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The Solar Uncertainty Integrator (SUNI) study was developed by the National Laboratory of the Rockies (NLR's) to provide a standardized "bulk uncertainty processing" method for solar irradiance data, which are essential for the successful deployment of solar energy systems. This poster provides an overview of key findings from NLR's SUNI application across 89 stations.
Over the past decade, techno-economical advancements in solar energy systems, particularly the improvement in the conversion efficiency of PV modules made with mono-crystalline silicon solar cells from 12% to 20%, as well as the cost reduction in manufacturing by a factor of about 10%, have made it possible to achieve a levelized cost of electricity (LCOE) in PV plants that is comparable to, or less than, the cost of deriving electricity from fossil fuels. Operating PV plants in mid-latitude sun-belt regions, where the solar irradiance level is highest, provides a high annual energy-yield (kWh/kWp) due to two factors: (1) the availability of predictable high solar irradiance throughout the year with the fewest interruptions in solar flux from clouds and rain, and (2) the increased conversion efficiency of crystalline solar cells, as recombination loss has decreased with increased intensity of the sunlight that illuminates the silicon solar cells. Semi-arid and desert regions, however, are plagued by high atmospheric dust concentrations and frequent sand storms. The deposition of a layer of dust on the optical surfaces of solar collectors such as PV modules and concentrating mirrors reduces the transmission efficiency of sunlight that actually reaches the solar cells or receivers, resulting in high energy-yield soiling loss. There are two major cost components to operating a solar plant: (1) installation costs, and (2) operation-and-maintenance (O&M) costs. There is no fuel cost; hence operating a solar plant in a semi-arid or desert region provides high returns on investment if soiling losses are mitigated via efficient cleaning methods and optimized cleaning frequency. If solar collectors are not cleaned, the accumulation of dust layers on solar collectors may cause the operation of such plants in arid regions to become economically unviable. Washing solar collectors with water and detergent, as is most commonly done now, is an efficient method for cleaning. The conventional approach in utility-scale solar plants is to use a large truck with a water tank and pump system for spraying deionized water on the surface of the solar collectors. Robotic cleaning with brushes, used for many solar plants, requires lesser water for cleaning. The water consumed using semi-automated cleaning of PV modules in utility-scale solar plants is approximately 2 liters/m 2 per cleaning cycle. The total optical surface area of the solar collectors in 1 TW-scale solar installation will be more than 3 × 109 m 2 ; hence an enormous amount of water be needed for cleaning. There simply is not enough fresh water in the sun-belt areas of the world for predicted cleaning needs. In solar power plants, the estimated cost of cleaning solar collectors includes expenses related to the equipment used, labor, cost of transportation of water, the energy required for cleaning, plus ancillary costs whereas the cost of water is not considered. The water used is obtained from sources located close to plant sites, unmindful of the environmental and societal impact as the power plants are oftentimes located in regions that face severe drought. This practice is very similar to the cost calculations in deriving the levelized cost of electricity (LCOE) in conventional power plants based on burning fossil fuel such as coal and gas, while disregarding the cost of climate change and health effects. Unless a water-free or low-water cleaning method is established, the expansion of solar plants may lose public support in areas suffering long intervals of drought. The goal of this research project has been the development and application of the Electrodynamic Screen (EDS) as a means for a water-free, scalable cleaning process applicable to solar-power installations, including rooftop applications. We describe here the development of an EDS film-based cleaning process as an emerging method for use on PV modules, parabolic troughs, and heliostats. This report aims to show the feasibility of integrating or retrofitting EDS films onto the optical surfaces of solar collectors (both PV and CSP) while maintaining high transmission or reflection efficiency. The cleaning action provided by the EDS film is an active method to remove dust deposits by electrodynamic force. Current lab-scale prototype EDS films, retrofitted onto solar panels and mirrors, have shown to be capable of maintaining optical transmission or specular-reflection efficiencies higher than 90% of initial values under clean conditions. The optical surfaces of solar collectors laminated with EDS films can remove more than 90% of deposited dust when the EDS is activated for less than two minutes. As an electrodynamic dust removal process, the EDS film-based method is designed primarily for the removal of dust in solar installations located in semi-arid and desert areas, where the atmosphere is often dry and dusty and rainfall is infrequent. While EDS film application minimizes water consumption and facilitates cleaning as frequently as needed, it has limitations in removing contaminants