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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Adaptive Runtime Features For Distributed Graph Algorithms

Performance of distributed graph algorithms can benefit greatly by forming rapport between algorithmic abstraction and the underlying runtime system that is responsible for scheduling work and exchanging messages. However, due to their dynamic and irregular nature of computation, distributed graph algorithms written in different programming models impose varying degree of workload pressure on the runtime. To cope with such vastly different workload characteristics, a runtime has to make several trade-offs. One such trade-off arises, for example, when the runtime scheduler has to choose among alternatives such as whether to execute algorithmic work, or progress the network by probing network buffers, or throttle sending messages (termed flow control). This tradeoff decides between optimizing the throughput of a runtime scheduler by increasing the rate of execution of algorithmic work, and reducing the latency of the network messages. Another trade-off exists when a decision has to be made about when to send aggregated messages in buffers (message coalescing). This decision chooses between trading off latency for network bandwidth and vice versa. At any instant, such trade-offs emphasize either on improving the quantity of work being executed (by maximizing the scheduler throughput) or on improving the quality of work (by prioritizing better work). However, encoding static policies for different runtime features (such as flow control, coalescing) can prevent graph algorithms from achieving their full potential, thus can undermine the actual performance of a distributed graph algorithm . In this paper, we investigate runtime support for distributed graph algorithms in the context of two paradigms: variants of wellknown Bulk-Synchronous Parallel model and asynchronous programming model. We explore generic runtime features such as message coalescing (aggregation) and flow control and show that execution policies of these features need to be adjusted over time to make a positive impact on the execution time of a distributed graph algorithm. Since synchronous and asynchronous graph algorithms have different workload characteristics, not all of such runtime features may be good candidates for adaptation. Each of these algorithmic paradigms may require different set of features to be adapted over time. We demonstrate which set of feature(s) can be useful in each case to achieve the right balance of work in the runtime layer. Existing implementation of different graph algorithms can benefit from adapting dynamic policies in the underlying runtime.

Firoz, Jesun S.↗
DOE OSTI2024

A Privacy Preserving Distributed Model Identification Algorithm for Power Distribution Systems

Distributed control/optimization is a promising approach for network systems due to its advantages over centralized schemes, such as robustness, cost-effectiveness, and improved privacy. However, distributed methods can have drawbacks, such as slower convergence rates due to limited knowledge of the overall network model. Additionally, ensuring privacy in the communication of sensitive information can pose implementation challenges. To address this issue, we propose a distributed model identification algorithm that enables each agent to identify the sub-model that characterizes the relationship between its local control and the overall system outputs. The proposed algorithm maintains the privacy of local agents by only communicating through dummy variables. We demonstrate the efficacy of our algorithm in the context of power distribution systems by applying it to the voltage regulation of a modified IEEE distribution system. The proposed algorithm is well-suited to the needs of power distribution controls and offers an effective solution to the challenges of distributed model identification in network systems.

data-driven modeling↗
DOE OSTI2023

A Privacy Preserving Distributed Model Identification Algorithm for Power Distribution Systems: Preprint

Distributed control/optimization is a promising approach for network systems due to its advantages over centralized schemes, such as robustness, cost-effectiveness, and improved privacy. However, distributed methods can have drawbacks, such as slower convergence rates due to limited knowledge of the overall network model. Additionally, ensuring privacy in the communication of sensitive information can pose implementation challenges. To address this issue, we propose a distributed model identification algorithm that enables each agent to identify the sub-model that characterizes the relationship between its local control and the overall system outputs. The proposed algorithm maintains the privacy of local agents by only communicating through dummy variables. We demonstrate the efficacy of our algorithm in the context of power distribution systems by applying it to the voltage regulation of a modified IEEE distribution system. The proposed algorithm is well-suited to the needs of power distribution controls and offers an effective solution to the challenges of distributed model identification in network systems.

