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Munkhammar, J. & Widén, J. (2018). A Markov-chain probability distribution mixture approach to the clear-sky index. Solar Energy, 170, 174-183
Open this publication in new window or tab >>A Markov-chain probability distribution mixture approach to the clear-sky index
2018 (English)In: Solar Energy, ISSN 0038-092X, E-ISSN 1471-1257, Vol. 170, p. 174-183Article in journal (Refereed) Published
Abstract [en]

This paper presents a Markov-chain probability distribution mixture approach to the clear-sky index (CSI). The main assumption is that the temporal variability of the state of clear and the state of cloudy can be described by a two-state Markov-chain, and the variability within each state can be approximated by a probability distribution, unique for each state. Measurables such as the mean clear-sky index, fraction of bright sunshine, expected duration of clearness and expected duration of cloudiness events are shown to be related to the parameters of the method. Additionally, the \AA ngstr\"om equation, which relates mean normalized solar irradiance to the fraction of bright sunshine, is shown to arise as the expectation of the method. In order to numerically verify the method, a simulation model is constructed based on data sets for two different climatic regions: Norrk\"oping, Sweden and Oahu, Hawaii, USA. Results from the simulation model based on training data shows good agreement with testing data, and when comparing the results to existing models in the literature it is comparable to the state of the art. It is shown that the simulation model generates a non-trivial, generally non-zero, autocorrelation function. Finally, challenges with the method and open problems are discussed.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:uu:diva-363524 (URN)
Available from: 2018-10-18 Created: 2018-10-18 Last updated: 2018-10-19
Munkhammar, J. & Widén, J. (2018). An N-state Markov-chain mixture distribution model of the clear-sky index. Solar Energy, 173, 487-495
Open this publication in new window or tab >>An N-state Markov-chain mixture distribution model of the clear-sky index
2018 (English)In: Solar Energy, ISSN 0038-092X, E-ISSN 1471-1257, Vol. 173, p. 487-495Article in journal (Refereed) Published
Abstract [en]

This paper presents an N-state Markov-chain mixture distribution approach to model the clear-sky index. The model is based on dividing the clear-sky index data into bins of magnitude and determining probabilities for transitions between bins. These transition probabilities are then used to define a Markov-chain, which in turn is connected to a mixture distribution of uniform distributions. When trained on measured data, this model is used to generate synthetic data as output. The model is an N-state generalization of a previously published two-state Markov-chain mixture distribution model applied to the clear-sky index. The model is tested on clear-sky index data sets for two different climatic regions: Norrköping, Sweden, and Oahu, Hawaii, USA. The model is also compared with the two-state model and a copula model for generating synthetic clear-sky index time-series as well as other existing clear-sky index generators in the literature. Results show that the N-state model is generally on par with, or superior to, state-of-the-art synthetic clear-sky index generators in terms of Kolmogorov–Smirnov test statistic, autocorrelation and computational speed.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:uu:diva-363525 (URN)10.1016/j.solener.2018.07.056 (DOI)
Available from: 2018-10-18 Created: 2018-10-18 Last updated: 2018-10-19Bibliographically approved
Lingfors, D. & Widén, J. (2018). Evaluation of Datasets and Methods to Derive 3D Building Models and their Influence on PV Power Integration Studies. In: Energynautics GmbH (Ed.), Proc. of the 8th International Workshop on the Integration of Solar Power into Power Systems: . Paper presented at 8th International Workshop on the Integration of Solar Power into Power Systems.
Open this publication in new window or tab >>Evaluation of Datasets and Methods to Derive 3D Building Models and their Influence on PV Power Integration Studies
2018 (English)In: Proc. of the 8th International Workshop on the Integration of Solar Power into Power Systems / [ed] Energynautics GmbH, 2018Conference paper, Published paper (Refereed)
Abstract [en]

A Geographic Information System (GIS) is apowerful tool for studying the impact of building-applied PV systems on the power system. The grid operator can be advised on exactly which parts of the grid may suffer from problems with operational performance (e.g., over-voltages and over- currents). However, this requires the PV power generation to be realistically modelled, for which a crucial first step is to find representative building models. In this study, we compare the accuracy of two types of aerial data, LiDAR (Light Detection And Ranging) and photogrammetry data, and two methods for identifying building models from these data, a raster- based and a vector-based model. The results show that with photogrammetry data, the roof topology of buildings is not identified correctly as often as with LiDAR data, and the vector- based method gives a far better representation of the roofs than the raster-based method. We exemplify this by comparing the results of power-flow simulations on a distribution grid with about 5000 customers where PV systems have been deployed on the roofs according to the two different methods. For the raster-based method the PV power potential is almost four times higher than for the vector-based method, which overestimates impacts on the simulated performance of the grid. The conclusion of the study is therefore that for accurate simulations of the impact of building-applied PV on grid performance based on GIS data, the proposed vector-based method should be used, rather than the raster-based, and it should be based on LiDAR data rather than photogrammetry data.

