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Probabilistic forecasting of solar power, electricity consumption and net load: Investigating the effect of seasons, aggregation and penetration on prediction intervals
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (BEESG)ORCID iD: 0000-0002-9473-4536
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics.ORCID iD: 0000-0003-0051-4098
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics.ORCID iD: 0000-0003-4887-9547
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.

Place, publisher, year, edition, pages
2018. Vol. 171, p. 397-413
Keywords [en]
Probabilistic forecasting, Quantile regression, Gaussian process, Solar power, Electric load, Net load
National Category
Environmental Engineering Energy Systems
Research subject
Engineering Science
Identifiers
URN: urn:nbn:se:uu:diva-362870DOI: 10.1016/j.solener.2018.06.103ISI: 000447113000042OAI: oai:DiVA.org:uu-362870DiVA, id: diva2:1255076
Funder
Swedish Energy AgencyAvailable from: 2018-10-11 Created: 2018-10-11 Last updated: 2018-12-10Bibliographically approved
In thesis
1. Spatio-temporal probabilistic forecasting of solar power, electricity consumption and net load
Open this publication in new window or tab >>Spatio-temporal probabilistic forecasting of solar power, electricity consumption and net load
2018 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The increasing penetration of renewable energy sources into the electricity generating mix poses challenges to the operational performance of the power system. Similarly, the push for energy efficiency and demand response—i.e., when electricity consumers are encouraged to alter their demand depending by means of a price signal—introduces variability on the consumption side as well.

Forecasting is generally viewed as a cost-efficient method to mitigate the adverse effects of the aforementioned energy transition because it enables a grid operator to reduce the operational risk by, e.g., unit-commitment or curtailment. However, deterministic—or point—forecasting is currently still the norm.

This thesis focuses on probabilistic forecasting, a method with which the uncertainty ac- companying the forecast is expressed by means of a probability distribution. In this framework, the thesis contributes to the current state-of-the-art by investigating properties of probabilistic forecasts of PV power production, electricity consumption and net load at the residential and distribution level of the electricity grid.

The thesis starts with an introduction to probabilistic forecasting in general and two models in specific: Gaussian processes and quantile regression. The former model has been used to produce probabilistic forecasts of PV power production, electricity consumption and net load of individual residential buildings—particularly challenging due to the stochasticity involved— but important for home energy management systems and potential peer-to-peer energy trading. Furthermore, both models have been utilized to investigate what effects spatial aggregation and increasing penetration have on the predictive distribution. The results indicated that only 20- 25 customers—out of a data set containing 300 customers—need to be aggregated in order to improve the reliability of the probabilistic forecasts. Finally, this thesis explores the potential of Gaussian process ensembles, which is an effective way to improve the accuracy of the forecasts.

Place, publisher, year, edition, pages
Uppsala: Institutionen för teknikvetenskaper, 2018. p. 61
National Category
Engineering and Technology
Identifiers
urn:nbn:se:uu:diva-363448 (URN)
Presentation
2018-11-16, 2005, Lägerhyddsvägen 1, Uppsala, 13:15 (English)
Opponent
Supervisors
Funder
Swedish Energy Agency
Available from: 2018-10-18 Created: 2018-10-18 Last updated: 2018-11-06

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Available from 2020-07-03 00:00

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van der Meer, DennisMunkhammar, JoakimWidén, Joakim

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