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The notion of graph filters can be used to define generative models for graph data. In fact, the data obtained from many examples of network dynamics may be viewed as the output of a graph filter. With this interpretation, classical signal processing tools, such as frequency analysis, have been successfully applied with analogous interpretation to graph data, generating new insights for data science. What follows is a user guide on a specific class of graph data, where the generating graph filters are low pass; i.e., the filter attenuates contents in the higher graph frequencies while retaining contents in the lower frequencies. Our choice is motivated by the prevalence of low-pass models in application domains such as social networks, financial markets, and power systems. We illustrate how to leverage properties of low-pass graph filters to learn the graph topology and identify its community structure; efficiently represent graph data through sampling; recover missing measurements; and denoise graph data. The low-pass property is also used as the baseline to detect anomalies.

We describe a distributed framework for resource sharing problems that arise in communications, micro-economics, and various networking applications. In particular, we consider a hierarchical multi-layer decomposition for network utility maximization (ML-NUM), where functionalities are assigned to different layers. The proposed methodology creates solutions with central management and distributed computations to the resource allocation problems. In non-stationary environments, the technique aims to respond quickly to the dynamics of the network by decreasing delay by partially shifting the communication and computational burden to the network edges. Our main contribution is a detailed analysis under the assumption that the network changes are on the same time-scale as the convergence time of the algorithms used for local computations. Moreover, assuming strong concavity and smoothness of the users’ objective functions, and under some stability conditions for each layer, we present convergence rates and optimality bounds for the ML-NUM framework. In addition, the main benefits of the proposed method are demonstrated with numerical examples.

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.

This paper presents an inference method to learn a model for complex system based on observations of the perturbed stationary points. We propose to jointly estimate the dynamics parameters and network topology through a regularized regression formulation. A distinguished feature of our approach rests on the direct modeling of rank deficient network data, which is widely found in network science but frequently ignored in the prior research. The new modeling technique allows us to provide the network identifiability condition under the scenarios of insufficient data. In the special case where the dynamics parameters are known, we show how the interplay between dynamics and sparsity in the graph structure can lead to verifiable conditions for network identifiability. Furthermore, relying only on the steady-states equations, our method avoids the necessity to record transient data, and allows to meaningfully combine data from multiple experiments. Numerical experiments are performed on examples with gene regulatory networks and opinion dynamics to justify our claims.

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.

A shortage of large power system data sets, as well as the frequent restrictions on sharing such models, have led to newfound interest in creating synthetic data that can be easily shared among researchers. This paper considers the problem of forming a power system test case from the constituent parts of realistic power grid samples. Starting from a topology, and samples of generation, load, and branches, we assemble systems while respecting the constraints imposed by a typical Optimal Power Flow problem. Expressed in this manner, the problem involves solving for permutations of the input data. Since permutations matrices are binary, the problem is linearized, allowing for a Mixed Integer Linear Problem formulation. The problem is further decomposed using the Alternating Direction Method of Multipliers as well as an Evolutionary Algorithm to facilitate scaling to larger system sizes. A post processing step is used to add shunt elements for reactive power planning. The resulting systems demonstrate statistically similar power flow behavior to reference systems. Finally, new analysis avenues, opened by synthesizing test cases according to the proposed method, are briefly introduced by creating fictitious systems with different topology models and examining how these affect power flow behavior.

Fast-acting smart inverters that utilize preset operating conditions to determine real and reactive power injection/consumption can create voltage instabilities (over-voltage, voltage oscillations and more) in an electrical distribution network if set-points are not properly configured. In this work, linear distribution power flow equations and droop-based Volt-Var and Volt-Watt control curves are used to analytically derive a stability criterion using \lyapnouv analysis that includes the network operating condition. The methodology is generally applicable for control curves that can be represented as Lipschitz functions. The derived Lipschitz constants account for smart inverter hardware limitations for reactive power generation. A local policy is derived from the stability criterion that allows inverters to adapt their control curves by monitoring only local voltage, thus avoiding centralized control or information sharing with other inverters. The criterion is independent of the internal time-delays of smart inverters. Simulation results for inverters with and without the proposed stabilization technique demonstrate how smart inverters can mitigate voltage oscillations locally and mitigate real and reactive power flow disturbances at the substation under multiple scenarios. The study concludes with illustrations of how the control policy can dampen oscillations caused by solar intermittency and cyber-attacks., Comment: Accepted for IET Renewable Power Generation

