Showing papers on "Distributed algorithm published in 2018"

Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

Abstract: In large-scale distributed learning, security issues have become increasingly important. Particularly in a decentralized environment, some computing units may behave abnormally, or even exhibit Byzantine failures -- arbitrary and potentially adversarial behavior. In this paper, we develop distributed learning algorithms that are provably robust against such failures, with a focus on achieving optimal statistical performance. A main result of this work is a sharp analysis of two robust distributed gradient descent algorithms based on median and trimmed mean operations, respectively. We prove statistical error rates for three kinds of population loss functions: strongly convex, non-strongly convex, and smooth non-convex. In particular, these algorithms are shown to achieve order-optimal statistical error rates for strongly convex losses. To achieve better communication efficiency, we further propose a median-based distributed algorithm that is provably robust, and uses only one communication round. For strongly convex quadratic loss, we show that this algorithm achieves the same optimal error rate as the robust distributed gradient descent algorithms.

Harnessing Smoothness to Accelerate Distributed Optimization

Abstract: There has been a growing effort in studying the distributed optimization problem over a network. The objective is to optimize a global function formed by a sum of local functions, using only local computation and communication. The literature has developed consensus-based distributed (sub)gradient descent (DGD) methods and has shown that they have the same convergence rate $O(\frac{\log t}{\sqrt{t}})$ as the centralized (sub)gradient methods (CGD), when the function is convex but possibly nonsmooth. However, when the function is convex and smooth, under the framework of DGD, it is unclear how to harness the smoothness to obtain a faster convergence rate comparable to CGD's convergence rate. In this paper, we propose a distributed algorithm that, despite using the same amount of communication per iteration as DGD, can effectively harnesses the function smoothness and converge to the optimum with a rate of $O(\frac{1}{t})$ . If the objective function is further strongly convex, our algorithm has a linear convergence rate. Both rates match the convergence rate of CGD. The key step in our algorithm is a novel gradient estimation scheme that uses history information to achieve fast and accurate estimation of the average gradient. To motivate the necessity of history information, we also show that it is impossible for a class of distributed algorithms like DGD to achieve a linear convergence rate without using history information even if the objective function is strongly convex and smooth.

A Fundamental Tradeoff Between Computation and Communication in Distributed Computing

Abstract: How can we optimally trade extra computing power to reduce the communication load in distributed computing? We answer this question by characterizing a fundamental tradeoff between computation and communication in distributed computing, ie, the two are inversely proportional to each other More specifically, a general distributed computing framework, motivated by commonly used structures like MapReduce, is considered, where the overall computation is decomposed into computing a set of “Map” and “Reduce” functions distributedly across multiple computing nodes A coded scheme, named “coded distributed computing” (CDC), is proposed to demonstrate that increasing the computation load of the Map functions by a factor of $r$ (ie, evaluating each function at $r$ carefully chosen nodes) can create novel coding opportunities that reduce the communication load by the same factor An information-theoretic lower bound on the communication load is also provided, which matches the communication load achieved by the CDC scheme As a result, the optimal computation-communication tradeoff in distributed computing is exactly characterized Finally, the coding techniques of CDC is applied to the Hadoop TeraSort benchmark to develop a novel CodedTeraSort algorithm, which is empirically demonstrated to speed up the overall job execution by $197\times $ – $339\times $ , for typical settings of interest

Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis

Abstract: Deep Neural Networks (DNNs) are becoming an important tool in modern computing applications. Accelerating their training is a major challenge and techniques range from distributed algorithms to low-level circuit design. In this survey, we describe the problem from a theoretical perspective, followed by approaches for its parallelization. We present trends in DNN architectures and the resulting implications on parallelization strategies. We then review and model the different types of concurrency in DNNs: from the single operator, through parallelism in network inference and training, to distributed deep learning. We discuss asynchronous stochastic optimization, distributed system architectures, communication schemes, and neural architecture search. Based on those approaches, we extrapolate potential directions for parallelism in deep learning.

Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

Abstract: In large-scale distributed learning, security issues have become increasingly important. Particularly in a decentralized environment, some computing units may behave abnormally, or even exhibit Byzantine failures -- arbitrary and potentially adversarial behavior. In this paper, we develop distributed learning algorithms that are provably robust against such failures, with a focus on achieving optimal statistical performance. A main result of this work is a sharp analysis of two robust distributed gradient descent algorithms based on median and trimmed mean operations, respectively. We prove statistical error rates for three kinds of population loss functions: strongly convex, non-strongly convex, and smooth non-convex. In particular, these algorithms are shown to achieve order-optimal statistical error rates for strongly convex losses. To achieve better communication efficiency, we further propose a median-based distributed algorithm that is provably robust, and uses only one communication round. For strongly convex quadratic loss, we show that this algorithm achieves the same optimal error rate as the robust distributed gradient descent algorithms.

