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NETL led a detailed scoping study of various direct power extraction systems and concepts for utility power generation. The study consisted of qualitative analysis along with quantitative techno-economic analysis of selected systems which use magnetohydrodynamic (MHD) power generators.
The U.S. installed more than 8,000 megawatts of wind generation in 2016. Wind energy is now powering more than 20 million homes. Idaho National Laboratory technology uses real time weather information and computer models to help utilities integrate more wind energy, relieving transmission line congestion, and extending the life time of existing grid infrastructure.
The need for proactive closed-loop integration of uncertainty information into system operations and probability-based controls is widely recognized, but rarely implemented in system operations. Proactive integration for this project means that the information concerning expected uncertainty ranges for net load and balancing requirements, including required balancing capacity, ramping and ramp duration characteristics, will be fed back into the generation commitment and dispatch algorithms to modify their performance so that potential shortages of these characteristics can be prevented. This basic, yet important, premise is the motivating factor for this project. The achieved project goal is to demonstrate the benefit of such a system. The project quantifies future uncertainties, predicts additional system balancing needs including the prediction intervals for capacity and ramping requirements of future dispatch intervals, evaluates the impacts of uncertainties on transmission including the risk of overloads and voltage problems, and explores opportunities for intra-hour generation adjustments helping to provide more flexibility for system operators. The resulting benefits culminate in more reliable grid operation in the face of increased system uncertainty and variability caused by solar power. The project identifies that solar power does not require special separate penetration level restrictions or penalization for its intermittency. Ultimately, the collective consideration of all sources of intermittency distributed over a wide area unified with the comprehensive evaluation of various elements of balancing process, i.e. capacity, ramping, and energy requirements, help system operators more robustly and effectively balance generation against load and interchange. This project showed that doing so can facilitate more solar and other renewable resources on the grid without compromising reliability and control performance. Efforts during the project included developing and integrating advanced probabilistic solar forecasts, including distributed PV forecasts, into closed –loop decision making processes. Additionally, new uncertainty quantifications methods and tools for the direct integration of uncertainty and variability information into grid operations at the transmission and distribution levels were developed and tested. During Phase 1, project work focused heavily on the design, development and demonstration of a set of processes and tools that could reliably and efficiently incorporate solar power into California’s grid operations. In Phase 2, connectivity between the ramping analysis tools and market applications software were completed, multiple dispatch scenarios demonstrated a successful reduction of overall uncertainty and an analysis to quantify increases in system operator reliability, and the transmission and distribution system uncertainty prediction tool was introduced to system operation engineers in a live webinar. The project met its goals, the experiments prove the advancements to methods and tools, when working together, are beneficial to not only the California Independent System Operator, but the benefits are transferable to other system operators in the United States.
Supercapacitor technology has reached a level of maturity as a viable energy storage option available to support a modern electric power system grid; however, its application is still limited because of its energy capacity and the cost of the commercial product. In this paper, we demonstrate transient models of supercapacitor energy storage plants operating in coordination with run-of-the-river (ROR), doubly-fed induction generator hydropower plants (HPP) using a system control concept and architecture developed. A detailed transient model of a supercapacitor energy storage device is coupled with the grid via a three-phase inverter/rectifier and bidirectional DC-DC converter. In addition, we use a version of a 14-bus IEEE test case that includes the models of the supercapacitor energy storage device, ROR HPPs, and synchronous condensers that use the rotating synchronous generators of retired coal-powered plants. The purpose of the synchronous condensers is to enhance the system stability by providing voltage and reactive power control, provide power system oscillations damping, and maintain system inertia at secure levels. The control layer provides coordinated, decentralized operation of distributed ROR HPPs and energy storage as aggregate support to power system operations.
The Unalakleet Microgrid Optimization Project aimed to strengthen the reliability and efficiency of the isolated electric power system that serves the Tribal community of Unalakleet, Alaska. The community relies entirely on a local wind-diesel microgrid, consisting of four 475 kW diesel generators and six 100 kW wind turbines, to provide electricity to approximately 745 residents and Tribal facilities. Because Unalakleet is not on a road system and is located nearly 400 miles from the nearest major power grid, maintaining a resilient and efficient local energy system is critical. The scope of this project included upgrading a portion of the transmission line between the wind farm and the power plant to increase voltage and reduce line losses, along with modernizing the Supervisory Control and Data Acquisition (SCADA) system to improve monitoring, control, and data management of the power system. These upgrades were designed to increase wind energy utilization, reduce diesel fuel consumption by tens of thousands of gallons annually, and improve overall grid stability. By allowing more of the community’s electricity to be supplied by local renewable wind resources, the project was designed to lower operating costs, reduce dependence on imported fuel, and strengthen the long-term resilience of the power system. These improvements represent an important step toward the community’s long-term energy vision of expanding renewable generation, incorporating energy storage, and eventually achieving “diesels-off” operation, where essential Tribal loads are powered primarily by local renewable resources.