The underlying theme of this paper is to explore the various facets of power systems data through the lens of graph signal processing (GSP), laying down the foundations of the Grid-GSP framework. Grid-GSP provides an interpretation for the spatio-temporal properties of voltage phasor measurements, by showing how the well-known power systems modeling supports a generative low-pass graph filter model for the state variables, namely the voltage phasors. Using the model we formalize the empirical observation that voltage phasor measurement data lie in a low-dimensional subspace and tie their spatio-temporal structure to generator voltage dynamics. The Grid-GSP generative model is then successfully employed to investigate the problems pertaining to the grid of data sampling and interpolation, network inference, detection of anomalies and data compression. Numerical results on a large synthetic grid that mimics the real-grid of the state of Texas, ACTIVSg2000, and on real-world measurements from ISO-New England verify the efficacy of applying Grid-GSP methods to electric grid data., Comment: 15 Pages, under review in IEEE Transactions on Signal Processing

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.

In this paper, we derive a Bayesian lower bound on the estimation of a scalar parameter whose prior distribution is assumed to have a bounded support. For such truncated prior distributions it is well known that the Bayesian Cramer-Rao bound (BCRB) does not hold. We also analyze the tightness of this bound for maximum a-posteriori estimators (MAP) in the case of conditionally Gaussian observations and highlight some interesting properties. Numerical results illustrate the tightness of this bound. We also study the utility of this bound in an application to a real-world system of fault detection in solar panels.

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.

In this paper, the performance limits of faults localization are investigated using synchrophasor data. The focus is on a non-trivial operating regime where the number of Phasor Measurement Unit (PMU) sensors available is insufficient to have full observability of the grid state. Proposed analysis uses the Kullback Leibler (KL) divergence between the distributions corresponding to different fault location hypotheses associated with the observation model. This analysis shows that the most likely locations are concentrated in clusters of buses more tightly connected to the actual fault site akin to graph communities. Consequently, a PMU placement strategy is derived that achieves a near-optimal resolution for localizing faults for a given number of sensors. The problem is also analyzed from the perspective of sampling a graph signal, and how the placement of the PMUs i.e. the spatial sampling pattern and the topological characteristic of the grid affect the ability to successfully localize faults. To highlight the superior performance of presented fault localization and placement algorithms, the proposed strategy is applied to a modified IEEE 34, IEEE-123 bus test cases and to data from a real distribution grid. Additionally, the detection of cyber-physical attacks is also examined where PMU data and relevant Supervisory Control and Data Acquisition (SCADA) network traffic information are compared to determine if a network breach has affected the integrity of the system information and/or operations.

This paper aims at modeling and inferring the influence among individuals from voting data (or more generally from actions that are selected by choosing one of $m$ different options). The voting data are modeled as outcomes of a discrete random process, which we refer to as the discuss-then-vote model, whose evolution is governed by the DeGroot opinion dynamics with stubborn nodes. Based on the proposed model, we formulate the maximum a posterior estimator for the opinions and influence matrix (or the transition matrix) and derive a tractable approximation that results in a convex optimization problem. In the paper, the identifiability of the network dynamics’ parameters and the vote prediction procedure based on the influence matrix are discussed in depth. Our methodology is tested through numerical simulations as well as through its application to a set of the U.S. Senate roll call data. Interestingly, in spite of the relatively small data record available, the influence matrix inferred from the real data is consistent with the common intuition about the influence structure in the U.S. Senate.

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.

As net-load becomes less predictable there is a lot of pressure in changing decision models for power markets such that they account explicitly for future scenarios in making commitment decisions. This paper proposes to make commitment decisions with multiple gate closures. Our proposed model also leverages a state-space formulation for the commitment variables, through which the operational constraints of generation units participating in the market are respected. We also study the problem of constructing scenario tree approximations for stochastic processes and evaluate our algorithms on scenario tree libraries derived from real net-load 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.