Virtual Reality Over Wireless Networks: Quality-of-Service Model and Learning-Based Resource Management

Abstract: In this paper, the problem of resource management is studied for a network of wireless virtual reality (VR) users communicating over small cell networks (SCNs). In order to capture the VR users’ quality-of-service (QoS) in SCNs, a novel VR model, based on multi-attribute utility theory, is proposed. This model jointly accounts for VR metrics, such as tracking accuracy, processing delay, and transmission delay. In this model, the small base stations (SBSs) act as the VR control centers that collect the tracking information from VR users over the cellular uplink. Once this information is collected, the SBSs will then send the 3-D images and accompanying audio to the VR users over the downlink. Therefore, the resource allocation problem in VR wireless networks must jointly consider both the uplink and downlink. This problem is then formulated as a noncooperative game and a distributed algorithm based on the machine learning framework of echo state networks (ESNs) is proposed to find the solution of this game. The proposed ESN algorithm enables the SBSs to predict the VR QoS of each SBS and is guaranteed to converge to mixed-strategy Nash equilibrium. The analytical result shows that each user’s VR QoS jointly depends on both VR tracking accuracy and wireless resource allocation. Simulation results show that the proposed algorithm yields significant gains, in terms of VR QoS utility, that reach up to 22.2% and 37.5%, respectively, compared with Q-learning and a baseline proportional fair algorithm. The results also show that the proposed algorithm has a faster convergence time than Q-learning and can guarantee low delays for VR services.

NOMA-Assisted Multi-Access Mobile Edge Computing: A Joint Optimization of Computation Offloading and Time Allocation

Abstract: Multi-access mobile edge computing (MEC), which enables mobile users (MUs) to offload their computation-workloads to the computation-servers located at the edge of cellular networks via multi-access radio access, has been considered as a promising technique to address the explosively growing computation-intensive applications in mobile Internet services. In this paper, by exploiting non-orthogonal multiple access (NOMA) for improving the efficiency of multi-access radio transmission, we study the NOMA-enabled multi-access MEC. We aim at minimizing the overall delay of the MUs for finishing their computation requirements, by jointly optimizing the MUs’ offloaded workloads and the NOMA transmission-time. Despite the non-convexity of the formulated joint optimization problem, we propose efficient algorithms to find the optimal offloading solution. For the single-MU case, we exploit the layered structure of the problem and propose an efficient layered algorithm to find the MU's optimal offloading solution that minimizes its overall delay. For the multi-MU case, we propose a distributed algorithm (in which the MUs individually optimize their respective offloaded workloads) to determine the optimal offloading solution for minimizing the sum of all MUs’ overall delay. Extensive numerical results have been provided to validate the effectiveness of our proposed algorithms and the performance advantage of our NOMA-enabled multi-access MEC in comparison with conventional orthogonal multiple access enabled multi-access MEC.

Distributed Transactive Energy Trading Framework in Distribution Networks

Abstract: In this paper, we propose a novel transactive energy trading (TET) framework to deal with the economic issues in energy trading and the technical issues in distribution system operation in a holistic manner In particular, we innovatively integrate a bilateral energy trading mechanism with the optimal power flow (OPF) technique to increase economic benefits to individual participants, and meanwhile ensure the reliability and security of the system operation In order to resolve the inherent conflict of interests, Nash bargaining theory is used to model the TET problem, which is further decomposed into a multiperiod OPF problem and a payment bargaining problem Moreover, we develop an efficient distributed algorithm for solving the TET problem base on alternating direction method of multipliers (ADMM) Instead of directly solving optimization subproblems like most ADMM-based distributed algorithms, we derive closed-form solutions to all subproblems to significantly improve the computational efficiency Finally, numerical tests on the IEEE 37-bus and 123-bus distribution systems demonstrate the effectiveness of our proposed framework and the efficiency of our distributed algorithm

Distributed Optimal Consensus Over Resource Allocation Network and Its Application to Dynamical Economic Dispatch