Purpose: A persistent challenge for the quality assessment of radiation therapy treatments (e.g. contouring accuracy) is the absence of the known, ground truth for patient data. Moreover, assessment results are often patient-dependent. Computer simulation studies utilizing numerical phantoms can be performed for quality assessment with a known ground truth. However, previously reported numerical phantoms do not include the statistical properties of inter-patient variations, as their models are based on only one patient. In addition, these models do not incorporate tumor data. In this study, a methodology was developed for generating numerical phantoms which encapsulate the statistical variations of patients within radiation therapy, including tumors. Methods: Based on previous work in contouring assessment, geometric attribute distribution (GAD) models were employed to model both the deterministic and stochastic properties of individual organs via principle component analysis. Using pre-existing radiation therapy contour data, the GAD models are trained to model the shape and centroid distributions of each organ. Then, organs with different shapes and positions can be generated by assigning statistically sound weights to the GAD model parameters. Organ contour data from 20 retrospective prostate patient cases were manually extracted and utilized to train the GAD models. As a demonstration, computer-simulated CT images of generated numerical phantoms were calculated and assessed subjectively and objectively for realism. Results: A cohort of numerical phantoms of the male human pelvis was generated. CT images were deemed realistic both subjectively and objectively in terms of image noise power spectrum. Conclusion: A methodology has been developed to generate realistic numerical anthropomorphic phantoms using pre-existing radiation therapy data. The GAD models guarantee that generated organs span the statistical distribution of observed radiation therapy patients, according to the training dataset. The methodology enables radiation therapy treatment assessment with multi-modality imaging and a known ground truth, and without patient-dependent bias.
The short-circuit response of inverter-interfaced distributed generators (IIDGs) is not adequately represented in many conventional protection studies. This paper presents an in-depth analysis of IIDG behavior during grid faults and proposes a more accurate reduced-order parameterized short-circuit current (RPSC) model of inverters. Typical inverter components are thoroughly investigated to identify those that play dominant roles during faults. The paper shows that the current limiter is the dominant factor for the steady-state fault-current of inverter; while the inverter filter along with the severity of the voltage disturbance largely determine the initial transient spike of inverter fault current. The proposed model is low-order and can be used in large scale simulations. The parameters of the proposed RPSC model can be extracted from laboratory experiments without requiring proprietary manufacturer information. The proposed fault-current model is analogous to the well-known synchronous machine model that segregates the inverter fault current into subtransient, transient, and steady-state fault currents. Finally, experimental and simulation tests are presented to validate the model.
Modern active distribution grids are characterized by the increasing penetration of distributed energy resources (DERs). The proper coordination and scheduling of a large numbers of these small-scale and spatially distributed DERs is necessary, and warrants the use of novel distributed approaches. In this paper, we propose a hybrid volt-var control architecture for the distribution grid, which leverages existing centralized and local approaches to planning, decision making, and control, and augments it with distributed optimization and distributed control for DER management. First, we propose a convex model to describe the power physics of distribution grids of meshed topology and unbalanced structure, based on current injection and McCormick Envelopes. Second, we employ the distributed proximal atomic coordination (PAC) algorithm to coordinate DERs to provide voltage support. We implement volt-var optimization by optimally coordinating DERs including PV smart inverters and demand response. We present results using the IEEE-34 bus network, using real data from a distribution feeder in Hawaii, to model load and PV generation. Different levels of DER penetration and objective functions are simulated. Finally, our results show the need for the coordination of DERs to improve voltage profiles, even in networks with existing voltage control devices. Further, we show the need for flexible reactive power capabilities to achieve desired grid performance.