data-driven modeling↗
DOE OSTI2022

Online Distribution System State Estimation via Stochastic Gradient Algorithm

Distribution network operation is becoming more challenging because of the growing integration of intermittent and volatile distributed energy resources (DERs). This motivates the development of new distribution system state estimation (DSSE) paradigms that can operate at fast timescale based on real-time data stream of asynchronous measurements enabled by modern information and communications technology. To solve the real-time DSSE with asynchronous measurements effectively and accurately, this paper formulates a weighted least squares DSSE problem and proposes an online stochastic gradient algorithm to solve it. The performance of the proposed scheme is analytically guaranteed and is numerically corroborated with realistic data on IEEE 123-bus feeder.

distribution system state estimation↗
DOE OSTI2020

Computation-Efficient Algorithm for Distributed Feedback Optimization of Distribution Grids

Feedback-based optimization algorithms use real-time measurements to update the optimal control for the underlying system which may not be fully identified. Recently, we have developed a distributed feedback-based algorithm [1] that avoids the requirement of fast communication between central computing and local actuator/sensor agents. This paper extends the work by greatly reducing the number of copies of variables involved in the distributed feedback-based algorithm, which results in faster convergence and lower communication requirement. The main idea is to leverage the specific structural properties of the admittance matrix for distribution systems with tree network topology. We also show the effectiveness of the proposed algorithm in simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Resilient Design of Continuous-time Distributed Optimization Algorithm in the Presence of Cyber-attacks

This paper presents a continuous-time resilient distributed optimization algorithm based on competitive interaction design method on connected graphs in the presence of adversaries. Here, the competitive interaction method allows us to design a network that protects the multi-agent systems from adversaries without requiring high network connectivity. In addition, the proposed algorithm does not require the global information about the number of adversaries. First, we show that the proposed distributed algorithm solves the resilient distributed optimization problem with no attack on the communication links. Second, we show that the proposed continuous-time distributed optimization algorithm on connected graphs converges to the small neighborhood of the optimal solution in the presence of cyber-attacks onto the communication channel. Simulations are presented to illustrate our theoretical results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Layered Coordination Architecture for Resilient Restoration of Power Distribution Systems

The current practices for restoring critical services in the distribution system during a disaster, align with the traditional centralized ideology of distribution systems operations. A central processor evaluates the distribution system after a disruption and attains a restoration plan. However, the centralized operational paradigm is susceptible to single-point failures, requires full situational awareness of the distribution system, and poses scalability challenges for large multifeeder distribution systems. This motivates a distributed decision-making paradigm where multiple agents solve smaller subproblems and jointly coordinate their individual decisions to achieve the global/network-level objective. Toward this goal, we propose a layered architecture for distributed algorithms for resilience and a two-stage distributed algorithm for distribution system restoration. The proposed distributed decision-making framework enables the bottom-up restoration of the distribution system using all available resources, including distributed generation, while only requiring local awareness and limited communications with neighboring connected regions. The proposed framework is robust to single-point failures, enables autonomy using distributed algorithms, and had reduced computational cost compared to centralized optimization solutions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

An Intelligent Distributed Ledger Construction Algorithm for IoT

Blockchain is the next generation of secure data management that creates near-immutable decentralized storage. Secure cryptography created a niche for blockchain to provide alternatives to well-known security compromises. However, design bottlenecks with traditional blockchain data structures scale poorly with increased network usage and are extremely computation-intensive. This made the technology difficult to combine with limited devices, like those in Internet of Things networks. In protocols like IOTA, replacement of blockchain's linked-list queue processing with a lightweight dynamic ledger showed remarkable throughput performance increase. However, current stochastic algorithms for ledger construction suffer distinct trade-offs between efficiency and security. This work proposed a machine-learning approach with a multi-arm bandit that resolved these issues and was designed for auditing on limited devices. This algorithm was tested in a reinforcement-learning environment simulating the IOTA ledger's construction with a decision tree. This study showed through regret analysis and experimentation that this approach was secure against impulse manipulation attacks while remaining energy-efficient. Although the IOTA protocol was a pioneer for lightweight distributed ledgers, it is expected that future blockchain protocols will adopt techniques similar to those presented in this work.

multi-arm bandit↗
NASA NTRS1991

Distributed genetic algorithms for the floorplan design problem

Designing a VLSI floorplan calls for arranging a given set of modules in the plane to minimize the weighted sum of area and wire-length measures. A method of solving the floorplan design problem using distributed genetic algorithms is presented. Distributed genetic algorithms, based on the paleontological theory of punctuated equilibria, offer a conceptual modification to the traditional genetic algorithms. Experimental results on several problem instances demonstrate the efficacy of this method and indicate the advantages of this method over other methods, such as simulated annealing. The method has performed better than the simulated annealing approach, both in terms of the average cost of the solutions found and the best-found solution, in almost all the problem instances tried.

