Short-term solar and wind variability in long-term energy system models - A European case study

• Published in: Energy (Oxford) Vol. 209; p. 118377
• Main Authors: Ringkjøb, Hans-Kristian, Haugan, Peter M., Seljom, Pernille, Lind, Arne, Wagner, Fabian, Mesfun, Sennai
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15-10-2020; Elsevier BV
• Subjects: Deterministic modeling; Deterministic models; Electricity generation; Energy; Energy modelling; Energy sources; Energy system model; Flexibility; Renewable energy resources; Renewable energy sources; Solar power generation; Stochastic approach; Stochastic modelling; Stochastic models; Stochastic solution; Stochastic systems; Stochasticity; Temporal resolution; TIMES energy-Models; Variability; Variable renewable energies; Variable renewable energy; Wind; Energy modelling; Variable renewable energy; Stochastic modelling; TIMES energy-Models
• ISSN: 0360-5442; 1873-6785; 1873-6785
• DOI: 10.1016/j.energy.2020.118377

Integration of variable renewables such as solar and wind has grown at an unprecedented pace in Europe over the past two decades. As the share of solar and wind rises, it becomes increasingly important for long-term energy system models to adequately represent their short-term variability. This paper uses a long-term TIMES model of the European power and district heat sectors towards 2050 to explore how stochastic modelling of short-term solar and wind variability as well as different temporal resolutions influence the model performance. Using a stochastic model with 48 time-slices as benchmark, the results show that deterministic models with low temporal resolution give a 15–20% underestimation of annual costs, an overestimation of the contribution of variable renewables (13–15% of total electricity generation) and a lack of system flexibility. The results of the deterministic models converge towards the stochastic solution when the temporal resolution is increased, but even with 2016 time-slices, the need for flexibility is underestimated. In addition, the deterministic model with 2016 time-slices takes 30 times longer to solve than the stochastic model with 48 time-slices. Based on these findings, a stochastic approach is recommended for long-term studies of energy systems with large shares of variable renewable energy sources.
•Assessment of short-term solar and wind variability in long-term energy models.•Investigation of the impact of temporal resolution on model performance.•Comparison of deterministic and stochastic modelling approaches.•Coarse deterministic models underestimate costs, emissions and flexibility needs.•Stochastic model preferred over deterministic models with much higher resolution.