pvlib python: a python package for modeling solar energy systems

Holmgren, William F.; Hansen, Clifford W.; Mikofski, Mark A.

doi:10.21105/joss.00884

Cited by 776 publications

(342 citation statements)

References 14 publications

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“…Nevertheless, when applying the method of Kara et al (2018) on the dataset used in this paper, the resulting RMSE was found to be 6.9% on average (6.3% in January and 7.4% in August). Therefore, the proposed method was shown to improve disaggregation per- Hansen, and Mikofski, 2018) to simulate the power output for two tilt/azimuth configurations given by the main PV system and the proxy shown in Table I. All other properties are fixed using the Typical Meteorological Year (TMY) data sets from the National Solar Radiation Data Base (Wilcox, 2007) as input.…”

Section: F Disaggregation and Computational Cost Results Of Selectedmentioning

confidence: 99%

Estimating PV Power from Aggregate Power Measurements within the Distribution Grid

Vrettos¹,

Kara²,

Stewart³

et al. 2019

Preprint

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The increased integration of photovoltaic (PV) systems in distribution grids reduces visibility and situational awareness for utilities, because the PV systems’ power production is usually not monitored by them. To address this problem, a method called Contextually Supervised Source Separation (CSSS) has been recently adapted for real-time estimation of aggregate PV active power generation from aggregate net active and reactive power measurements at a point in a radially configured distribution grid (e.g., substation). In its original version, PV disaggregation is formulated as an optimization problem that fits linear regression models for the aggregate PV active power generation and true substation active power load. This paper extends the previous work by adding regularization terms in the objective function to capture additional contextual information such as smoothness, by adding new constraints, by introducing new regressors such as ambient temperature, and by investigating the use of time-varying regressors. Furthermore, we perform extensive parametric analysis to inform tuning of the objective function weighting factors in a way that maximizes performance and robustness. The proposed PV disaggregation method can be applied to networks with either a single PV system (e.g., MW scale) or many distributed ones (e.g., residential scale) connected downstream of the substation. Simulation studies with real field recorded data show that the enhancements of the proposed method reduce disaggregation error by 58% in winter and 35% in summer compared with previous CSSS-based work. When compared against a commonly used transposition model based approach, the reduction in disaggregation error is more pronounced (78% reduction in winter and 45% in summer). Additional simulations indicate that the proposed algorithm is applicable also for PV systems with time-varying power factors. Overall, our results show that – with appropriate modeling and tuning – it is possible to accurately estimate the aggregated PV active power generation of a distribution feeder with minimal or no additional sensor deployment.

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Section: F Disaggregation and Computational Cost Results Of Selectedmentioning

confidence: 99%

Estimating PV Power from Aggregate Power Measurements within the Distribution Grid

Vrettos¹,

Kara²,

Stewart³

et al. 2019

Preprint

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“…As shown in Figure 1, it is possible to integrate a conversion model between GREECE and the optimization module, in order to obtain energy data from the previously aggregated GHI resource. Regarding this study, we have thus integrated solar PV conversion into our model framework, by adapting the pvlib Python library from the Sandia National Laboratory (SNL) [66]. This model makes use of the hourly GHI data described beforehand as well as both endogenous and exogenous factors.…”

Section: Pv System's Output Energymentioning

confidence: 99%

An integrated GIS and robust optimization framework for solar PV plant planning scenarios at utility scale