Cohoon, James P.↗
DOE OSTI2018

Accelerated Distributed Energy Management for Microgrids

This paper considers the optimal energy management for a microgrid. The goal of optimal energy management is to minimize the total generation cost while meeting total demand and satisfying individual generator output limits. To achieve this goal, we develop a distributed algorithm based on the consensus theory and gradient estimation technique. Compared to the existing algorithms with diminishing step-sizes, the proposed algorithm enjoys a faster convergence speed due to the fixed step-size, and thus increases the computation speed, and reduces the computation and communication burden. The proposed distributed algorithm is applicable to both fixed and time-varying directed communication networks. We show that the convergence of the proposed distributed algorithm is achieved if the fixed communication network is strongly connected, or if the time-varying communication network is uniformly jointly strongly connected. Numerical simulation results are used to illustrate and demonstrate the effectiveness of the proposed algorithm.

Du, Wen↗
DOE OSTI2018

A Scalable Distributed Louvain Algorithm for Large-Scale Graph Community Detection

We present a new distributed community detection algorithm for large graphs based on the Louvain method. We exploit a distributed delegate partitioning to ensure the workload and communication balancing among processors. In addition, we design a new heuristic strategy to carefully coordinate the community constitution in a distributed environment, and ensure the convergence of the distributed clustering algorithm. Our intensive experimental study has demonstrated the scalability and the correctness of our algorithm with various large-scale real-world and synthetic graph datasets using up to 32,768 processors.

Computer Science↗
DOE OSTI2018

Two-stage State Estimation Algorithm for Distribution Network Analysis

Practical distribution system contains many nodes and short branches. As a result, a more efficient and stable distribution state estimation (DSE) algorithm is needed to properly handle the inverse operation of its large-scale ill-conditioned information matrix. This paper proposed a two-stage DSE algorithm to transform the inverse operation into addition and multiplication operations in a recursive manner. It includes pseudo measurement preprocessing in the offline stage where an approximate linear estimator is adopted to relieve most of computation burdens, and real-time correction in the online stage where a nonlinear estimator is used to update the estimation. Numerical results of the method applied to both IEEE standard test systems and an actual distribution system show the accuracy and effectiveness of the proposed algorithm.

Dong, Jiaojiao↗
DOE OSTI2025

Cyber‐Resilient Distributed Energy Resource Control Algorithms for Smart Distribution Grids

ABSTRACT This paper focuses on the development of cyber‐resilient gradient‐based optimisation algorithms and theoretical proof for grid‐interactive distributed energy resource (DER) control to enable two grid services of virtual power plants (VPPs) dispatch and grid voltage regulation, considering the communication and security impacts. Firstly, the combined DER dispatch and voltage regulation as a real‐time gradient‐based optimisation problem is recapped. Thereafter, we consider a probabilistic traffic model to characterise packet delays and loss in a communication network, and study how the delays enter the process of information exchange among the grid measurement units, local DER controllers and the grid control centre that execute this control algorithm in a coordinated manner. Then, a strategy combining delay thresholds and message update rules is proposed to immunity the asynchrony resulting from the communications traffic and it avoids possible numerical instabilities and sensitivities of the power tracking and voltage regulation capabilities, resulting as cyber‐resilient DER control algorithms. Additionally, their convergence is theoretically proved. Effectiveness of proposed cyber‐resilient algorithms has been validated on the IEEE 37‐bus system in terms of convergence, VPP tracking and voltage regulation performance for smart distribution systems with high penetration of DERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Reverse and Forward Engineering of Local Voltage Control in Distribution Networks

