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Optimal expansion for a clean power sector transition in Mexico based on predicted electricity demand using deep learning scheme

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  • Serrano-Arévalo, Tania Itzel
  • López-Flores, Francisco Javier
  • Raya-Tapia, Alma Yunuen
  • Ramírez-Márquez, César
  • Ponce-Ortega, José María

Abstract

This study presents a mathematical programming approach for the strategic planning of electrical energy production. The proposed approach seeks the optimal selection of conventional and clean technologies and their production capacity in a given period. In addition, it aims to assess the amount of emissions generated and water consumption, without neglecting economic aspects. The planning considered in this study extends from 2020 to 2040, where the approach considers the implementation of deep learning models to forecast the future electricity demand of each year through historical data; thus obtaining a more precise energy configuration. Fossil fuels and biofuels are used as primary energy in the operation of different technologies. Therefore, both economic and environmental objectives are considered. The economic objective function determines the minimum total discounted cost, which encompasses energy production, operation, maintenance, primary energy, and investment costs. While the environmental objective focuses on reducing water consumption and emissions. Through Pareto curves, using the ɛ-constraint method, the proposed model determines the trade-offs between the total discounted cost and the CO2eq emissions generated. The Mexican electrical system is presented as a specific case study with real production data and available resources. The results show that it is possible to reduce the total discounted cost by up to 11.02%, while in environmental aspects, up to 28.27% in emissions and up to 20.23% in water consumption, achieving that the power produced by renewable energies increases progressively until reaching 70% of the energy demand in 2040, where the system is committed to implementing clean technologies and replaces a percentage of fossil fuels with biofuels in conventional technologies. The results highlight that by increasing the investment cost with clean technologies, the system is balanced with a lower total future cost in the planning presented, thus reducing the environmental burden, and determining trade-offs between cost and environmental aspects.

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  • Serrano-Arévalo, Tania Itzel & López-Flores, Francisco Javier & Raya-Tapia, Alma Yunuen & Ramírez-Márquez, César & Ponce-Ortega, José María, 2023. "Optimal expansion for a clean power sector transition in Mexico based on predicted electricity demand using deep learning scheme," Applied Energy, Elsevier, vol. 348(C).
    • Handle: RePEc:eee:appene:v:348:y:2023:i:c:s0306261923009613
    DOI: 10.1016/j.apenergy.2023.121597
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    References listed on IDEAS

