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At least 127 records · Page 7

Hierarchical Transactive Control of Flexible Building Loads Under Distribution LMP

With grid modernization efforts, future distribution networks, which consist of various distributed generators and flexible loads, will be more flexible and active. All new network components of distributed energy resources (DERs) drive and enable the transition towards a market-based distribution net-work that seeks the optimal allocation of all DERs. To address challenges associated with DERs, one promising solution is to utilize demand-side flexibility of building loads facilitated by demand response (DR) programs and provide ancillary grid services through distribution-level markets. Under this new paradigm, this paper proposes an efficient DR management strategy incorporating emerging price signals of distribution markets, i.e., distribution locational marginal price (DLMP), based on a hierarchical transactive control approach. The proposed approach establishes a two-layer decision-making framework; the upper layer formulates a bilevel model to obtain an optimal demand response (ODR) under DLMP, and the lower layer employs model-free control to dispatch the (aggregated) ODR to individual end users. Numerical case studies using a modified IEEE 33 test network are performed to verify the effectiveness of the proposed approach; load shifting and peak shaving for the distribution system operator and payments’ reduction for end users while maintaining their comfort.

Park, Byungkwon↗

Solid State Power Substations (SSPS): A Multi-Hierarchical Architecture from Substation to Grid Edge

With the growing deployment of distributed generation, or power electronic interfaced renewable energy and storage technologies, the nature and behavior of the grid is changing. Synchronous machine-driven asset contributions to the generation mix are shrinking, leading to concerns regarding grid stability. Furthermore, the scale of smaller distributed PE resources needed for managing the electrical network could dwarf the existing system leading to more complex optimization problems and communication interconnections. This paper introduces the concept of a hierarchal system of controllers that spans the grid edge or the customer end to distribution scale substations or solid-state power substation (SSPS). This concept focuses on minimizing the number of interfaces and optimization considerations in the grid by clustering resources into nodes and hubs. The work validates the concept in a controller hardware-in-the-loop (cHIL) platform.

Chinthavali, Madhu Sudhan↗

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Prototype Installation and Testing Awardee: Accelerate Wind

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Accelerate Wind for Prototype Installation and Testing. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NREL on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Prototype Installation and Testing Awardee: Pecos Wind Power

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Pecos Wind Power for Prototype Installation and Testing. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NREL on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Inverter Listing Awardee: Eocycle America Corporation

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Eocycle America Corporation for Inverter Listing. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NLR on behalf of DOE, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗

U.S. Department of Energy Competitiveness Improvement Project 2024 Technology Commercialization Awardee: Intelligent Energy Systems

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Intelligent Energy Systems for technology commercialization. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NLR on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed-generation technology option.

17 WIND ENERGY↗

Deep Reinforcement Learning for Distribution System Operations: A Tutorial and Survey

Here, the rapid evolution of modern electric power distribution systems into complex networks of interconnected active devices, distributed generation (DG), and storage poses increasing difficulties for system operators. The large-scale integration of distributed energy resources (DERs) and the rapid exchange of measurement data via communication networks present major opportunities for advancing grid operations but also introduce greater uncertainty, higher data dimensionality, more complex network and device models, and challenging control and optimization problems. Deep reinforcement learning (DRL) algorithms are promising in addressing these challenges. However, they have not been effectively adapted for power systems applications, requiring extensive customization for implementation and evaluation. This has resulted in reproducibility challenges and a steep learning curve for researchers new to applying DRL algorithms to the power systems domain. To bridge these gaps, this tutorial aims to serve as a valuable resource for researchers interested in exploring learning-based algorithms to operate active power distribution networks. Specifically, this work presents a generalized process for translating sequential decision-making problems in power distribution systems into Markov decision process (MDP) formulations, illustrated through concrete grid service examples. Additionally, we introduce a simple environment design strategy to develop and evaluate example DRL algorithms for distribution system applications, complete with an included code repository to guide users through environment construction.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Optimal Siting of EV Fleet Charging Station Considering EV Mobility and Microgrid Formation for Enhanced Grid Resilience

Coordinating infrastructure planning for transportation and the power grid is essential for enhanced reliability and resilience during operation and disaster management. This paper presents a two-stage stochastic model to optimize the location of electric vehicle fleet charging stations (FEVCSs) to enhance the resilience of a distribution network. The first stage of this model deals with the decision to place an FEVCS at the most favorable and optimized location, whereas the second stage aims to minimize the weighted sum of the value of lost load in multiple potential scenarios with different faults. Indeed, the second stage is a joint grid restoration scheme with network reconfiguration and microgrid formation using available distributed generators and fleet electric vehicles. The proposed model is tested on a modified IEEE-33 node distribution network and a four-node transportation network. Case studies demonstrate the effectiveness of the proposed model.