Abstract: The resource allocation problem is studied and reformulated by a distributed interior point method via a $\theta$ - logarithmic barrier. By the facilitation of the graph Laplacian, a fully distributed continuous-time multiagent system is developed for solving the problem. Specifically, to avoid high singularity of the $\theta$ - logarithmic barrier at boundary, an adaptive parameter switching strategy is introduced into this dynamical multiagent system. The convergence rate of the distributed algorithm is obtained. Moreover, a novel distributed primal–dual dynamical multiagent system is designed in a smart grid scenario to seek the saddle point of dynamical economic dispatch, which coincides with the optimal solution. The dual decomposition technique is applied to transform the optimization problem into easily solvable resource allocation subproblems with local inequality constraints. The good performance of the new dynamical systems is, respectively, verified by a numerical example and the IEEE six-bus test system-based simulations.

Deployment Algorithms for UAV Airborne Networks Toward On-Demand Coverage

Abstract: Due to the flying nature of unmanned aerial vehicles (UAVs), it is very attractive to deploy UAVs as aerial base stations and construct airborne networks to provide service for on-ground users at temporary events (such as disaster relief, military operation, and so on). In the constructing of UAV airborne networks, a challenging problem is how to deploy multiple UAVs for on-demand coverage while at the same time maintaining the connectivity among UAVs. To solve this problem, we propose two algorithms: a centralized deployment algorithm and a distributed motion control algorithm. The first algorithm requires the positions of user equipments (UEs) on the ground and provides the optimal deployment result (i.e., the minimal number of UAVs and their respective positions) after a global computation. This algorithm is applicable to the scenario that requires a minimum number of UAVs to provide desirable service for already known on-ground UEs. Differently, the second algorithm requires no global information or computation, instead, it enables each UAV to autonomously control its motion, find the UEs and converge to on-demand coverage. This distributed algorithm is applicable to the scenario where using a given number of UAVs to cover UEs without UEs’ specific position information. In both algorithms, the connectivity of the UAV network is maintained. Extensive simulations validate our proposed algorithms.

Cooperative platoon control for a mixed traffic flow including human drive vehicles and connected and autonomous vehicles

Abstract: This study seeks to develop a cooperative platoon control for a platoon mixed with connected and autonomous vehicles (CAVs) and human-drive vehicles (HDVs), aiming to ensure system level traffic flow smoothness and stability as well as individual vehicles’ mobility and safety. Specifically, our study integrated/contributed the following technical approaches. First, the car-following behavior of human-drive vehicles is modeled by well-accepted Newell car-following models. Accordingly, an online curve matching algorithm is integrated to anticipate the aggregated response delay of the human-drive vehicles using real-time trajectory data. Built upon that, constrained One- or P-step MPC models are developed to control the movement of the CAV platoon upstream or downstream of the HDV platoon so that we can ensure both transient traffic smoothness and asymptotic stability of this sample mixed flow platoon, leveraging the communication and computation technologies equipped on CAVs. Considering the lack of the centralized computation facilities and severe changes of the platoon topology, this study develops a distributed algorithm to solve the MPCs according to the properties of the optimizers, such as solution uniqueness, sequentially feasibility, and nonempty interior point of the solution space. The convergence of the distributed algorithm as well as the stability of the MPC control is proved by both the theoretical analysis and the experimental study. Extensive numerical experiments based on the field data indicate that the distributed algorithm can solve the One-step and P-step MPCs efficiently. The cooperative MPC can dampen traffic oscillation propagation and stabilize the traffic flow more efficiently for the entire mixed flow platoon. Moreover, the cooperative platoon control scheme outperforms the other three control strategies, including the non-cooperative control strategy and a latest CACC strategy in literature.

Distributed Optimal Power Flow Algorithm for Radial Networks, I: Balanced Single Phase Case

Abstract: The optimal power flow (OPF) problem determines a network operating point that minimizes a certain objective such as generation cost or power loss. Traditionally, OPF is solved in a centralized manner. With increasing penetration of renewable energy in distribution system, we need faster and distributed solutions for real-time feedback control. This is difficult due to the nonlinearity of the power flow equations. In this paper, we propose a solution for balanced radial networks. It exploits recent results that suggest solving for a globally optimal solution of OPF over a radial network through the second-order cone program relaxation. Our distributed algorithm is based on alternating direction method of multiplier (ADMM), but unlike standard ADMM-based distributed OPF algorithms that require solving optimization subproblems using iterative method, our decomposition allows us to derive closed form solutions for these subproblems, greatly speeding up each ADMM iteration. We illustrate the scalability of the proposed algorithm by simulating it on a real-world 2065-bus distribution network.

