Chance constrained programming using non-Gaussian joint distribution function in design of standalone hybrid renewable energy systems
- Submitting institution
-
University of Northumbria at Newcastle
- Unit of assessment
- 12 - Engineering
- Output identifier
- 25210686
- Type
- D - Journal article
- DOI
-
10.1016/j.energy.2014.01.027
- Title of journal
- Energy
- Article number
- -
- First page
- 677
- Volume
- 66
- Issue
- -
- ISSN
- 0360-5442
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This research proved that the common approach to consider uncertainties of renewable resources based on the assumption that these to follow a Gaussian distribution does not result in an optimum solution for sizing of hybrid renewable energy system components. This research co-funded by Synchron Technology Ltd, proposed a new method to determine the optimal size. Led to EU Interreg SEEV4-City grant (WP5) with Putrus as PI (€5M, http://www.northsearegion.eu/seev4-city/). Led to keynote speech at IEEE Conference on Applied Electrical Engineering and Computing Technologies (Amman, Jordan 2017) and invited talk at 15th World Renewable Energy Congress (Jakarta, Indonesia 2016).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
