Publications

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The Waste Isolation Pilot Plant (WIPP) facility is a U.S. Department of Energy (DOE) operating repository 654 m below the surface in a thick salt formation in southeastern New Mexico. The DOE disposes transuranic (TRU) waste produced from atomic energy defense activities at the WIPP facility. A portion of the waste shipped to the WIPP facility contains TRU radionuclides co-mingled with polychlorinated biphenyls (PCBs), which fall under U.S. Environmental Protection Agency (EPA) regulations implementing the Toxic Substances Control Act (TSCA). This report documents the risks of PCBs co-mingled with TRU waste (hereafter designated as PCB/TRU waste) designated for disposal at the WIPP facility. This analysis is input to the National Environmental Policy Act (NEPA) assessment by the DOE Carlsbad Field Office (CBFO) for the proposed increase of the WIPP facility disposal area to include additional waste panels (but not to increase the legislated WIPP volume). This analysis is not a compliance calculation to support a certification renewal nor does it support a planned change request (PCR) or planned change notice (PCN) to be submitted to the EPA.

Sandia National Laboratories sponsored a three-year internally funded Laboratory Directed Research and Development (LDRD) effort to investigate the vulnerabilities and mitigations of a high-altitude electromagnetic pulse (HEMP) on the electric power grid. The research was focused on understanding the vulnerabilities and potential mitigations for components and systems at the high voltage transmission level. Results from the research included a broad array of subtopics, covered in twenty-three reports and papers, and which are highlighted in this executive summary report. These subtopics include high altitude electromagnetic pulse (HEMP) characterization, HEMP coupling analysis, system-wide effects, and mitigating technologies.

The Waste Isolation Pilot Plant (WIPP), located in southeastern New Mexico, has been developed by the U.S. Department of Energy (DOE) for the geologic (deep underground) disposal of defense-related transuranic (TRU) waste. Containment of TRU waste at the WIPP facility is derived from standards set forth in Title 40 of the Code of Federal Regulations (CFR), Part 191. The DOE assesses compliance with the containment standards according to the Certification Criteria in Title 40 CFR Part 194 by means of Performance Assessment (PA) calculations performed by Sandia National Laboratories (SNL). WIPP PA calculations estimate the probability of radionuclide releases from the repository to the accessible environment for a regulatory period of 10,000 years after facility closure. The DOE Carlsbad Field Office (CBFO) has initiated a National Environmental Policy Act (NEPA) action for a proposal to excavate and use additional transuranic (TRU) waste disposal panels at the WIPP facility. This report documents an analysis undertaken as part of an effort to evaluate the potential environmental consequences of the proposed action. Although not explicitly required for a NEPA analysis, evaluations of a dose indicator to hypothetical members of the public after final facility closure are presented in this report. The analysis is carried out in two stages: first, Performance Assessment (PA) calculations quantify the potential releases to the accessible environment over a 10,000-year post-closure period. Second, dose was evaluated for three hypothetical exposure pathways using the conservative radionuclide concentrations assumed to be released to the accessible environment.

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Understanding the effect of a high-altitude electromagnetic pulse (HEMP) on the equipment in the United States electrical power grid is important to national security. A present challenge to this understanding is evaluating the vulnerability of transformers to a HEMP. Evaluating vulnerability by direct testing is cost-prohibitive, due to the wide variation in transformers, their high cost, and the large number of tests required to establish vulnerability with confidence. Alternatively, material and component testing can be performed to quantify a model for transformer failure, and the model can be used to assess vulnerability of a wide variety of transformers. This project develops a model of the probability of equipment failure due to effects of a HEMP. Potential failure modes are cataloged, and a model structure is presented which can be quantified by the results of small-scale coupon tests.

Grid operators are now considering using distributed energy resources (DERs) to provide distribution voltage regulation rather than installing costly voltage regulation hardware. DER devices include multiple adjustable reactive power control functions, so grid operators have the difficult decision of selecting the best operating mode and settings for the DER. In this work, we develop a novel state estimation-based particle swarm optimization (PSO) for distribution voltage regulation using DER-reactive power setpoints and establish a methodology to validate and compare it against alternative DER control technologies (volt-VAR (VV), extremum seeking control (ESC)) in increasingly higher fidelity environments. Distribution system real-time simulations with virtualized and power hardware-in-the-loop (PHIL)-interfaced DER equipment were run to evaluate the implementations and select the best voltage regulation technique. Each method improved the distribution system voltage profile; VV did not reach the global optimum but the PSO and ESC methods optimized the reactive power contributions of multiple DER devices to approach the optimal solution.

Optimum and reliable photovoltaic (PV) plant performance requires accurate diagnostics of system losses and failures. Data-driven approaches can classify such losses however, the appropriate PV data features required for accurate classification remains unclear. To avoid misclassification, this study reviews the potential issues associated with inabilities to separate fault conditions that overlap using certain data features. Feature selection techniques that define each feature's importance and identify the set of features necessary for producing the most accurate results are also explored. The experiment quantified the amount of overlap using both maximum power point (MPP) and current and voltage (I-V) curve data sets. The I -V data provided an overall increase in classification accuracy of 8% points above the case where only MPP was available.

