A solar energy desalination analysis tool, sedat, with data and models for selecting technologies and regions

Producing fresh water via desalination is essential for arid, water-scarce regions, but it is expensive and energy-intensive. Energy costs are a significant contributor, and fossil-fueled desalination emits greenhouse gases and other pollutants. Recent cost reductions and technological advances in solar energy create opportunities for low-cost, emission-free desalination. This paper discusses the development of sedat, a user-friendly, open-source software enabling comparative evaluation of solar desalination technology options and geospatial identification of high-potential regions for solar thermal desalination. Sedat integrates geospatial analysis with an energy and desalination technology modeling framework describing current and emerging processes at industrial scales. Its use enables streamlined identification of locations where solar thermal desalination can be most competitive and system-level simulation and optimization of solar energy–desalination integration. The tool aggregates diverse data layers—solar and saline water resources, infrastructure, regulatory contexts, and cost/price inputs—into one GUI for desktop/laptop use. Users can supply alternative or proprietary datasets through the GUI. A web interface supports quick visualization and simple calculations; a desktop application integrates comprehensive techno-economic models (leveraging NREL SAM CSP models and CIEMAT-PSA thermal desalination models). Sedat’s modular architecture supports expansion, providing default parameters for technical design and cost, quantifying performance and listing assumptions that users can modify. Technology suggestions are based mainly on saline water TDS, levelized cost of water (LCOW), product purity, and target brine concentration. Desalination models include LT-MED, MED-TVC, MED-ABS, VAGMD (continuous and batch), RO (multi-pass), OARO, FO, and hybrids (RO-VAGMD, RO-FO). The workflow proceeds from site selection, model selection (solar, desalination, financial), to simulations with time-series outputs.