The increasing penetration of renewable and distributed energy resources in distribution networks calls for real-time and distributed voltage control. As such, in this paper we investigate local Volt/VAR control with a general class of control functions, and show that the power system dynamics with non-incremental local voltage control can be seen as a distributed algorithm for solving a well-defined optimization problem (reverse engineering). The reverse engineering further reveals a fundamental limitation of the non-incremental voltage control: the convergence condition is restrictive and prevents better voltage regulation at equilibrium. This motivates us to design two incremental local voltage control schemes based on the subgradient and pseudo-gradient algorithms respectively for solving the same optimization problem (forward engineering). The new control schemes decouple the dynamical property from the equilibrium property, and have much less restrictive convergence conditions. This work presents another step towards developing a new foundation—network dynamics as optimization algorithms—for distributed real-time control and optimization of future power networks.

42 ENGINEERING↗
DOE OSTI2017

Best effort broadcast under cascading failures in interdependent critical infrastructure networks

Here, we present a novel study of reliable broadcast in interdependent networks, in which the failures in one network may cascade to another network. In particular, we focus on the interdependency between a communication network and a power grid network, where the power grid depends on the communication network for control and the communication network depends on the grid for power. In this paper, we propose a best effort broadcast algorithm to handle crash failures in the communication network that may cause cascading failures. We guarantee that all the correct nodes, which operate correctly according to the protocol and do not experience any software or hardware or network failures, eventually deliver the message if the sender is correct. We provide a centralized algorithm and a fully distributed algorithm for nodes to analyze and handle cascading failures. At the core of our work is the fully distributed algorithm which enjoys great performance and scalability. Our evaluation results show that the algorithm handles cascading failures with low overhead.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Optimizing Microgrid Energy Delivery Under High Uncertainty

One of the largest transitions in the power system today is the shift to a more sustainable and resilient power system. This is being driven by public opinion, changes in regulatory policies, and advancements in smart grid technologies. The most noticeable changes taking place is the integration of distributed energy sources (DERs); this study uses the term DER in the most general way as a resource that can be manipulated to alter energy delivery and flow in the transmission and distribution networks. Also, here it is preferred to focus on energy as the true need while power is a function of the equipment rating. As such, wind and solar, demand that can be manipulated, electric vehicles, electric energy storage, thermal storage, and storage in water system are all considered DERs. These additions to the distribution system are evolving the operation of distribution feeders into microgrids- communication, computing, and control-enabled resources that produce, transport, and utilize energy in a manner that provides cost, reliability, and resilience benefits. As this evolution progresses, the planning and operational management (scheduling and control) must explicitly include the consideration of risk. The management of system risk is currently in the purview of the utility and will likely remain so in the future. However, as each microgrid, as well as federation of microgrids, sees autonomy in order to provide maximum benefits to their constituents, they must assume responsibility to manage their internal risk. The primary scope of this study is the scheduling of resources in a distribution feeder(s) operating as microgrids. The study explores a distribution algorithm to develop the transactive schedule for the DERs, to minimize cost and risk over a time horizon, and an initial laboratory-scale to conduct implementation on distributed hardware. Results from case studies are presented that show that solutions derived by the distributed algorithm are valid. This study also discusses the continuing work on the expansion of: 1) the distributed algorithm from a deterministic to stochastic optimization formulation, and 2) implementation of the distributed algorithm into real-time simulation within the Power System laboratory at New Mexico State University (NMSU) and expanding to the Southwest Technology Development Institute located at NMSU where actual solar, energy storage, and demand response resources are installed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Performance Evaluation of Distributed Energy Resource Management Algorithm in Large Distribution Networks

This paper presents performance evaluation of hierarchical optimization and control for distributed energy resource management system (DERMS) in large distribution networks via an advanced hardware-in-the-loop (HIL) platform. The HIL platform provides realistic testing in a laboratory environment, including the accurate modeling of a full-scale distribution system of 11,000 nodes, the DERMS software controller, and 90 power hardware photovoltaics (PVs) and battery inverters. The applied DERMS algorithm is designed based on a realtime optimal power flow algorithm and implemented with acceleration design that performs fast dispatch of simulated PVs and real physical hardware DER devices every 4 seconds.

DERMS↗