such as soot, organic pollutants deposited as fine films on the surface, and bird droppings. The dust removal efficiency of the EDS is maximum at relative humidity RH is 40 to 50% and decreases at when RH > 65%. Many solar plant sites undergo diurnal and seasonal cycles of high ambient, early afternoon temperatures and an RH that reaches the dew point early in the morning. These variations in atmospheric conditions do not limit the operation of the EDS film. This report presents a brief review of the progress and the potential of EDS film technology for mitigating the impact of dust on solar collectors via water-free cleaning, as well as current technical challenges regarding efficiency and durability. Our experimental data on the performance of EDS films show that: (1) the dust-removal efficiency (DRE) can reach levels higher than 90%, (2) the specular reflectivity (SR) of EDS film-laminated second-surface mirrors reach levels in excess of 90%, (3) the specular reflectivity restoration (SRR) can exceed 90%, (4) the output-power restoration (OPR) of PV modules can exceed 95%, and (5) the optical transmission efficiency (TE) of the EDS films can be greater than 90%. Working with Sandia National Laboratories (NM), Corning Research and Development Corporation (NY), Eastman Kodak (NY), Tomark-Worthen Industries (NH), and EDS Chile SPA (Chile), we have produced EDS film-laminated PV modules and demonstrated their self-cleaning functions without requiring water. We have demonstrated that the operational range of EDS films will cover the expected ambient temperature of solar fields at RH cycling varying from 20 to 95% as long as the EDS films are activated in the RH range 20 to 50%. (Typical solar-field climates in deserts and semi-arid lands often reach near dew point in coastal areas.) Our experiments on the application of hydrophobic-fluorinated nanoparticle coatings on EDS film surfaces show that the EDS operational range can be extended to higher RH levels that approach the dew point. At Eastman Kodak, as one of our industrial partners, we were able to establish a process for manufacturing EDS films using flexographic printing of the electrodes onto transparent polymer films. This process utilizes an existing manufacturing line at Eastman Kodak that allows fabrication of medium-scale EDS films (26 cm × 30 cm). The manufacturing process has the capacity to produce EDS films at high production speeds. The medium-scale EDS films that have been printed at Kodak were evaluated in the lab at Boston University. The EDS films produced at Kodak were laminated at Tomark-Worthen using an industrial scale vacuum laminator to produce EDS film stacks, which can be affixed onto the optical surfaces of PV modules or concentrating mirrors. The EDS film stack consists of the EDS films that have Willow® Glass (WG) which has a thickness of 100 μm as the front surface. The WG sheets obtained from Corning Research and Development Corporation are customized in size and shape to cover the active area of the EDS films. The back surface of the EDS film stack is integrated onto the optical surface of the solar panel or mirror using optically clear adhesive (OCA) films or silicone adhesives. The OCA films (thickness 25 μm) are produced by 3M. The EDS film stacks have the architecture: WG/OCA/EDS Film/OCA/ over PV module or solar mirror. The power-supply units needed for activating the electrodes of the EDS film were designed and produced at Boston University. These power supply units provide three-phase, 1.2 kV voltage pulses at a very low current (micro-ampere) level and at a low frequency (≈ 5 Hz). The voltage pulses are applied to the electrodes in a sequence such that the train of pulses resembles a unidirectional traveling wave of electrical field on the surface of the EDS film. The dust particles on the surface become charged electrostatically and are levitated by the Coulomb force. The lateral sweeping action of the traveling electric field created by the three-phase voltages pulses then sweeps the dust off the surface. The energy consumed by the EDS electrodes is less than 0.2 Wh/m 2 /cleaning cycle, enabling energy-efficient restoration of output power (OPR) of PV module or specular reflectivity restoration (SRR) for solar mirrors. The EDS system consists of (1) an EDS film stack laminated onto the solar collectors, (2) connection of the EDS film to its power supply unit and (3) Interconnection of the power supply to PV modules or solar mirrors. Design, construction and assessment of field-testing units that have EDS stack laminated PV modules for evaluating the performance of the EDS films in solar fields is being carried out at BU. Our progress under this project, aimed at the advancement of EDS film technology, has reached DOE Technology Readiness Level (TRL) 6. Based on the extensive laboratory evaluations and limited field trials, as well as contacts with potential users, we believe that the technology has reached its commercial stage. We are conducting a cost analysis using the National Renewable Energy Laboratory (NREL) System Advisory Model (SAM) and are preparing for field trials of EDS films in different solar fields in the US, Chile, India and in the Middle East. A brief description of EDS film performance, construction and testing of the field-test unit, autonomous operation of the field-test unit for evaluating EDS performance in increasing energy yield, associated revenue savings, and water conservation is presented.