25 ENERGY STORAGE↗

Wind as a Distributed Energy Resource

Distributed wind can be installed in a wide range of locations and wind conditions, supporting millions of systems and thousands of gigawatts of power production capacity. As a result, utilities, communities, and nations are looking to distributed generation as an effective way to meet future energy needs. This fact sheet provides an overview of distributed wind, including where distributed wind projects can be located, and how U.S. and international research supports distributed wind applications. This fact sheet was produced as a resource for the International Energy Agency Task 41 members to use as an educational resource.

behind-the-meter wind energy↗

Autonomous Energy Systems

Energy systems are increasingly complicated by the proliferation of clean energy technologies such as solar, wind, storage, electric vehicles, and building automations. Future energy systems will require secure, autonomous, and reliable communications, control, and interoperability among millions of distributed generation points and billions of buildings, vehicles, and more. To enable effective management of the anticipated growth of distributed devices and the deluge of data and extensive metering that will follow, the National Renewable Energy Laboratory (NREL) has developed the concept of autonomous energy systems (AES).

autonomous↗

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Small Turbine Certification and/or Listing Awardee: Sonsight Wind

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Sonsight Wind for Small Turbine Certification and/or Listing. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NREL on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Technology Commercialization Awardee: Siva Powers America Inc.

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Siva Powers America Inc. for a Technology Commercialization Award. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NREL on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Manufacturing Process Innovation Awardee: Bergey Windpower Co.

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Bergey Windpower Co. for manufacturing process innovation. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NREL on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Component Innovation Awardee: Windurance LLC

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Windurance LLC for component innovation. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NREL on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Small Turbine Certification and/or Listing Awardee: NPS Solutions LLC

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by NPS Solutions LLC for Small Turbine Certification and/or Listing. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NREL on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Small Turbine Certification and Listing Awardee: Uprise Energy

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by Uprise Energy for small turbine certification and listing. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NREL on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗

U.S. Department of Energy Competitiveness Improvement Project (CIP) 2024 Technology Commercialization Awardee: EWT Americas Inc.

This fact sheet describes the 2024 Competitiveness Improvement Project (CIP) award received by EWT Americas, Inc., for technology commercialization. The U.S. Department of Energy's (DOE's) CIP awards cost-shared subcontracts and technical support to manufacturers of small and medium-sized wind turbines. Managed by NREL on behalf of DOE's Wind Energy Technologies Office, CIP helps advance wind energy as a cost-effective, distributed generation technology option.

17 WIND ENERGY↗

Production of b b ¯ at forward rapidity in p + p collisions at s = 510 GeV

The cross section of bottom quark-antiquark ( b b ¯ ) production in p + p collisions at s = 510 GeV is measured with the PHENIX detector at the Relativistic Heavy Ion Collider. The results are based on the yield of high mass, like-sign muon pairs measured within the PHENIX muon arm acceptance ( 1.2 < | y | < 2.2 ). The b b ¯ signal is extracted from like-sign dimuons by utilizing the unique properties of neutral B meson oscillation. We report a differential cross section of d σ b b ¯ → μ ± μ ± / d y = 0.16 ± 0.01 ( stat ) ± 0.02 ( syst ) ± 0.02 ( global ) nb for like-sign muons in the rapidity and p T ranges 1.2 < | y | < 2.2 and p T > 1 GeV / c , and dimuon mass of 5 – 10 GeV / c 2 . The extrapolated total cross section at this energy for b b ¯ production is 13.1 ± 0.6 ( stat ) ± 1.5 ( syst ) ± 2.7 ( global ) μ b . The total cross section is compared to a perturbative quantum chromodynamics calculation and is consistent within uncertainties. The azimuthal opening angle between muon pairs from b b ¯ decays and their p T distributions are compared to distributions generated using ps pythia 6, which includes next-to-leading order processes. The azimuthal correlations and pair p T distribution are not very well described by pythia calculations, but are still consistent within uncertainties. Flavor creation and flavor excitation subprocesses are favored over gluon splitting.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