| dc.description.abstract | The sustainable development of the entire world is confronting considerable challenges due to the tremendous expansion in energy demand that synchronizes with fresh water scarcity, vast depletion of conventional energy sources and climate change. Consequently, the necessity has emerged for creating suitable management strategies for existing water resources (e.g., wastewater treatment) and for integrating traditional energy sources with renewables (e.g., solar energy, wind energy, biofuels, etc.). The objective of this study is to develop a novel design framework of the water-energy nexus system, which optimized according to economic and environmental metrics using certain parameters (leading to deterministic optimization) and uncertain parameters (leading to stochastic optimization). The system comprises multiple energy sources, cogeneration process, and desalination technologies. Solar energy is incorporated to provide thermal power directly to a multi-effect distillation plant (MED) exclusively (to be more feasible economically), or to the entire system through a steam generator. Thus, MED is driven by direct solar energy, indirect solar energy (thermal energy storage), and surplus heat from the cogeneration process. Additionally, electric power production is intended to meet a reverse osmosis plant (RO) demand and the local electric grid (if it is connected to the system). The deterministic optimization problem is formulated as a multi-period Mixed Integer Non-Linear Programming (MINLP) to discretize operation period to track the diurnal fluctuations of solar energy.
However, the stochastic optimization problem is formulated as a multiscenario MINLP problem that is a deterministic equivalent of a two-stage stochastic programming model for handling uncertainty in operational parameters (normal direct irradiance, fossil fuel price) through a finite set of scenarios. A case study is solved for water treatment and energy management for Eagle Ford Basin in Texas to obtain the maximum annual profit of the entire system. The long-term evaluation for the techno-economic performance of solar energy conversion systems is highly dependent on the availability of solar radiation data and their accuracy. This study offers hierarchical calculation methodologies to estimate solar irradiance values for a specific location under different sky conditions. A case study is solved to predict hourly direct normal irradiance for San Antonio city in Texas. | en |