Photovoltaic (PV) generation is a critical component of microgrids, but its accurate modeling is challenging due to the complex and dynamic interactions between solar irradiance, temperature, and PV system installation. This paper develops a multilayer perceptron (MLP) model that inputs solar irradiance and temperature to estimate the PV generation, and it compares the proposed data-driven model’s performance to two well-known physical models: the single-diode model and the inverter model. The results demonstrate that all the models can reach high levels of accuracy. However, the MLP model outperforms the physical models on average by 4.5 to 6.6 percent in R squared scores and 220 to 290 Watts in RMSE scores, and it does not require physical system parameters. Moreover, the data-driven model can overcome the limitations of the lack of real-time PV generation data.
In this study, a novel sizing methodology was developed for centralized and interconnected operating strategies of transactive microgrids and several variables were investigated including starting month, initial charge of battery, load variability, unit cost of solar panels and energy storage, number of systems, climate, and required reliability to determine their effect on total cost. The centralized strategy improved cost by seven to ten percent compared to the isolated strategy in every case. The interconnected strategy saved an incremental amount of money consistently compared to the isolated standard. The number of connected systems was not a strong effect. It was thought that increasing the number of systems would increase the benefit of energy sharing. Climate zones studied (“Cold”; “Hot-Dry/Mixed Dry”; “Mixed Humid”; and “Cold but with lower solar irradiation”) showed a large variation on cost with the Hot-Dry/Mixed Dry being the least expensive and Cold, with lower solar irradiation being the most expensive. Cost sensitivity analysis was performed showing that the unit cost of solar has a greater effect on the total cost. Required reliability of power, measured in outage hours, exhibited an inverse relation with cost.
A two-step solar thermochemical looping cycle based on Co 3 Mo 3 N/Co 6 Mo 6 N reduction/nitridation reactions offers a pathway for green NH 3 production that utilizes concentrated solar irradiation, H 2 O, and air as feedstocks. The NH 3 production cycle steps both derive process heat from concentrated solar irradiation and encompass 1) the reduction of Co 3 Mo 3 N in H 2 to Co 6 Mo 6 N and NH 3 ; and 2) nitridation of Co 6 Mo 6 N to Co 3 Mo 3 N with N 2 . Co 3 Mo 3 N reduction/nitridation reactions are examined at different H 2 and/or N 2 partial pressures and temperatures. NH 3 production is quantified in situ using liquid conductivity measurements coupled with mass spectrometry (MS). Solid-state characterization is performed to identify a surface oxygen layer that necessitates the addition of H 2 during cycling to prevent surface oxidation by trace amounts of O 2 . H 2 concentrations of > 5% H 2 /Ar and temperatures >500 °C are required to reduce Co 3 Mo 3 N to Co 6 Mo 6 N and form NH 3 at 1 bar. Complete regeneration of Co 3 Mo 3 N from Co 6 Mo 6 N is achieved at conditions of 700 °C under 25–75% H 2 /N 2 . H 2 pressure-swings are observed to increase NH 3 production during Co 3 Mo 3 N reduction. In conclusion, the results represent the first comprehensive characterization of and definitive non-catalytic production of NH 3 via chemical looping with metal nitrides and provide insights for technology development.
A high-resolution, long-term solar dataset is essential for capturing the variability of solar energy resources and informing strategies to ensure grid reliability and resilience in systems with high levels of solar energy integration. This study focuses on generating unbiased, high-resolution projections of solar irradiance through a statistical downscaling framework, using Earth system model (ESM) simulations obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). The National Solar Radiation Database (NSRDB) is used to calibrate statistical downscaling models. The newly developed dataset provides solar irradiance, surface air temperature, and surface wind speed at 4-km and hourly resolutions across the contiguous United States (CONUS), based on two future scenarios (RCP4.5 and RCP8.5). This study outlines key steps in developing the high-resolution future solar dataset, including (1) regridding ESM data to a common 20-km resolution grid, (2) correcting ESM biases using the NSRDB, and (3) applying temporal and spatial downscaling methods to generate high-resolution (4-km, hourly) solar projections. Preliminary results indicate that downscaled projections (4-km) captured reasonable spatial patterns when compared to observations across CONUS for four variables. On average across all pixels, 4-km daily-total GHI and DNI projections showed normalized bias (nBias) less than 1% and 6% for GHI and DNI against NSRDB, respectively (nBias less than 1% and 5% for daily-average surface air temperature and surface wind speed). In terms of long-term trend for GHI and DNI, there was no strong increasing or decreasing trend (when compared to surface air temperature), but it showed a very weak decreasing trend.
