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DOE OSTI2021

Representing DC-Coupled PV+Battery Hybrids in a Capacity Expansion Model

Recent technology cost and performance improvements and the federal investment tax credit (ITC) have driven growing interest in coupling solar photovoltaic (PV) and battery systems. Combining these technologies into co-located or hybridized PV+battery systems has the potential to lower costs and increase energy output relative to multiple independent systems. In this work, we provide an overview of PV+battery systems and demonstrate methods for incorporating them into NREL’s Regional Energy Deployment System (ReEDS) capacity expansion model. Although the methods are applied to a specific model, we anticipate that the approaches used here can be useful for informing PV+battery method development for other capacity expansion models. The implemented method relies heavily on the native representations of PV and battery technologies; therefore, the focus of this work is on capturing and parameterizing the interactions between them for a configuration in which the PV and battery technologies share a single bi-directional inverter. This work also demonstrates the impacts of including PV+battery systems in the ReEDS optimization for the conterminous United States through 2050. In particular, we perform parametric sensitivities for input assumptions that are uncertain and expected to influence PV+battery deployment levels, including (a) the cost of PV+battery systems relative to independent PV and battery systems, (b) the battery component’s qualification for the ITC, and (c) future cost trajectories for PV and battery systems. We find that PV+battery deployment could occur throughout the conterminous United States if there are cost savings associated with DC coupling PV and battery technologies. If even modest (5%) cost savings can be achieved (through a shared inverter and balance-of-system costs), then approximately one-third of utility-scale PV deployment through 2050 adopts the DC-coupled hybrid configuration, resulting in total PV+battery deployment that exceeds the magnitude of PV+battery projects in U.S. interconnection queues in 2020. If greater cost savings can be achieved through DC coupling (e.g., due to a growing amount of shared balance-of-system costs, reduced financial risk, or modularity) or more rapid cost and performance improvements are realized for PV and battery technologies, then total PV+battery deployment and the share of PV and battery deployment that adopts the hybrid configuration grows (to >50%). In all cases, growing PV+battery deployment primarily displaces independent PV and battery technologies, indicating the strong competition between the hybrid and independent configurations comprising technologies with similar performance characteristics.

14 SOLAR ENERGY↗
DOE OSTI2022

Influence of Hybridization on the Capacity Value of PV and Battery Resources

Utility-scale systems that combine solar photovoltaic and battery (PV+battery) technologies are growing in popularity on the U.S. bulk power system. The business case for PV+battery systems depends on both their ability to reduce costs and their ability to generate value synergies associated with the provision of energy, capacity, and ancillary services. Capacity value can constitute a significant portion of the value PV+battery hybrids provide to the grid (e.g., through avoided or deferred capacity) and receive through revenues. Throughout this report, we define capacity value as the monetary value of a plant's contribution towards the planning reserve margin, which ultimately depends on market rules and structures. PV+battery hybrids do not always fit into current market structures because of the interactions between the PV and battery components. Unique considerations for the capacity value of PV+battery hybrids include the disparate nature of participation models for PV and battery technologies in existing market rules and the potential influence of a shared interconnection capacity; limitations imposed by a shared inverter; limited ability to charge the battery in advance of capacity events if charging must be sourced from the coupled PV; and challenges or uncertainties associated with co-optimizing the operations of the PV and battery components. Grid operators are currently considering how market structures can be modified to optimally determine the capacity value provided by PV+battery systems, and the rules of how they are integrated into markets are still being written. As with any resource, poorly designed rules could increase the cost of energy and reduce system reliability, while well-designed rules could allow markets to receive the full benefits hybrid systems can offer without overcompensating them for the services they provide. Well-designed rules for PV+battery systems must consider the unique aspects listed above, while leveraging the commonalities with existing resource types. In this report, we summarize the technical capability and market rules that influence the capacity value of PV+battery systems. We further discuss the potential tradeoffs between computational complexity and accuracy for the various ways in which grid operators can credit PV+battery systems for capacity. Finally, we describe markets for capacity, survey current wholesale market rules applying to PV+battery systems, and provide a snapshot of the current regulatory landscape for PV+battery systems.

