Energy systems optimization
Computational optimization techniques can be used to significantly improve the economics and reduce the environmental impact of our energy technologies. Optimization has been applied to numerous technologies, from boilers and combustion turbines to electricity grids and battery storage systems.
Our group focuses on two main challenges: the production of low-carbon electricity, and the functioning of global fuel markets.
Low carbon power production
Our group actively developes the HyPPO (Hybrid Power Plant Optimization) software package - a C++ code that combines economics, thermodynamics, statistics, and computational optimization to explore how power plants are (and could be) designed and operated.
A core focus of HyPPO has been on carbon dioxide capture and storage (CCS). Carbon capture and storage could significantly reduce emissions from existing energy facilities, without the time and expense of completely rebuilding our energy system. However, CCS is capital intensive, and it reduces the output of power per unit of energy input, thus increasing the cost of electricity. Lost power output could be especially problematic in a capacity-constrained future grid, where high renewables penetration increases the need for dispatchable, reliable power capacity.
Our work in CCS focuses on "energy parks" that allow for the direct integration of CCS with different types of conventional fuels and renewable power sources, including coal, natural gas, wind, and solar thermal. We find that designing a CCS system to account for the intermittency and variability of renewable power production results in reduced costs of capture compared to non-optimized systems. Benefits can be achieved by shifting capture of CO2 to times when the grid is able to handle the loss of capacity. Also, excess CO2 can be captured at some times to meet emissions reductions targets without requiring constant capture rates. This flexibility can result in significant improvements in operating economics for CCS systems.
Modeling of oil substitution pathways
Our group also uses optimization techniques to understand transitions to oil substitutes in the face of depletion of conventional oil resources. The functioning of global fuels markets can be cast as an optimization problem, where the goal is to supply fuels to society at least cost. The fuels market optimization problem can be solved over many time periods to model the optimal rates of investment, production, and shipment of fuels between world regions. This results in time paths of development of oil substitutes, which can be studied for their economic and environmental impacts.
Publications
2018
*P.G. Brodrick, A.R. Brandt., L.J. Durlofsky. Optimal design and operation of integrated solar combined cycles under emissions intensity constraints. Applied Energy
DOI: 10.1016/j.apenergy.2018.06.052
*Sun, Y., G. Szucs, A.R. Brandt. Solar PV output prediction from video streams using convolutional neural networks. Energy & Environmental Science 2018, DOI: 10.1039/C7EE03420B
2017
*Brodrick, P.G., A.R. Brandt., L.J. Durlofsky. Operational optimization of an integrated solar combined cycle under practical time-dependent constraints. Energy. DOI: 10.1016/j.energy.2017.11.059
*Kolster, C., M.S. Masnadi, S. Krevor, N. MacDowell, and A.R. Brandt. CO2 enhanced oil recovery: a catalyst for gigatonne-scale carbon capture and storage deployment? Energy & Environmental Science. DOI: 10.1039/c7ee02102j
Teichgraeber, H., Brodrick, P. G., Brandt, A. R. Optimal design and operations of a flexible oxyfuel natural gas plant. Energy. DOI:10.1016/j.energy.2017.09.087.
2016
*Kang, C.A., A.R. Brandt, L. Durlofsky, I. Jayaweera. Assessment of advanced solvent-based post-combustion CO2 capture processes using a bi-objective optimization technique. Applied energy. DOI: 10.1016/j.apenergy.2016.07.062
2015
Brodrick, P.A., Kang, C.A., Brandt, A.R., Durlofsky, L.J. (2015) Optimization of carbon-capture-enabled coal-gas-solar power generation. Energy. DOI: 10.1016/j.energy.2014.11.003
Kang, C.A., Brandt, A.R., Durlofsky, L. (2015). A new carbon capture proxy model for optimizing the design and time-varying operation of a coal-natural gas power station. International Journal of Greenhouse Gas Control. DOI: 10.1016/j.ijggc.2015.11.023
2014
Kang, C.A., Brandt, A.R., Durlofsky, L.J. (2014) Optimizing heat integration in a flexible coal-natural gas power station with CO2 capture. International Journal of Greenhouse Gas Control. DOI: 10.1016/j.ijggc.2014.09.019
Site content
- Fan, Y, M Masnadi, L Jing, B Ren, and A Brandt. “The Pyxis Project: A Geospatial Data System for Emission Estimation Monitoring in the Oil and Gas Industry”, Energy and AI, 22 (December 1, 2025): 100601. https://doi.org/10.1016/j.egyai.2025.100601.