Keywords
Solar Energy;Digital Surface Model; Viewshed analysis; PV integration
National Category
Energy Engineering
Research subject
Engineering Science
Identifiers
urn:nbn:se:uu:diva-363670 (URN)978-3-9820080-0-4 (ISBN)
Conference
8th International Workshop on the Integration of Solar Power into Power Systems
Available from: 2018-10-19 Created: 2018-10-19 Last updated: 2018-10-19
Shepero, M., Munkhammar, J., Widén, J., Bishop, J. D. K. & Bostrom, T. (2018). Modeling of photovoltaic power generation and electric vehicles charging on city-scale: A review. Renewable & sustainable energy reviews, 89, 61-71
Open this publication in new window or tab >>Modeling of photovoltaic power generation and electric vehicles charging on city-scale: A review
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2018 (English)In: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 89, p. 61-71Article, review/survey (Refereed) Published
Abstract [en]

Photovoltaics (PV) and electric vehicles (EVs) are promising technologies for increasing energy efficiency and the share of renewable energy sources in power and transport systems. As regards the deployment, use and system integration of these technologies, spatio-temporal modeling of PV power production and EV charging is of importance for several purposes such as urban planning and power grid design and operation. There is an abundance of studies and reviews on modeling of PV power production and EV charging available in the literature. However, there is a lack of studies that review the opportunities for combined modeling of the power consumption and production associated with these technologies. This paper aims to fill this research gap by presenting a review of previous research regarding modeling of spatio-temporal PV power production and charging load of EVs. The paper provides a summary of previous work in both fields and the combination of the fields. Finally, research gaps that need to be further explored are identified. This survey revealed some research gaps that need to be further addressed. Improving the accuracy of PV power production ramp-rate modeling in addition to quantifying the aggregate clear-sky index on city-scale are two priorities for the PV potential studies. For the EV charging load models, differences in model assumptions, such as charging locations, charging powers and charging profiles, need to be studied more extensively. Moreover, there is an imminent need for metering the load of charging stations. This is essential in developing accurate models and time series forecasting techniques. For studies exploring both the PV and EV impacts, local weak points in a spatial network need to be discovered, especially for the city-scale studies. Cooperation between eminent researchers in the PV and EV fields might propagate state-of-the-art models from the separate fields to the combined studies.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD, 2018
Keywords
Photovoltaics power production models, Electric vehicle charging models
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:uu:diva-357484 (URN)10.1016/j.rser.2018.02.034 (DOI)000430853300007 ()
Funder
Swedish Energy Agency, 40864-1Swedish Energy Agency, 40199-1EU, European Research Council, 004525-2015
Available from: 2018-08-23 Created: 2018-08-23 Last updated: 2018-09-03Bibliographically approved
Lingfors, D., Shepero, M., Good, C., Bright, J. M., Widén, J., Boström, T. & Munkhammar, J. (2018). Modelling City Scale Spatio-temporal Solar Energy Generation and Electric Vehicle Charging Load. In: Energynautics GmbH (Ed.), Proc. of the 8th International Workshop on the Integration of Solar Power into Power Systems: . Paper presented at 8th International Workshop on the Integration of Solar Power into Power Systems.
Open this publication in new window or tab >>Modelling City Scale Spatio-temporal Solar Energy Generation and Electric Vehicle Charging Load
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2018 (English)In: Proc. of the 8th International Workshop on the Integration of Solar Power into Power Systems / [ed] Energynautics GmbH, 2018Conference paper, Published paper (Refereed)
Abstract [en]

This study presents a model for estimatingbuilding-applied photovoltaic (PV) energy yield and electric ve- hicle (EV) charging temporally over time and spatially on a city scale. The model enables transient assessment of the synergy between EV and PV, thus is called the EV-PV Synergy Model. Spatio-temporal data on solar irradiance is used in combination with Light Detection and Ranging (LiDAR) data to generate realistic spatio-temporal solar power generation profiles. The spatio-temporal EV charging profiles are produced with a stochastic Markov chain model trained on a large Swedish data set of travel patterns combined with OpenStreetMap (OSM) for deterministically identifying parking spaces in cities. The modelled estimates of solar power generation andEV charging are combined to determine the magnitude and correlation between PV power generation and EV charging over time on city scale for Uppsala, Sweden. Two months (January and July) were simulated to represent Sweden’s climate extremes. The EV penetration level was assumed to be 100% and all the roofs with yearly irradiation higher than 1000 kWh/m2 were assumed to have PV panels. The results showed that, even in January with the lowestsolar power generation and maximum EV load, there can be a positive net-generation (defined as the integration of PV generation minus EV charging load over time) in some locations within the city. Central locations exhibited a positive temporal correlation between EV charging load and PV generation. Negative temporal correlations were observed in the outskirts of the city, where typically night time home-charging was prevalent. In the highest PV power generation month (July) the solar generation was 16 times higher than the EV charging load. Spatially, the net-generation was positive in almost the entire city. However, the time-series correlation between the EV charging load and the PV generation reached more extreme positive and negative values in comparison with January. This was a result of the higher variability in irradiance during July in comparison with January. In summary, we find that there is a favorable synergy of EV-PV technology within the city center with assumptions of workplace charging behaviors for both winter and summer months. An unfavorable synergy with suburban areas where typically nighttime charging behaviors negatively correlate to PV generation. This suggests that distributed PV should be targeted around city center/workplace EV charging stations.