14 SOLAR ENERGY↗
DOE OSTI2023

Hybrid renewable energy systems: the value of storage as a function of PV-wind variability

As shares of variable renewable energy (VRE) on the electric grid increase, sources of grid flexibility will become increasingly important for maintaining the reliability and affordability of electricity supply. Lithium-ion battery energy storage has been identified as an important and cost-effective source of flexibility, both by itself and when coupled with VRE technologies like solar photovoltaics (PV) and wind. In this study, we explored the current and future value of utility-scale hybrid energy systems comprising PV, wind, and lithium-ion battery technologies (PV-wind-battery systems). Using a price-taker model with simulated hourly energy and capacity prices, we simulated the revenue-maximizing dispatch of a range of PV-wind-battery configurations across Texas, from the present through 2050. Holding PV capacity and point-of-interconnection capacity constant, we modeled configurations with varying wind-to-PV capacity ratios and battery-to-PV capacity ratios. We found that coupling PV, wind, and battery technologies allows for more effective utilization of interconnection capacity by increasing capacity factors to 60%–80%+ and capacity credits to close to 100%, depending on battery capacity. We also compared the energy and capacity values of PV-wind and PV-wind-battery systems to the corresponding stability coefficient metric, which describes the location-and configuration-specific complementarity of PV and wind resources. Our results show that the stability coefficient effectively predicts the configuration-location combinations in which a smaller battery component can provide comparable economic performance in a PV-wind-battery system (compared to a PV-battery system). These PV-wind-battery hybrids can help integrate more VRE by providing smoother, more predictable generation and greater flexibility.

14 SOLAR ENERGY↗
DOE OSTI2026

Knowledge-Based Hazardous Waste Determinations for Solar Photovoltaic (PV) Modules: A Foundational Study

This report explores using "generator knowledge" to determine whether a solar photovoltaic (PV) module must be managed as hazardous waste prior to recycling or landfilling. Generator knowledge is a legal term and existing regulatory pathway for making a hazardous waste determination that has been used by other industries but is a relatively unknown option to the PV industry. In the United States, a hazardous waste determination often acts as a pre-requisite to recycle or landfill a PV module. The results of the hazardous waste determination dictate whether the PV module must be managed as hazardous waste or nonhazardous solid waste. Managing a PV module as hazardous waste requires compliance with stringent U.S. federal and state hazardous waste law. In addition to increased management costs, which can be ten times higher, legal liability for PV modules regulated as hazardous waste is also heightened with both civil and criminal penalties for noncompliance which includes making an inaccurate or faulty hazardous waste determination. The most common reason a PV module would be regulated as hazardous is if it contains a regulated metal in an amount that equals or exceeds the toxicity characteristic limits. To determine whether a PV module exhibits a hazardous characteristic, the regulated person/entity must "apply knowledge...in light of the materials and processes used." In the absence of adequate knowledge to determine whether the PV module is hazardous, it must be tested using Test Method 1311 Toxicity Characteristic Leaching Procedure (TCLP) or an equivalent EPA-approved method. Although TCLP is the predominant method used to today to make a hazardous waste determination for PV modules, evidence from this study concludes it is not practical to TCLP test every PV module even in a single utility-scale installation, and a scalable solution is needed. This study finds that knowledge-based hazardous waste determinations may allow a regulated person/entity to make a hazardous waste determination for more than one PV module at one time - making this regulatory pathway a potential scalable solution. Through legal analysis and interviews with 44 experts, the authors explore what it means to make an accurate knowledge-based hazardous determination for PV modules considering sources and forms of information as well as potential limitations. The work aims to provide a foundation for building consensus on whether knowledge-based hazardous determinations are a viable, scalable industry approach for solar.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Evaluating Utility-Scale PV-Battery Hybrids in an Operational Model for the Bulk Power System