In this paper we revisit the problem of False Data Injection (FDI) attacks to electric power systems synchrophasors measurements through the lens of graph signal processing (GSP). First, we introduce a physics based model that supports the empirical evidence that Phasor Measurement Unit (PMU) data are low-pass graph signals. This insight is used to investigate how one can leverage such a structure to construct more effective bad data detection (BDD) algorithms that can detect FDI attack signatures through appropriate utilization of the features of the PMU graph-signal.

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.

In this paper, we consider the problem of solving a distributed (consensus-based) optimization problem in a network that contains regular and malicious nodes (agents). The regular nodes are performing a distributed iterative algorithm to solve their associated optimization problem, while the malicious nodes inject false data with a goal to steer the iterates to a point that serves their own interest. The problem consists of detecting and isolating the malicious agents, thus allowing the regular nodes to solve their optimization problem. We propose a method to dwarf data injection attacks on distributed optimization algorithms, which is based on the idea that the malicious nodes (individually or in collaboration) tend to give themselves away when broadcasting messages with the intention to drive the consensus value away from the optimal point for the regular nodes in the network. In particular, we provide a new gradient-based metric to detect the neighbors that are likely to be malicious. We also provide some simulation results demonstrating the performance of the proposed approach.

Many recent works on inference of graph structure assume that the graph signals are fully observable. For large graphs with thousands or millions of nodes, this entails high complexity on the data collection and processing steps. Here, we study a community inference problem on partially observed (sub-sampled) graph signals which sidesteps topology inference, while revealing the coarse structure of the graph directly. Two variants of the inference task are studied: (i) a blind method that infers the communities that the observable nodes belong to; and (ii) a semi-blind method that infers the communities of all nodes using, in addition, side information about the sub-graph between observable and hidden nodes. These techniques for community inference are shown to be efficient and suitable for large graphs analytically and empirically.

This work proposes a framework to generate synthetic distribution feeders mapped to real geo-spatial topologies using available OpenStreetMap data. The synthetic power networks can facilitate power systems research and development by providing thousands of realistic use cases. The location of substations is taken from recent efforts to develop synthetic transmission test cases, with underlying real and reactive power in the distribution network assigned using population information gathered from United States 2010 Census block data. The methods illustrate how to create individual synthetic distribution feeders, and groups of feeders across entire ZIP Code, with minimal input data for any location in the United States. The framework also has the capability to output data in \OpenDSS format to allow further simulation and analysis., Comment: North American Power Symposium 2019

Decentralized optimization has found a significant utility in recent years, as a promising technique to overcome the curse of dimensionality when dealing with large-scale inference and decision problems in big data. While these algorithms are resilient to node and link failures, they however, are not inherently Byzantine fault-tolerant towards insider data injection attacks. This paper proposes a decentralized robust subgradient push (RSGP) algorithm for detection and isolation of malicious nodes in the network for optimization non-strongly convex objectives. In the attack considered in this work, the malicious nodes follow the algorithmic protocols, but can alter their local functions arbitrarily. However, we show that in sufficiently structured problems, the method proposed is effective in revealing their presence. The algorithm isolates detected nodes from the regular nodes, thereby mitigating the ill-effects of malicious nodes. We also provide performance measures for the proposed method., Comment: 6 pages, 2 figures, 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems

The adoption of blockchain for Transactive Energy has gained significant momentum as it allows mutually non-trusting agents to trade energy services in a trustless energy market. Research to date has assumed that the built-in Byzantine Fault Tolerance in recording transactions in a ledger is sufficient to ensure integrity. Such work must be extended to address security gaps including random bilateral transactions that do not guarantee reliable and efficient market operation, and market participants having incentives to cheat when reporting actual production/consumption figures. Work herein introduces the Electron Volt Exchange framework with the following characteristics: 1) a distributed protocol for pricing and scheduling prosumers' production/consumption while keeping constraints and bids private, and 2) a distributed algorithm to prevent theft that verifies prosumers' compliance to scheduled transactions using information from grid sensors (such as smart meters) and mitigates the impact of false data injection attacks. Flexibility and robustness of the approach are demonstrated through simulation and implementation using Hyperledger Fabric., Comment: Accepted for Applied Energy