    as
    1. Frate, Guido Francesco & Ferrari, Lorenzo & Desideri, Umberto, 2021. "Energy storage for grid-scale applications: Technology review and economic feasibility analysis," Renewable Energy, Elsevier, vol. 163(C), pages 1754-1772.
    2. Mobarak Abumohsen & Amani Yousef Owda & Majdi Owda, 2023. "Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms," Energies, MDPI, vol. 16(5), pages 1-31, February.
    3. Sareen, Siddharth & Haarstad, Håvard, 2018. "Bridging socio-technical and justice aspects of sustainable energy transitions," Applied Energy, Elsevier, vol. 228(C), pages 624-632.
    4. Wang, Lu & Wei, Yi-Ming & Brown, Marilyn A., 2017. "Global transition to low-carbon electricity: A bibliometric analysis," Applied Energy, Elsevier, vol. 205(C), pages 57-68.
    5. Meha, Drilon & Pfeifer, Antun & Sahiti, Naser & Rolph Schneider, Daniel & Duić, Neven, 2021. "Sustainable transition pathways with high penetration of variable renewable energy in the coal-based energy systems," Applied Energy, Elsevier, vol. 304(C).
    6. Feng, Zhong-kai & Niu, Wen-jing & Wang, Wen-chuan & Zhou, Jian-zhong & Cheng, Chun-tian, 2019. "A mixed integer linear programming model for unit commitment of thermal plants with peak shaving operation aspect in regional power grid lack of flexible hydropower energy," Energy, Elsevier, vol. 175(C), pages 618-629.
    7. Oscar Trull & Juan Carlos García-Díaz & Alicia Troncoso, 2020. "Initialization Methods for Multiple Seasonal Holt–Winters Forecasting Models," Mathematics, MDPI, vol. 8(2), pages 1-16, February.
    8. Limpens, Gauthier & Moret, Stefano & Jeanmart, Hervé & Maréchal, Francois, 2019. "EnergyScope TD: A novel open-source model for regional energy systems," Applied Energy, Elsevier, vol. 255(C).
    9. Ikeda, Shintaro & Ooka, Ryozo, 2015. "Metaheuristic optimization methods for a comprehensive operating schedule of battery, thermal energy storage, and heat source in a building energy system," Applied Energy, Elsevier, vol. 151(C), pages 192-205.
    10. Himeur, Yassine & Ghanem, Khalida & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2021. "Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives," Applied Energy, Elsevier, vol. 287(C).
    11. Li, Chuang & Li, Guojie & Wang, Keyou & Han, Bei, 2022. "A multi-energy load forecasting method based on parallel architecture CNN-GRU and transfer learning for data deficient integrated energy systems," Energy, Elsevier, vol. 259(C).
    12. Liu, Jia & Zhou, Yuekuan & Yang, Hongxing & Wu, Huijun, 2022. "Uncertainty energy planning of net-zero energy communities with peer-to-peer energy trading and green vehicle storage considering climate changes by 2050 with machine learning methods," Applied Energy, Elsevier, vol. 321(C).
    13. Wang, Jian Qi & Du, Yu & Wang, Jing, 2020. "LSTM based long-term energy consumption prediction with periodicity," Energy, Elsevier, vol. 197(C).
    14. Gondia Sokhna Seck & Vincent Krakowski & Edi Assoumou & Nadia Maïzi & Vincent Mazauric, 2020. "Embedding power system's reliability within a long-term Energy System Optimization Model: Linking high renewable energy integration and future grid stability for France by 2050," Post-Print hal-02418375, HAL.
    15. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
    16. Simsek, Yeliz & Sahin, Hasret & Lorca, Álvaro & Santika, Wayan G. & Urmee, Tania & Escobar, Rodrigo, 2020. "Comparison of energy scenario alternatives for Chile: Towards low-carbon energy transition by 2030," Energy, Elsevier, vol. 206(C).
    17. Cerdeira Bento, João Paulo & Moutinho, Victor, 2016. "CO2 emissions, non-renewable and renewable electricity production, economic growth, and international trade in Italy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 142-155.
    18. Fan, Guo-Feng & Yu, Meng & Dong, Song-Qiao & Yeh, Yi-Hsuan & Hong, Wei-Chiang, 2021. "Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling," Utilities Policy, Elsevier, vol. 73(C).
    19. Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    20. Rial A. Rajagukguk & Raden A. A. Ramadhan & Hyun-Jin Lee, 2020. "A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power," Energies, MDPI, vol. 13(24), pages 1-23, December.
    21. Zhang, Zhihui & Jing, Rui & Lin, Jian & Wang, Xiaonan & van Dam, Koen H. & Wang, Meng & Meng, Chao & Xie, Shan & Zhao, Yingru, 2020. "Combining agent-based residential demand modeling with design optimization for integrated energy systems planning and operation," Applied Energy, Elsevier, vol. 263(C).
