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This report summarizes the activities of this project, which aims at design and testing a scalable, robust, and online framework that provides secure monitoring of photovoltaic generation in the face of potential cyber-attacks.
Visits to four utilities concerned with the use of DSG power sources on their distribution networks yielded useful impressions of present and future approaches to the integration of DSGs into electrical distribution network. Different approaches to future utility systems with DSG are beginning to take shape. The new DSG sources will be in decentralized locations with some measure of centralized control. The utilities have yet to establish firmly the communication and control means or their organization. For the present, the means for integrating the DSGs and their associated monitoring and control equipment into a unified system have not been decided.
This report describes the results of PSCAD simulations that were performed in 2020 to assess the impacts of PacWave generation on CLPUD's 12.47-kV distribution and 69-kV subtransmission systems. PacWave South (PacWave) is a wave energy test facility planned by Oregon State University. PacWave shore facilities will be located south of Seal Rock on the Oregon coast. PacWave is expected to be operational in 2022 and will connect up to 10 MW of generation to CLPUD's Seal Rock distribution feeder.
The Department of Defense maintains an inventory of over 1,800 MW of engine-generators 15 KW and larger, with an estimated procurement rate of over 140 MW/year. Nearly the entire requirement could be met by advanced heat engines of the types being developed as point-focussing, distributed receiver power plants. A conceptual system consisting of a heat engine which efficiently burns liquid fossil or synthetic fuels, with a 'solarization kit' for conversion to hybrid solar operation could meet existing DOD requirements for new systems which are quieter, lighter, and multi-fueled. An estimated 24 percent (33 MW/year) or more could operationally benefit from the solar option. Baseline cost projections indicate levelized energy cost goals of 210 to 120 mills/KWh (15 to 1000 KW systems). Fuel cost escalation is the major factor affecting the value of the solar option. A baseline calculation for fuel at $0.59/gal in spring, 1979, escalating at 8 percent above general inflation indicates a value of $2700/KWe for a solarization kit.
From an operational and planning perspective, it is important to quantify the impact of increasing penetration of photovoltaics on the distribution system. Most existing impact assessment studies are scenario-based where derived results are scenario specific and not generalizable. Moreover, stochasticity in the temporal behavior of spatially distributed PVs requires a large number of scenarios that increase with the size of the network and the level of penetration. Therefore, we propose a new computationally efficient analytical framework of voltage sensitivity analysis that allows for stochastic analysis of voltage change due to random changes in PV generation. We first derive an analytical approximation for voltage change at any node of the network due to change in power at other nodes in an unbalanced distribution network. The quality of this approximation is reinforced via bounds on the approximation error. Then, we derive the probability distribution of voltage change at a certain node due to random changes in power injections/consumptions at multiple locations of the network. The accuracy of the proposed PVSA is illustrated using a modified version of the IEEE 37 bus test system. As a result, the proposed PVSA can serve as a powerful tool for proactive monitoring/control and ease the computational burden associated with perturbation based cybersecurity mechanisms.
Structural configuration analysis of two advanced aircraft concepts with distributed hybrid-electric propulsion is presented. These concepts are characterized by multiple wing-mounted electric propulsors, which are powered by turbo-generators. Based on lessons learned from previous structural analysis of unconventional concepts, high-fidelity finite element models of the aircraft wing with embedded electric propulsors are developed. Although a hybrid-electric propulsion system has noise and emission benefits, it also adds electrical power system weights. Hence, efficient structural integration of the wing and propulsors is investigated for design improvement, structural analysis, and weight reduction. Wing structural weights of the two designs are compared with a baseline conventional transport aircraft wing for benefit assessment. In one design, the wing structural weight reduction partially compensates for the additional weight associated with the distributed electric propulsion system.
