Abstract
Due to its insular condition, Gran Canaria operates an isolated energy system that requires high self-sufficiency in energy generation and that covers its energy need, 3,327,872.76 MWh/year. The future integration of the Chira-Soria Pumped Hydroelectric Power Plant, scheduled for 2030, is expected to radically transform the energy dynamics of Gran Canaria's electricity system, facing different challenges and opportunities. Challenges include the need for greater flexibility due to growing renewable energy sources, going from 381,000 MW of renewables today to an increase of more than 750,000 MW, environmental commitments, and the operation must address potential operational constraints related to downstream hydrological effects. On the other hand, the opportunities lie in the ability of the reversible hydropower plant to provide balancing services during off-peak hours, improving the integration of intermittent energy sources such as wind and solar power, in addition, the Pumped Hydroelectric (PHES) technology is recognized as mature and efficient for large-scale energy storage, contributing significantly to the integration of renewable energy sources. Implementing innovative approaches such as integrating Big Data into construction projects can also improve efficiency and decision-making in the project delivery process. This facility will facilitate energy storage through the pumping of water at high levels, allowing it to be subsequently turbined in periods of high demand, which will be essential to improve the management and efficiency of the island's energy system. This energy demand will be studied, which follows certain patterns according to the day of the week and, continuing with the line of research established in previous works, a detailed analysis of the existing system, simulation and algorithmic optimization of the integration of the Chira-Soria Pumped Hydroelectric Power Plant into the energy system of Gran Canaria in the year 2023 will be carried out, providing the expected results of such optimal integration by differentiating the demand patterns of each day of the week, previously establishing these annual representative days.
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