Operational Challenges of Solar PV Plus Storage Power Plants and Modeling Recommendations
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14 SOLAR ENERGY↗
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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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Generalized Analytical Estimation of Sensitivity Matrices in Unbalanced Distribution Networks
Fast and accurate estimation of sensitivity matrices is significant for the enhancement of distribution system modeling and automation. Analytical estimations have mainly focused on voltage magnitude sensitivity to active/reactive power injections for unbalanced networks with Wye-connected loads and neglecting DERs' smart inverter functionality. Hence, this paper enhances the scope of analytical estimation of sensitivity matrices for unbalanced networks with 1- Φ, 2- Φ, and 3- Φ Delta/Wye-connected loads, DERs with smart inverter functionality, and substation/line step-voltage regulators (SVR). A composite bus model comprising of DER, Delta- and Wye-connected load is proposed to represent a generic distribution bus, which can be simplified to load, PV, or voltage-controlled bus as required. Furthermore, the proposed matrix-based analytical method consolidates voltage magnitude and angle sensitivity to active/reactive power injection and tap-position of all SVRs into a single algorithm. Extensive case studies on IEEE and EPRI networks show the accuracy and wide scope of the proposed algorithm compared to the existing benchmark method.
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Grid Resiliency with a 100% Renewable Microgrid
San Diego Gas & Electric Company (SDG&E) installed America’s first and largest utility-scale microgrid in Borrego Springs in 2013. The first generation Borrego Springs Microgrid utilized diesel generators to form and stabilize the microgrid island, with support from grid-scale batteries and local solar photovoltaic (PV) generation. In this project, SDG&E in partnership with National Renewable Energy Laboratory (NREL) demonstrated through modeling, simulation and utility field testing that blackstart and islanding of the microgrid can be led with 100% renewable, inverter based resources (IBRs), to help reduce community reliance on conventional generation resources. Through equipment upgrades, grid-forming island leader capability was transitioned to a battery IBR instead of the Borrego Springs Microgrid diesel generators. A new microgrid controller was integrated to the microgrid and programmed to control and manage multiple energy storage systems. Synchrophasor and other power quality data verified autonomous, high-speed response of the IBRs through blackstart, islanding, and load step testing. Results of project field evaluations provide distribution systems operators (DSO) with increased confidence that renewable, IBR can replace traditional generators to blackstart and island microgrids and rapidly establish stable island frequency with rapid changes in peak power demand. Importantly, the project validated the integration feasibility of a distributed energy resource management system (DERMS) controller that manages multiple grid-forming and grid-following IBRs, establishing a standard design interface to reduce the complexity of integrating new DERs in the future and supporting replication by the industry. As a result of learnings in this project, SDG&E has implemented the microgrid controller strategy at multiple other microgrid sites, thereby validating the replicability of the solution. Hardware-in-the-loop (HIL) simulations including power and controller HIL hardware — along with electromagnetic transient (EMT) simulations of Borrego Springs Microgrid —informed adjustments to inverter parameters and were important to characterize the performance of the IBRs in relevant operating conditions before deployment. The EMT and HIL simulations of islanding the entire community are important contributions in providing confidence in IBR performance prior to future islanding of the community in the field. High-fidelity EMT and/or HIL simulation of IBRs can de-risk field operations, and its relevance and importance as a tool is increasing as distribution grids and microgrids become more complex and dynamic with an increasing proportion of renewable generation, distributed energy storage, and two-way power and energy flows.
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Multiport Control with Partial Power Processing in Solid-State Transformer for PV, Storage, and Fast-Charging Electric Vehicle Integration
This article proposes a multiport control method to enable partial power processing (PPP) in a medium-voltage (MV) multiport solid-state transformer (SST). MV multiport SSTs are promising in integrating low-voltage DC sources or loads such as solar photovoltaic, energy storage, and electric vehicles into smart grids without bulky line-frequency transformers. Compared to voltage-source SST, current-source (CS) SST features single-stage isolated bidirectional AC/AC, AC/DC, or DC/DC conversion using an inductive DC link. For a multiport CS SST, it is revealed in this article that the PPP capability can be enabled through the proposed control without extra hardware, different from the case of voltage-source converters where special hardware architecture is required for the PPP. With the PPP, most power exchange between LV ports is processed by only a fraction of the entire conversion stage, leading to reduced DC-link current, volume, loss, and improved efficiency. The proposed multiport PPP control scheme is analyzed to verify the advantages across a wide voltage and power range against conventional full power processing (FPP) multiport control, using the soft-switching solid-state transformer (S4T) with reduced conduction loss as an example. Comparative experimental results based on a SiC three-port S4T prototype verify the effectiveness of the proposed PPP scheme against the FPP scheme under both steady state and dynamic conditions. Here, the DC-link current reduction is measured to be more than 36%. Significantly, the proposed multiport PPP control scheme is generic and applicable to any hard-switching or soft-switching CS SSTs without extra hardware.