Accurate and high-resolution data reflecting different climate scenarios are vital for policy makers when deciding on the development of future energy resources, electrical infrastructure, transportation networks, agriculture, and many other societally important systems. However, state-of-the-art long-term global climate simulations are unable to resolve the spatiotemporal characteristics necessary for resource assessment or operational planning. We introduce an adversarial deep learning approach to super resolve wind velocity and solar irradiance outputs from global climate models to scales sufficient for renewable energy resource assessment. Using adversarial training to improve the physical and perceptual performance of our networks, we demonstrate up to a 50 × resolution enhancement of wind and solar data. In validation studies, the inferred fields are robust to input noise, possess the correct small-scale properties of atmospheric turbulent flow and solar irradiance, and retain consistency at large scales with coarse data. An additional advantage of our fully convolutional architecture is that it allows for training on small domains and evaluation on arbitrarily-sized inputs, including global scale. We conclude with a super-resolution study of renewable energy resources based on climate scenario data from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report.
As a major entry point of mercury (Hg) to aquatic food webs, algae play an important role in taking up and transforming Hg species in aquatic ecosystems. However, little is known how and to what extent Hg reduction, uptake, and species transformations are mediated by algal cells and their exudates, algal organic matter (AOM), under either sunlit or dark conditions. In this study, using Chlorella vulgaris (CV) as one of the most prevalent freshwater model algal species, we show that solar irradiation could enhance the reduction of mercuric Hg(II) to elemental Hg(0) by both CV cells and AOM. AOM reduced more Hg(II) than algal cells themselves due to cell surface adsorption and uptake of Hg(II) inside the cells under solar irradiation. Synchrotron radiation X-ray absorption near-edge spectroscopy (SR-XANES) analyses indicate that sunlight facilitated the transformation of Hg to less bioavailable species, such as β-HgS and Hg-phytochelatins, compared to Hg(Cysteine) 2 -like species formed in algal cells in the dark. These findings highlight important functional roles and potential mechanisms of algae in Hg reduction and immobilization under varying lighting conditions and how these processes may modulate Hg cycling and bioavailability in the aquatic environment.
Existing frameworks to forecast time-series photovoltaic (PV) output power and consumer load for microgrid operations and controls assume a near-continuous availability of real-time input features from the field assets such as PV inverters, energy meters, and weather station. These incoming data points are used to periodically retrain models and update forecast snapshots over a moving horizon window, be it one hour-ahead, one-day ahead, or one-week ahead. However, such frameworks are not resilient to disruptions in data availability caused by losses in communications between the field sensors and data loggers. Hence, there is a need for programs that assume no availability of real-time microgrid asset data and still make reliable forecasts that can be used for decision-making. Such programs would be apt to function in extreme weather events such as hurricanes and would use lightweight recursive time-series models to independently forecast solar irradiance and ambient temperature, then compute PV power from those forecasts, as well as independently forecast consumer load. The codebase performs forecasting for the scenario of when the microgrid does not have a reliable access to forecasts or real-time observations of solar irradiance (I) and ambient temperature (AT) and load (Load) to be able to adequately forecast, in real-time, the PV power production or a business' load. In this case, using historical values of PV power and load, a univariate forecasting of generation and consumption are respectively made. The use-case in particular has two sub-scenarios: one, a normal 7-day ahead forecast where the unavailability of real-time data is assumed due to infrastructure issues such as loss of communication or sensor maintenance or service downtimes. Whereas a hurricane-caused unavailability of real-time data requires a second model trained specifically on historical hurricane days to be able to capture the extreme day behavior of generation in particular, and load if applicable. A gradient boosted regression tree comprises an ensemble of additive models that map between the input of historical values (be it irradiance, temperature, or load) and their corresponding output forecasts of a given horizon such that the individual learner predictions are summed up over the total number of such learners in the ensemble to produce an aggregate forecast. A weighting mechanism is applied to the training data in each iteration, where actual and forecast values are compared to penalize incorrect forecasts by increasing the weight and reducing it to reward correct forecasts. The code's benefits are that it: (a) accounts for a contingency where communication loss renders newly measured real-time data unavailable for model tuning and snapshot updates; (b) presents blind forecasting that recursively determines the next time-step value in a horizon using the forecast of the same attribute from a prior step; and (c) employs lightweight models that, once trained, can reliably generalize for different horizons, which make them suitable for enhancing the resilience of field microgrids prone to extreme events that encounter disruptions to data availability.
