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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Decentralized Control of Cascaded H-Bridge Inverters for Medium-Voltage Grid Integration: Preprint

This paper introduces a fully decentralized control strategy that allows for individualized active and reactive power control of H-bridge inverters connected in series. After formulating a model of the system power flows, we outline a communications-free approach where each inverter can act on independent commands. Inverter-level feedback is accomplished locally via a measurement of current flowing through the stack. A key feature is the lack of any centralized control routines or phase-locked loop. In this framework, the reactive power control loops achieve voltage synchronization among the converters and the grid, and the active power control loops modulate the terminal voltage amplitudes that influence power delivery. Furthermore, pole placement via state feedback is used to ensure stability of voltage synchronization while also enabling tracking of bidirectional power commands. Simulations are presented to validate the proposed approach.

communication-free modular converter control↗
DOE OSTI2024

Efficient Unitary Designs from Random Sums and Permutations

A unitary k-design is an ensemble of unitaries that matches the first k moments of the Haar measure. In this work, we provide two efficient constructions of k-designs on n-qubits using new random matrix theory techniques. Our first construction is based on exponentiating sums of random i.i.d. Hermitian matrices and uses O(k2n2)-many gates. In the spirit of central limit theorems, we show that this random sum approximates the Gaussian Unitary Ensemble (GUE). We then show that the product of just two exponentiated GUE matrices is already approximately Haar random. Our second construction is based on products of exponentiated sums of random permutations and uses Õ(k poly (n)) many gates. The k dependence is optimal (up to polylogarithmic factors) and is inherited from the efficiency of existing k-wise independent permutations. Furthermore, replacing random permutations with quantum-secure pseudorandom permutations (PRPs), we also obtain a pseudorandom unitary (PRU) ensemble that is secure under nonadaptive queries. A central feature of both proofs is a new connection between the polynomial method in quantum query complexity and the large-dimension (N) expansion in random matrix theory. In particular, the first construction uses the polynomial method to control high moments of certain random matrix ensembles without requiring delicate Weingarten calculations. In doing so, we define and solve a moment problem on the unit circle, asking whether a finite number of equally weighted points can reproduce a given set of moments. In our second construction, the key step is to exhibit an orthonormal basis for irreducible representations of the partition algebra that has a low-degree large-N expansion. This allows us to show that the distinguishing probability is a low-degree rational polynomial of the dimension N.

algebra↗
DOE OSTI2023

Fast Parallel Tensor Times Same Vector for Hypergraphs

Hypergraphs are a popular paradigm to rep- resent complex real-world networks exhibiting multi-way relationships of varying sizes. Mining centrality in hyper- graphs via symmetric adjacency tensors has only recently become computationally feasible for large and complex datasets. To enable scalable computation of these and related hypergraph analytics, here we focus on the Sparse Symmetric Tensor Times Same Vector (S3TTVC) oper- ation. We introduce the Compound Compressed Sparse Symmetric (CCSS) format, an extension of the compact CSS format for hypergraphs of varying hyperedge sizes and present a shared-memory parallel algorithm to compute S3TTVC. We experimentally show S3TTVC computation using the CCSS format achieves better performance than the naive baseline, and is subsequently more performant for hypergraph H-eigenvector centrality.

Shivakumar, Shruti↗
DOE OSTI2022

Control of Grid-Connected Multiport MV Power Electronics Energy Hub: Preprint

In this paper a new concept of multiport modular medium-voltage power electronics hub (M3PE-HUB) is introduced for future power grid. The goal for this project is to design, develop, and demonstrate foundational technologies and capabilities for multiport power electronics energy hubs that can serve as intelligent devices to coordinate and control several different sources and loads. In this paper, the architecture of the controller, the central controls, and its verification is presented.

controls↗
DOE OSTI2021

Segmented Energy Routing for a Modular AC/DC Hybrid System

This article presents a modular ac/dc system with both distributed and centralized power ports for energy router (ER) applications. In each module of the described system, photovoltaic (PV) power generation units, battery-type energy storage (ES) units, and critical loads are connected to the cascaded H-bridge (CHB)-organized medium-voltage (MV) dc links, with fully distributed low-voltage (LV) dc power ports. Copies of modules share the centralized load bus and interact with an MV ac grid in parallel. Hybrid power port (HPO) assigns flexibility to the system but makes energy routings a necessity for stable operation. In this article, a segmented energy management strategy for the HPO-ER is proposed. In terms of the grid-side power transferring, the system ratings are intentionally designed to match significant power imbalance. Focally, a segmented energy management strategy is proposed to realize fully autonomous energy routing involving MV ac grid, distributed PV generations, distributed battery storages, and LV load. The system is proven to be stable using the derived impedance-based model considering the interaction between power blocks. The feasibility of the topology and control strategy is also verified through software simulation, laboratorial hardware prototype experiment, and hardware-in-the-loop (HIL) emulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Securely Aggregated Coded Matrix Inversion