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A CHIL Validation of Machine Learning-Assisted Methods for Real-Time Controls of Solar PV for Grid Services
Recent research has highlighted the potential for solar to act as a zero-marginal-cost and zero-emission flexibility resource on the bulk power system when operated with advanced control systems. To increase the performance of these systems, leading technologies, including machine learning (ML) and hierarchical inverter set point allocation, have been proposed; however, these technologies lack comprehensive validation under real-world application scenarios. This paper addresses this gap by designing and developing a controller-hardware-in-the-loop framework to evaluate the performance of different flexible solar technologies in responding to automatic generation control signals in a closed-loop fashion. Simulation results indicate the superior performance of an ML-based approach compared to the conventional reference-control grouping-based approach, showcasing its potential to support grid stability and operational efficiency.
closed-loop validation↗
PVAnalytics: A Python Package for Automated Processing of Solar Time Series Data
Multiple publicly available software packages exist that analyze solar time series data, including RdTools and Solar Data Tools, among others. Several of these packages contain their own unique quality assurance (QA) and feature recognition algorithms. The python PVAnalytics package was developed to offer an internally consistent source for these analysis tools, making it easier for the end user to deploy these routines on his or her solar data. The PVAnalytics package currently contains routines for outlier detection, inverter clipping detection, irradiance and temperature checks, orientation checks, and data shift detection, among other functions. These functions have been aggregated from various sources including Solar Forecast Arbiter, RdTools, and the QA process developed by NREL's PV Fleets Initiative. We are continuously adding new functionality to the package, including documentation, examples and algorithms. By bundling QA functionality into a single software package, we hope to make PVAnalytics a comprehensive software library to support analysis of solar metadata and time series data.
data cleaning↗
A CHIL Validation of Machine Learning-Assisted Methods for Real-Time Controls of Solar PV for Grid Services: Preprint
Recent research has highlighted the potential for solar to act as a zero-marginal-cost and zero-emission flexibility resource on the bulk power system when operated with advanced control systems. To increase the performance of these systems, leading technologies, including machine learning (ML) and hierarchical inverter set point allocation, have been proposed; however, these technologies lack comprehensive validation under real-world application scenarios. This paper addresses this gap by designing and developing a controller-hardware-in-the-loop framework to evaluate the performance of different flexible solar technologies in responding to automatic generation control signals in a closed-loop fashion. Simulation results indicate the superior performance of an ML-based approach compared to the conventional reference-control grouping-based approach, showcasing its potential to support grid stability and operational efficiency.
closed-loop validation↗
Perovskite Quantum Dot Solar Cells: Mapping Interfacial Energetics for Improving Charge Separation
Colloidal halide perovskite nanocrystals or quantum dots (QDs) show similar defect tolerance as thin film perovskite materials with added nanoscale phenomena. Perovskite QD solar cells have demonstrated efficiencies of 16.6%, greater than that of any other QD material system. While the efficiency lags behind the best thin-film perovskite devices, these solar cells could have advantages over the thin-film versions in terms of processability, phase stability, and high open-circuit voltages. However, some operating principles behind perovskite quantum dot device stacks and the associated electric field properties are still unknown. Here, we characterize the junction structure within perovskite QD solar cells, by exposing functioning cross-sections and using nanometer-scale Kelvin probe force microscopy to offer insight into the selection and performance of charge selective contacts. We also evaluated various solar cell device architectures with different selective contacts to isolate the role of each junction in device performance. We show that in high-performance n-i-p architectures, both electron- and hole-transport layer (HTL) interfaces possess a strong electric field, but in the case of the inverted p-i-n architecture, we find that high interfacial recombination at the HTL/QD junction is responsible for subpar device performance. Perovskite QD and thin film materials can synergistically be combined to offer more design flexibility in PV devices, and here we demonstrate that the interface between perovskite thin films and QDs are relatively benign and amenable for synergistic device design.