This document is prepared as part of the Department of Energy’s Grid Modernization Laboratory Consortium (GMLC) 2.5.2 project, whose goal is to develop and socialize a common set of grid service definitions relevant to grid-related interactions with distributed energy resources (DER: responsive generation, storage, and loads), and to advance the concept and requirements of the Energy Services Interface (ESI) to the point of launching related interface standards and guides that can be implemented in communication protocols and business process definitions. The notion of “grid services” is integral to the definition of an ESI because a key principle of the ESI is that it permits coordination between grid operators and DER facilities in a way that is service-oriented, with an understanding of performance expectations. This document reviews the current state of grid service definitions, including those actively used in the market today as well as new services that have been proposed for future implementation. The document describes grid services used in transmission as well as distribution systems. In defining grid services, this document also distinguishes between two fundamental concepts: an “operational objective” and a “grid service,” which describes a generator’s or customer’s expected physical performance in delivering power to or consuming power from the grid.
Transportation electrification is an integral component of the energy decarbonization transition. This paper investigates the impact of distributed energy resources (DERs), including distributed photovoltaics (DPV) and electric vehicles (EVs), in the primary frequency response of the power grid. Increasing DER adoption poses challenges to maintaining grid frequency stability. However, DERs' ability to provide fast frequency regulation services-primary frequency response (PFR) and secondary frequency response (SFR)-can be exploited to recover the frequency after an N-1 contingency event in the system. This paper also investigates the importance of a droop control strategy through dynamic models of DPV and EV to provide the primary frequency regulation services following the contingency event. A dynamic EV model, based on the PVD1 model Western Electricity Coordinating Council (WECC) introduced, has been used for the simulation. Further, DERs' primary frequency response is studied for five different cases of DER penetration levels after the system is exposed to the generator trip. Additionally, different frequency regulation capacities of EVs are analyzed. The studies show that an increment in DERs capacity providing effective PFR can improve the system frequency nadir and stabilize the frequency faster after the generation trip contingency.
Distribution grid planning, control, and optimization require accurate estimation of solar photovoltaic (PV) generation and electric load in the system. Most of the small residential solar PV systems are installed behind-the-meter making only the net load readings available to the utilities. This paper presents an unsupervised framework for joint disaggregation of the net load readings of a group of customers into the solar PV generation and electric load. Our algorithm synergistically combines a physical PV system performance model for individual solar PV generation estimation with a statistical model for joint load estimation. The electric loads for a group of customers are estimated jointly by a mixed hidden Markov model (MHMM) which enables modeling the general load consumption behavior present in all customers while acknowledging the individual differences. At the same time, the model can capture the change in load patterns over a time period by the hidden Markov states. The proposed algorithm is also capable of estimating the key technical parameters of the solar PV systems. Our proposed method is evaluated using the net load, electric load, and solar PV generation data gathered from residential customers located in Austin, Texas. Testing results show that our proposed method reduces the mean squared error of state-of-the-art net-load disaggregation algorithms by 67%.
The power grid is undergoing massive changes to ensure resiliency and reliability in a more decentralized world. Distributed energy resources are becoming a prominent source of generation, potentially leading to a lack of centralized generation sources. Due to these new behaviors and system topologies, it is important to install measurement devices that are 1) accurate and 2) self-aware of their measurement quality. In this paper, a residential-scale microgrid is used to generate voltage and current waveforms, captured by Verivolt and National Instruments measurement equipment. A least-squares approach is used to separate the “clean” signals from the noise. Finally, Gaussian mixture modeling is used to approximate noise distributions, and it is shown these higher-order distribution estimates are a better fit to voltage and current noise profiles than single-mode Gaussian estimates.