The development of sedat followed four major steps: (1) compilation of geospatial resource databases and map layers; (2) creation of software modules (open-source libraries) to assemble, transform, and pass geospatial data to techno-economic models; (3) development of GUI components for selecting solar/desalination/financial models and inputs; (4) development of modules to display model outputs and identify locations where desalinated water costs undercut local tariffs, with exportable CSV tables. Geospatial database and display: Integrated datasets include solar resources (DNI, GHI; NSRDB and PVGIS), Typical Meteorological Year (TMY) files for 1,397 locations globally (1,016 US), with PVGIS UTC time series adjusted to local time; water resources (USGS brackish groundwater assessment: dissolved solids, major ions, depth, temperature, yields; aggregation to county level; alternative sources like agricultural drainage and USGS produced waters with salinity); US desalination plants (GWI, TWDB) and brine management options; power plants (EIA) for potential co-location and waste heat use; water transfer networks (USGS canals/aqueducts and a municipal water network proxy based on road data); energy and water markets (OpenEI utility rates and EIA fuel prices; IBNET tariffs with links to utilities); regulatory/permitting examples (TX, AZ, NV, FL, CA, CO); and population projections by SSP at county level (CIESIN/SEDAC). Dash-Leaflet enables efficient map rendering (layers retrieved from Mapbox and loaded locally), improving load and interaction speeds versus Dash-Plotly alone. Spatial query and performance engineering: Python libraries (OSGeo stack, Fiona, Shapely, Xarray, SciPy.spatial) implement nearest-feature and overlap queries. Spatial indexes (KDTrees for points, RTrees for polygons) accelerate lookups. Line features are generalized (15 m tolerance) and converted to points for KDTree queries. For massive point layers (e.g., water network proxy with 81,185,710 points), data are subdivided to county-level compressed shapefiles (reducing storage from 46.1 GB to 4.6 GB) to achieve sub-minute query times; two-stage queries first identify state/county polygons, then query local indexes. Compressed shapefile access was shown to have negligible overhead (~0.04 s difference for largest archive) versus uncompressed. Model integration and GUI: Solar generation models (CSP and PV) and financials from NREL’s SAM are integrated via Python wrappers. SAM source code was modified to expose variables needed for thermal desalination integration (e.g., condenser temperature, exhaust steam mass flow). Wrappers initially with >300 inputs per module were refactored: a JSON-driven GUI schema (Dash DataTable) programmatically constructs model menus, sets defaults (from SAM GitHub JSON), and implements parameter constraints and inter-parameter connections (GUI callbacks) to mirror SAM’s GUI logic (e.g., calculating number of loops from solar multiple and aperture parameters). Weather file linkage is dynamically set from the selected site. Unit tests and logging support reliability. Outputs from SAM (hourly energy generation, LCOE/LCOH) feed desalination and cost models. Desalination models: Multiple technologies are implemented with unified code structure and data frames. Each technology has (a) a fast design model (e.g., specific heat transfer area, design-point performance) allowing parameter adjustments, and (b) a simulation model driven by hourly solar outputs to assess annual performance and costs. Examples include: LT-MED (transformed from design to simulation, with fixed heat exchange areas and solved effect temperatures), MED-TVC, MED-ABS, VAGMD (one-pass and batch, empirically modeled from PSA pilots), RO (multi-pass), OARO, FO, and hybrids (RO-FO, RO-VAGMD). Integration workflows couple solar models (e.g., flat-plate, LF-DSG, parabolic trough with storage) with desalination units, and optional thermal energy storage (TES). A modular six-step process supports adding new models: develop Python scripts (solar outputs must include hourly energy, LCOH/LCOE; desalination needs design, simulation, cost); create JSON inputs/defaults; configure selectable combinations; link map-derived inputs (weather, salinity); wire design/simulation/financial I/O; configure outputs for charts/reports. User workflow and outputs: The GUI guides users through site selection (visualizing solar resources, water sources/prices, infrastructure, regulatory overlays), model selection (solar, desalination, financial), and simulations. Upon site selection, required variables populate and are stored as JSON. Results include system specifications (thermal power needs, STEC/SEEC, recovery, brine concentration, GOR/performance ratio), time-series charts (irradiation, power generation, water production), and project reports (site/model specs; solar field performance with capacity factor, curtailment; desalination plant performance; LCOE/LCOH; LCOW). Performance-based guidance issues warnings (e.g., high curtailment >20%) and suggests remedies (reduce solar field size, add TES, adjust ITD, add external heat). Data and code availability: open-source data via figshare (DOI: 10.6084/m9.figshare.c.5874125.v5); source code on GitHub (https://github.com/gyetman/DOE_CSP_PROJECT).

- The sedat platform successfully integrates large geospatial datasets with techno-economic models of solar energy and desalination, enabling rapid site screening and detailed system simulations via a web/desktop GUI. Dash-Leaflet dramatically improved map load and interaction performance versus Dash-Plotly alone. - Example: Flat-plate collectors + VAGMD-batch at Phoenix, AZ (design water 1000 m³/day; 5.0 MW flat-plate solar field; no TES): • Solar field annual thermal energy: 13.58 GWh; capacity factor: 31.0%. • Curtailed thermal energy: 6.19 GWh (45.6%) → High curtailment warning. • Desalination performance: average daily water production 327 m³; recovery 30.42%; brine concentration 71.4 g/L; STEC 61.95 kWh/m³; SEEC 0.30 kWh/m³. • Costs: LCOW 2.84 $/m³; LCOH (solar) 0.017 $/kWh; assumed LCOH (other) 0.010 $/kWh; LCOE 0.05 $/kWh; capital cost 1.50 $/m³; O&M 1.34 $/m³; unit energy cost 1.08 $/m³. • Parametric TES integration indicated 12 h TES as most cost-effective for the case/location, using all thermal energy from a 5 MW field, increasing water output by ~140% and reducing LCOW by ~15% versus no TES. - Example: LF-DSG CSP + LT-MED (2000 m³/day) with 60 g/L feedwater: • Reducing solar field from 2.5 MW to 1.8 MW decreased curtailment from 31.7% to 12.2%, with ~5.8% annual reduction in water production; similar production on sunny days (excess energy) and reduced output on cloudy days with the smaller field. - Curtailment and LCOW optimization (Tucson, AZ; LF-DSG 2.5 MW + MD-batch 1000 m³/day baseline): • Base: curtailment 57.3%; LCOW 5.32 $/m³. • Reduce CSP to 1 MW: curtailment 13.8%; LCOW 5.15 $/m³. • Add 12 h TES (keep 2.5 MW): curtailment 10.1%; LCOW 4.51 $/m³. • Combine actions (2 MW + 12 h TES): curtailment 2.3%; LCOW 4.14 $/m³. - Seasonal/operational tuning when solar thermal resource is lacking: • Increasing ITD from 40 to 60 °C (reducing power cycle efficiency 0.37→0.30) increased annual thermal energy (9.85→14.77 GWh) and slightly reduced LCOW (4.14→4.07 $/m³). • Adding external heat at 0.04 $/kWh with higher ITD further reduced LCOW to 3.52 $/m³. - Integration with TES effectively reduces curtailment (e.g., from 21.1% to 1.1% in a parabolic trough + MED case with 4 h TES) and smooths diurnal variability, improving utilization and water production. - The tool provides actionable performance-based guidance (e.g., curtailment warnings and recommended mitigations), linking geospatial context to technology choice (e.g., TDS levels guiding technology selection, proximity to water networks affecting delivery costs).

