Engineering topics

Hansen, Clifford W.

Publications and source records attributed to Hansen, Clifford W..

At least 19 records

DOE OSTI2024

User Stories for PV Operations and Maintenance: Findings from the 2024 PVPMC Workshop

At the 2024 Photovoltaic (PV) Performance Modeling Workshop we collected user stories related to PV system monitoring, analytics, and operations and maintenance (O&M). In this context, user stories describe challenges with current practices and tools or imagining opportunities for improvements. From these user stories, we identified several near-term opportunities to improve photovoltaic system monitoring, analytics and O&M.

14 SOLAR ENERGY↗

DOE OSTI2024

Digital twin advanced distribution management systems (ADMS) and methods

Advanced Distribution Management Systems not generally optimize over the entire feeder because there are few high-fidelity distribution circuit models and real-time distribution-connected sensors are rare. The limited observability at the distribution level makes it difficult to globally optimize distribution operations and issue control setpoints to power systems equipment or Distributed Energy Resources (DER) to perform grid-support services. For example, setpoints can be issued to DER based on results from an optimization module that incorporates a static or time-series feeder simulation. Feeder simulation initial conditions are populated with photovoltaic (PV) and load forecasts, state estimation results, and/or digital twin measurements or state output. The real-time (RT) digital twin runs a model of the feeder to generate state estimation pseudo-measurements since there are limited live feeder measurements.

Johnson, Jay Tillay↗

DOE OSTI2023

Clear-Sky Detection Using Time-Averaged, Tilted-Plane Data

A method is presented to detect clear-sky periods for plane-of-array, time-averaged irradiance data that is based on the algorithm originally described by Reno and Hansen. We show this new method improves the state-of-the-art by providing accurate detection at longer data intervals, and by detecting clear periods in plane-of-array data, which is novel. We illustrate how accurate determination of clear-sky conditions helps to eliminate data noise and bias in the assessment of long-term performance of PV plants.

clear-sky conditions↗

DOE OSTI2023

pvlib python: 2023 project update

pvlib python is a community-developed, open-source software toolbox for simulating the performance of solar photovoltaic (PV) energy components and systems. It provides reference implementations of over 100 empirical and physics-based models from the peer-reviewed scientific literature, including solar position algorithms, irradiance models, thermal models, and PV electrical models. In addition to individual low-level model implementations, pvlib python provides high-level workflows that chain these models together like building blocks to form complete “weather-to-power” photovoltaic system models. It also provides functions to fetch and import a wide variety of weather datasets useful for PV modeling. pvlib python has been developed since 2013 and follows modern best practices for open-source python software, with comprehensive automated testing, standards-based packaging, and semantic versioning. Its source code is developed openly on GitHub and releases are distributed via the Python Package Index (PyPI) and the conda-forge repository. pvlib python’s source code is made freely available under the permissive BSD-3 license. Here we (the project’s core developers) present an update on pvlib python, describing capability and community development since our 2018 publication (Holmgren, Hansen, & Mikofski, 2018).

14 SOLAR ENERGY↗

DOE OSTI2023

pvlib iotools—Open-source Python functions for seamless access to solar irradiance data

Access to accurate solar resource data is critical for numerous applications, including estimating the yield of solar energy systems, developing radiation models, and validating irradiance datasets. However, lack of standardization in data formats and access interfaces across providers constitutes a major barrier to entry for new users. pvlib python’s iotools subpackage aims to solve this issue by providing standardized Python functions for reading local files and retrieving data from external providers. All functions follow a uniform pattern and return convenient data outputs, allowing users to seamlessly switch between data providers and explore alternative datasets. The pvlib package is community-developed on GitHub: https://github.com/pvlib/pvlib-python. As of pvlib python version 0.9.5, the iotools subpackage supports 12 different datasets, including ground measurement, reanalysis, and satellite-derived irradiance data. The supported ground measurement networks include the Baseline Surface Radiation Network (BSRN), NREL MIDC, SRML, SOLRAD, SURFRAD, and the US Climate Reference Network (CRN). Additionally, satellite-derived and reanalysis irradiance data from the following sources are supported: PVGIS (SARAH & ERA5), NSRDB PSM3, and CAMS Radiation Service (including McClear clear-sky irradiance).