An A‑ and B‑site substitutional study of SrFeO3−δ perovskites (A’xA1−xB’yB1−yO3−δ, where A = Sr and B = Fe) was performed for a two‑step solar thermochemical air separation cycle. The cycle steps encompass (1) the thermal reduction of A’xSr1−xB’yFe1−yO3−δ driven by concentrated solar irradiation and (2) the oxidation of A’xSr1−xB’yFe1−yO3−δ in air to remove O2, leaving N2. The oxidized A’xSr1−xB’yFe1−yO3−δ is recycled back to the first step to complete the cycle, resulting in the separation of N2 from air and concentrated solar irradiation. A-site substitution fractions between 0 ≤ x ≤ 0.2 were examined for A’ = Ba, Ca, and La. B-site substitution fractions between 0 ≤ y ≤ 0.2 were examined for B’ = Cr, Cu, Co, and Mn. Samples were prepared with a modified Pechini method and characterized with X-ray diffractometry. The mass changes and deviations from stoichiometry were evaluated with thermogravimetry in three screenings with temperature- and O2 pressure-swings between 573 and 1473 K and 20% O2/Ar and 100% Ar at 1 bar, respectively. A’ = Ba or La and B’ = Co resulted in the most improved redox capacities amongst temperature- and O2 pressure-swing experiments.
This paper analyzes a novel, cost-effective planar waveguide solar concentrator design that is inspired by cellular hexagonal structures in nature with the benefits of facile installation and low operation and maintenance cost. A coupled thermal and optical analysis of solar irradiation through an ideal hexagonal waveguide concentrator integrated with a linear receiver is presented, along with a cost analysis methodology, to establish the upper limit of performance. The techno-economic model, coupled with numerical optimization, is used to determine designs that maximized power density and minimized the cost of heat in the temperature range of 100–250 °C, which constitutes more than half of the industrial process heat demand. Depending on the incident solar irradiation and the application temperature, the cost of heat for the optimal design configuration ranged between 0.1–0.27 $/W and 0.075–0.18 $/W for waveguide made of ZK7 glass and polycarbonate, respectively. A techno-economic analysis showed the potential of the technology to achieve cost as low as 80 $/m2 and 61 $/m2 for waveguide made of ZK7 glass and polycarbonate material, respectively, which is less than half the cost of state-of-the-art parabolic trough concentrators. Overall, the hexagonal waveguide solar concentrator technology shows immense potential for decarbonizing the industrial process heat and thermal desalination sectors.
Abstract WRF-Solar is a numerical weather prediction model specifically designed to meet the increasing demand for accurate solar irradiance forecasting. The model provides flexibility in the representation of the aerosol–cloud–radiation processes. This flexibility can be argued to make it more difficult to improve the model’s performance because of the necessity of inspecting different configurations. To alleviate this situation, WRF-Solar has a reference configuration to use as a benchmark in sensitivity experiments. However, the scarcity of high-quality ground observations is a handicap to accurately quantify the model performance. An alternative to ground observations are satellite irradiance retrievals. Herein we analyze the adequacy of the National Solar Radiation Database (NSRDB) to validate the WRF-Solar performance using high-quality global horizontal irradiance (GHI) observations across the contiguous United States (CONUS). Based on the sufficient performance of NSRDB, we further analyze the WRF-Solar forecast errors across the CONUS, the growth of the forecasting errors as a function of the lead time, and sensitivities to the grid spacing and the representation of the radiative effects of unresolved clouds. Our results based on WRF-Solar forecasts spanning 2018 reveal a 7% median degradation of the mean absolute error (MAE) from the first to the second daytime period. Reducing the grid spacing from 9 to 3 km leads to a 4% improvement in the MAE, whereas activating the radiative effects of unresolved clouds is desirable over most of the CONUS even at 3 km of grid spacing. A systematic overestimation of the GHI is found. These results illustrate the potential of GHI retrievals to contribute to increasing the WRF-Solar performance.
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This study is part of a broader program that aims to generate reliable ultraviolet (UV) data for several applications. This work presents a simple and fast parametrization to estimate UV solar irradiation at the ground level from global horizontal irradiance (GHI) information. The work focuses on UVB (280-315 nm) and erythemic UV (UVE) irradiance weighted by the average skin response.