National Category
Energy Engineering
Research subject
Engineering Science
Identifiers
urn:nbn:se:uu:diva-363672 (URN)978-3-9820080-0-4 (ISBN)
Conference
8th International Workshop on the Integration of Solar Power into Power Systems
Available from: 2018-10-19 Created: 2018-10-19 Last updated: 2018-10-19
van der Meer, D., Munkhammar, J. & Widén, J. (2018). Probabilistic clear-sky index forecasts using Gaussian process ensembles. In: : . Paper presented at IEEE PVSC 45.
Open this publication in new window or tab >>Probabilistic clear-sky index forecasts using Gaussian process ensembles
2018 (English)Conference paper, Published paper (Refereed)
Keywords
Gaussian process, Gaussian mixture model, probabilistic forecasting, ensembles
National Category
Engineering and Technology Energy Systems
Identifiers
urn:nbn:se:uu:diva-362972 (URN)
Conference
IEEE PVSC 45
Funder
Swedish Energy Agency
Available from: 2018-10-12 Created: 2018-10-12 Last updated: 2018-10-19
van der Meer, D. W., Shepero, M., Svensson, A., Widén, J. & Munkhammar, J. (2018). Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes. Applied Energy, 213, 195-207
Open this publication in new window or tab >>Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes
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2018 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 213, p. 195-207Article in journal (Refereed) Published
Abstract [en]

This paper presents a study into the utilization of Gaussian Processes (GPs) for probabilistic forecasting of residential electricity consumption, photovoltaic (PV) power generation and net demand of a single household. The covariance function that encodes prior belief on the general shape of the time series plays a vital role in the performance of GPs and a common choice is the squared exponential (SE), although it has been argued that the SE is likely suboptimal for physical processes. Therefore, we thoroughly test various (combinations of) covariance functions. Furthermore, in order bypass the substantial learning and inference time accompanied with GPs, we investigate the potential of dynamically updating the hyperparameters using a moving training window and assess the consequences on predictive accuracy. We show that the dynamic GP produces sharper prediction intervals (PIs) than the static GP with significant lower computational burden, but at the cost of the ability to capture sharp peaks. In addition, we examine the difference in accuracy between a direct and indirect forecasting strategy in case of net demand forecasting and show that the latter is prone to producing wider PIs with higher coverage probability.

National Category
Control Engineering Energy Systems
Identifiers
urn:nbn:se:uu:diva-340597 (URN)10.1016/j.apenergy.2017.12.104 (DOI)000425576900017 ()
Funder
Swedish Energy Agency
Available from: 2018-01-28 Created: 2018-02-01 Last updated: 2018-10-18Bibliographically approved
van der Meer, D., Munkhammar, J. & Widén, J. (2018). Probabilistic forecasting of solar power, electricity consumption and net load: Investigating the effect of seasons, aggregation and penetration on prediction intervals. Solar Energy, 171, 397-413
Open this publication in new window or tab >>Probabilistic forecasting of solar power, electricity consumption and net load: Investigating the effect of seasons, aggregation and penetration on prediction intervals
2018 (English)In: Solar Energy, ISSN 0038-092X, E-ISSN 1471-1257, Vol. 171, p. 397-413Article in journal (Refereed) Published
Abstract [en]

This paper presents a study into the effect of aggregation of customers and an increasing share of photovoltaic (PV) power in the net load on prediction intervals (PIs) of probabilistic forecasting methods applied to dis- tribution grid customers during winter and spring. These seasons are shown to represent challenging cases due to the increased variability of electricity consumption during winter and the increased variability in PV power production during spring. We employ a dynamic Gaussian process (GP) and quantile regression (QR) to produce probabilistic forecasts on data from 300 de-identified customers in the metropolitan area of Sydney, Australia. In case of the dynamic GP, we also optimize the training window width and show that it produces sharp and reliable PIs with a training set of up to 3 weeks. In case of aggregation, the results indicate that the aggregation of a modest number of PV systems improves both the sharpness and the reliability of PIs due to the smoothing effect, and that this positive effect propagates into the net load forecasts, especially for low levels of aggregation. Finally, we show that increasing the share of PV power in the net load actually increases the sharpness and reliability of PIs for aggregations of 30 and 210 customers, most likely due to the added benefit of the smoothing effect.