Systems that combine solar photovoltaic and battery energy storage technologies (PV-BES) are increasingly being proposed and deployed on the bulk power system. The operations and value of PV-BES systems have been extensively studied from the project developer's perspective through analyses that maximize plant-level revenue. However, PV-BES hybrids' operational characteristics are seldom studied from the perspective of bulk power system operators, who seek to optimize the performance of a suite of generation and storage assets that are connected via the transmission network. This work presents modeling approaches for representing and evaluating PV-BES hybrids in a model that optimizes operations across the bulk power system. Its novel contributions include demonstrating a technique to modify a unit commitment and dispatch model to represent the operational synergies of PV-BES hybrids. In particular, we describe the challenges and an approach for representing so-called DC-coupled PV-BES - which utilize a single bi-directional inverter - as a dispatchable resource in a commercial, production cost model (PCM), PLEXOS. We demonstrate this technique in a PCM study of the Los Angeles Department of Water and Power (LADWP) test system, by replacing existing PV and battery generators on the test system with our PV-BES hybrids. We then pursue scenario analysis that is designed to isolate the various drivers of operational strategies for DC-coupled PV-BES hybrids, including the nature of coupling, PV penetration on the system, and varying inverter loading ratios (or degrees of over-sizing of the PV field). Results from the analysis include utilization profiles for the PV DC energy across available pathways, dispatch profiles for the battery component, and the hybrid technologies' impacts on system-wide production costs. The approach presented in this paper can be used in any PCM that is looking to study PV-BES hybrids as a resource in different power system configurations and services.

14 SOLAR ENERGY↗
DOE OSTI2023

Grid-Forming PV Inverter: Technology Development and Microgrid Applications

Presently, excluding residential backup power applications, grid-forming (GFM) inverters are commercially available for battery energy storage system (BESS) and some PV plus battery systems but not yet for stand-alone PV. This tech update investigates the control design for GFM PV inverter, especially the control required to provide active power reserve and to stabilize the dc link voltage. Subsequently, the use cases of GFM PV plants in utility-level microgrids are discussed considering fully inverter-based microgrids (i.e., with PV and BESS) as well as mixed-source microgrids (i.e., with PV and diesel generator). Scenarios where active power up-reserve from GFM PV plants is beneficial are illustrated. Moreover, requirements and specifications for GFM PV plant, especially for its active power reserve function are developed. In addition, stability of an example utility-level microgrid with two GFM PV plants and a diesel generator is analyzed to shed light on the potential unstable interaction between multiple GFM resources and the countermeasures. This research aims to advance GFM PV technology to offer more flexibility in microgrid design. With GFM PV available, depending on the level of reliability required, cost-benefit analysis, etc., the GFM BESS or diesel generator may be sized smaller while leveraging the full potential of PV generation inside the microgrid.

14 SOLAR ENERGY↗
DOE OSTI2024

Multi-year analysis of physical interactions between solar PV arrays and underlying soil-plant complex in vegetated utility-scale systems

Concerns over the land use changes impacts of solar photovoltaic (PV) development are increasing as PV energy development expands. Co-locating utility-scale solar energy with vegetation may maintain or rehabilitate the land's ability to provide ecosystem services. Previous studies have shown that vegetation under and around the panels may improve the performance of the co-located PV and that PV may create a favorable environment for the growth of vegetation. While there have been some pilot-scale experiments, the existence and magnitude of these benefits of vegetation has not been confirmed in a utility-scale PV facility over multiple years. In this study we use power output data coupled with microclimatic measurements in temperate climates to assess these potential benefits. Here this study combines multi-year microclimatic measurements to analyze the physical interactions between PV arrays and the underlying soil-vegetation system in three utility-scale PV facilities in Minnesota, USA. No significant cooling of PV panels or increased power production was observed in PV arrays with underlying vegetation. Fine soil particle fraction was the highest in soils within PV arrays with the vegetation which was attributable to the lowest wind speeds from the compounding suppression of wind by vegetation and PV arrays. Soil moisture and soil nutrient response to re-vegetation varied between PV facilities, which could be attributed to differing soil texture. No statistically significant vegetation-driven panel cooling was observed in this climate. This finding prompts a need for site-specific studies to identify contributing factors for environmental co-benefits in co-located systems.