Knowing the degradation in performance of a photovoltaic (PV) system over time is important for estimating the lifetime energy produced and the financial return. A key parameter for normalizing performance and determining degradation is the plane-of-array (POA) irradiance. Because accurate long-term POA measurements are not always readily available, three methods of providing irradiance data for determining the degradation rate of PV systems were evaluated—a method using irradiance data modeled with the Ineichen clear-sky model and monthly Linke turbidity coefficients, a method using the supplemental clear-sky irradiance data from the National Solar Radiation Data Base (NSRDB), and a method using the NSRDB solar irradiance data for both cloudy and clear-sky conditions (all-sky). The irradiance data from the three methods were evaluated using measured irradiance data from 1998 through 2018 for the seven-station SURFRAD network and for 3-, 5-, and 10-year periods that might be used for evaluating PV system performance. Only the two clear-sky methods for the 10-year periods had less uncertainty with respect to determining PV system degradation than the expected median degradation rate for PV systems of -0.5%/year to -0.6%/year. Shorter periods and the all-sky method had larger uncertainties, making their use questionable for determining the degradation rates of PV systems.
Solar-driven interfacial evaporation shows great prospects for seawater desalination with its rapid fast evaporation rate and high photothermal conversion efficiency. Here, a sustainable, biodegradable, non-toxic, and highly efficient full ocean biomass-based solar-driven evaporator is reported, which is composed of chitosan (CS) hydrogel as the hydratable skeleton and cuttlefish ink (CI) as the photothermal material. Under solar irradiation, the cuttlefish ink powder harvests solar energy and heats the surrounding water. Simultaneously, the water in the three-dimensional network of chitosan hydrogel is rapidly replenished by the interconnected porous structure and the hydrophilic functional groups attached to the polymer chains. With its enlarged evaporation surface, high solar absorptance, adequate water transportation, good salt drainage, and heat localization, the CI/CS-based evaporator achieves a remarkable evaporation rate of 4.1 kg m-2 h-1 under one sun irradiance (1 kW m-2) with high-quality freshwater yields. This full ocean biomass-based evaporator with abundant raw material availability provides new possibilities for an efficient, stable, sustainable, and environmentally friendly solar evaporator with guaranteed water quality.
Integrating solar resource uncertainties due to radiometer measurement performance and operational data quality assessment can provide improved estimates of economic bankability, system design performance, and compliance of solar energy conversion systems. Estimating radiometer measurement uncertainty is an established procedure consistent with recognized best practices and international guidelines. SERI QC is a robust solar data quality assessment software tool that has been in continuous use for more than three decades. This report, the fourth of six for the Data Quality and Uncertainty Integration Project, presents a refined algorithm description for software to translate solar resource data quality assessment results into estimated uncertainty values in a Solar Resource Operational Uncertainty Integrator (SROUI) application. This algorithm requires three-component solar irradiance measurements - global horizontal irradiance, direct normal irradiance, and diffuse horizontal irradiance - collected at 1- to 60-minute intervals, as described in the previous deliverables. The development of this report as Deliverable 6.4 was an iterative process that included reviews and feedback from the project team on initial drafts designed to refine how the new software could best support determining solar resource data uncertainty. The results of this effort will contribute to the final software system development by National Renewable Energy Laboratory staff.
Distributed photovoltaic (PV) generation often occurs ``behind the meter": a grid operator can only observe the net load, which is the sum of the gross load and distributed PV generation. This lack of observability poses a challenge to system operation at both bulk level and distribution level. The lack of real-time or near-future disaggregated estimates of gross load and PV generation will lead to over scheduling of energy production and regulation reserves, reliability constraints violations, wear and tear of controller devices, and potentially cascading failures of a system. In this paper we propose the use of a Bayesian Structural Time Series (BSTS) model with local solar irradiance measurements to disaggregate the summed PV generation and gross load signals at a downstream measurement site. BSTSs are a highly expressive model class that blends classic time series models with the powerful Bayesian state space estimation framework. Disaggregation is done probabilistically, which automatically quantifies the uncertainties of the estimated PV generation and gross load consumption. Depending on the data availability in real-time, it can be used to disaggragate PV and gross load at customer site, or can be used at the feeder level. In this paper, we focus on solving the problem at feeder level. We compare the performance of a BSTS model as well as a handful of state-of-the-art methods on a Pecan Street AMI dataset, using the National Solar Radiation Database (NSRDB) to estimate local irradiance.