14 SOLAR ENERGY↗

DOE OSTI2023

Parameter estimation for incidence angle modifier models for photovoltaic modules

We present methods to estimate parameters for models for the incidence angle modifier for simulating irradiance on a photovoltaic array. The incidence angle modifier quantifies the fraction of direct irradiance that is reflected away at the array’s face, as a function of the direct irradiance’s angle of incidence. Parameters can be estimated from data and the fitting method can be used to convert between models. We show that the model conversion procedure results in models that produce similar annual insolation on a fixed plane.

14 SOLAR ENERGY↗

DOE OSTI2023

Open Source Evaluation Framework for Solar Forecasting

The Solar Forecast Arbiter is an open-source evaluation framework for solar forecasting. The framework enables evaluations of solar irradiance, solar power, and net-load forecasts that are impartial, repeatable and auditable. The Solar Forecast Arbiter addresses stakeholder-informed use cases including evaluation of forecast skill, comparisons to reference data sets, private forecast trials, and evaluation of probabilistic forecast skill. The framework includes a data validation toolkit, reference data sources, data privacy protocols, and benchmark forecast capabilities for intra-hour and day ahead forecast horizons. Reports and metrics communicate the relative merits of the test and benchmark forecasts. The reports are created from standardized templates and include graphics for qualitatively evaluating deterministic and probabilistic forecasts and standard metrics for quantitatively evaluating forecasts. The Solar Forecast Arbiter is designed to support all solar forecasting stakeholders, including Solar Forecasting 2 Topic Area 2 and Topic Area 3 teams.

14 SOLAR ENERGY↗

DOE OSTI2023

Determining circuit model parameters from operation data for PV system degradation analysis: $\mathrm{PVPRO}$

Physics-based circuit parameters like series and shunt resistance are essential to provide insights into the degradation status of photovoltaic (PV) arrays. However, calculating these parameters typically requires a full current-voltage characteristic (I-V curve), the acquisition of which involves specific measurement devices and costly methods. Thus, I-V curves of the PV system level are often not available. Here this paper proposes a methodology (PVPRO) to estimate these I-V curve parameters using only operation (string-level DC voltage and current) and weather data (irradiance and temperature). PVPRO first performs multi-stage data pre-processing to remove noisy data. Next, the time-series DC data are used to fit an equivalent circuit single-diode model (SDM) to estimate the circuit parameters by minimizing the differences between the measured and estimated values. In this way, the time evolutions of the SDM parameters are obtained. We evaluate PVPRO on synthetic datasets and find an excellent estimation of both SDM and the key I-V parameters (e.g., open-circuit voltage, short-circuit current, maximum power, etc.) with an average relative error of 0.55%. The performance, especially the extracted degradation rate of parameters, is robust to various measurement noises and the presence of faults. In addition, PVPRO is applied to a 271 kW PV field system. The relative error between the real and estimated operation voltage and current is less than 1%, suggesting that degradation trends are well captured. PVPRO represents a promising open-source tool to extract the time-series degradation trends of key PV parameters from routine operation data.