14 SOLAR ENERGY↗
DOE OSTI2022

Understanding Economic and Deployment Benefits of Wind-PV Hybrid Power Plants

The least-cost approach to decarbonizing the U.S. electricity supply will likely involve a significantly expanded share of variable renewable energy (VRE) generation. Many strategies and approaches have been identified to address integration challenges in a VRE-dominated system, including standalone and coupled battery systems. In this study, we explored the current and future potential of utility-scale hybrid energy systems comprising PV, wind, and lithium-ion battery technologies (PV-wind-battery systems). Analysis results include temporal complementarity of wind and PV resources across the contiguous United States, with an emphasis on metrics that quantify reductions in variability that can be achieved through hybridization. We further use a price-taker model with simulated hourly energy and capacity prices to determine the revenue-maximizing dispatch of a range of PV-wind-battery configurations across Texas, from the present through 2050. We find that coupling PV, wind, and battery technologies allows for more effective utilization of interconnection capacity by increasing capacity factors to 60-80%+ and capacity credits to close to 100%, depending on battery capacity. We also compared the energy and capacity values of PV-wind and PV-wind-battery systems to the corresponding stability coefficient metric, which describes the location-and configuration-specific complementarity of PV and wind resources. Our results show that the stability coefficient effectively predicts the configuration-location combinations in which a smaller battery component can provide comparable economic performance in a PV-wind-battery system (compared to a PV-battery system). These PV-wind-battery hybrids can help integrate more VRE by providing smoother, more predictable generation and greater flexibility. Finally, capacity expansion modeling of PV-wind systems indicates that hybridization can reduce transmission expansion requirements associated with a heavily decarbonized U.S. electricity system.

complementarity↗
DOE OSTI2020

Will Solar Panels Help When the Power Goes Out? Planning for PV Resilience

PV systems, like all energy systems, must chart a difficult course in order to improve resilience. They need to simultaneously withstand extreme conditions that cause grid power disruptions, be configured in a way that allows electricity to be delivered to meet the PV user's needs, and overcome disruptions arising from solar resource intermittency. Certain considerations reveal themselves as key to a PV system's resilience benefits. Choice of hardware is critical, as is site selection for the PV array. Voluminous evidence in a hurricane context demonstrates that durable hardware, properly maintained and sited, leads to PV systems surviving extreme weather with their functionality intact. Structurally weak or poorly maintained PV equipment, on the other hand, has demonstrated failures during extreme weather events. PV system stakeholders should ensure that best practices are followed for hardware procurement and siting. It is also important to understand that PV systems are not one-size-fits-all. While incorporating islanding capabilities and energy storage into the system's electrical topology will typically enhance resilience, the user's needs will determine the optimally resilient conformation of any given system. These factors - appropriate hardware, siting, maintenance, and system design - are the primary drivers of PV resilience. PV systems possess high potential for resilient energy generation due to their distributed, abundant, and free fuel supply, but the technology itself does not inherently lead to resilient outcomes. Realizing PV's potential for resilience requires consideration of these determinative factors.