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 )

A significant portion of the literature on fault localization assumes (more or less explicitly) that there are sufficient reliable measurements to guarantee that the system is observable. While several heuristics exist to break the observability barrier, they mostly rely on recognizing spatio-temporal patterns, without giving insights on how the performance are tied with the system features and the sensor deployment. In this paper, we try to fill this gap and investigate the limitations and performance limits of fault localization using Phasor Measurement Units (PMUs), in the low measurements regime, i.e., when the system is unobservable with the measurements available. Our main contribution is to show how one can leverage the scarce measurements to localize different type of distribution line faults (three-phase, single-phase to ground, ...) at the level of sub-graph, rather than with the resolution of a line. We show that the resolution we obtain is strongly tied with the graph clustering notion in network science., Accepted in IEEE SmartGridComm 2018 Conference

This paper describes a distributed statistical estimation problem, corresponding to a network of agents. The network may be vulnerable to data injection attacks, in which the attackers’ main goal is to steer the network’s final state to a state of their choice. We show that the detection metric of the straightforward attack scheme proposed by Wu et. at in [1], is vulnerable to a more sophisticated attack. To overcome this attack we propose a novel metric that can be computed locally by each agent to detect the presence of an attacker in the network, as well as a metric that localizes the attackers in the network. We conclude the paper with simulations supporting our findings.

We propose and analyze a new stochastic gradient method, which we call Stochastic Unbiased Curvature-aided Gradient (SUCAG), for finite sum optimization problems. SUCAG constitutes an unbiased total gradient tracking technique that uses Hessian information to accelerate con- vergence. We analyze our method under the general asynchronous model of computation, in which each function is selected infinitely often with possibly unbounded (but sublinear) delay. For strongly convex problems, we establish linear convergence for the SUCAG method. When the initialization point is sufficiently close to the optimal solution, the established convergence rate is only dependent on the condition number of the problem, making it strictly faster than the known rate for the SAGA method. Furthermore, we describe a Markov-driven approach of implementing the SUCAG method in a distributed asynchronous multi-agent setting, via gossiping along a random walk on an undirected communication graph. We show that our analysis applies as long as the graph is connected and, notably, establishes an asymptotic linear convergence rate that is robust to the graph topology. Numerical results demonstrate the merits of our algorithm over existing methods., to appear in CDC 2018, 17 pages, 2 figures

We describe a distributed framework for resource sharing problems that we face in communications, microeconomics and various networking applications. In particular, we consider a hierarchical multi-layer decomposition for network utility maximization (NUM), where functionalities are assigned to different layers. The proposed methodology creates solutions having central management and distributed computations. The technique aims to respond to the dynamics of the network by decreasing the communication cost, while shifting more computational load to the edges of the network. The main contribution of this work is the provision of a detailed analysis under the assumption that the network changes are in the same time-scale with the convergence time of the algorithms used for local computations. For this scenario, assuming strong concavity and smoothness of the users' objective functions, we present convergence rates for each layer.

To address the need for trusted information across bulk power systems, our paper proposes a new type of distributed ledger (or Blockchain), for a shared management of sensitive information in power systems. We call our Blockchain design the Assured Volt Ampere Information Ledger (AVAIL). The AVAIL` abstractions fit data needs of prototypical grid applications in wide area protection and control, energy management systems, and markets. The contribution of this paper is to draw directly from the distinct requirements of these applications and the valid assumptions about the adversaries, to shape the AVAIL abstractions. AVAIL is unique for the following features: 1) Adversarial model: Our design principles consider an adversarial model where attacks affect physical resources; 2) Non-binary validity: in our setting we allow for a spectrum of validity; 3) Validity enforcement: validity in our setting is governed by physical laws.

