The Role of Gas to Power in Supporting Large-scale Renewable Energy Integration in Morocco: Insights from Optimization through Long-term Bottom-up Modelling

Jabrane Slimani, Abdeslam Kadrani, Imad El Harraki, El Hadj Ezzahid

2021

Abstract

To strengthen its energy sector, Morocco adopted at the end of 2009 an energy strategy based mainly on the increased penetration of renewable sources, the improvement of energy efficiency, and the reinforcement of regional integration. Morocco's energy strategy called for increasing the share of renewable electricity to 42% of installed capacity by 2020 and over 52% of installed renewable capacity by 2030. However, as renewable energy becomes more widespread, the national power system will face new challenges due to its intermittent nature. It is, therefore, necessary to deploy flexible resources to cope with this intermittency and improve the stability of the power system. In this work, we use a bottom-up linear optimization model to identify the optimal options for gas to power technology development in Morocco. We find that developing new gas-to-power facilities fuelled by importing liquefied natural gas (LNG) is not the optimal solution. Instead, it would be interesting for Morocco to negotiate its natural gas supply via the GME with one of its neighbours, Spain or Algeria.

Paper Citation

in Harvard Style

Slimani J., Kadrani A., El Harraki I. and Ezzahid E. (2021). The Role of Gas to Power in Supporting Large-scale Renewable Energy Integration in Morocco: Insights from Optimization through Long-term Bottom-up Modelling. In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML, ISBN 978-989-758-559-3, pages 327-332. DOI: 10.5220/0010733600003101

in Bibtex Style

@conference{bml21,
author={Jabrane Slimani and Abdeslam Kadrani and Imad El Harraki and El Hadj Ezzahid},
title={The Role of Gas to Power in Supporting Large-scale Renewable Energy Integration in Morocco: Insights from Optimization through Long-term Bottom-up Modelling},
booktitle={Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,},
year={2021},
pages={327-332},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010733600003101},
isbn={978-989-758-559-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,
TI - The Role of Gas to Power in Supporting Large-scale Renewable Energy Integration in Morocco: Insights from Optimization through Long-term Bottom-up Modelling
SN - 978-989-758-559-3
AU - Slimani J.
AU - Kadrani A.
AU - El Harraki I.
AU - Ezzahid E.
PY - 2021
SP - 327
EP - 332
DO - 10.5220/0010733600003101