@misc{datasets for widespread residential space heating electrification in texas_2024, DOI={10.57931/2331202}, journal={MultiSector Dynamics - Living, Intuitive, Value-adding, Environment}, year={2024} }
 @article{ssembatya_kern_oikonomou_voisin_burleyson_akdemir_2024, title={Dual Impacts of Space Heating Electrification and Climate Change Increase Uncertainties in Peak Load Behavior and Grid Capacity Requirements in Texas}, volume={12}, ISSN={2328-4277 2328-4277}, url={http://dx.doi.org/10.1029/2024EF004443}, DOI={10.1029/2024EF004443}, abstractNote={Abstract Around 60% of households in Texas currently rely on electricity for space heating. As decarbonization efforts increase, non‐electrified households could adopt electric heat pumps, significantly increasing peak (highest) electricity demand in winter. Simultaneously, anthropogenic climate change is expected to increase temperatures, the potential for summer heat waves, and associated electricity demand for cooling. Uncertainty regarding the timing and magnitude of these concurrent changes raises questions about how they will jointly affect the seasonality of peak demand, firm capacity requirements, and grid reliability. This study investigates the net effects of residential space heating electrification and climate change on long‐term demand patterns and load shedding potential, using climate change projections, a predictive load model, and a direct current optimal power flow (DCOPF) model of the Texas grid. Results show that full electrification of residential space heating by replacing existing fossil fuel use with higher efficiency heat pumps could significantly improve reliability under hotter futures. Less efficient heat pumps may result in more severe winter peaking events and increased reliability risks. As heating electrification intensifies, system planners will need to balance the potential for greater resource adequacy risk caused by shifts in seasonal peaking behavior alongside the benefits (improved efficiency and reductions in emissions).}, number={6}, journal={Earth's Future}, publisher={American Geophysical Union (AGU)}, author={Ssembatya, Henry and Kern, Jordan D. and Oikonomou, Konstantinos and Voisin, Nathalie and Burleyson, Casey D. and Akdemir, Kerem Ziya}, year={2024}, month={Jun} }
 @article{go ercot version used for the "dual impacts of space heating electrification and climate change …" analysis_2024, DOI={10.5281/zenodo.10475841}, abstractNote={This is the GO ERCOT version used in the Ssembatya et. al : "The dual Impacts ..." work}, journal={Zenodo}, year={2024}, month={Jan} }
 @article{go ercot version used for the "dual impacts of space heating electrification and climate change …" analysis_2024, DOI={10.5281/zenodo.10475965}, abstractNote={This is the GO ERCOT version used in the Ssembatya et. al : "The dual Impacts ..." work}, journal={Zenodo}, year={2024}, month={Jan} }
 @misc{go ercot version used for the hp_hecc analysis_2024, DOI={10.5281/zenodo.10475842}, journal={Zenodo}, year={2024}, month={Jan} }
 @article{immm-sfa/ssembatya-etal_2024_earths_future: v1.0.0 release_2024, DOI={10.5281/zenodo.10934192}, journal={Zenodo}, year={2024}, month={Apr} }
 @article{immm-sfa/ssembatya-etal_2024_earths_future: v1.0.0 release_2024, DOI={10.5281/zenodo.10934193}, journal={Zenodo}, year={2024}, month={Apr} }
 @article{akdemir_oikonomou_kern_voisin_ssembatya_qian_2023, title={An Open-Source Framework for Balancing Computational Speed and Fidelity in Production Cost Models}, DOI={10.2139/ssrn.4507380}, abstractNote={
 Studies of bulk power system operations need to incorporate uncertainty and sensitivity analyses, especially around exposure to weather and climate variability and extremes, but this remains a computational modeling challenge. Commercial production cost models have shorter runtimes, but also important limitations (opacity, license restrictions) that do not fully support stochastic simulation. Open-source production cost models represent a potential solution. They allow for multiple, simultaneous runs in high-performance computing environments and offer flexibility in model parameterization. Yet, developers must balance computational speed (i.e., runtime) with model fidelity (i.e., accuracy). In this paper, we present GO (Grid Operations), a framework for instantiating open-source, scale-adaptive production cost models. GO allows users to search across parameter spaces to identify model versions that appropriately balance computational speed and fidelity based on experimental needs and resource limits. Results provide generalizable insights on how to navigate the fidelity and computational speed tradeoff through parameter selection. We show that models with coarser network topologies can accurately mimic market operations, sometimes better than higher-resolution models. It is thus possible to conduct large simulation experiments that characterize operational risks related to climate and weather extremes while maintaining sufficient model accuracy.}, journal={SSRN Electronic Journal}, publisher={Elsevier BV}, author={Akdemir, Kerem Ziya and Oikonomou, Konstantinos and Kern, Jordan D. and Voisin, Nathalie and Ssembatya, Henry and Qian, Jingwei}, year={2023} }