The electric power industry landscape is continually evolving. As emerging technologies such as wind and solar generating systems become more cost effective, traditional power system operating strategies will need to be re-evaluated. The presence of wind and solar generation (commonly referred to as variable generation or VG) can increase variability and uncertainty in the net-load profile. One mechanism to mitigate this issue is to schedule and dispatch additional operating reserves. These operating reserves aim to ensure that there is enough capacity online in the system to account for the increased variability and uncertainty occurring at finer temporal resolutions. A new operating reserve strategy, referred to as flexibility reserve, has been introduced in some regions. A similar implementation is explored in this study, and its implications on power system operations are analyzed. Results show that flexibility reserve products can improve economic metrics, particularly in significantly reducing the number of scarcity pricing events, with minimal impacts on reliability metrics and production costs. Furthermore, the production costs increased due to increased VG curtailment - i.e. including the flexible ramping product in the commitment of excess thermal capacity that needed to remain online at the expense of VG output.
Many conventional power systems are evolving due to the growth of renewable energy and distributed energy resources (DERs). Modeling the interplay of transmission and distribution systems is critical to analyze how DERs impact a system’s conventional operation and which electric infrastructure improvements are needed to achieve a balance between centralized generation and DERs. This article describes the process, tools, and resources used to model electric power systems with a centralized infrastructure in an isolated context and limited access to actual utility data. Photovoltaic systems installed on residential rooftops were the main design option. This work broadened the typical power system modeling to include planning and social considerations. This integrative engineering-social method allows for interdisciplinary teams to work in the development of a model as part of broader design goals for a renewable-dominant energy system. The Puerto Rico electric power system was used as a case study to demonstrate the process. The integrative engineering-social perspective in developing the model and the actions to manage data limitations are aspects that could be followed in other locations with aggressive renewable energy goals and where utility data are not readily available.
A system and method for control of dispatchable resources on an electrical power distribution grid is disclosed. The disclosed method provides improved response to power imbalance based on a communications signal independent of system frequency. The communication signal provides a feed-forward control of dispatchable power resources. A communications network is employed to generate a signal to dispatchable resources that a power imbalance has occurred. Due to fast response power transmission network interfaces associated with the dispatchable resources, the respective resources respond in significantly less time than feedback controlled conventional generators which depend on line frequency sensing. The rapid response mitigates the impact of power imbalances and enables greater penetrations of converter-interfaced power generation units such as renewable energy sources to be connected to the grid.
Synchronous machines have traditionally acted as the foundation of large-scale electrical infrastructures and their physical properties have formed the cornerstone of system operations. However, with the increased integration of distributed renewable resources and energy-storage technologies, there is a need to systematically acknowledge the dynamics of power-electronics inverters - the primary energy-conversion interface in such systems - in all aspects of modeling, analysis, and control of the bulk power network. In this paper, we assess the properties of coupled machine-inverter systems by studying an elementary system comprised of a synchronous generator, three-phase inverter, and a load. The inverter model is formulated such that its power rating can be scaled continuously across power levels while preserving its closed-loop response. Accordingly, the properties of the machine-inverter system can be assessed for varying ratios of machine-to-inverter power ratings. After linearizing the model and assessing its eigenvalues, we show that system stability is highly dependent on the inverter current controller and machine exciter, thus uncovering a key concern with mixed machine-inverter systems and motivating the need for next-generation grid-stabilizing inverter controls.
We use near-infrared observations obtained as part of the Visible and Infrared Survey Telescope for Astronomy (VISTA) Survey of the Magellanic Clouds (VMC), as well as two complementary Hubble Space Telescope (HST) data sets, to study the luminosity and mass functions (MFs) as a function of clustercentric radius of the main-sequence stars in the Galactic globular cluster 47 Tucanae. The HST observations indicate a relative deficit in the numbers of faint stars in the central region of the cluster compared with its periphery, for 18.75 ≤ m{sub F606W} ≤ 20.9 mag (corresponding to a stellar mass range of 0.55 < m{sub *}/M{sub ⊙} < 0.73). The stellar number counts at 6.′7 from the cluster core show a deficit for 17.62 ≤ m{sub F606W} ≤ 19.7 mag (i.e., 0.65 < m{sub *}/M{sub ⊙} < 0.82), which is consistent with expectations from mass segregation. The VMC-based stellar MFs exhibit power-law shapes for masses in the range 0.55 < m{sub *}/M{sub ⊙} < 0.82. These power laws are characterized by an almost constant slope, α. The radial distribution of the power-law slopes α thus shows evidence of the importance of both mass segregation and tidal stripping, for both the first- and second-generation stars in 47 Tuc.
