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Simulating dispersed photovoltaic power generation using a bimodal mixture model of the clear-sky index
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (BEESG)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (BEESG)
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (Built Environment Energy Systems Group)
2015 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Improved probability distribution models for power generation are useful e.g. forprobabilistic power flow simulations. This paper presents a distribution modelfor photovoltaic (PV) power generation based on the clear-sky index.With the use of minute-resolution data on globalhorizontal irradiation (GHI) we fit unimodal normal,bimodal normal and trimodal normal mixture distributionfamilies to the clear-sky index. Based on Kolmogorov-Smirnov (K-S) teststhe best fit distribution family consisting of a bimodal normal distribution isthen used for estimating an aggregate clear-sky index for multipledispersed locations that are assumed to be uncorrelated in terms of sky clearness.For five or more locations the aggregate clear-sky indexfollows a normal distribution due to the central limit theorem.Models for solar radiation on tilted planes and PV power generation areapplied to the clear-sky index to generate probability distributions for anarbitrary PV system.

Place, publisher, year, edition, pages
2015.
National Category
Engineering and Technology
Research subject
Engineering Science
Identifiers
URN: urn:nbn:se:uu:diva-242439OAI: oai:DiVA.org:uu-242439DiVA: diva2:783458
Conference
EU PVSEC - 31st European Photovoltaic Solar Energy Conference and Exhibition, 14-18 September, 2016, Hamburg, Germany
Available from: 2015-01-26 Created: 2015-01-26 Last updated: 2016-05-27Bibliographically approved
In thesis
1. Distributed Photovoltaics, Household Electricity Use and Electric Vehicle Charging: Mathematical Modeling and Case Studies
Open this publication in new window or tab >>Distributed Photovoltaics, Household Electricity Use and Electric Vehicle Charging: Mathematical Modeling and Case Studies
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Technological improvements along with falling prices on photovoltaic (PV) panels and electric vehicles (EVs) suggest that they might become more common in the future. The introduction of distributed PV power production and EV charging has a considerable impact on the power system, in particular at the end-user in the electricity grid.

In this PhD thesis PV power production, household electricity use and EV charging are investigated on different system levels. The methodologies used in this thesis are interdisciplinary but the main contributions are mathematical modeling, simulations and data analysis of these three components and their interactions. Models for estimating PV power production, household electricity use, EV charging and their combination are developed using data and stochastic modeling with Markov chains and probability distributions. Additionally, data on PV power production and EV charging from eight solar charging stations is analyzed.

Results show that the clear-sky index for PV power production applications can be modeled via a bimodal Normal probability distribution, that household electricity use can be modeled via either Weibull or Log-normal probability distributions and that EV charging can be modeled by Bernoulli probability distributions. Complete models of PV power production, household electricity use and EV home-charging are developed with both Markov chain and probability distribution modeling. It is also shown that EV home-charging can be modeled as an extension to the Widén Markov chain model for generating synthetic household electricity use patterns. Analysis of measurements from solar charging stations show a wide variety of EV charging patterns. Additionally an alternative approach to modeling the clear-sky index is introduced and shown to give a generalized Ångström equation relating solar irradiation to the duration of bright sunshine.

Analysis of the total power consumption/production patterns of PV power production, household electricity use and EV home-charging at the end-user in the grid highlights the dependency between the components, which quantifies the mismatch issue of distributed intermittent power production and consumption. At an aggregate level of households the level of mismatch is shown to be lower.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2015. 93 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1224
Keyword
Distributed Photovoltaics, Household Electricity Use, Electric Vehicle Charging, Markov Chain Modeling, Probability Distribution Modeling, Data Analysis, Self-Consumption, Grid Interaction.
National Category
Energy Systems
Research subject
Engineering Science
Identifiers
urn:nbn:se:uu:diva-243159 (URN)978-91-554-9162-8 (ISBN)
Public defence
2015-03-27, Polhemsalen, Ångström Laboratory, Lägerhyddsvägen 1, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2015-03-04 Created: 2015-02-05 Last updated: 2015-03-12Bibliographically approved

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Munkhammar, JoakimRydén, JesperWidén, JoakimLingfors, David

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