14 SOLAR ENERGY↗

DOE OSTI2023

Quantifying Uncertainty in PV Energy Estimates Final Report

Uncertainty in PV energy estimates is "one of the most critical areas of lack of understanding" according to independent engineers, financiers, PV model developers, and other industry stakeholders. The primary problem is a lack of rigorous, transparent, widely accepted methods for quantifying uncertainty in energy production estimates. Uncertainty in energy production estimates arises from variability of the solar resource, inexact PV performance models and their parameters, and system reliability considerations. Uncertainty in annual energy production is frequently calculated for larger projects in order to quantify financial risk. Key statistics for energy, such as the P-values "P50" and "P90" (the annual energy values that are exceeded in future years with 50\% and 90\% probability, respectively) are used by financing institutions to calculate the repayment risk for the project. The current methods to estimate these statistics are typically proprietary, specialized, and involve significant post-processing of commercial performance model results. This black-box approach leads to inconsistent P-value estimates from different parties, which reduces investors' confidence in the results. Since the financial community bases its risk assessment on these estimates, reduced confidence increases perceived project risk, and consequently financing costs. The goal of this project was to establish a set of best practices for quantifying uncertainty in energy production estimates, including identifying what sources of uncertainty must be considered with clear definitions and metrics, determining which sources are the biggest drivers of uncertainty, and providing a computationally efficient framework for combining different sources of uncertainty that is flexible enough to accommodate substitutions of data or methods when better information is available. We engaged a wide set of stakeholders to ensure industry endorsement and adoption, and leveraged complementary projects investigating individual sources of uncertainty in great detail, as well as others' work that started down this path.

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗

DOE OSTI2021

Measuring Irradiance for Bifacial PV Systems

The advent of bifacial PV systems drives new requirements for irradiance measurement at PV projects for monitoring and assessment purposes. While there are several approaches, there is still no uniform guidance for what irradiance parameters to measure and for the optimal selection and placement of irradiance sensors at bifacial arrays. Standards are emerging to address these topics but are not yet available. In this paper we review approaches to bifacial irradiance monitoring which are being discussed in the research literature and pursued in early systems, to provide a preliminary guide and framework for developers planning bifacial projects.

bifacial↗

DOE OSTI2021

Measuring Irradiance for Bifacial PV Systems: Preprint

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

DOE OSTI2020

Modeling Failure of Electrical Transformers due to Effects of a HEMP Event

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.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

Determining Series Resistance for Equivalent Circuit Models of a PV Module

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. Furthermore, 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.

42 ENGINEERING↗

DOE OSTI2018

Stochastic Optimization with Risk Aversion for Virtual Power Plant Operations: A Rolling Horizon Control

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.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

A Detailed Model of Rear-Side Irradiance for Bifacial PV Modules

We describe and validate a method for modeling irradiance on the back surface of bifacial PV modules at the scale of individual cells using view factors. We compare model results with irradiance measurements on the back of PV modules in various configurations. Our analysis illustrates the relative accuracy of the model as well as the potential variation in back surface irradiance among the cells.

14 SOLAR ENERGY↗

DOE OSTI2018

pvlib python: a python package for modeling solar energy systems

pvlib python is a community-supported open source tool that provides a set of functions and classes for simulating the performance of photovoltaic energy systems. pvlib python aims to provide reference implementations of models relevant to solar energy, including for example algorithms for solar position, clear sky irradiance, irradiance transposition, DC power, and DC-to-AC power conversion. pvlib python is an important component of a growing ecosystem of open source tools for solar energy (William F. Holmgren, Hansen, Stein, & Mikofski, 2018).

14 SOLAR ENERGY↗

DOE OSTI2017

2017 IEEE PVSC tutorial PV system modeling.

Abstract not provided.

Hansen, Clifford W.↗

DOE OSTI2016

Analysis of Irradiance Models for Bifacial PV Modules

We describe and compare two methods for modeling irradiance on the back surface of rack-mounted bifacial PV modules: view factor models and ray-tracing simulations. For each method we formulate one or more models and compare each model with irradiance measurements and short circuit current for a bifacial module mounted a fixed tilt rack with three other similarly sized modules. Our analysis illustrates the computational requirements of the different methods and provides insight into their practical applications. We find a level of consistency among the models which indicates that consistent models may be obtained by parameter calibrations.

14 SOLAR ENERGY↗