Coded computing is a method for mitigating straggling workers in a centralized computing network, by using erasure-coding techniques. Federated learning is a decentralized model for training data distributed across client devices. In this work we propose approximating the inverse of an aggregated data matrix, where the data is generated by clients; similar to the federated learning paradigm, while also being resilient to stragglers. To do so, we propose a coded computing method based on gradient coding. We modify this method so that the coordinator does not access the local data at any point; while the clients access the aggregated matrix in order to complete their tasks. Here, the network we consider is not centrally administrated, and the communications which take place are secure against potential eavesdroppers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Decentralized Voltage Control of Large-Scale Distribution System with PVs Based on MADRL

This paper proposes a model-free decentralized control framework for the voltage regulation of large-scale distribution systems through the coordinated control of PV inverters. This is achieved by developing a novel interaction mechanism between the surrogate model and the centralized training and decentralized execution multiagent deep reinforcement learning framework. Specifically, the sparse Gaussian processes regression method is first utilized to develop the surrogate model of the original distribution system for reward calculation during the training stage, where each agent represents a sub-region in the centralized fashion for coordination strategy learning. After that, the learned control rules are used to inform controllers within each sub-region for real-time decisions with only local measurements. Comparative tests among various methods on the EPRI Ckt5 test system demonstrate the effectiveness of the proposed method.

distribution system↗
DOE OSTI2022

Performance Evaluation of Peer-to-Peer Distributed Microgrids Coordination for Voltage Regulation

This paper presents the performance evaluation of a peer-to-peer microgrids coordination algorithm for sub-transmission systems. As distributed energy resources (DERs) in distribution system start to show negative impact to the bulk power system, a paradigm shift is needed for transmission planning and operation. Because distribution substations are located far from the sub-transmission system, and it is hard to use traditional centralized control for real-time control and coordination. Thus, distributed control is a natural choice because it requires less communication and central computation. In this paper, each distribution substation is treated as a microgrid, and the peer-to-peer distributed microgrids control is formulated as a real-time optimal power flow problem to reduce the negative impact in sub-transmission systems. A distributed primal-dual optimization algorithm is adopted to solve the problem. Validation of the peer-to-peer algorithm is performed through the simulation of a real-world sub-transmission system composing of many distribution systems with high renewable penetration. Simulation results show that the peer-to-peer algorithm can achieve satisfactory performance (e.g., voltage regulation) in sub-transmission system by coordinating and controlling DERs in distribution systems.

distributed control↗
DOE OSTI2022

Decentralized Distribution System Restoration with Grid-Forming/Following Inverter-Based Resources

The high penetration of distributed energy resources (DERs) in active distribution systems has posed challenges to the centralized distribution system restoration (DSR) strategies in current practice. On the other hand, the advancement in smart inverter technologies enables the bottom-up restoration capability. This paper is motivated to develop a 3-layered hierarchical framework for decentralized DSR, based on the grid-forming (GFM) and grid-following (GFL) grid-edge inverters. The first layer presents the tertiary control, which determines the load pickup schedule and generation dispatch of DERs, using the alternating direction method of the multipliers algorithm. The second layer consists of two control functions: GFM control, which regulates voltage and frequency, establishing a stable grid for GFL inverters to follow; and GFL control, which regulates the real and reactive power. In the third layer, the primary control is proposed to regulate the inverter voltage and current, which is developed based on the virtual oscillator control (VOC). Furthermore, the developed framework is tested in the modified IEEE 13-node test feeder. Two scenarios of grid-connected and islanded operating modes are designed, and simulation results demonstrate the effectiveness of decentralized DSR strategies for controlling grid-edge inverters to enhance the distribution system resilience.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Collaborative Decision Approach for Electricity Pricing-demand Response Stackelberg Game