14 SOLAR ENERGY↗
DOE OSTI2024

Regional Real-Time PV Spinning Reserve Estimator

Curtailed photovoltaic (PV) generation is a zero-marginal-cost spinning reserve that can be used for a number of active power control services. Unlike traditional spinning reserve providers, however, i.e., fossil-fueled generators, which have well-defined operating characteristics, e.g., available headroom or potential high limit (PHL), PV plants have by nature variable and uncertain operating characteristics. To ensure the effective coordination between PV plants and the system operator during an active power control event, accurate knowledge of the PV PHL is essential. It ensures that enough headroom is reserved by the PV plants to deliver the award services in real time and informs feasible dispatch decisions made by the market operator. To tackle this challenge, a novel reference-control grouping-based PV plant reserve estimation method has been proposed by the National Renewable Energy Laboratory under past projects funded by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office. The estimation method separates inverters within a plant into two groups: a control group and a reference group. While the reference group is reserved to operate at its PHL, the control group can be curtailed to provide the grid services. Real-time outputs from the reference inverters are used to estimate the PHL for the whole plant based on the ratio between capacities of the reference group and of the plant. This work further enhances the methodology by (1) improving the model accuracy through machine learning; (2) automating the reference inverter selection through correlation analysis; (3) considering estimation look-ahead windows; and (4) applying to regional spinning reserve estimation. Significant performance improvement has been observed based on real-world data collected by CAISO, Southern Company, and Terabase Energy. Compared with the original scaling method, the newly proposed machine learning-based approach reduces the estimation errors by 30% and 13% at the plant level and region level, respectively. Results obtained from this project are intended to be used by grid operators, market operators, balancing authorities, and PV plant owners and operators to facilitate PV participation in ancillary service markets. Regulators, policymakers, and system planners can also consider the results of this work in their decision-making processes. In addition to the performance improvement on the existing reference-control based grouping method, we also investigated how the variability of PV generation from a single PV inverter can be used to represent the variability of PV generation at the plant level.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Agent-Based Coordination Scheme for PV Integration (ABC4PV)

Renewables and especially photovoltaics (PV) have benefitted significantly from a host of incentives and policies targeted toward enhanced integration and adoption of specific energy technologies. However, with the push to move forward into a subsidy-free market framework, behind-the-meter residential PV applications have generally struggled to retain their value (unlike utility scale and commercial projects) [1]. This project focused on developing control-theoretic solutions aimed at improving the integration and interaction of behind-the-meter residential PV with other distribution system assets (controllable and non-controllable) to enhance the integrated value of residential PV. To this end, a suite of decentralized control methodologies have been developed to enable effective coordination and control of behind-the-meter residential load customers’ PV, battery storage systems (BSS), controllable loads and other similar assets within a distribution feeder. This interaction aims at procuring energy savings and, thus, energy bill savings. The main source of savings is drawn from reducing the effect of demand charge pricing and is realized at the feeder level, assuming community level interaction and management among the aforementioned assets. Optimal control of the assets is implemented with a distributed optimization methodology, leveraging consensus-based algorithms. The results gathered from the optimal control simulations demonstrates that the savings can be duly achieved and the algorithm decision times (to dynamically control asset set points, for example) are fast. As for the overall efficiency of PV+BSS systems, to procure energy savings from curtailment of the demand charge pricing effects, the optimal control is set up so as to minimize the variance of the load for all customers, throughout a feeder and throughout time in a rolling horizon scheduling with model predictive control. The control takes into account inter-temporal electrochemical storage (battery) degradation costs: specifically, we have developed a long-term lifetime model for the BSS that weighs in the effect of the degradation factor in the dispatch formulations, thus, a considerable operating cost that affects energy decision making. The levelized cost of energy (LCOE – redefined for the purpose of quantifying asset integration effectiveness through the customers’ energy cost) is shown to be below the threshold set for the combined PV+BSS topology of $ 0.14/kWh for multiple cases of PV penetration all the way up to 50%, provided that a policy of shared ownership of and savings is in place. Further, the LCOE calculated for the case before the deployment PV+BSS systems is also achievable, i.e. the deployment of PV+BSS, if planned and scheduled optimally. will have no effect on customers’ energy costs. From the control methodology viewpoint, the developed consensus-based algorithms are shown to converge for a wide range of problem cases (spanning normal operating scenarios and contingencies), guaranteeing dispatch solutions under forecasting errors, communication break-downs and cyber-security attacks. The proposed control solutions are scalable and real-time implementable, with dispatch computations and device set-point updates converging in less than 2s in most practical instances of the above events.