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Long Term Damage Testing (NREL)
DOE's Regional Test Center program has fielded several strings of PV modules as part of their Long Term Testing program. Some of these modules have been installed since 2016 and have been exposed to severe weather events. A key to developing long lifespans for PV modules (in excess of 30 years) is understanding how damage and defects develop, propagate and progress. Researchers at the NREL Regional Test Center have started developing testing procedures and analysis tools in order to characterize modules in a controlled manner with the goal of understanding how damage spreads under normal operations. Characterization methods include EL and IR imaging, outdoor and indoor IV curves, and long term exposure monitoring. Modules to be evaluated will be chosen based on severity of defect, ability to track changes in defect and space requirements. The samples will be loaded at maximum power using grid-tied inverters or other means. Gathering and reporting on this data may help engineers and scientists design for damage and improve long-term performance.
crack damage↗
PV Fleet Performance Data Initiative: Performance Index-Based Analysis
In this analysis, we report on 250 PV systems throughout the US, comprising 157 MWdc of system capacity and over 10,000 monthly PI values based on high-frequency (subhourly) energy data and satellite-based resource data. The distribution of PI values is analyzed, and multiple causes of underperformance are assessed, including first-year startup issues, snowfall and inverter downtime. An initial distribution of raw monthly PI values was collected with mean measured / modeled performance of PI = 0.935. After correcting for the three identifiable loss factors mentioned above, an average monthly performance of PI = 0.994 was obtained, with a distribution closely following a Gumbel Extreme Value distribution. In particular, inverter availability was found to contribute a system energy loss of 2.3% on average, except in the first six months of operation when availability losses are closer to 8%. Other startup issues beyond inverter downtime such as partial string outage contributed an additional 5% underperformance in the first year of operation across the fleet. Winter performance was also found to be 5%-10% below summer performance on average, likely due to snowfall. A simple linear relationship was found between snow loss and monthly snowfall accumulation in cm, indicating between 6% - 40% loss in months with nonzero snowfall, depending on climate.
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Probing-Based Inertia Estimation Method Using Hybrid Power Plants
With the displacement of synchronous generation by inverter-based resources (IBRs), power systems could face the challenge of reduced inertia since IBRs do not inherently contribute to system inertia. Therefore, there is rising interest in monitoring system inertia in real-time applications for situational awareness. In addition, there is a growing number of IBRs that provide fast frequency responses (FFR) in the form of synthetic inertia and P-f droop. It is desirable to quantify the contribution of these FFR controls as equivalent inertia. This paper proposes a probing-based inertia estimation method using a PV-battery hybrid power plant in the Kauai island power system. The method is validated under different operating conditions.
hybrid power plant↗
Power Electronics Based Self-Monitoring and Diagnosing for Photovoltaic Systems
Faults in photovoltaic (PV) systems can seriously affect the efficiency, energy yield, cost, safety, and reliability of PV plants. Condition monitoring of PV plants is, therefore, a very important approach to estimating the health condition of PV modules and power electronics in the system. However, additional hardware for PV system monitoring adds cost to the system's operation; delayed maintenance service also causes additional energy production loss. The Center for Power Electronics Systems (CPES) at the Virginia Polytechnic Institute and State University and Siemens Cooperate Research developed the online impedance measurement for a PV panel self-monitoring and diagnosing technology using the DC-DC converter connected to the panel. Small-signal impedances of a monocrystalline silicon PV panel were modeled and simulated to reflect fault conditions such as the short-circuit, hot-spot, and junction box faults. Modeling and simulation results were validated firstly using a test setup consisting of a solar simulator, a network analyzer, small-signal injectors, and a monocrystalline PV panel rated at 300 W.
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CCM vs. CRM Design Optimization of a Boost-derived Parallel Active Power Decoupler for Microinverter Applications
Single-phase inverter or rectifier systems often make use of an auxiliary active power decoupler (APD) to balance the mismatch between steady DC power and fluctuating AC power. This paper deals with efficiency and size optimization of a parallel boost-type APD circuit for PV microinverter applications. Specifically, design of an eGaNFET-based, 400 W APD circuit, employing planar inductor and operating in either continuous conduction mode (CCM) or critical conduction mode (CRM) is considered. Available design variables including inductance value, inductor core geometry, capacitor voltage, switching frequency, and modulation scheme (CCM vs. CRM) are explored to identify Pareto-optimal configurations, which can achieve low California Energy Commission (CEC) efficiency drop while also reducing the footprint area of the inductor. The theoretical study predicts that the optimal CRM design can achieve 37% reduced inductor size, while operating with similar efficiency drop, compared to the optimal CCM design. Experimental results, obtained using two separate 40 V, 400 W hardware prototypes for CCM and CRM, are presented to verify the analyses.
