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Smarter Grid Solutions used the National Renewable Energy Laboratory’s (NREL’s) simulation capabilities at the Energy Systems Integration Facility to expand its Active Network Management technology for smart campus power control.
In a companion manuscript, we developed a novel optimization method for placement, sizing, and operation of Flexible Alternating Current Transmission System (FACTS) devices to relieve transmission network congestion. Specifically, we addressed FACTS that provide Series Compensation (SC) via modification of line inductance. In this manuscript, this heuristic algorithm and its solutions are explored on a number of test cases: a 30-bus test network and a realistically-sized model of the Polish grid (~2700 nodes and ~3300 lines). The results on the 30-bus network are used to study the general properties of the solutions including non-locality and sparsity. The Polish grid is used as a demonstration of the computational efficiency of the heuristics that leverages sequential linearization of power flow constraints and cutting plane methods that take advantage of the sparse nature of the SC placement solutions. Using these approaches, the algorithm is able to solve an instance of Polish grid in tens of seconds. We explore the utility of the algorithm by analyzing transmission networks congested by (a) uniform load growth, (b) multiple overloaded configurations, and (c) sequential generator retirements
This report presents the project results for the NREL INTEGRATE demonstration project: Active Network Management (ANM) Integration. Consistent with the INTEGRATE program's objective of increasing the grid's clean energy hosting capacity and providing grid services in a holistic manner, the project demonstrated the use of ANM to enable very high penetrations of solar PV and wind energy systems. Results obtained using a representative North American distribution system showed ANM's autonomous distributed control doubled the interconnected wind and solar systems by managing thermal and voltage constraints in real-time. These generators flexibly interconnected to the grid via ANM's end-to-end control architecture, and were managed and coordinated with other smart distributed energy resources (DER; e.g. EVs, battery ESS, controllable load) to unlock the grid's dynamic hosting capacity. As a smart grid application to standardize grid integration of diverse renewable generation sources, ANM provides the industry with a scalable, cost-effective alternative to traditional transmission and distribution (T&D) reinforcements. This project considered three use cases of the application of ANM: a Smart Home demonstration of ANM control of residential scale devices (PV, EV, and battery ESS); a Smart Campus demonstration of high penetration megawatt-scale DER (PV, EVs, battery ESS, and controllable load) behind a single point-of-common-coupling (PCC), and a Smart Distribution demonstration that combined prior use cases along with additional community/utility-scale DER at very high aggregate penetration levels. The Smart Distribution use case provided an integrated perspective of ANM across all distribution grid DER device scales (incorporating aggregated Smart Homes and the Smart Campus) in order to showcase how these devices can support smart grid services to manage DER related constraints in a safe and reliable manner; and likewise how ANM integrates with grid modernization initiatives. The ANM system was deployed in NREL's Energy Systems Integration Facility (ESIF), and project industry advisors attended an on-site demonstration and a set of workshops in order to review project outcomes and evaluate the relevance of ANM for increasing the grid hosting capacity. The outcomes of this effort include an assessment of opportunities for field deployment of ANM to address existing interconnection challenges, as well as consolidating project advisor suggestions for investment in ANM and potential future pilots for more advanced schemes.
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Many countries worldwide are setting clean energy targets to decarbonize the energy sector and add higher wind and solar capacities. Given their distributed nature, variable renewable energy (VRE) assets can have unique grid integration considerations, and require grid operation practices to be modernized. When transitioning to higher renewable energy levels, many system operators configure a dedicated renewable energy desk to manage renewable energy resource operation in the system control center. Further, system operation planning is conducted with VRE and load forecasting to enable reliable real-time system operation. NREL has partnered with many system operators in the region to identify appropriate technology and grid modernization solutions. NREL and partners from system operators in the region will present on the challenges and some solutions that have been applied and share best practices such as energy management system upgrades, VRE forecasting framework, grid flexibility improvements, and staff training.
Multilevel Monte Carlo (MLMC) is a recently proposed variation of Monte Carlo (MC) simulation that achieves variance reduction by simulating the governing equations on a series of spatial (or temporal) grids with increasing resolution. Instead of directly employing the fine grid solutions, MLMC estimates the expectation of the quantity of interest from the coarsest grid solutions as well as differences between each two consecutive grid solutions. When the differences corresponding to finer grids become smaller, hence less variable, fewer MC realizations of finer grid solutions are needed to compute the difference expectations, thus leading to a reduction in the overall work. Here this paper presents an extension of MLMC, referred to as multilevel control variates (MLCV), where a low-rank approximation to the solution on each grid, obtained primarily based on coarser grid solutions, is used as a control variate for estimating the expectations involved in MLMC. Cost estimates as well as numerical examples are presented to demonstrate the advantage of this new MLCV approach over the standard MLMC when the solution of interest admits a low-rank approximation and the cost of simulating finer grids grows fast.