Demand response programs are considered as a valuable resource in smart grids that provide several advantages of load shifting, peak load reduction, mediating intermittency of renewable energy integration, etc. Flexible price-based incentives have been recognized as a critical strategy in motivating and compensating consumers' load adjustment actions for successful implementation of demand response. Game theoretical approaches, especially Stackelberg games are popularly adopted to model the relationship between electricity price and customers' demand response and solved by the classical centralized backward induction (BI) method. However, the BI method generally requires convexity of the follower's model for necessary optimality conditions, and the computational time of any centralized approach increases sharply with larger problem instances. In this paper, the Stackelberg game of electricity pricing-demand response between a distribution system operator (DSO) and load aggregators (LAs) is decomposed based on a collaborative optimization (CO) framework, where each LA is treated as a discipline with its own domain constraints (e.g. building temperature control), while the DSO at the system level tries to reduce the solution discrepancy and guide the searching towards optimality. Several groups of comparison experiments have demonstrated the effectiveness of the proposed collaborative decision approach in solving the demand response game.

Chen, Yang↗
DOE OSTI2023

Traffic Signal Optimization by Integrating Reinforcement Learning and Digital Twins

Machine learning (ML) methods, especially reinforcement learning (RL), have been widely considered for traffic signal optimization in intelligent transportation systems. Most of these ML methods are centralized, lacking in scalability and adaptability in large traffic networks. Further, it is challenging to train such ML models due to the lack of training platforms and/or the cost of deploying and training in a real traffic networks. This paper presents an approach for the integration of decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin (DT) to optimize traffic signals for the reduction of traffic congestion and network-wide fuel consumption related to stopping. Specifically, the DGMARL agents learn traffic state patterns and make decisions regarding traffic signal control with assistance from a Digital Twin module, which simulates and replicates the traffic behaviors of a real traffic network. The proposed approach was evaluated using PTV-Vissim [1], a microscopic traffic simulation platform. PTV-Vissim is also the simulation engine of the DT, enabling emulation and optimization of the traffic signals on the MLK Smart Corridor in Chattanooga, Tennessee. Compared to an actuated signal control baseline approach, experiment results show that Eco_PI, a developed performance measure capturing the impact of stops on fuel consumption, was reduced by 44.27% in a 24-hour and an average of 29.88% in a PM-peak-hour scenario.

Multi-Agent Reinforcement Learning, Digital Twin, ↗
DOE OSTI2020

Deep Reinforcement Learning-Based Edge Caching in Wireless Networks

With the purpose to offload data traffic in wireless networks, content caching techniques have recently been studied intensively. Using these techniques and caching a portion of the popular files at the local content servers, the users can be served with less delay. Most of the content replacement policies are based on the content popularity, that depends on the users' preferences. In practice, such information varies over time. Therefore, an approach to determine the file popularity patterns must be incorporated into caching policies. In this context, we study content caching at the wireless network edge using a deep reinforcement learning framework with Wolpertinger architecture. In particular, we propose deep actor-critic reinforcement learning based policies for both centralized and decentralized content caching. For centralized edge caching, we aim at maximizing the cache hit rate. In decentralized edge caching, we consider both the cache hit rate and transmission delay as performance metrics. These proposed frameworks are assumed to neither have any prior information on the file popularities nor know the potential variations in such information. Via simulation results, the superiority of the proposed frameworks is verified by comparing them with other policies, including least frequently used (LFU), least recently used (LRU), and first-in-first-out (FIFO) policies.

42 ENGINEERING↗
DOE OSTI2020

A robust dynamic state estimation approach against model errors caused by load changes

Dynamic state estimation (DSE) plays an important role in power system security monitoring and online control. In practice, there are two approaches to implementing DSE. The first approach is distributed DSE, which is based on the assumption that the terminal bus of each generator can be measured by PMUs (phasor measurement units). The assumption cannot be satisfied currently, however, because PMUs usually are installed at important high-voltage buses such as 500-kV buses installed in portions of the grid overseen by the Western Electricity Coordinating Council. Another issue of this approach is that performance of DSE is vulnerable to bad measurement data. The reason for this vulnerability is that DSE is performed separately through measurements at each terminal bus, and measurements at terminal buses are the only measurement upon which DSE can rely. Therefore, important redundant measurements are not included in this approach. The second approach is centralized DSE. This approach does not have the requirement for PMU location, and redundant measurements can be considered fully. However, load changes and grid topology changes impact centralized DSE. In this paper, we propose a new approach for handling the impact of load changes on DSE. We have developed a new algorithm that includes two sequential steps. In the first step, errors caused by load changes are detected by analyzing the difference between prediction results and measured results. In the second step, once model error is detected, a model optimization procedure is run to correct the error so the state estimation error can be mitigated. Simulation results from the IEEE 68 bus system show that the proposed approach can effectively handle model errors caused by load changes.