This article presents the performance evaluation of ratio consensus-based distributed power apportioning engine along with centralized net load management (NLM) engine that ensures viable and stable operation of an islanded microgrid. Managing net load variability in a microgrid with high penetrations of uncertain renewable generation and ever-changing load demands is a crucial need in order to ensure viable and stable operation of the microgrid. Centralized “dispatch-rule”-based and/or multi-agent-based distributed control of distributed energy resources (DERs) in microgrid are well accepted for microgrid by adapting ANSI/ISA-95-based hierarchical control architecture. In the application where microgrid network has large geographical span with multiple DERs dispersed in the network, high penetration of uncertain renewable energy resources, and ever-changing load demands, a judicious selection of techniques/solutions for managing net-load resources for maintaining viability and stability is required. With this motivation, this article proposes a novel solution to mitigate the challenges by incorporating a mixed centralized NLM engine and distributed power apportioning control of DERs and loads. A power-hardware-in-the-loop (PHIL) -based experiment is conducted with the centralized NLM engine and the distributed power apportioning engine along with two commercial inverters. The experimental results validates the efficacy of the proposed method in ensuring viability and stability of a microgrid.
Waveforms in power grids typically reveal a certain pattern with specific features and peculiarities driven by the system operating conditions, internal and external uncertainties, etc. This prompts an observation of different types of waveforms at the measurement points (substations). An innovative next-generation smart sensor technology includes a measurement unit embedded with sophisticated analytics for power grid online surveillance and situational awareness. The smart sensor brings additional levels of smartness into the existing phasor measurement units (PMUs) and intelligent electronic devices (IEDs). It unlocks the full potential of advanced signal processing and machine learning for online power grid monitoring in a distributed paradigm. Within the smart sensor are several interconnected units for signal acquisition, feature extraction, machine learning-based event detection, and a suite of multiple measurement algorithms where the best-fit algorithm is selected in real-time based on the detected operating condition. Embedding such analytics within the sensors and closer to where the data is generated, the distributed intelligence mechanism mitigates the potential risks to communication failures and latencies, as well as malicious cyber threats, which would otherwise compromise the trustworthiness of the end-use applications in distant control centers. The smart sensor achieves a promising classification accuracy on multiple classes of prevailing conditions in the power grid and accordingly improves the measurement quality across the power grid.
The focus of this paper is on analyzing the impact of conservation voltage reduction in the presence of active devices such as solar photovoltaic (PV) and developing controls that leverage these distributed energy resources. An event-driven predictive approach for real-time volt/volt-ampere reactive (VAR) optimization, along with local two-level adaptive volt/VAR droop-based control algorithm for advanced distribution management systems, is introduced. The methodology covers aggregated and autonomous controls under different timescale operations, including the impact and effect of unpredicted events such as cloud transients on PV power production. In addition, the control schemes include the uncertainties in PV power generation and load power demand. The proposed methodology is validated in a real-time framework using the real-time digital simulator platform through co-simulation with models based on Python and OpenDSS (Open Distribution System Simulator). The developed methodology is tested on the modified IEEE 123-feeder test system. The results reveal that the proposed methodology works well in the presence of high penetrations of PV power, produces significant energy savings, and mitigates over-/undervoltage problems.
Solid particles are being considered in several high temperature thermal energy storage systems and as heat transfer media in a variety of advanced power generation systems, particularly in concentrated solar power plants. The downside of such an approach is the low overall heat transfer coefficients caused by the inherently low thermal conductivity values of the low-cost solid media when coupled to heat exchanger for the power cycle working fluid. Choosing the right particle size distribution, emittance, and material of the solid media can all make a substantial difference in packed bed thermal conductivity. Current research though exclusively focuses on continuous unimodal distributions of particles. Here, in this work, we propose the use of a binary particle system with a bimodal size distribution to significantly increase packed bed thermal conductivity by reducing packed bed porosity. This is the first study related to ceramic solid particle heat transfer that has considered the thermal conductivity of non-unimodal size distributions at room and elevated temperatures. The following study found that for the binary particle system using Carbo particles CP 16/30 – CP 70/140 where the large particle volume fraction was 50% there was an 17–47% increase in packed bed thermal conductivity when compared with a nearly unimodal particle size distribution of CP 16/30 between 50 and 300 °C. Two different porosity and effective thermal conductivity models were studied, with one providing better prediction of porosity but both effective thermal conductivity models providing less predictive capacity. Importantly this approach can have a substantial impact of thermal performance, with little to no impact on the particle cost.
