@article{ford_sankarasubramanian_2023, title={Generalizing Reservoir Operations Using a Piecewise Classification and Regression Approach}, volume={59}, ISSN={["1944-7973"]}, url={https://doi.org/10.1029/2023WR034890}, DOI={10.1029/2023WR034890}, abstractNote={Abstract}, number={9}, journal={WATER RESOURCES RESEARCH}, author={Ford, Lucas and Sankarasubramanian, A.}, year={2023}, month={Sep} }
 @article{ford_queiroz_decarolis_sankarasubramanian_2022, title={Co-Optimization of Reservoir and Power Systems (COREGS) for seasonal planning and operation}, volume={8}, ISSN={["2352-4847"]}, url={https://doi.org/10.1016/j.egyr.2022.06.017}, DOI={10.1016/j.egyr.2022.06.017}, abstractNote={Climate variability accounts for distinct seasonal differences in electricity demand and streamflow potential, which power systems rely on to assess available hydropower and to cool thermal power plants. Understanding the interactions between reservoir and power networks under varying climate conditions requires an integrated analysis of both systems. In this study, we develop Co-Optimization of Reservoir and Electricity Generation Systems (COREGS), a generalized, open-source, modeling framework that optimizes both systems with respect to reducing power generation costs using a multireservoir model (GRAPS) and an electricity system model (TEMOA). Three optimization schemes of varying degrees of model integration are applied to Tennessee Valley Authority’s reservoir and electricity systems for the summer and winters from 2003 to 2015. We find that co-optimization of the systems results in more efficient water allocation decisions than separate optimization. Co-optimization solutions reduce reservoir spill and allocate water for hydropower only when and where it is beneficial to the power system as compared to stand-alone water system optimization. As the penetration of solar and wind power continues to increase, power systems will be more reliant on flexible reliable generating services such as reservoir systems and co-optimization of both systems will become more essential for efficient seasonal planning and operation.}, journal={ENERGY REPORTS}, publisher={Elsevier BV}, author={Ford, Lucas and Queiroz, Anderson and DeCarolis, Joseph and Sankarasubramanian, A.}, year={2022}, month={Nov}, pages={8061–8078} }
 @article{xuan_ford_mahinthakumar_de souza filho_lall_sankarasubramanian_2020, title={GRAPS: Generalized Multi-Reservoir Analyses using probabilistic streamflow forecasts}, volume={133}, ISSN={1364-8152}, url={http://dx.doi.org/10.1016/j.envsoft.2020.104802}, DOI={10.1016/j.envsoft.2020.104802}, abstractNote={A multi-reservoir simulation-optimization model GRAPS, Generalized Multi-Reservoir Analyses using Probabilistic Streamflow Forecasts, is developed in which reservoirs and users across the basin are represented using a node-link representation. Unlike existing reservoir modeling software, GRAPS can handle probabilistic streamflow forecasts represented as ensembles for performing multi-reservoir prognostic water allocation and evaluate the reliability of forecast-based allocation with observed streamflow. GRAPS is applied to four linked reservoirs in the Jaguaribe Metropolitan Hydro-System (JMH) in Ceará, North East Brazil. Results from the historical simulation and the zero-inflow policy over the JMH system demonstrate the model's capability to support monthly water allocation and reproduce the observed monthly releases and storages. Additional analyses using streamflow forecast ensembles illustrate GRAP's abilities in developing storage-reliability curves under inflow-forecast uncertainty. Our analyses show that GRAPS is versatile and can be applied for 1) short-term operating policy studies, 2) long-term basin-wide planning evaluations, and 3) climate-information based application studies.}, journal={Environmental Modelling & Software}, publisher={Elsevier BV}, author={Xuan, Yi and Ford, Lucas and Mahinthakumar, Kumar and De Souza Filho, Assis and Lall, Upmanu and Sankarasubramanian, A.}, year={2020}, month={Nov}, pages={104802} }