    22. Ahn, Hyeunguk & Rim, Donghyun & Pavlak, Gregory S. & Freihaut, James D., 2019. "Uncertainty analysis of energy and economic performances of hybrid solar photovoltaic and combined cooling, heating, and power (CCHP + PV) systems using a Monte-Carlo method," Applied Energy, Elsevier, vol. 255(C).
    23. Florinda Martins & Carlos Felgueiras & Miroslava Smitkova & Nídia Caetano, 2019. "Analysis of Fossil Fuel Energy Consumption and Environmental Impacts in European Countries," Energies, MDPI, vol. 12(6), pages 1-11, March.
    24. Seck, Gondia Sokhna & Krakowski, Vincent & Assoumou, Edi & Maïzi, Nadia & Mazauric, Vincent, 2020. "Embedding power system’s reliability within a long-term Energy System Optimization Model: Linking high renewable energy integration and future grid stability for France by 2050," Applied Energy, Elsevier, vol. 257(C).
    25. Lei, Yang & Wang, Dan & Jia, Hongjie & Li, Jiaxi & Chen, Jingcheng & Li, Jingru & Yang, Zhihong, 2021. "Multi-stage stochastic planning of regional integrated energy system based on scenario tree path optimization under long-term multiple uncertainties," Applied Energy, Elsevier, vol. 300(C).
    26. Fuentes-Cortés, Luis Fabián & Ma, Yan & Ponce-Ortega, Jose María & Ruiz-Mercado, Gerardo & Zavala, Victor M., 2018. "Valuation of water and emissions in energy systems," Applied Energy, Elsevier, vol. 210(C), pages 518-528.
    27. Lee, Hwarang & Lee, Jeongeun & Koo, Yoonmo, 2022. "Economic impacts of carbon capture and storage on the steel industry–A hybrid energy system model incorporating technological change," Applied Energy, Elsevier, vol. 317(C).
    28. Lara, Yolanda & Petrakopoulou, Fontina & Morosuk, Tatiana & Boyano, Alicia & Tsatsaronis, George, 2017. "An exergy-based study on the relationship between costs and environmental impacts in power plants," Energy, Elsevier, vol. 138(C), pages 920-928.
    29. Bajwa, Dilpreet S. & Peterson, Tyler & Sharma, Neeta & Shojaeiarani, Jamileh & Bajwa, Sreekala G., 2018. "A review of densified solid biomass for energy production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 296-305.
    30. Greg Muttitt & Sivan Kartha, 2020. "Equity, climate justice and fossil fuel extraction: principles for a managed phase out," Climate Policy, Taylor & Francis Journals, vol. 20(8), pages 1024-1042, September.
    31. Ram, Manish & Gulagi, Ashish & Aghahosseini, Arman & Bogdanov, Dmitrii & Breyer, Christian, 2022. "Energy transition in megacities towards 100% renewable energy: A case for Delhi," Renewable Energy, Elsevier, vol. 195(C), pages 578-589.
    32. Ahmad, Najid & Du, Liangsheng, 2017. "Effects of energy production and CO2 emissions on economic growth in Iran: ARDL approach," Energy, Elsevier, vol. 123(C), pages 521-537.
    33. Horak, Daniel & Hainoun, Ali & Neugebauer, Georg & Stoeglehner, Gernot, 2022. "A review of spatio-temporal urban energy system modeling for urban decarbonization strategy formulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    34. Bompard, Ettore & Ciocia, Alessandro & Grosso, Daniele & Huang, Tao & Spertino, Filippo & Jafari, Mehdi & Botterud, Audun, 2022. "Assessing the role of fluctuating renewables in energy transition: Methodologies and tools," Applied Energy, Elsevier, vol. 314(C).
    35. Neofytou, H. & Nikas, A. & Doukas, H., 2020. "Sustainable energy transition readiness: A multicriteria assessment index," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    36. Bedi, Jatin & Toshniwal, Durga, 2019. "Deep learning framework to forecast electricity demand," Applied Energy, Elsevier, vol. 238(C), pages 1312-1326.
    37. Sanya Carley & David M. Konisky, 2020. "The justice and equity implications of the clean energy transition," Nature Energy, Nature, vol. 5(8), pages 569-577, August.
    38. Mavromatidis, Georgios & Petkov, Ivalin, 2021. "MANGO: A novel optimization model for the long-term, multi-stage planning of decentralized multi-energy systems," Applied Energy, Elsevier, vol. 288(C).
    39. Thompson, James R. & Frezza, Damon & Necioglu, Burhan & Cohen, Michael L. & Hoffman, Kenneth & Rosfjord, Kristine, 2019. "Interdependent Critical Infrastructure Model (ICIM): An agent-based model of power and water infrastructure," International Journal of Critical Infrastructure Protection, Elsevier, vol. 24(C), pages 144-165.
    40. Joos, Michael & Staffell, Iain, 2018. "Short-term integration costs of variable renewable energy: Wind curtailment and balancing in Britain and Germany," Renewable and Sustainable Energy Reviews, Elsevier, vol. 86(C), pages 45-65.
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