Momentary cessation refers to an inverter control mode. When the inverter terminal voltage falls below (or exceeds) a certain level, the inverter ceases to output any current, but attempts to maintain (or quickly regain) phase-locked loop synchronization to allow for quick reinjection of current when the voltage recovers to a certain point. This paper presents a photovoltaic (PV) momentary cessation model developed in PSS/E. Simulations are presented for a high voltage transmission line fault contingency in the Hawaiian island of Oahu power system on a validated PSS/E model, modified to include a custom distributed PV inverter model, and different near-future distributed PV penetration levels. Simulations for the island power system include different penetration levels of PV, and different recovery times (ramp rates and delays) after momentary cessation. The results indicate that during low voltage events, such as faults, momentary cessation can produce severe under frequency events, causing significant load shed and shortly thereafter, in some cases, over frequency events that cause generation to trip offline. The problem is exacerbated with higher penetration levels of PV. If momentary cessation is used (as is typically the case for distribution-connected resources), the recovery process after momentary cessation should be carefully considered to minimize impacts to bulk power system stability.
This study investigates the data requirements of generative artificial intelligence (AI), particularly generative adversarial networks (GANs), for reliable data augmentation in energy applications. Generative AI, though seen as a solution to data limitations, requires substantial data to learn meaningful distributions—a challenge often overlooked. This study addresses the challenge through synthetic data generation for critical heat flux (CHF) and power grid demand, focusing on renewable and nuclear energy. Two variants of GAN employed are conditional GAN (cGAN) and Wasserstein GAN (wGAN). Our findings include the strong dependency of GAN on data size, with performance declining on smaller datasets and varying performance when generalizing to unseen experiments. Mass flux and heated length significantly influence CHF predictions. wGAN is more robust to feature exclusion, making it suitable for constrained synthetic data generation. In energy demand forecasting, wGAN performed well for solar, wind, and load predictions. Longer lookback hours and larger datasets improved predictions, especially for load power. Seasonal variations posed challenges, with wGAN achieving a relatively high error of Root Mean Squared Error (RMSE) of 0.32 for load power prediction, compared to RMSE of 0.07 under same-season conditions. Feature exclusions impacted cGAN the most, while wGAN showed greater robustness. This study concludes that, while generative AI is effective for data augmentation, it requires substantial data and careful training to generate realistic synthetic data and generalize to new experiments in engineering applications.
This dataset consists of a table containing the distribution of literature estimates of greenhouse gas emissions for the following electricity generation and storage technologies: biopower, coal, concentrating solar power, geothermal, hydrogen storage, hydropower, lithium-ion battery storage, natural gas, nuclear, ocean, oil, photovoltaic, pumped-storage hydropower, and wind. Quartile estimates of life cycle emissions factors in units of grams of carbon dioxide equivalent per kilowatt hour of generation (g CO2e/kWh) are provided for the following life cycle stages: one-time upstream, ongoing combustion, ongoing non-combustion, one-time downstream, and total. Literature estimates were compiled by the LCA Harmonization study and subsequent updates, as detailed in the factsheet which accompanies this dataset, https://www.nlr.gov/docs/fy21osti/80580.pdf .
The continuum spectra of radio-quiet active galactic nuclei, which are steeper in the near-IR and flatter in the X-ray regime, can be produced in a synchrotron self-Compton model with a broken power law electron distribution that is flat at low, and steep at large, Lorentz factor values. While the steeper part generates the IR spectrum via synchrotron process, both parts together produce the flatter X-ray spectrum via Compton scattering. The intersection of the two spectra corresponds to equipartition between the magnetic field and synchrotron photon energy densities, giving approximately the same luminosities in the synchrotron and Compton components. The photon spectra are self-consistently computed here, taking the effects of cooling, e(+)e(-) pair production, and higher order Compton scattering into account.