robust dynamic state estimation, load change, powe↗
DOE OSTI2021

Distributed Optimization in Distribution Systems: Use Cases, Limitations, and Research Needs

We report electric distribution grid operations typically rely on both centralized optimization and local non-optimal control techniques. As an alternative, distribution system operational practices can consider distributed optimization techniques that leverage communications among various neighboring agents to achieve optimal operation. With the rapidly increasing integration of distributed energy resources (DERs), distributed optimization algorithms are growing in importance due to their potential advantages in scalability, flexibility, privacy, and robustness relative to centralized optimization. Implementation of distributed optimization offers multiple challenges and also opportunities. This paper provides a comprehensive review of the recent advancements in distributed optimization for electric distribution systems and classifications using key attributes. Problem formulations and distributed optimization algorithms are provided for example use cases, including volt/var control, market clearing process, loss minimization, and conservation voltage reduction. Finally, this paper also presents future research needs for the applicability of distributed optimization algorithms in the distribution system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Distributed Outage Detection in Power Distribution Networks

Real time topology knowledge is essential for situational awareness of power distribution networks. Line outages change the topology of a distribution network. Hence, outage detection is an important task. Most of the existing outage detection algorithms are centralized, in which sensors communicate their data to a control center which performs outage detection using the received data. However, with the increasing size of the distribution network and with different areas of the network being monitored by different operators, communication is a bottleneck and scalability is a major concern. To address these issues, we propose a novel outage detection algorithm using a divide and conquer approach. First, we divide a distribution network into sub-networks, such that outage detection can be run in parallel in each sub-network independently ensuring scalability to large networks. Further, to reduce the latency, bandwidth and attenuation challenges associated with communications in a large network, we divide each sub-network into multiple control areas which communicate only with their neighbors. We employ a distributed iterative load estimation across the control areas of each sub-network and then use the load estimate for local outage detection in each control area. Here, the performance of our algorithm is evaluated for multiple feeder models and compared against traditional centralized outage detection algorithms.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Development of an Encoding Method on an Co-simulation Platform for Mitigating the Impact of Unreliable Communication

This report presents a hardware-in-the-loop (HIL) based modeling approach for simulating impacts of unreliable communication on the performance of centralized volt-var control and for developing an encoding method to mitigate the impacts. First, an asynchronous real-time HIL simulation platform is introduced to enable multi-rate co-simulation of a distribution system with many inverter-based distributed energy resources (DERs). The distribution system is modeled by milliseconds phasor-based models and the DERs are modeled by micro-seconds power electronic models. Communication connections between a centralized volt-var controller (modeled externally to the HIL testbed) and smart inverters are built by implementing Modbus links and the Long Term Evolution network. On this co-simulation platform, an enhanced, augmented Lagrangian multiplier based encoded data recovery (EALM-EDR) algorithm for mitigating the impact of unreliable communication is developed and validated. Simulation results demonstrate the efficacy of using the HIL-based co-simulation platform as a power grid digital twin for developing algorithms that coordinate a large number of heterogeneous control systems through wired and wireless communication links.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Data-Driven Multi-agent Deep Reinforcement Learning for Distribution System Decentralized Voltage Control with High Penetration of PVs

This paper proposes a novel model-free/data-driven centralized training and decentralized execution multi-agent deep reinforcement learning (MADRL) framework for distribution system voltage control with high penetration of PVs. The proposed MADRL can coordinate both the real and reactive power control of PVs with existing static var compensators and battery storage systems. Unlike the existing DRL-based voltage control methods, our proposed method does not rely on a system model during both the training and execution stages. This is achieved by developing a new interaction scheme between the surrogate modeling of the original system and the multi-agent soft actor critic (MASAC) MADRL algorithm. In particular, the sparse pseudo-Gaussian process with a few-shots of measurements is utilized to construct the surrogate model of the original environment, i.e., power flow model. This is a data-driven process and no model parameters are needed. Furthermore, the MASAC enabled MADRL allows to achieve better scalability by dividing the original system into different voltage control regions with the aid of real and reactive power sensitivities to voltage, where each region is treated as an agent. This also serves as the foundation for the centralized training and decentralized execution, thus significantly reducing the communication requirements as only local measurements are required for control. Comparative results with other alternatives on the IEEE 123-nodes and 342-nodes systems demonstrate the superiority of the proposed method.

14 SOLAR ENERGY↗