The pair functions that minimize the correlation energy of second-order many-body perturbation (MP2) theory (the Hylleraas functional) are obtained as solutions to the corresponding Sinanoglu equation by expanding them on a six-dimensional, multicenter, radial-angular grid of two electrons. Cusps in the pair functions at the nuclei are described numerically accurately by the multicenter grid. A cusp in each singlet pair function at the coalescence of the two electrons is taken into account analytically by a correlation factor. With a grid of approximately 10,000 points per atom, the MP2 correlation energies for atoms and polyatomic molecules are obtained usually within 0.1 mE h of the complete-basis-set results. The correlation factor, auxiliary basis functions, and a judicious choice of integration algorithms are all necessary to stabilize the grid-based MP2 and underlying Hartree–Fock (HF) calculations. The auxiliary basis set, in particular, largely restores the Hermiticity and diagonal dominance of the Fock matrix as well as furnishes virtual orbitals used in a resolution-of-the-identity approximation to lower the dimension of some integrals. The results of the grid-based HF and MP2 calculations without a correlation factor are found to suffer from large, nonsystematic errors frequently.
ORNL is developing technology solutions to modernize the U.S. power grid in tandem with industry and academic partners to ensure the secure, reliable flow of electricity essential for a vibrant economy.
Advanced grid solutions are comprised of advanced transmission technologies and grid enhancing technologies. In this webinar, experts will provide participants with insights into eight advanced grid solutions. The advanced transmission technologies that will be discussed include point-to-point high voltage direct current and advanced conductoring and the grid enhancing technologies that experts will explore include topology optimization, advanced power flow control, dynamic line rating, energy storage, virtual power plants, and advanced flexible transformers
In a companion manuscript, we developed a novel optimization method for placement, sizing, and operation of Flexible Alternating Current Transmission System (FACTS) devices to relieve transmission network congestion. Specifically, we addressed FACTS that provide Series Compensation (SC) via modification of line inductance. In this manuscript, this heuristic algorithm and its solutions are explored on a number of test cases: a 30-bus test network and a realistically-sized model of the Polish grid (~ 2700 nodes and ~ 3300 lines). The results on the 30-bus network are used to study the general properties of the solutions including non-locality and sparsity. The Polish grid is used as a demonstration of the computational efficiency of the heuristics that leverages sequential linearization of power flow constraints and cutting plane methods that take advantage of the sparse nature of the SC placement solutions. Using these approaches, the algorithm is able to solve an instance of Polish grid in tens of seconds. We explore the utility of the algorithm by analyzing transmission networks congested by (a) uniform load growth, (b) multiple overloaded configurations, and (c) sequential generator retirements.
Utilidata Inc. is an energy software company that provides clean energy and digital solutions for utilities. This work was the result of Utilidata Inc. being awarded a New York State Energy Research and Development Agency award to integrate Utilidata volt-VAR optimization (VVO) software with smart inverters to explore enhanced voltage control. This project was conducted in partnership with Utilidata Inc., National Grid, the New York State Energy and Research Development Authority, and the National Renewable Energy Laboratory (NREL). As part of the project NREL conducted modeling and simulation analysis, developed cost-benefit analysis framework, and conducted laboratory testing to understand the potential for smart inverter integration to a centralized VVO control scheme. The remainder of the report provides an executive summary of the work, an introduction to the challenges, sections detailing specific aspects of the work, and the overall findings and conclusions.
This presentation briefly summarizes some challenges related to the stability of power systems with very high levels of inverter-based generation. It also summarizes some solutions and provides an example of a study that implemented some of those solutions.
Overview fact sheet about the Smarter Grid Solutions Integrated Network Testbed for Energy Grid Research and Technology Experimentation (INTEGRATE) project at the Energy Systems Integration Facility. INTEGRATE is part of the U.S. Department of Energy's Grid Modernization Initiative.