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On-Orbit Measurement of Next Generation Space Solar Cell Technology on the International Space Station
Measurement is essential for the evaluation of new photovoltaic (PV) technology for space solar cells. NASA Glenn Research Center (GRC) is in the process of measuring several solar cells in a supplemental experiment on NASA Goddard Space Flight Center's (GSFC) Robotic Refueling Mission's (RRM) Task Board 4 (TB4). Four industry and government partners have provided advanced PV devices for measurement and orbital environment testing. The experiment will be on-orbit for approximately 18 months. It is completely self-contained and will provide its own power and internal data storage. Several new cell technologies including four- junction (4J) Inverted Metamorphic Multijunction (IMM) cells will be evaluated and the results compared to ground-based measurements.
solar cell↗
Fast Grid Frequency Support from Distributed Energy Resources
This report describes research related to electric power system frequency support from inverter-coupled distributed energy resources (DERs). This research was initiated under the U.S. Department of Energy’s Grid Modernization Laboratory Consortium (GMLC) and also contains work funded through a Cooperative Research and Development Agreement with Hawaiian Electric. The purpose of this report is to summarize the findings of simulations, hardware testing, power hardware-in-the-loop (PHIL) simulation, and analysis examining the effects of DER frequency support on the Oahu power system. This report also includes recommendations for consideration in the use of DER grid frequency support functionality, and related topics. Note that a previous GMLC Technical Report from this project also provided specific recommendations for the activation of one specific grid frequency support function, frequency-watt control, for overfrequency events in Hawaii. Those recommendations included recommended frequency-watt control settings, which have since been approved by the Hawaii Public Utilities Commission on October 20, 2017, for system-wide activation. This report builds on the previous report by considering not only overfrequency events but also underfrequency events, and by considering distributed energy storage systems in addition to photovoltaic (PV) systems.
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Analysis of 0.1-Hz Var Oscillations in Solar Photovoltaic Power Plants
Oscillations with very low frequency at 0.1 Hz, have been observed in voltage and var in practical solar photovoltaic (PV) systems when power exporting ramps up to a certain level. Here, this letter provides an explanation on the formation of 0.1-Hz oscillations and identifies three critical factors that lead to the oscillations: communication delay between the plant-level control and the inverter-level control, high volt/var sensitivity at a high power exporting level, and the volt-var feedback system consisting of the plant control, inverter control and the grid impact. Furthermore, a critical feature of the 0.1-Hz oscillation is also explained: why oscillations appear only in voltage and var, but not in real power.
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NASA Glenn Research Center Solar Cell Experiment Onboard the International Space Station
Accurate air mass zero (AM0) measurement is essential for the evaluation of new photovoltaic (PV) technology for space solar cells. The NASA Glenn Research Center (GRC) has flown an experiment designed to measure the electrical performance of several solar cells onboard NASA Goddard Space Flight Center's (GSFC) Robotic Refueling Missions (RRM) Task Board 4 (TB4) on the exterior of the International Space Station (ISS). Four industry and government partners provided advanced PV devices for measurement and orbital environment testing. The experiment was positioned on the exterior of the station for approximately eight months, and was completely self-contained, providing its own power and internal data storage. Several new cell technologies including four-junction (4J) Inverted Metamorphic Multi-junction (IMM) cells were evaluated and the results will be compared to ground-based measurement methods.
Solar Cell Calibration Facility↗
Further Analyses of the NASA Glenn Research Center Solar Cell and Photovoltaic Materials Experiment Onboard the International Space Station
Accurate air mass zero (AM0) measurement is essential for the evaluation of new photovoltaic (PV) technology for space solar cells. The NASA Glenn Research Center (GRC) has flown an experiment designed to measure the electrical performance of several solar cells onboard NASA Goddard Space Flight Center's (GSFC) Robotic Refueling Mission's (RRM) Task Board 4 (TB4) on the exterior of the International Space Station (ISS). Four industry and government partners provided advanced PV devices for measurement and orbital environment testing. The experiment was positioned on the exterior of the station for approximately eight months, and was completely self-contained, providing its own power and internal data storage. Several new cell technologies including four-junction (4J) Inverted Metamorphic Multi-Junction (IMM) cells were evaluated and the results will be compared to ground-based measurement methods.
multi-junction↗