Keywords
Probabilistic forecasting, Quantile regression, Gaussian process, Solar power, Electric load, Net load
National Category
Environmental Engineering Energy Systems
Research subject
Engineering Science
Identifiers
urn:nbn:se:uu:diva-362870 (URN)10.1016/j.solener.2018.06.103 (DOI)
Funder
Swedish Energy Agency
Available from: 2018-10-11 Created: 2018-10-11 Last updated: 2018-10-18Bibliographically approved
Shepero, M., van der Meer, D., Munkhammar, J. & Widén, J. (2018). Residential probabilistic load forecasting: A method using Gaussian process designed for electric load data. Applied Energy, 218, 159-172
Open this publication in new window or tab >>Residential probabilistic load forecasting: A method using Gaussian process designed for electric load data
2018 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 218, p. 159-172Article in journal (Refereed) Published
Abstract [en]

Probabilistic load forecasting (PLF) is of important value to grid operators, retail companies, demand response aggregators, customers, and electricity market bidders. Gaussian processes (GPs) appear to be one of the promising methods for providing probabilistic forecasts. In this paper, the log-normal process (LP) is newly introduced and compared to the conventional GP. The LP is especially designed for positive data like residential load forecasting—little regard was taken to address this issue previously. In this work, probabilisitic and deterministic error metrics were evaluated for the two methods. In addition, several kernels were compared. Each kernel encodes a different relationship between inputs. The results showed that the LP produced sharper forecasts compared with the conventional GP. Both methods produced comparable results to existing PLF methods in the literature. The LP could achieve as good mean absolute error (MAE), root mean square error (RMSE), prediction interval normalized average width (PINAW) and prediction interval coverage probability (PICP) as 2.4%, 4.5%, 13%, 82%, respectively evaluated on the normalized load data.

Keywords
Gaussian process, Probabilistic load forecasting, Residential load
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Energy Systems
Research subject
Engineering Science with specialization in Science of Electricity
Identifiers
urn:nbn:se:uu:diva-345282 (URN)10.1016/j.apenergy.2018.02.165 (DOI)000430994500014 ()
Funder
Swedish Energy Agency
Available from: 2018-03-08 Created: 2018-03-08 Last updated: 2018-10-17Bibliographically approved
van der Meer, D., Widén, J. & Munkhammar, J. (2018). Review on probabilistic forecasting of photovoltaic power production and electricity consumption. Renewable & sustainable energy reviews, 1484-1512
Open this publication in new window or tab >>Review on probabilistic forecasting of photovoltaic power production and electricity consumption
2018 (English)In: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, p. 1484-1512Article, review/survey (Refereed) Published
Abstract [en]

Accurate forecasting simultaneously becomes more important and more challenging due to the increasing penetration of photovoltaic (PV) systems in the built environment on the one hand, and the increasing stochastic nature of electricity consumption, e.g., through electric vehicles (EVs), on the other hand. Until recently, research has mainly focused on deterministic forecasting. However, such forecasts convey little information about the possible future state of a system and since a forecast is inherently erroneous, it is important to quantify this error. This paper therefore focuses on the recent advances in the area of probabilistic forecasting of solar power (PSPF) and load forecasting (PLF). The goal of a probabilistic forecast is to provide either a complete predictive density of the future state or to predict that the future state of a system will fall in an interval, defined by a confidence level. The aim of this paper is to analyze the state of the art and assess the different approaches in terms of their performance, but also to what extent these approaches can be generalized so that they not only perform best on the data set for which they were designed, but also on other data sets or different case studies. In addition, growing interest in net demand forecasting, i.e., demand less generation, is another important motivation to combine PSPF and PLF into one review paper and assess compatibility. One important finding is that there is no single preferred model that can be applied to any circumstance. In fact, a study has shown that the same model, with adapted parameters, applied to different case studies performed well but did not excel, when compared to models that were optimized for the specific task. Furthermore, there is need for standardization, in particular in terms of filtering night time data, normalizing results and performance metrics. 

National Category
Energy Systems
Identifiers
urn:nbn:se:uu:diva-332709 (URN)10.1016/j.rser.2017.05.212 (DOI)000417070500106 ()
Funder
Swedish Energy Agency
Available from: 2017-10-31 Created: 2017-10-31 Last updated: 2018-10-18Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4887-9547

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