Optimum and reliable photovoltaic (PV) plant performance requires accurate diagnostics of system losses and failures. Data-driven approaches can classify such losses however, the appropriate PV data features required for accurate classification remains unclear. To avoid misclassification, this study reviews the potential issues associated with inabilities to separate fault conditions that overlap using certain data features. Feature selection techniques that define each feature's importance and identify the set of features necessary for producing the most accurate results are also explored. The experiment quantified the amount of overlap using both maximum power point (MPP) and current and voltage (I-V) curve data sets. The I -V data provided an overall increase in classification accuracy of 8% points above the case where only MPP was available.

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The New York State Public Service Commission recently made significant changes to the compensation mechanisms for distributed energy resources, such as solar generation. The new mechanisms, called the Value of Distributed Energy Resources (VDER), alter the value proposition of potential installations. In particular, multiple time-of-generation based pricing alternatives were established, which could lead to potential benefits from pairing energy storage systems with solar installations. This paper presents the calculations to maximize revenue from a solar photovoltaic and energy storage system installation operating under the VDER pricing structures. Two systems in two different zones within the New York Independent System Operator area were modeled. The impact of AC versus DC energy storage system interconnections with solar generation resources was also explored. The results show that energy storage systems could generate significant revenue depending on the pricing alternative being targeted and the zone selected for the project.

We describe an open source evaluation framework for solar forecasting to support the DOE Solar Forecasting 2 program and the broader solar forecast community. The framework enables evaluations of solar irradiance, solar power, and net-load forecasts that are impartial, repeatable and auditable. First, we define the use cases of the framework. The use cases, developed from the project's initial stakeholder engagement sessions, include comparisons to reference data sets, private forecast trials, evaluation of probabilistic forecast skill, and examinations of forecast errors during critical periods. We discuss the framework's data validation toolkit, reference data sources, and data privacy protocols. We describe the framework's benchmark forecast capabilities for intra-hour and day ahead forecast horizons. Finally, we summarize the reports and metrics that communicate the relative merits of the test and benchmark forecasts. The reports are created from standardized templates and include graphics for quantitatively evaluating deterministic and probabilistic forecasts and standard metrics for quantitatively evaluating forecasts.

In this paper, we present a new, light-weight approach for extracting the five single diode parameters (IL, Io, RS, RSH, and nNsVt) for advanced, in-field monitoring of in situ current and voltage (I-V) tracing devices. The proposed procedure uses individual I-V curves, and does not require the irradiance or module temperature measurement to calculate the parameters. It is suitable for operation on a small, single board computer at the point of I-V curve measurement. This allows for analysis to occur in the field, and eliminates the need to transfer large amounts of data to centralized databases. Observers can receive alerts directly from the in-field devices based on the extraction, and analysis of the commonly used single diode equivalent model parameters. This paper defines the approach and evaluates its accuracy by subjecting it to I-V curves with known parameters. Its performance is defined using actual I-V curves generated from an in situ scanning devices installed within an actual photovoltaic production field. The algorithm is able to operate at a high accuracy for multiple module types and performed well on actual curves extracted in the field.

In this paper, we present a new, light-weight approach for extracting the five single diode parameters (IL, Io, RS, RSH, and nNsVt) for advanced, in-field monitoring of in situ current and voltage (I-V) tracing devices. The proposed procedure uses individual I-V curves, and does not require the irradiance or module temperature measurement to calculate the parameters. It is suitable for operation on a small, single board computer at the point of I-V curve measurement. This allows for analysis to occur in the field, and eliminates the need to transfer large amounts of data to centralized databases. Observers can receive alerts directly from the in-field devices based on the extraction, and analysis of the commonly used single diode equivalent model parameters. This paper defines the approach and evaluates its accuracy by subjecting it to I-V curves with known parameters. Its performance is defined using actual I-V curves generated from an in situ scanning devices installed within an actual photovoltaic production field. The algorithm is able to operate at a high accuracy for multiple module types and performed well on actual curves extracted in the field.

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Literature describes various methods for determining a series resistance for a photovoltaic device from measured IV curves. We investigate use of these techniques to estimate the series resistance parameter for a single diode equivalent circuit model. With simulated IV curves we demonstrate that the series resistance values obtained by these techniques differ systematically from the known series resistance parameter values used to generate the curves, indicating that these methods are not suitable for determining the series resistance parameter for the single diode model equation. We present an alternative method to determine the series resistance parameter jointly with the other parameters for the single diode model equation, and demonstrate the accuracy and reliability of this technique in the presence of measurement errors.

While the concept of aggregating and controlling renewable distributed energy resources (DERs) to provide grid services is not new, increasing policy support of DER market participation has driven research and development in algorithms to pool DERs for economically viable market participation. Sandia National Laboratories recently undertook a three-year research program to create the components of a real-world virtual power plant (VPP) that can simultaneously participate in multiple markets. Our research extends current state-of-the-art rolling horizon control through the application of stochastic programming with risk aversion at various time resolutions. Our rolling horizon control consists of (1) day-ahead optimization to produce an hourly aggregate schedule for the VPP operator and (2) sub-hourly optimization for real-time dispatch of each VPP subresource. Both optimization routines leverage a two-stage stochastic program (SP) with risk aversion, and integrate the most up-to-date forecasts to generate probabilistic scenarios in real operating time. Our results demonstrate the benefits to the VPP operator of constructing a stochastic solution regardless of the weather. In more extreme weather, applying risk optimization strategies can dramatically increase the financial viability of the VPP. As a result, the methodologies presented here can be further tailored for optimal control of any VPP asset fleet and its operational requirements.

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