Low-dimensional scenario generation method of solar and wind availability for representative days in energy modeling. (15th January 2022)

• Record Type: Journal Article
• Authors: Densing, Martin; Wan, Yi
• Is Part Of: Applied energy. Volume 306:Part B(2022)
• Publication Date: 2022-01-15
• Subjects: Scenario generation -- Solar and wind variability -- Representative days -- Electricity market modeling; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.118075
• Languages: English
• ISSNs: 0306-2619

Abstract

Abstract: We present a scenario generation method for representative days of wind and solar power availability for use in energy-system models. The method uses principal component analysis (PCA) such that the correlations between solar and wind can be captured. PCA is applied to daily time series of hourly profiles of regional solar and wind power availability to yield low-dimensional scenarios, which can be used in regional energy system or energy market models that represent the year with a limited set of representative days. Subsequently, the scenarios generated with PCA are used as building blocks for daily multi-regional scenarios under different assumption on dependence, which can include extreme joint events. As an application, the impact of variability of intermittent renewables with a – numerically tractable – low number of scenarios is applied in an electricity market model, where the increase in resulting price variation caused by solar and wind variability is investigated. Strengths and limits of the approach are also shown in terms of dimensional extensions and by comparison with hierarchical clustering. – The documented software code of the statistical analysis is freely available. Highlights: Correlation of wind and solar are captured in representative days by PCA. Principal components of wind and solar are interpretable weather events. Cross-regional correlation structures capture extreme joint events. Cross-regional correlation structure given by copula of t-distribution. Application of method in electricity system model yielding power price impacts.