et al. 2020

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Today, the overall goal of energy transition planning is to seek an optimal strategy for increasing the share of renewable sources in existing power networks, such that the growing power demand is satisfied at manageable short/long term investment. In this paper we address the problem of PV penetration in electricity networks, by considering both 1) the spatial issue of site selection and size, and 2) the temporal aspect of hourly load and demand satisfaction, in addition with the investment and maintenance costs to guarantee a viable and reliable solution. We propose to address this spatio-temporal optimization problem through an integrated GIS and robust optimization model, that allows handling of the ubiquitous dependencies between resource and demand time variability and the selection of optimal sites of renewable power generation. Our approach contributes to the integration of the multi-dimensional and combinatorial aspects of this problem, gathering geographical layers (regional or national scale) and temporal packing (hourly time stamp) constraints, and cost functions. This model computes the optimal geographical location and size of PV facilities allowing energy planning targets to be met at minimal cost in a reliable manner. In this paper, we illustrate our approach by studying the penetration of large-scale solar PV in the French Guiana's power system. Among the results, we show for instance that: 1) our approach performs geographical aggregation with real contextual data, i.e. balances the intermittency of RE sources by spreading out the corresponding installations (location + size) across the territory; 2) the total installed PV capacity can be doubled by removing the 35 % penetration limit on intermittent power without exceeding hourly demand; 3) the safest investment scenario is below 30 MW of new PV facilities (≈ 45 Me and 2 plants), though it is theoretically possible to install up to 45 MW (>120 Me and 11 plants). Nomenclature B iBoolean variable equal to 1 if site PS i is selected, 0 otherwise Ccap i Capital cost for implementation of a new PV power plant (e) Ccon i Connection cost for each new PV plant, transmission lines and substation (e) Clan Transmission line unit cost (e/m) Cop i Annual fixed operational cost per new PV plant (e) Csta Substation power unit cost (e/kW) Csta i Capital cost for new substation (e) Dem h Estimated hourly power demand (kWh) Dg i Minimum distance from the grid to the centroid of a candidate (m) Eint h Current hourly production from intermittent sources (kWh) * Corresponding author Email addresses: benjamin.pillot@ird.fr (Benjamin Pillot), nadeem.alkurdi@ird.fr (Nadeem Al-Kurdi), carmen.gervet@umontpellier.fr (Carmen Gervet), laurent.linguet@univ-guyane.fr (Laurent Linguet)

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“…Step 1: Calculation of the total solar irradiance on a horizontal surface, which is called GHI, under clear-sky conditions. GHI was estimated with the help of pvlib, a special open-source python toolbox for modeling PV system performance [22]. This toolbox provides three different simple clear-sky models in order to estimate solar irradiance on horizontal surface under clear-sky conditions: Ineichen, Haurwitz, and simplified soils [22].…”

Section: Pv Power Under Clear-sky Conditionsmentioning

confidence: 99%

A Machine Learning Approach to Low-Cost Photovoltaic Power Prediction Based on Publicly Available Weather Reports

Maitanova¹,

Telle²,

Hanke³

et al. 2020

Energies

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A fully automated transferable predictive approach was developed to predict photovoltaic (PV) power output for a forecasting horizon of 24 h. The prediction of PV power output was made with the help of a long short-term memory machine learning algorithm. The main challenge of the approach was using (1) publicly available weather reports without solar irradiance values and (2) measured PV power without any technical information about the PV system. Using this input data, the developed model can predict the power output of the investigated PV systems with adequate accuracy. The lowest seasonal mean absolute scaled error of the prediction was reached by maximum size of the training set. Transferability of the developed approach was proven by making predictions of the PV power for warm and cold periods and for two different PV systems located in Oldenburg and Munich, Germany. The PV power prediction made with publicly available weather data was compared to the predictions made with fee-based solar irradiance data. The usage of the solar irradiance data led to more accurate predictions even with a much smaller training set. Although the model with publicly available weather data needed greater training sets, it could still make adequate predictions.

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pvlib python: a python package for modeling solar energy systems

Cited by 776 publications

References 14 publications

Estimating PV Power from Aggregate Power Measurements within the Distribution Grid

Estimating PV Power from Aggregate Power Measurements within the Distribution Grid

An integrated GIS and robust optimization framework for solar PV plant planning scenarios at utility scale

A Machine Learning Approach to Low-Cost Photovoltaic Power Prediction Based on Publicly Available Weather Reports

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