Distributed Energy Management for Networked Microgrids Using Online ADMM With Regret

Abstract: We propose a distributed algorithm for online energy management in networked microgrids with a high penetration of distributed energy resources (DERs) A high penetration of DERs introduces high uncertainty of power generation to the microgrids In general, the state-of-the-art forecasting for non-dispatchable DERs such as solar energy is not sufficiently accurate, which results in inaccurate energy scheduling To address the high uncertainty issue in the networked microgrids, we propose an online energy management based on the online alternating direction method of multipliers algorithm with the past power generation information from the DERs The online algorithm provides less conservative schedule than the robust optimization-based approach The effectiveness of the proposed algorithm is verified by various numerical examples

Second-Order Continuous-Time Algorithms for Economic Power Dispatch in Smart Grids

Abstract: This paper proposes two second-order continuous-time algorithms to solve the economic power dispatch problem in smart grids. The collective aim is to minimize a sum of generation cost function subject to the power demand and individual generator constraints. First, in the framework of nonsmooth analysis and algebraic graph theory, one distributed second-order algorithm is developed and guaranteed to find an optimal solution. As a result, the power demand constraints can be kept all the time under appropriate initial condition. The second algorithm is under a centralized framework, and the optimal solution is robust in the sense that different initial power conditions do not change the convergence of the optimal solution. Finally, simulation results based on five-unit system, IEEE 30-bus system, and IEEE 300-bus system show the effectiveness and performance of the proposed continuous-time algorithms. The examples also show that the convergence rate of second-order algorithm is faster than that of first-order distributed algorithm.

Distributed Continuous-Time Algorithms for Resource Allocation Problems Over Weight-Balanced Digraphs

Abstract: In this paper, a distributed resource allocation problem with nonsmooth local cost functions is considered, where the interaction among agents is depicted by strongly connected and weight-balanced digraphs. Here the decision variable of each agent is within a local feasibility constraint described as a convex set, and all the decision variables have to satisfy a network resource constraint, which is the sum of available resources. To solve the problem, a distributed continuous-time algorithm is developed by virtue of differentiated projection operations and differential inclusions, and its convergence to the optimal solution is proved via the set-valued LaSalle invariance principle. Furthermore, the exponential convergence of the proposed algorithm can be achieved when the local cost functions are differentiable with Lipschitz gradients and there are no local feasibility constraints. Finally, numerical examples are given to verify the effectiveness of the proposed algorithms.

On Derandomizing Local Distributed Algorithms

Abstract: The gap between the known randomized and deterministic local distributed algorithms underlies arguably the most fundamental and central open question in distributed graph algorithms. In this paper, we combine the method of conditional expectation with network decompositions to obtain a generic and clean recipe for derandomizing LOCAL algorithms. This leads to significant improvements on a number of problems, in cases resolving known open problems. Two main results are: - An improved deterministic distributed algorithm for hypergraph maximal matching, improving on Fischer, Ghaffari, and Kuhn [FOCS '17]. This yields improved algorithms for edge-coloring, maximum matching approximation, and low out-degree edge orientation. The last result gives the first positive resolution in the Open Problem 11.10 in the book of Barenboim and Elkin. - Improved randomized and deterministic distributed algorithms for the Lovasz Local Lemma, which get closer to a conjecture of Chang and Pettie [FOCS '17].

An ADMM-Based Distributed Algorithm for Economic Dispatch in Islanded Microgrids

Abstract: In this paper, based on an alternating direction method of multipliers (ADMM), a novel distributed algorithm is proposed to address the economic dispatch problem (EDP) in islanded microgrids. Unlike most of the existing studies that investigate the EDP with quadratic cost functions, the algorithm proposed in this paper is able to solve the EDP with general convex cost functions. Moreover, by using the center-free algorithm and the projection method, the traditional centralized ADMM is extended to the distributed implementation framework, and a fully distributed solution to the EDP is obtained. Furthermore, the proposed algorithm can deal with both equality and inequality constraints in the EDP, and the supply–demand balance can be guaranteed at any time provided that the sum of initial power outputs is equal to the total demand. The convergence property of the proposed algorithm is strictly proved. Finally, some examples are provided to further demonstrate the effectiveness of the proposed algorithm.