14 SOLAR ENERGY↗
DOE OSTI2024

PV Performance Modeling and Stakeholder Engagement (Final Technical Report)

This core capability project’s objective is to increase the value of photovoltaic (PV) performance models by improving their functionality, demonstrating, and quantifying their validity, and offering a wide range of stakeholder engagement opportunities. In FY22-24, we developed new and improved modeling algorithms and functions to represent PV performance more accurately in a variety of environments and conditions. The “Model parameter toolkit” was developed and includes functions to translate between different module temperature models, incidence angle modifier models, and single-diode models. A new modeling capability named “PV Atlas” was also developed leveraging Sandia’s High Performance Computing resources. This capability allows us to investigate several questions and provide climate-specific best practices and geographic data files; all these are hosted on an interactive website on Sandia’s GitHub and can be used for training, system optimization, or to provide best practices for uncertainty reduction. For model validation, we published high-quality PV performance, and weather data; these data are well documented, filtered, and processed for quality and include examples on how to run PV simulations. We also developed well documented, standardized methods for validating PV models and ran independent model validation and 2 blind modeling intercomparisons engaging with 49 organizations from 17 countries. We co-led and contributed to a growing, well documented and maintained suite of open-source functions for PV modeling (i.e., the pvlib-python) and we outreached to the PV modeling stakeholders via the PVPMC workshops and web resources. In addition, this project supported US representation and leadership for the International Energy Agency (IEA) PVPS Task 13; specifically, members of our team led and supported 3 subtasks on: 1) Best practices for the optimization of bifacial photovoltaic tracking, 2) Extreme weather events and their multiple impact on PV power plants: Risks, failure mechanisms and mitigation strategies, and 3) Best practice guidelines for the use of economic and technical Key Performance Indicators (KPIs). This project resulted in the publications of 14 peer reviewed journal papers, 37 conference presentations, 6 SAND reports, 5 public datasets and 6 new webpages on the PVPMC website. It supported the release of 13 pvlib-python versions where 28 enhancements were from this PV Performance Modeling project. We co-organized 5 PVPMC workshops in FY22-24 with the participation of 214 unique institutions and around 700 participants. The PVPMC website was redesigned, and its reliability was improved; it receives over 50,000 visitors/year from 202 unique countries.

14 SOLAR ENERGY↗
DOE OSTI2020

Frequency Analysis of Solar PV Power to Enable Optimal Building Load Control

In this paper, we present a flexibility estimation mechanism for buildings’ thermostatically controlled loads (TCLs) to enable the distribution level consumption of the majority of solar photovoltaic (PV) generation by local building TCLs. The local consumption of PV generation provides several advantages to the grid operation as well as the consumers, such as reducing the stress on the distribution network, minimizing voltage fluctuations and two-way power flows in the distribution network, and reducing the required battery storage capacity for PV integration. This would result in increasing the solar PV generation penetration levels. The aims of this study are twofold. First, spectral (frequency) analyses of solar PV power generation together with the power consumption of multiple building TCLs (such as heating, ventilation, and air conditioning (HVAC) systems, water heaters, and refrigerators) are performed. These analyses define the bandwidth over which these TCLs can operate and also describe the PV generation frequency bandwidth. Such spectral analyses, in frequency domain, can help identify the flexible components of PV generation that can be consumed by the various TCLs through optimal building load utilization. Second, a quadratic optimization problem based on model predictive control is formulated to allow consuming most of the low and medium frequency content of the PV power locally by building TCLs, while maintaining occupants’ comfort and TCLs’ physical constraints. The solution to the proposed optimization problem is achieved using optimal control strategies. Numerical results show that most of the low and medium frequency content of the PV generation can be consumed locally by building TCLs. The remaining high-frequency content of the PV generation can then be stored/offset using energy storage systems.