 @article{akdemir_oikonomou_kern_voisin_ssembatya_qian_2024, title={An open-source framework for balancing computational speed and fidelity in production cost models}, volume={1}, ISSN={2753-3751}, url={http://dx.doi.org/10.1088/2753-3751/ad1751}, DOI={10.1088/2753-3751/ad1751}, abstractNote={Abstract
               Studies of bulk power system operations need to incorporate uncertainty and sensitivity analyses, especially around exposure to weather and climate variability and extremes, but this remains a computational modeling challenge. Commercial production cost models (PCMs) have shorter runtimes, but also important limitations (opacity, license restrictions) that do not fully support stochastic simulation. Open-source PCMs represent a potential solution. They allow for multiple, simultaneous runs in high-performance computing environments and offer flexibility in model parameterization. Yet, developers must balance computational speed (i.e. runtime) with model fidelity (i.e. accuracy). In this paper, we present Grid Operations (GO), a framework for instantiating open-source, scale-adaptive PCMs. GO allows users to search across parameter spaces to identify model versions that appropriately balance computational speed and fidelity based on experimental needs and resource limits. Results provide generalizable insights on how to navigate the fidelity and computational speed tradeoff through parameter selection. We show that models with coarser network topologies can accurately mimic market operations, sometimes better than higher-resolution models. It is thus possible to conduct large simulation experiments that characterize operational risks related to climate and weather extremes while maintaining sufficient model accuracy.}, number={1}, journal={Environmental Research: Energy}, publisher={IOP Publishing}, author={Akdemir, Kerem Ziya and Oikonomou, Konstantinos and Kern, Jordan D and Voisin, Nathalie and Ssembatya, Henry and Qian, Jingwei}, year={2024}, month={Jan}, pages={015003} }
 @misc{datasets used for "heat pump - heating electrification and climate change - grid impact studies"_2023, DOI={10.5281/zenodo.10150609}, abstractNote={Summary In this work, we explore long term patterns in electricity demand driven by the dual effects of space heating electrification and climate change. We use an open source nodal power system model of the Electric Reliability Council of Texas (ERCOT) system to investigate a wide range of future climate and technology scenarios that evolve over time, and report results in terms of market prices, reliability and corresponding relative capacity requirements About The technical analysis aimed to: 1) Understand the Long-Term Patterns: We aim to analyze patterns in peak load, total load, loss of load, and the seasonality of these phenomena, driven by widespread heat pump adoption of Heat Pumps alongside climate change. 2) Use Extensive Scenario Analysis: Explore a wide range of future scenarios, including variations in climate and technology pathways, to capture the uncertainty associated with these long-term changes. In total, 2560 simulation years. 3) Use a validated open source DC OPF model(reproducibility) Use an open-source nodal power system model of the ERCOT system to simulate and understand the potential impacts on market prices, reliability, and relative capacity requirements. Similar models are available for all interconnections of the conterminous US. 4) Assess Grid Vulnerability: Assess the vulnerability of the grid to these simultaneous changes, identify potential vulnerability. 5) Provide Insights for System Planners: Offer results that can assist long-term system planners in anticipating and preparing for potential shifts in grid reliability.}, journal={Zenodo}, year={2023}, month={Nov} }
 @misc{datasets used for "heat pump - heating electrification and climate change - grid impact studies"_2023, DOI={10.5281/zenodo.10150610}, abstractNote={Summary In this work, we explore long term patterns in electricity demand driven by the dual effects of space heating electrification and climate change. We use an open source nodal power system model of the Electric Reliability Council of Texas (ERCOT) system to investigate a wide range of future climate and technology scenarios that evolve over time, and report results in terms of market prices, reliability and corresponding relative capacity requirements About The technical analysis aimed to: 1) Understand the Long-Term Patterns: We aim to analyze patterns in peak load, total load, loss of load, and the seasonality of these phenomena, driven by widespread heat pump adoption of Heat Pumps alongside climate change. 2) Use Extensive Scenario Analysis: Explore a wide range of future scenarios, including variations in climate and technology pathways, to capture the uncertainty associated with these long-term changes. In total, 2560 simulation years. 3) Use a validated open source DC OPF model(reproducibility) Use an open-source nodal power system model of the ERCOT system to simulate and understand the potential impacts on market prices, reliability, and relative capacity requirements. Similar models are available for all interconnections of the conterminous US. 4) Assess Grid Vulnerability: Assess the vulnerability of the grid to these simultaneous changes, identify potential vulnerability. 5) Provide Insights for System Planners: Offer results that can assist long-term system planners in anticipating and preparing for potential shifts in grid reliability.}, journal={Zenodo}, year={2023}, month={Nov} }