Distributed Nonsmooth Optimization With Coupled Inequality Constraints via Modified Lagrangian Function

Abstract: This note considers a distributed convex optimization problem with nonsmooth cost functions and coupled nonlinear inequality constraints. To solve the problem, we first propose a modified Lagrangian function containing local multipliers and a nonsmooth penalty function. Then, we construct a distributed continuous-time algorithm by virtue of a projected primal-dual subgradient dynamics. Based on the nonsmooth analysis and Lyapunov function, we obtain the existence of the solution to the nonsmooth algorithm and its convergence.

Delay Effects on Consensus-Based Distributed Economic Dispatch Algorithm in Microgrid

Abstract: For microgrids with connected but sparse communication networks, consensus-based approach can provide a distributed solution to the economic dispatch problem. However, as time delays in communication networks are nonnegligible, performances of consensus-based distributed economic dispatch algorithms are not well disclosed and investigated. Considering the effects of time delays, we first provide a novel consensus-based economic dispatch algorithm. The algorithm is fully distributed such that the optimal dispatch of energy resources in microgrid can be implemented in a distributed manner. The influence of time delays on distributed economic dispatch is strictly analyzed. The maximum allowable delay bounds are derived by applying the generalized Nyquist criterion. Several simulations are presented to verify the effectiveness of the algorithm and the correctness of the theoretical results.

Fault-Tolerant Message-Passing Distributed Systems: An Algorithmic Approach

Abstract: This book presents the most important fault-tolerant distributed programming abstractions and their associated distributed algorithms, in particular in terms of reliable communication and agreement, which lie at the heart of nearly all distributed applications. These programming abstractions, distributed objects or services, allow software designers and programmers to cope with asynchrony and the most important types of failures such as process crashes, message losses, and malicious behaviors of computing entities, widely known under the term "Byzantine fault-tolerance". The author introduces these notions in an incremental manner, starting from a clear specification, followed by algorithms which are first described intuitively and then proved correct. The book also presents impossibility results in classic distributed computing models, along with strategies, mainly failure detectors and randomization, that allow us to enrich these models. In this sense, the book constitutes an introduction to the science of distributed computing, with applications in all domains of distributed systems, such as cloud computing and blockchains. Each chapter comes with exercises and bibliographic notes to help the reader approach, understand, and master the fascinating field of fault-tolerant distributed computing.

Robust Finite-Time Frequency and Voltage Restoration of Inverter-Based Microgrids via Sliding-Mode Cooperative Control

Abstract: In this paper, we present a novel control strategy to perform the exact finite-time restoration among voltages and frequencies of an islanded inverter-based microgrid. The problem is attacked from a cooperative-based control perspective inspired to the tracking consensus paradigm. Ad hoc chattering-free sliding-mode-based distributed algorithms are designed to enhance the underlying robustness and convergence properties of the system with respect to the existing solutions. Particularly, the restoration is achieved while dispensing with the knowledge of the distributed generators’ models and parameters. Performance of the control system is analyzed by Lyapunov tools, and a simple set of tuning rules are derived. The effectiveness of the proposed scheme is verified by simulations on a realistic inverter-based microgrid modelization.

Distributed Constrained Optimization and Consensus in Uncertain Networks via Proximal Minimization

Abstract: We provide a unifying framework for distributed convex optimization over time-varying networks, in the presence of constraints and uncertainty, features that are typically treated separately in the literature. We adopt a proximal minimization perspective and show that this set-up allows us to bypass the difficulties of existing algorithms while simplifying the underlying mathematical analysis. We develop an iterative algorithm and show the convergence of the resulting scheme to some optimizer of the centralized problem. To deal with the case where the agents’ constraint sets are affected by a possibly common uncertainty vector, we follow a scenario-based methodology and offer probabilistic guarantees regarding the feasibility properties of the resulting solution. To this end, we provide a distributed implementation of the scenario approach, allowing agents to use a different set of uncertainty scenarios in their local optimization programs. The efficacy of our algorithm is demonstrated by means of a numerical example related to a regression problem subject to regularization.

Resilient Distributed Estimation Through Adversary Detection

Abstract: This paper studies resilient multiagent distributed estimation of an unknown vector parameter when a subset of the agents is adversarial. We present and analyze a flag raising distributed estimator ( $\mathcal {FRDE}$ ) that allows the agents under attack to perform accurate parameter estimation and detect the adversarial agents. The $\mathcal {FRDE}$ algorithm is a consensus+innovations estimator in which agents combine estimates of neighboring agents (consensus) with local sensing information (innovations). We establish that, under $\mathcal {FRDE}$ , either the uncompromised agents' estimates are almost surely consistent, or the uncompromised agents detect compromised agents (with arbitrarily small false alarm probability) if and only if the network of uncompromised agents is connected and globally observable. Numerical examples illustrate the performance of $\mathcal {FRDE}$ .