Impedance-based methods are widely used for power system stability analysis with inverter-based resources (IBRs), e.g., assessing dynamic interactions between the power grid and an IBR, control interactions between multiple IBRs, and the sub-synchronous oscillation and damping phenomenon. Since it is difficult to get a numerical model 100% matching with the hardware IBR, using the hardware inverter directly to obtain its output impedance has become a prominent approach nowadays. Therefore, this article presents the impedance scan using hardware IBRs, and also a hardware diesel generator as it still stays with the grid before the grid completely goes to renewable. The devices under test (DuTs) for the impedance scan includes two 3-..phi.., 480 V, 60 Hz commercial grid-forming IBRs (one of 250 kVA and another of 125 kVA rating) in series with ..delta..-Y transformers, one 3-..phi.., 480 V, 60 Hz commercial grid-following IBR (of 125 kVA rating), and a 3-..phi.., 480 V, 60 Hz commercial diesel generator (of 187.5 kVA rating). Using voltage signals perturbed with sub-, inter-, and higher harmonic components, and measuring the current response, the positive-sequence impedances are computed via an offline-based post-analysis. Moreover, best-fit transfer functions are estimated that closely resemble the measured data points of the positive-sequence impedances. Based on the observations from various outcomes of the hardware experiments, this article also provides some fundamental insights on the equivalent positive-sequence impedance of a combination of multiple hardware components by comparing the estimated and the empirically computed impedances. A comparative insight on the damping capability of the DuTs using the positive-sequence impedances of the hardware is also discussed.
With grid modernization, smart inverters are increasingly used to execute advanced controls for distribution network reliability. However, this also increases the cyber-attack space. Here this paper focuses on the defense approaches to restore the system to normal operation circumstances in the presence of cyber-attacks. A unique deep reinforcement learning (DRL) method is developed to minimize voltage violations and reduce power losses for impacted feeders. The defense problem is reformulated as a Markov decision-making process to dynamically control DERs while minimizing load shedding. This is achieved via an improved soft actor-critic (SAC)-based DRL algorithm, which can govern DER set points and load-shedding scenarios in discrete and continuous modes via the auto-tune entropy and Gaussian policy features. Numerical comparison results on the modified IEEE 123-node system with other control approaches, such as Volt-VAR (VV), Volt-Watt (VW), and model predictive control (MPC) show that the proposed method can eliminate voltage violations and provide feasible control actions that perform complete mitigation of cyber-threats.
Residential scale energy storage devices have shown great promise to add value and improve the ability to integrate distributed and variable generation into the distribution grid. Battery energy storage systems combined with PV and advanced inverters can provide many services. These include extended backup power in case of grid failure, reductions in feeder and system level peak load, system level net load ramp reduction, grid management services both locally (e.g., voltage management) and bulk system (e.g., reserves) level, and improved grid reliability through grid voltage and frequency support. Even though the cost of the energy storage systems are decreasing, one of the main challenges related to the widespread deployment of energy storage, as found by the U.S. Department of Energy (DOE), is utility acceptance of storage technologies. Various interconnection challenges exist when connecting distributed PV-energy storage into the electrical distribution grid in terms of safety, reliability and stability of the electric power systems. Some of the urgent challenges, as seen by the manufacturers and installers are: safety of the battery cells and the packs, performance of the power conversion systems, effect of bidirectional power flow on existing utility equipment and qualification of solutions such as volt-ampere reactive (VAR) support and frequency regulation. Under this project, National Renewable Energy Laboratory (NREL) researchers will work with SunPower to address these challenges by research, testing and analysis at Energy System Integration Facility (ESIF) and energy storage testing laboratory at NREL. NREL's unique power hardware-in-the-loop (PHIL) capability will be utilized to evaluate system-level issues such as reverse power flow and VAR support (if available) for these PV-energy storage systems. Through the analysis of test results, the proposed project will demonstrate use of ESIF capabilities in a way that advances both the DOE and SunPower's goals of moving clean, renewable energy technologies onto the electrical grid.
Distributed Energy Resources (DER) is increasing in the U.S and affect the utility grid operation and consumer power access. This report summaries the current status and the best practices of DER application in the U.S.
This report, originally published in December 2023, has been revised in January 2024. There were three changes in the revision. First, Anthony Lopez was added as a contributing author, as he had been erroneously omitted in the original publication. Second, in appendix section A.1 the cost of upgrading a hydrogen combustion turbine from a natural gas turbine was erroneously reported as 20% when it should have been 33%. Lastly, appendix Figure A-8 was erroneously a duplication of the same figure from the Standard Scenarios 2022, it has now been updated to reflect this year’s data.