This project aims to develop and enhance a low-cost, highly scalable control solution for Small and Medium-Sized Commercial Buildings (SMCB), assess the business potential at multiple sites, and perform commercialization efforts. The technology can be applied to any buildings served by multiple units, with the benefits being greatest for open-spaced buildings, such as banks, retail stores, restaurants, and factories. This project aims to develop an affordable control solution for: 1) SMCB grid responsiveness, 2) reduction of GHG by changing unit operations, 3) greater reduction in utility costs, and 4) rapid adoption in the marketplace. The proposed technology will be built on a previously developed and demonstrated MPC solution. The minimal sensor requirement and less need of control expertise are the unique feature of the algorithm that leads to low capital and maintenance costs, and short installation and implementation time. These attributes contribute to low capital and maintenance costs, as well as a short installation and implementation time. However, these advantages come with a trade-off: increased difficulties and unreliability when applying traditional modeling and MPC control approaches due to limited information. This final report describes the modeling approaches developed and tested to overcome these challenges. It begins by outlining the modeling challenge posed by minimal sensor requirements, then delves into the proposed modeling approaches, which primarily involve system identification. Finally, preliminary test results for a simulation case study are presented.
In this paper we consider the pricing of American options, governed by a partial differential complementarity problem. The differential problem is first approximated by a semi-linear PDE using two distinct penalty approaches which are well known in computational finance. We then initiate the two-grid algorithm by solving the nonlinear problem on a coarse grid and further the linearized in the interpolated coarse-grid solution problem on a fine grid. By means of the maximum principle the algorithm is shown to be of fourth order convergence rate in space. Numerical experiments verify the presented two-grid approach where we draw some interesting conclusions.
Owing to its efficiency, simplicity and robustness, the FFT-based method has become the standard for computation of mechanical fields in a heterogeneous periodic unit cell. One of the main disadvantages of the FFT-based method is the inaccurate representation of the initial microstructure on a regular grid of voxels, which can be alleviated through the use of distorted initial grids. Here, in this paper, a method for generation of distorted initial grids conforming to the microstructural features (e.g. straight/curved boundaries) is proposed. The method determines the positions of the grid nodes in the initial configuration by solving a system of springs connecting the nodes. Microstructures consisting of layers, Voronoi tessellation and circular/spherical inclusions are considered, and mechanical fields simulated using the FFT-based method. It is found that distorted initial grids, conforming to the microstructural features, lead to more accurate mechanical fields in comparison to the corresponding non-distorted initial grid solution. The effect of initial grid distortion on the convergence of the FFT-based method is analyzed and discussed.
As electric utilities install more solar power, they must manage the quick changes in voltage that these plants can produce. NREL teamed-up with GE’s Grid Solutions business to identify ways a major U.S. electric utility could use its existing grid management system to address this challenge.
Within the field of Transactive Energy Systems (TES), there is an active need for tools that can support and accelerate the development of these new grid solutions. Among the many tools available, blockchain stands out as a viable instrument that can help researchers develop decentralized, autonomous, and tamper-resistant grid applications. In this work, we explore the use of smart contracts (SCs), a subset of blockchain technology, and analyze their applicability to facilitating the implementation of TES solutions. In particular, we focus on presenting areas of opportunity and potential drawbacks, along with use cases that can benefit from this technology building upon previous research developed by Pacific Northwest National Laboratory and other research organizations. This work builds upon the fundamentals of TES and smart contract technology to develop a series of software templates that can be used by industry to build TES-oriented grid solutions. These templates are intended to be platform agnostic and take into consideration the unique properties of SCs and distributed ledger storage mechanisms to ensure actual code implementations remain aware of the limitations of the technology. The proposed templates have the potential to enable software architects to mix and match components to satisfy their application requirements, thereby reducing the number of resources required to implement blockchain-based solutions. These templates are divided into two main components—data and behavioral models. The data models are intended to help software engineers represent the underlying grid objects along with their properties in a ledger-based storage system. The behavioral models are used to describe the processes and actions that actors within a system must perform to achieve a given outcome such as registering an asset, placing a bid, and performing bid clearances. These two components are documented in a Unified Modeling Language (UML) format and are intended for use in SC-based implementations, with special behavioral considerations to account for the asynchronous properties of the underlying ledger and the typical execution model of smart contracts. Finally, future research ideas and potential extensions to this work are discussed. In particular, known limitations and potential improvements of the developed product are identified and expected to be addressed in future revisions of the template model.