This paper considers a new framework to detect communities in a graph from the observation of signals at its nodes. We model the observed signals as noisy outputs of an unknown network process, represented as a graph filter that is excited by a set of unknown low-rank inputs/excitations. Application scenarios of this model include diffusion dynamics, pricing experiments, and opinion dynamics. Rather than learning the precise parameters of the graph itself, we aim at retrieving the community structure directly. The paper shows that communities can be detected by applying a spectral method to the covariance matrix of graph signals. Our analysis indicates that the community detection performance depends on a `low-pass' property of the graph filter. We also show that the performance can be improved via a low-rank matrix plus sparse decomposition method when the latent parameter vectors are known. Numerical experiments demonstrate that our approach is effective., Single column format, 32 pages, 9 figures

We treat the emerging power systems with direct current (DC) MicroGrids, characterized with high penetration of power electronic converters. We rely on the power electronics to propose a decentralized solution for autonomous learning of and adaptation to the operating conditions of the DC Mirogrids; the goal is to eliminate the need to rely on an external communication system for such purpose. The solution works within the primary droop control loops and uses only local bus voltage measurements. Each controller is able to estimate (i) the generation capacities of power sources, (ii) the load demands, and (iii) the conductances of the distribution lines. To define a well-conditioned estimation problem, we employ decentralized strategy where the primary droop controllers temporarily switch between operating points in a coordinated manner, following amplitude-modulated training sequences. We study the use of the estimator in a decentralized solution of the Optimal Economic Dispatch problem. The evaluations confirm the usefulness of the proposed solution for autonomous MicroGrid operation., Comment: Extended version including the appendix with proofs and derivations (This version should be used as a supplementary material to the original paper, soon to appear in IEEE Transactions on Signal Processing)

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

Validation is a critical component in the development of synthetic models, which aims to convince a user that a model achieves its claims of realism. While users of power system test cases are primarily interested in operational results, which could be considered outputs, it is more convenient and feasible to control distributions of data inputs, both structural and otherwise. Validation metrics, are therefore generally focused on input rather than operation features. This paper investigates the link between input and output statistics in power system. Better understanding of this relationship allows validation to continue to focus largely on input statistics, while at the same time offering some assurance about operational behavior.

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.

In this paper, a mathematical relationship between temperature and solar irradiance is established in order to reduce the sample space and provide joint probabilistic forecasts. These forecasts can then be used for the purpose of stochastic optimization in power systems. A Volterra system type of model is derived to characterize the dependence of temperature on solar irradiance. A dataset from NOAA weather station in California is used to validate the fit of the model. Using the model, probabilistic forecasts of both temperature and irradiance are provided and the performance of the forecasting technique highlights the efficacy of the proposed approach. Results are indicative of the fact that the underlying correlation between temperature and irradiance is well captured and will therefore be useful to produce future scenarios of temperature and irradiance while approximating the underlying sample space appropriately.

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’.

This paper studies the security aspect of gossip-based decentralized optimization algorithms for multi agent systems against data injection attacks. Our contributions are two-fold. First, we show that the popular distributed projected gradient method (by Nedic et al.) can be attacked by coordinated insider attacks, in which the attackers are able to steer the final state to a point of their choosing. Second, we propose a metric that can be computed locally by the trustworthy agents processing their own iterates and those of their neighboring agents. This metric can be used by the trustworthy agents to detect and localize the attackers. We conclude the paper by supporting our findings with numerical experiments.

This paper considers the problem of inferring the topology of a graph from noisy outputs of an unknown graph filter excited by low-rank signals. Limited by this low-rank structure, we focus on solving the community detection problem, whose aim is to partition the node set of the unknown graph into subsets with high edge densities. We propose to detect the communities by applying spectral clustering on the low-rank output covariance matrix. To analyze the performance, we show that the low-rank covariance yields a sketch of the eigenvectors of the unknown graph. Importantly, we provide theoretical bounds on the error introduced by this sketching procedure based on spectral features of the graph filter involved. Finally, our theoretical findings are validated via numerical experiments.

In this paper we introduce a continuous time multi stage stochastic optimization for scheduling generating units, their commitment, reserve capacities and their continuous time generation profiles in the day-ahead wholesale electricity market. Our formulation approximates the solution of a variational problem, in which the balance, generation capacity and ramping constraints are in continuous time. Due to the greater accuracy of our representation of ramping events this approach improves the system reliability and lowers the real-time cost., Comment: Accepted for the 2018 IEEE Power System Computation Conference

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