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Optimization of All-Renewable Generation Mix According to Different Demand Response Scenarios to Cover All the Electricity Demand Forecast by 2040: The Case of the Grand Canary Island

Author

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  • Carlos Vargas-Salgado

    (Instituto Universitario de Investigación en Ingeniería Energética, Universitat Politècnica de València (UPV), 46022 Valencia, Spain)

  • César Berna-Escriche

    (Instituto Universitario de Investigación en Ingeniería Energética, Universitat Politècnica de València (UPV), 46022 Valencia, Spain)

  • Alberto Escrivá-Castells

    (Instituto Universitario de Investigación en Ingeniería Energética, Universitat Politècnica de València (UPV), 46022 Valencia, Spain)

  • Dácil Díaz-Bello

    (Instituto Universitario de Investigación en Ingeniería Energética, Universitat Politècnica de València (UPV), 46022 Valencia, Spain)

Abstract

The decarbonization of the electric generation system is fundamental to reaching the desired scenario of zero greenhouse gas emissions. For this purpose, this study describes the combined utilization of renewable sources (PV and wind), which are mature and cost-effective renewable technologies. Storage technologies are also considered (pumping storage and mega-batteries) to manage the variability in the generation inherent to renewable sources. This work also analyzes the combined use of renewable energies with storage systems for a total electrification scenario of Grand Canary Island (Spain). After analyzing the natural site’s resource constraints and focusing on having a techno-economically feasible, zero-emission, and low-waste renewable generation mix, six scenarios for 2040 are considered combining demand response and business as usual. The most optimal solution is the scenario with the maximum demand response, consisting of 3700 MW of PV, around 700 MW of off-shore wind system, 607 MW of pump storage, and 2300 MW of EV batteries capacity. The initial investment would be EUR 8065 million, and the LCOE close to EUR 0.11/kWh, making the total NPC EUR 13,655 million. The payback is 12.4 years, and the internal rate of return is 6.39%.

Suggested Citation

  • Carlos Vargas-Salgado & César Berna-Escriche & Alberto Escrivá-Castells & Dácil Díaz-Bello, 2022. "Optimization of All-Renewable Generation Mix According to Different Demand Response Scenarios to Cover All the Electricity Demand Forecast by 2040: The Case of the Grand Canary Island," Sustainability, MDPI, vol. 14(3), pages 1-29, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1738-:d:741039
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    References listed on IDEAS

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    Cited by:

    1. Cruz-Pérez, Noelia & Santamarta, Juan C. & Rodríguez-Martín, Jesica & Beltrán, Rubén Fuentes & García-Gil, Alejandro, 2023. "Photovoltaic potential of public buildings in a world Heritage city: The case of San Cristóbal de La Laguna (Canary Islands, Spain)," Renewable Energy, Elsevier, vol. 209(C), pages 357-364.
    2. César Berna-Escriche & Carlos Vargas-Salgado & David Alfonso-Solar & Alberto Escrivá-Castells, 2022. "Hydrogen Production from Surplus Electricity Generated by an Autonomous Renewable System: Scenario 2040 on Grand Canary Island, Spain," Sustainability, MDPI, vol. 14(19), pages 1-29, September.
    3. Wenich Vattanapuripakorn & Sathapon Sonsupap & Khomson Khannam & Natthakrit Bamrungwong & Prachakon Kaewkhiaw & Jiradanai Sarasamkan & Bopit Bubphachot, 2022. "Advanced Electric Battery Power Storage for Motors through the Use of Differential Gears and High Torque for Recirculating Power Generation," Clean Technol., MDPI, vol. 4(4), pages 1-14, October.
    4. Przemysław Ogarek & Michał Wojtoń & Daniel Słyś, 2023. "Hydrogen as a Renewable Energy Carrier in a Hybrid Configuration of Distributed Energy Systems: Bibliometric Mapping of Current Knowledge and Strategies," Energies, MDPI, vol. 16(14), pages 1-18, July.
    5. Berna-Escriche, César & Rivera, Yago & Alvarez-Piñeiro, Lucas & Muñoz-Cobo, José Luis, 2024. "Best estimate plus uncertainty methodology for forecasting electrical balances in isolated grids: The decarbonized Canary Islands by 2040," Energy, Elsevier, vol. 294(C).
    6. Marinko Barukčić & Toni Varga & Tin Benšić & Vedrana Jerković Štil, 2022. "Optimal Allocation of Renewable Energy Sources and Battery Storage Systems Considering Energy Management System Optimization Based on Fuzzy Inference," Energies, MDPI, vol. 15(19), pages 1-17, September.

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