Sedat addresses the dual objectives of (i) identifying favorable locations for solar thermal desalination and (ii) optimizing integration between solar energy systems and desalination technologies. The results demonstrate that siting informed by geospatial layers (solar resource, saline water availability, infrastructure, tariffs, regulations) and subsequent techno-economic simulation can reveal mismatches (e.g., oversized solar fields causing high curtailment) and guide corrective actions. Case studies show that curtailment can be reduced substantially by right-sizing solar fields and incorporating TES, with meaningful LCOW reductions. Seasonal effects (e.g., lower winter production due to insufficient waste heat temperatures) can be mitigated by increasing ITD or supplementing with external thermal sources; the trade-offs between power block efficiency and thermal availability are quantified and can further reduce LCOW when combined with low-cost heat. The time-series outputs provide visibility into diurnal/seasonal performance, enabling additional design adjustments (e.g., storage sizing, hybridization, brine concentration targets). Overall, the findings validate sedat as a decision-support tool that links spatial context with system design to achieve lower costs and better utilization of solar resources for desalination.

This work introduces sedat, an open-source, modular analysis tool that integrates extensive GIS datasets with detailed solar and desalination techno-economic models in a user-friendly GUI. The software streamlines site screening, system design, and performance-cost evaluation, providing diagnostics and recommendations (e.g., curtailment warnings, TES sizing) to optimize solar–desalination coupling. Example applications in the U.S. Southwest highlight how right-sizing solar fields, adding TES, and adjusting operating parameters reduce curtailment and LCOW. All input datasets are openly available (figshare), and source code is hosted on GitHub, facilitating transparency, reproducibility, and extension. Future work can expand regulatory databases, incorporate additional desalination and storage technologies, refine cost modules (e.g., brine management), and extend global data coverage to support broader adoption of solar-powered desalination.

- Regulatory and permitting database is preliminary and not comprehensive; intended as a foundation for further development. - Water tariff data (IBNET) are limited to available years (up to ~2017 for many utilities) and may require local updates via provided links. - Waste heat availability near power plants is mostly low-temperature condenser heat (≤41.5 °C), generally not practically recoverable for thermal desalination; only a small fraction of higher-temperature exhaust heat is potentially usable and requires proximity. - Performance of large geospatial queries necessitated data subdivision and compression; while optimized, extremely large or new datasets may still affect responsiveness and require further tuning. - Some SAM parameter dependencies are defined in the GUI rather than source code, requiring custom callback logic that may need updates with new SAM releases. - Example results are scenario-specific (locations, configurations, assumptions such as LCOE/LCOH, external heat price) and may not generalize; local data overrides are recommended for project-specific decisions.