Round compression for parallel matching algorithms

Abstract: For over a decade now we have been witnessing the success of massive parallel computation (MPC) frameworks, such as MapReduce, Hadoop, Dryad, or Spark. One of the reasons for their success is the fact that these frameworks are able to accurately capture the nature of large-scale computation. In particular, compared to the classic distributed algorithms or PRAM models, these frameworks allow for much more local computation. The fundamental question that arises in this context is though: can we leverage this additional power to obtain even faster parallel algorithms? A prominent example here is the maximum matching problem—one of the most classic graph problems. It is well known that in the PRAM model one can compute a 2-approximate maximum matching in O(logn) rounds. However, the exact complexity of this problem in the MPC framework is still far from understood. Lattanzi et al. (SPAA 2011) showed that if each machine has n1+Ω(1) memory, this problem can also be solved 2-approximately in a constant number of rounds. These techniques, as well as the approaches developed in the follow up work, seem though to get stuck in a fundamental way at roughly O(logn) rounds once we enter the (at most) near-linear memory regime. It is thus entirely possible that in this regime, which captures in particular the case of sparse graph computations, the best MPC round complexity matches what one can already get in the PRAM model, without the need to take advantage of the extra local computation power. In this paper, we finally refute that possibility. That is, we break the above O(logn) round complexity bound even in the case of slightly sublinear memory per machine. In fact, our improvement here is almost exponential: we are able to deliver a (2+є)-approximate maximum matching, for any fixed constant є>0, in O((loglogn)2) rounds. To establish our result we need to deviate from the previous work in two important ways that are crucial for exploiting the power of the MPC model, as compared to the PRAM model. Firstly, we use vertex–based graph partitioning, instead of the edge–based approaches that were utilized so far. Secondly, we develop a technique of round compression. This technique enables one to take a (distributed) algorithm that computes an O(1)-approximation of maximum matching in O(logn) independent PRAM phases and implement a super-constant number of these phases in only a constant number of MPC rounds.

Distributed Convex Optimization with Inequality Constraints over Time-Varying Unbalanced Digraphs

Abstract: This paper considers a distributed convex optimization problem with inequality constraints over time-varying unbalanced digraphs, where the cost function is a sum of local objective functions, and each node of the graph only knows its local objective and inequality constraints. Although there is a vast body of literature on distributed optimization, most of them require the graph to be balanced, which is quite restrictive and not necessary. To solve it, this work proposes a novel idea of using the epigraph form of the constrained optimization, which can be easily used to study time-varying unbalanced digraphs. Under local communications, a simple iterative algorithm is then designed for each node. We prove that if the graph is uniformly jointly strongly connected, each node asymptotically converges to some common optimal solution.

SCAPE: Safe Charging With Adjustable Power

Abstract: Wireless power transfer technology is considered as one of the promising solutions to address the energy limitation problems for end-devices, but its incurred potential risk of electromagnetic radiation (EMR) exposure is largely overlooked by most existing works. In this paper, we consider the Safe Charging with Adjustable PowEr (SCAPE) problem, namely, how to adjust the power of chargers to maximize the charging utility of devices, while assuring that EMR intensity at any location in the field does not exceed a given threshold $R_{t}$ . We present novel techniques to reformulate SCAPE into a traditional linear programming problem, and then remove its redundant constraints as much as possible to reduce computational effort. Next, we propose a series of distributed algorithms, including a fully distributed algorithm that provably achieves $(1-\epsilon)$ approximation ratio and requires only communications with neighbors within a constant distance for each charger. Through extensive simulation and testbed experiments, we demonstrate that our proposed algorithms can outperform the set-cover algorithm by up to 17.05%, and has an average performance gain of 41.1% over the existing algorithm in terms of the overall charging utility.

A Novel Consensus-Based Economic Dispatch for Microgrids

Abstract: In this letter, a novel consensus-based approach is proposed to solve the energy management problem for islanded microgrids. By taking the incremental cost of each agent as the consensus variable and through limited communication between neighboring agents, the proposed approach can quickly converge to optimal solutions.