking at this problem is the scarcity of ramping resources and their increasing importance motivated by the variability of power resources, particularly due to the addition of solar power, which exacerbates the need of fast ramping units in the early morning and early evening hours. We show that the solution for the marginal price can be found through Euler-Lagrange equations and we argue that this price signal better reflects the market value of power in the presence of significant ramps in net-load., Comment: Information Theory and Applications Workshop, 2016, Special Session on "Energy and Power Grid I"

In this paper, we first introduce a variational formulation of the Unit Commitment (UC) problem, in which generation and ramping trajectories of the generating units are continuous time signals and the generating units cost depends on the three signals: the binary commitment status of the units as well as their continuous-time generation and ramping trajectories. We assume such bids are piecewise strictly convex time-varying linear functions of these three variables. Based on this problem derive a tractable approximation by constraining the commitment trajectories to switch in a discrete and finite set of points and representing the trajectories in the function space of piece-wise polynomial functions within the intervals, whose discrete coefficients are then the UC problem decision variables. Our judicious choice of the signal space allows us to represent cost and constraints as linear functions of such coefficients, thus, our UC models preserves the MILP formulation of the UC problem. Numerical simulation over real load data from the California ISO demonstrate that the proposed UC model reduces the total dayahead and real-time operation cost, and the number of ramping scarcity events in the real-time operations.

Relay-assisted cooperative wireless communication has been shown to have significant performance gains over the legacy direct transmission scheme. Compared with single relay based cooperation schemes, utilizing multiple relays further improves the reliability and rate of transmissions. Distributed space-time coding (DSTC), as one of the schemes to utilize multiple relays, requires tight coordination between relays and does not perform well in a distributed environment with mobility. In this paper, a cooperative medium access control (MAC) layer protocol, called \emph{STiCMAC}, is designed to allow multiple relays to transmit at the same time in an IEEE 802.11 network. The transmission is based on a novel DSTC scheme called \emph{randomized distributed space-time coding} (\emph{R-DSTC}), which requires minimum coordination. Unlike conventional cooperation schemes that pick nodes with good links, \emph{STiCMAC} picks a \emph{transmission mode} that could most improve the end-to-end data rate. Any station that correctly receives from the source can act as a relay and participate in forwarding. The MAC protocol is implemented in a fully decentralized manner and is able to opportunistically recruit relays on the fly, thus making it \emph{robust} to channel variations and user mobility. Simulation results show that the network capacity and delay performance are greatly improved, especially in a mobile environment., This paper is a revised version of a paper with the same name submitted to IEEE Transaction on Wireless Communications. STiCMAC protocol with RTS/CTS turned off is presented in the appendix of this draft

Is Power Line Communications (PLC) a good candidate for Smart Grid applications? The objective of this paper is to address this important question. To do so we provide an overview of what PLC can deliver today by surveying its history and describing the most recent technological advances in the area. We then address Smart Grid applications as instances of sensor networking and network control problems and discuss the main conclusion one can draw from the literature on these subjects. The application scenario of PLC within the Smart Grid is then analyzed in detail. Since a necessary ingredient of network planning is modeling, we also discuss two aspects of engineering modeling that relate to our question. The first aspect is modeling the PLC channel through fading models. The second aspect we review is the Smart Grid control and traffic modeling problem which allows us to achieve a better understanding of the communications requirements. Finally, this paper reports recent studies on the electrical and topological properties of a sample power distribution network. Power grid topological studies are very important for PLC networking as the power grid is not only the information source \textit{but also} the information delivery system - a unique feature when PLC is used for the Smart Grid., Comment: 26 pages, 6 figures, 2 tables

The Pulse-Coupled Oscillator (PCO) is a novel protocol inspired by models used in mathematical biology to justify the emergence of synchrony in the natural world. Our paper introduces and demonstrates the efficacy of a new PCO protocol implementation that, by disabling all collision resolution mechanisms for a suitable portion of the node operations, lets the rapid establishment of a common clock and its maintenance. The key idea is to allow signals to be superimposed in time, a feature that is absent in previous implementations, because it is prevented by traditional medium access schemes. We map the PCO protocol into an event-driven asynchronous coloring algorithm, based on the local exchange of information to explain its convergence properties. The event-based description of the PCO protocol sets the stage for our experimental comparison with a competing decentralized network synchronization approach, namely, the Reference Broadcast Protocol (RBS). For comparison, we combined RBS with an asynchronous average consensus protocol, running exactly on the same MicaZ platforms. The experimental results showcase the better scalability of the PCO scheme compared to the competing method based on RBS, proving that the PCO primitive is a reasonable option to consider for wireless sensor network applications.