14 SOLAR ENERGY↗
DOE OSTI2020

IEEE 1547-2018 Based Interoperable PV Inverter with Advanced Grid-Support Functions

Grid integration of photovoltaic (PV) inverters has been increasing in the past decade. As a result of the uncertainties introduced with high penetrations of PV, better monitoring and control of the PV inverters becomes crucial for improving overall system stability. This paper focuses on the communications capability of the inverter controller and on enabling interoperability. Multiple standards are available to enable interoperability in PV inverters. In this paper, an interoperable controller, enabled by Distributed Network Protocol 3 (DNP3) communications protocols, is developed for a grid-connected, three-phase PV inverter. The DNP3 server for the PV inverter is programmed on the real-time layer of the field-programmable gate array (FPGA)-based inverter controller. Set points for advanced inverter control functions, such as volt/VAr curves, ride-through curves, are sent from a DNP3 client, a simulated distribution management system application, to the PV inverter through DNP3. This communications capability of the inverter controller is validated using a controller-hardware-in-the-loop experimental setup. The code developed to achieve the interoperability is available in the public domain through open-source software licensing. This interoperability will enable smoother grid integration of smart PV inverters with advanced grid-support functions as well as allow better monitoring and control of PV inverters for grid stability.

14 SOLAR ENERGY↗
DOE OSTI2023

Net Load Forecasting With Disaggregated Behind-the-Meter PV Generation

As worldwide use of residential photovoltaic (PV) systems grows, system operators and utilities will need to transition from forecasting pure demand to forecasting net load with behind-the-meter (BTM) PV generation. However, PV generation can be difficult to predict and the measurements of PV generation from BTM residential systems are often invisible behind a measurement of the net load, making net load forecasting challenging. This paper proposes a novel two-stage framework for net load forecasting in areas with limited observability and high BTM PV generation. First, the profiles of observable customers are used to disaggregate the net load measurements into the pure load and PV generation. Then, separate models are used to forecast the PV generation and pure load individually, and the results are combined for a net load forecast. Further, this paper also proposes a compensator for correcting the error of the net load forecast, using historical forecast errors of the PV generation, pure load, and net load. The proposed framework is tested through two case studies for areas with high BTM PV penetration and less than 10% observable customers. The two-stage forecasting model is compared to two benchmark methods - a time series forecasting model, and a model that forecasts the net load directly using historical net load measurements. Results show that the proposed disaggregation-forecasting framework reduces the error of the net load forecast compared to both benchmark models. In addition, when the net load forecast error is periodic, the compensator can correct the error to improve the forecast accuracy.