 @article{broman_voisin_kern_steinschneider_ssembatya_wi_turner_2023, title={How Hydropower Operations Mitigate Flow Forecast Uncertainties to Maintain Grid Services in the Western US}, volume={2}, DOI={10.5194/egusphere-egu23-16887}, abstractNote={Hydropower facilities in the United States (US) most often have non-powered objectives, for example storage and release of water for water supply or environmental benefit, or flood control. These objectives can limit the flexibility available to hydropower operations to generate power to provide maximum benefit to the power grid. There does exist however flexibility within a week to optimize hydropower generation while still ensuring non-powered objectives are met. We examine the flexibility available to optimize generation and the value of medium-range inflow forecasts using a dynamic programing reservoir optimization model applied at ~250 hydropower facilities over the US Western Interconnection. Optimization is performed using day-ahead hourly scheduling to reflect existing electricity markets, using Locational Marginal Prices (LMPs) provided by a production cost model, and using three flavors of medium-range inflow forecasts – perfect forecasts representing an upper limit on performance, persistence forecasts representing a lower benchmark, and synthetic forecasts as a surrogate for operational streamflow forecast products. Measures of direct performance and flexibility are examined at the grid-scale for Balancing Authorities within the Western Interconnection. This study highlights where and under what conditions medium-range forecasts influence flexibility in hydropower operations which will be increasingly valuable under an evolving grid with increased renewable penetration.}, publisher={Copernicus GmbH}, author={Broman, Daniel and Voisin, Nathalie and Kern, Jordan and Steinschneider, Scott and Ssembatya, Henry and Wi, Sungwook and Turner, Sean}, year={2023}, month={Feb} }
 @inproceedings{ssembatya_kern_oikonomou_voisin_2022, title={The Dual Impacts of Space Heating Electrification and Climate Change on Seasonal Peaking and Reliability of the Texas Power Grid.}, booktitle={AGU Fall Meeting Abstracts}, author={Ssembatya, Henry and Kern, Jordan D. and Oikonomou, Konstantinos and Voisin, Nathalie}, year={2022}, month={Dec} }
 @inproceedings{voisin_kern_steinschneider_turner_ssembatya_wi_2022, title={Value of medium range inflow forecast for hydropower scheduling flexibility}, booktitle={AGU Fall Meeting Abstracts}, author={Voisin, Nathalie and Kern, Jordan D. and Steinschneider, Scott and Turner, Sean William Donald and Ssembatya, Henry and Wi, Sungwook}, year={2022}, month={Dec} }
 @article{ssembatya_ershaghi_2019, title={A Prediction Method for Estimating Time to Convert From Cyclic to Drive in Steam Injection Processes}, DOI={10.2118/195301-ms}, abstractNote={Abstract
               There is significant history on the use of steam injection processes in California heavy oil reservoirs. In this paper, we introduce a graphical method for estimating the optimal timing of conversion from Cyclic Steam Stimulation (CSS) to steam drive and compare our estimation to actual cases where CSS had continued with the buildup of significant water saturation around the wellbore. We use as a measure, the SOR (Steam Oil Ratio) to ascertain the optimality of the conversion points and compare it to our modeling work. During the CSS process, steam injectivity is gradually improved, resulting in low SOR which is the characteristic of the steam stimulation process. We see of course some relation between the behaviors of various oil viscosity types and the SOR during CSS. But in general, the rapid heating of the formation relates to limitations associated with contact volume. Based on our numerical modeling, we demonstrate that a log-log plot of cumulative injection vs. cumulative production for all the cycles leads to a linear relationship. When there is an indication of flattening of oil steam ratio or an increase in SOR, it is time to change to steam drive allowing the steam to contact a larger volume of the reservoir. Thus, the heat-scavenging effects of water buildup around the injection well and low relative permeabilities of oil traveling through the high water saturation interval are avoided. The estimated SOR during the CSS process depends on the oil viscosities and vertical conformance. The optimum time corresponds to the point where there is deterioration of the SOR. We showcase studies on several CSS wells from fields in Central California validating the graphical method and demonstrating the steam savings had the proposed timing been implemented. The methodology presented confirms a practical and smart way for operators to decide on the conversion timing.}, journal={Day 3 Thu, April 25, 2019}, publisher={SPE}, author={Ssembatya, Henry and Ershaghi, Iraj}, year={2019}, month={Apr} }