Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Day-ahead Market Modelling of Large-scale Highly-renewable Multi-energy Systems: Analysis of the North Sea Region Towards 2050

Version 1 : Received: 9 November 2020 / Approved: 10 November 2020 / Online: 10 November 2020 (10:34:00 CET)

How to cite: Gea Bermúdez, J.; Das, K.; Koduvere, H.; Koivisto, M.J. Day-ahead Market Modelling of Large-scale Highly-renewable Multi-energy Systems: Analysis of the North Sea Region Towards 2050. Preprints 2020, 2020110300 (doi: 10.20944/preprints202011.0300.v1). Gea Bermúdez, J.; Das, K.; Koduvere, H.; Koivisto, M.J. Day-ahead Market Modelling of Large-scale Highly-renewable Multi-energy Systems: Analysis of the North Sea Region Towards 2050. Preprints 2020, 2020110300 (doi: 10.20944/preprints202011.0300.v1).

Abstract

This paper proposes a mathematical model to simulate Day-ahead markets of large-scale multi-energy systems with high share of renewable energy. Furthermore, it analyses the importance of including unit commitment when performing such analysis. The results of the case study, which is performed for the North Sea region, show the influence of massive renewable penetration in the energy sector and increasing electrification of the district heating sector towards 2050, and how this impacts the role of other energy sources such as thermal and hydro. The penetration of wind and solar is likely to challenge the need for balancing in the system as well as the profitability of thermal units. The degree of influence of the unit commitment approach is found to be dependent on the configuration of the energy system. Overall, including unit commitment constraints with integer variables leads to more realistic behaviour of the units, at the cost of increasing considerably the computational time. Relaxing integer variables reduces significantly the computational time, without highly compromising the accuracy of the results. The proposed model, together with the insights from the study case, can be specially useful for system operators for optimal operational planning.

Subject Areas

Energy System; Large Scale; Day Ahead Market; Operational Planning; Unit Commitment

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