14 SOLAR ENERGY↗
DOE OSTI2024

Availability and Performance Loss Factors for U.S. PV Fleet Systems

In the PV Fleet Performance Data Initiative, we partner with photovoltaic (PV) fleet owners to collect time-series PV production data and publish aggregated, anonymized results. This report is an update of our previous publications, specifically a FY 2021 performance index publication and a FY 2022 fleet degradation analysis. In this analysis, we have increased our data participants and system totals by around 10% to 8.5 GW and 24,000 separate inverter data channels. Four major analysis topics are considered in this report: Performance Index (PI) trends, PV system availability, soiling losses, and PV system degradation. Performance Index and inverter availability are assessed on a larger set of data from our FY 2021 report: 1,128 systems compared with 200 systems from before. The increased number of systems is due to an improved data quality methodology, as well as introducing new systems to the analysis. Overall results are similar to previously published values - overall inverter availability is low in the first six months of system performance before reaching steady-state by the end of the first year. Excluding this six-month startup period, system-level aggregated data shows a median (P50) system availability of 0.99 and a lower 10th percentile (P90) value of 0.95 (Figure ES-1). A dependence on system size is also demonstrated, with worse inverter availability results for larger PV systems. Causes of this effect are under investigation, but may be impacted by inverter size, which also show lower availability for larger inverter sizes. This report also investigates PI, correcting for degradation, soiling, snow, and availability. Following these corrections, the median system PI over its entire lifetime is 0.95. PI values reported here are approximately 3% lower than what we presented in our previous FY 2021 report. Soiling loss is assessed in a comprehensive way for the first time in this report. Results are presented using the COmbined Degradation and Soiling (CODS) method, as implemented in RdTools (v3.0.0a4). Soiling values are presented for 255 systems, which indicated irradiance-weighted soiling loss greater than 1%. The values have been published in an updated NREL soiling map at nrel.gov/pv/soiling.html. Finally, we investigated system degradation using three different data analysis techniques: conventional RdTools (year-on-year (YOY)), CODS, and Performance Loss Rate (PLR) analysis. Overall degradation results are consistent with our previous publications. Rerunning conventional RdTools on our updated fleet shows that some data partners have systematically fallen below the median system degradation rate (change over time) of -0.75 %/year. A comparison with PLR analysis, which looks at change in annual PI over time, shows that median system degradation is consistent with -0.5% to -0.75% per year change. However, at the P90 value, system degradation is substantially faster. These two results are consistent and indicate that resulting degradation statistics depend to a great degree on the population of PV systems making up the analysis cohort and whether soiling impacts the systems. The use of CODS for degradation analysis provides a different method for degradation assessment, which explicitly excludes the impact of recoverable soiling on degradation analysis. Excluding soiling effects yields an annual system degradation around -0.5% per year on average. This indicates that a portion of system performance loss may be attributed to periodic soiling that is not fully recovered. This report provides PV system owners/operators with background and methods to analyze PV system performance, give guidance for expected cohort performance, and performance loss values for use in pro-forma financial models, which guide new-build system design and bankability reports.

14 SOLAR ENERGY↗
DOE OSTI2020

Provision of Grid Services by PV Plants with Integrated Battery Energy Storage System: Preprint

Battery energy storage systems (BESS), due to their tremendous range of uses and configurations, may assist PV integration in any number of ways by increasing power system flexibility. In this paper we describe results of a research project conducted by NREL and First Solar for developing controls and demonstrating many use cases for PV-BESS systems including (1) matching generation to loads through time shifting, reducing PV curtailment,; (2) promoting higher levels of PV penetrations by balancing the grid through ancillary services; (3) use of PV-BESS systems to provide wide-area stability services in the form of oscillations damping controls; and (r) use of gird forming BESS for enabling black start and islanded applications for PV-BESS systems. In the paper we will also describe the unique multi-MW test and validation platform developed at NREL for conducting research on fielded utility-scale PV-BESS systems. The platform consists of First Solar 0.43 MW PV power plant, NREL’s 1MW/1MW BESS, 2MW PV power supplies and 7MVA power electronic grid simulator interfaced with industrial plant controllers and power-hardware-in-the-loop (PHIL) capability.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2022

PV DMFA [SWR-21-105]

The Photovoltaic Dynamic Material Flow Assessment (PV DMFA) model (also referred to here as “The model”) is a computational framework written in Python based on utility-scale PV electricity generation to quantify time-series stocks and flows of PV materials primarily in crystalline silicon PV technologies. The model evaluates cradle-to-cradle life cycle of utility-scale solar PV systems in the United States in the period 2000-2100. PV DMFA serves as a sustainability analysis tool to assess the impacts of different material circularity practices (i.e., reduce, reuse/refurbish, remanufacture, and recycle), PV module design shifts and sensitivity of material processing and technology related parameters to material installations, waste creation and raw material depletion in PV material supply chains. This tool enables advanced planning for future material needs and informs sustainable pathways for PV material management in the circular economy. This tool could be helpful to a wide range of stakeholders; Particularly, researchers and manufacturers looking for technoeconomic and/or environmental life cycle analysis (LCA) feedback for renewable energy (RE) systems.

Khalifa, SherifA.↗