Abstract
The integration of more variable renewable energy sources (vRES) like wind and solar photovoltaics (PV) is expected to play a significant role in reducing carbon dioxide emissions from the power sector. However, unlike conventional thermal generators, the generation patterns of vRES are spatially dependent, and the spatial distributions of wind and PV capacity can help or hinder their integration into the power system. After reviewing existing approaches for spatially distributing vRES, we present a new method to optimise the mix and spatial distribution of wind and PV capacity in Europe based on minimising residual demand. We test the potential of this method by modelling several scenarios exploring the effects of vRES penetration, alternative demand profiles, access to wind sites located far offshore, and alternative PV configurations. Assuming a copper-plate Europe without storage, we find an optimum vRES penetration rate of 82% from minimising residual demand, with an optimum capacity mix of 74% wind and 26% PV. We find that expanding offshore wind capacity in the North Sea is a ‘no regret’ option, though correlated generation patterns with onshore wind farms in neighbouring countries at high vRES penetration rates may lead to significant surplus generation. The presented method can be used to build detailed vRES spatial distributions and generation profiles for power system modelling studies, incorporating different optimisation objectives, spatial and technological constraints. However, even under the ideal case of a copper-plate Europe, we find that neither peak residual demand nor total residual demand can be significantly reduced through the spatial optimisation of vRES.
| Original language | English |
|---|---|
| Pages (from-to) | 1192-1216 |
| Number of pages | 25 |
| Journal | Renewable and Sustainable Energy Reviews |
| Volume | 94 |
| DOIs | |
| Publication status | Published - 2018 |
Funding
The authors would like to thank Gerard Sleijpen from the Mathematical Institute at Utrecht University for his expertise, and the JRC for providing access to their EV charging profile database. The CDDA data used in this paper included copyrighted data from the Estonian Environmental Register (02/03/2015) and Finnish Environment Institute (2015). Load data was provided by ENTSO-E. The UK building footprint data contained OS data © Crown copyright and database right 2016. This research did not receive any funding from agencies in the public, commercial, or not-for-profit sectors. Appendix A
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Optimisation
- Power system
- Solar photovoltaic
- Spatial distribution
- Variable renewable energy
- Wind power
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