Browsing by Author "Lorca Galvez, Álvaro Hugo"

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    Adaptation to Climate Change in Basins within the Context of the Water-Energy-Food Nexus
    (2023) Jander Palma, Vicente Gabriel; Vicuña Díaz, Sebastián; Melo Contreras, Óscar Alfredo; Lorca Galvez, Álvaro Hugo
    This study uses a water-food-energy nexus model, which connects water-based productive activities and allocation policies, to simulate water resources management strategies for adapting the Maule Basin in Chile to climate change impacts. Two strategies are considered: introducing linear hedging rules to a reservoir and establishing adaptative water rights linked to irrigation efficiency of crops in the area. The simulations are run over 14 different climatic scenarios that project different water availability conditions. A multiobjective optimization of environmental, agricultural, and energy outcome indicators is conducted to generate noninferior portfolios that combine the strategies proposed. Simulation results, though limited to a deterministic approach and pessimistic climate scenarios, show that 79 out of the 84 combinations of objective-scenarios increase their performance because of the strategies, with notable increases in agricultural resilience (64%), outflow (92%), and total benefits (26%). We find considerable trade-offs between the food/energy production and water outflow, alongside agricultural vulnerability and resilience.
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    An Efficient Forecasting-Optimization Scheme for the Intraday Unit Commitment Process Under Significant Wind and Solar Power
    (2018) Cordova, Samuel; Rudnick, Hugh; Lorca Galvez, Álvaro Hugo; Martinez Aranza, Victor Julio
    Due to their uncertain and variable nature, the large-scale integration of wind and solar power poses significant challenges to the generator scheduling process in power systems. To support this process, system operators require using repeatedly updated forecasts of the best possible quality for renewable power. Motivated by this, the present work aims to study the benefits of incorporating spatiotemporal dependence and seasonalities into probabilistic forecasts for the intraday unit commitment (UC) process. With this purpose, a highly efficient forecasting-optimization scheme is proposed, which is composed of a detrended periodic vector autoregressive model and a technology-clustered interval UC model. The proposed approach is tested on a 120-GW power system with 210 conventional generators using real wind and solar measurements and compared to existing deterministic and stochastic UC techniques alongside standard forecasting methods. Extensive computational experiments show that the incorporation of spatiotemporal dependence and seasonalities into forecasts translates in a reduction of up to 1.55% in operational costs for a daily UC relative to standard practice, the application of intraday instead of daily UC runs further reduces operational costs in up to 1.51%, and the proposed forecasting-optimization scheme takes less than 10 h to simulate a whole year.