Desalination is crucial for water-scarce regions but is expensive and energy-intensive, often relying on fossil fuels. The decreasing cost and advancements in solar energy technologies present an opportunity for sustainable and low-cost desalination. This research focuses on the development of sedat, a user-friendly software designed to facilitate the comparative evaluation of various solar desalination technologies and identify optimal locations for implementation. Sedat leverages geospatial analysis alongside a robust energy and desalination technology modeling framework to streamline site identification and optimize the integration of solar and desalination systems. The software incorporates diverse data layers encompassing solar and saline water resources, water and energy infrastructure, regulations, costs, and market prices, much of which was previously unavailable in a consolidated database. This integration simplifies the planning, design, and valuation of solar desalination systems globally, focusing initially on the United States.

The introduction adequately references the high cost and energy intensity of traditional desalination methods, highlighting the environmental impact of fossil fuel reliance. It cites the potential of solar energy to provide a sustainable alternative, setting the stage for the introduction of sedat. While not a comprehensive literature review, it positions the research within the existing knowledge base by acknowledging the need for efficient tools to analyze and optimize solar desalination systems.

The development of sedat involved four key steps: 1. **Geospatial Database Compilation and Display:** A comprehensive database was assembled, integrating diverse data sources such as solar irradiation (DNI and GHI from NSRDB and PVGIS), brackish water resources (USGS), alternative water sources (agricultural drainage and oil & gas produced water), power plant locations and capacities (EIA), water infrastructure (USGS canals and aqueducts, road networks as proxies), water prices (IBNET), and regulatory information from various state agencies. Data aggregation techniques were employed, such as spatial joins to county boundaries, to improve visualization and usability. Data preprocessing involved techniques like creating spatial indexes (KD-trees and R-trees) to optimize spatial queries, enhancing the efficiency of the site selection process. 2. **Software Modules for Data Selection, Mapping, and Integration:** Open-source libraries (OSGeo, Fiona, Shapely, Xarray, SciPy.spatial) were used to create modules for querying and accessing the geospatial data. A two-step query process was implemented for polygons and points to optimize memory usage and improve performance. The data were also compressed to reduce storage requirements and improve loading times. A custom map framework based on Leaflet was developed to improve performance, particularly during site selection and theme changes. 3. **GUI Model Development and Integration:** NREL's System Advisor Model (SAM) was integrated for solar energy generation modeling (CSP and PV). The integration involved creating Python wrappers, restructuring the input variables using JSON for better GUI management, and developing callback functions to handle interdependencies between parameters. The use of JSON streamlined the creation of the GUI menu system, making it easier to add new models and maintain the software. Data validation and error handling were built into the GUI to guide user inputs and provide feedback. 4. **Desalination Model Development:** Multiple desalination technologies were incorporated into the model including Low-temperature multi-effect distillation (LT-MED), multi-effect distillation with thermal vapor compression (MED-TVC), multi-effect distillation with absorption heat pumps (MED-ABS), vacuum air gap membrane distillation (VAGMD), reverse osmosis (RO) with multiple passes, osmotically assisted reverse osmosis (OARO), forward osmosis (FO), and hybrid RO-VAGMD and RO-FO systems. These models were structured for easy integration into the GUI, and their outputs (design, simulation, and cost) were formatted consistently for unified display.

Sedat provides a user-friendly interface for evaluating solar desalination options and identifying suitable locations. The integration of various data layers allows for comprehensive site assessments, considering factors such as solar resources, water availability, infrastructure, and regulatory constraints. Example results demonstrated the potential of different combinations of solar technologies (flat-plate collectors, Linear Fresnel CSP, parabolic trough CSP) and desalination methods (MD, LT-MED) in specific locations within the US Southwest. Sedat's ability to model energy curtailment and provide suggestions for optimization (e.g., adding thermal storage, adjusting solar field size, utilizing waste heat) enhances its practical application. Parametric analyses illustrated the cost-effectiveness of incorporating thermal energy storage to mitigate energy curtailment and improve water production. The tool also highlights regions where the levelized cost of water (LCOW) from desalination is competitive with existing municipal water prices.

Sedat directly addresses the need for a comprehensive and user-friendly tool to evaluate the techno-economic feasibility of solar desalination projects. Its integration of GIS data, detailed solar and desalination models, and optimization features provides valuable insights for decision-making. The example results highlight the potential of solar desalination to provide a sustainable and cost-effective water solution in water-scarce regions, particularly when coupled with thermal energy storage to optimize energy utilization and reduce LCOW. Future development could focus on expanding the database to include more regions and technologies, incorporating more sophisticated modeling capabilities, and further refining the optimization algorithms.

Sedat provides a valuable tool for planning and designing solar desalination systems. Its open-source nature and modular design facilitate expansion and adaptation to diverse contexts. Future research could focus on incorporating more advanced modeling techniques, expanding the database to cover a wider range of geographic locations and desalination technologies, and improving the optimization algorithms for enhanced efficiency and cost-effectiveness.

While sedat provides a comprehensive framework, some limitations exist. The regulatory database is not exhaustive and may not completely capture the nuances of all permitting processes in different jurisdictions. The accuracy of the models depends on the quality and availability of input data. Further refinement of the optimization algorithms and incorporation of more sophisticated economic models could enhance the tool's predictive capabilities. The model's current reliance on publicly available data may limit its ability to consider certain proprietary information or site-specific characteristics that could influence project feasibility.