Uncertainties towards a fossil-free system with high integration of wind energy in long-term planning

• Published in: Applied energy Vol. 253; p. 113528
• Main Authors: Pizarro-Alonso, Amalia, Ravn, Hans, Münster, Marie
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2019
• Subjects: Bottom-up optimization; Energy systems modelling; Fluctuating renewable energy; Global sensitivity analysis; Long-term planning; Uncertainty; Global sensitivity analysis; Uncertainty; Energy systems modelling; Fluctuating renewable energy; Long-term planning; Bottom-up optimization
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2019.113528

•Energy systems analysis must cover the whole multi-dimensional uncertainty space.•Local sensitivity analyses may not identify influential parameters in planning.•Offshore wind technological learning would promote lower electricity prices.•Offshore wind upscaling would depend on the efficient integration of large shares.•District heating markets would depend on heat pumps and electricity markets. There is a large amount of parametric uncertainties that might affect long-term energy planning, due to the inherent variability connected to the future. Most of these uncertainties are stochastic, i.e. they cannot be reduced, but can be better characterized. In an attempt to address this issue, studies often explore different alternative scenarios or perform local sensitivity analyses. While acknowledging their importance, it is evident that their traditional scope must be rethought, as those methods cannot consider interactions among parameters and hence might omit parameters that are highly influential. This study aims to explore the whole uncertainty range in order to identify the most critical parameters towards fossil-free energy systems with high integration of wind-based electricity. Denmark is used as a case study of a country with large wind resources, which are increasingly exploited. It pursues three steps: (1) selection of parameters and characterization of their uncertainties, (2) global sensitivity analyses through Morris sampling, and (3) uncertainty propagation and Monte Carlo runs using Latin Hypercube sampling. Offshore wind upscaling will depend on technological improvements related to capital costs or efficiencies as well as on the system integration constraints. Hence, increasing deployments of offshore wind would require policies that foster technological learning, while promoting the cost-efficient integration of an increase in participation in the power mix, such as grid transmission expansion. Therefore, methods that deal with the whole uncertainty space should, to a larger extent, be implemented when uncertainties are assessed in association with long-term planning of systems with high integration of fluctuating renewable energy.