- Sodwatana, M, D Saad, M Ahumada-Paras, and A Brandt. “Appliance Decarbonization and Its Impacts on California’s Energy Transition”, Applied Energy, 390 (July 15, 2025): 125769. https://doi.org/10.1016/j.apenergy.2025.125769.
- Long, W, A Brandt, T Demayo, and L Verduzco. “Technical Validation of Oil Production Greenhouse Gas Emissions Estimator Using Field Data from Thermal Enhanced Oil Recovery Operations”, Energy & Fuels, May 7, 2025. https://doi.org/10.1021/acs.energyfuels.5c00079.
- Chen, Z, R Zhong, W Long, H Tang, A Wang, Z Liu, X Yang, B Ren, J Littlefield, S Koyejo, M Masnadi, and A Brandt. “Advancing Oil and Gas Emissions Assessment through Large Language Model Data Extraction”, Energy and AI, 20 (May 1, 2025): 100481. https://doi.org/10.1016/j.egyai.2025.100481.
- Saad, D, M Sodwatana, E Sherwin, and A Brandt. “Energy Storage in Combined Gas-Electric Energy Transitions Models: The Case of California”, Applied Energy, 385 (May 1, 2025): 125480. https://doi.org/10.1016/j.apenergy.2025.125480.
- Aljubran, M, D Saad, M Sodwatana, A Brandt, and R Horne. “The Value of Enhanced Geothermal Systems for the Energy Transition in California”, Sustainable Energy & Fuels, February 3, 2025. https://doi.org/10.1039/D4SE01520G.
- Mukherjee, M, J Littlefield, H Khutal, K Kirchner-Ortiz, K Davis, L Jing, F Ramadan, H El-Houjeiri, M Masnadi, and A Brandt. “Greenhouse Gas Emissions from the US Liquefied Natural Gas Operations and Shipping through Process Model Based Life Cycle Assessment”, Communications Earth & Environment, 6, no. 1 (January 19, 2025): 16. https://doi.org/10.1038/s43247-024-01988-2.
- Sodwatana, M, S Kazi, K Sundar, A Brandt, and A Zlotnik. “Locational Marginal Pricing of Energy in Pipeline Transport of Natural Gas and Hydrogen With Carbon Offset Incentives”, International Journal of Hydrogen Energy, 96 (December 27, 2024): 574-88. https://doi.org/10.1016/j.ijhydene.2024.11.191.
- Nie, Y, Q Paletta, A Scott, L Pomares, G Arbod, S Sgouridis, J Lasenby, and A Brandt. “Sky Image-Based Solar Forecasting Using Deep Learning With Heterogeneous Multi-Location Data: Dataset Fusion Versus Transfer Learning”, Applied Energy, 369 (September 1, 2024): 123467. https://doi.org/10.1016/j.apenergy.2024.123467.
- Nie, Y, E Zelikman, A Scott, Q Paletta, and A Brandt. “SkyGPT: Probabilistic Ultra-Short-Term Solar Forecasting Using Synthetic Sky Images from Physics-Constrained VideoGPT”, Advances in Applied Energy, 14 (July 15, 2024): 100172. https://doi.org/10.1016/j.adapen.2024.100172.
- Chen, Z, S El Abbadi, E Sherwin, P Burdeau, J Rutherford, Y Chen, Z Zhang, and A Brandt. “Comparing Continuous Methane Monitoring Technologies for High-Volume Emissions: A Single-Blind Controlled Release Study”, ACS ES&T Air, 1, no. 8 (June 4, 2024): 871-84. https://doi.org/10.1021/acsestair.4c00015.
- Negron, A, E Kort, G Plant, A Brandt, Y Chen, C Hausman, and M Smith. “Measurement-Based Carbon Intensity of US Offshore Oil and Gas Production”, Environmental Research Letters, 19, no. 6 (May 28, 2024): 064027. https://doi.org/10.1088/1748-9326/ad489d.
- El Abbadi, S, Z Chen, P Burdeau, J Rutherford, Y Chen, Z Zhang, E Sherwin, and A Brandt. “Technological Maturity of Aircraft-Based Methane Sensing for Greenhouse Gas Mitigation”, Environmental Science & Technology, 58, no. 22 (May 17, 2024). https://doi.org/10.1021/acs.est.4c02439.
- Sherwin, E, J Rutherford, Z Zhang, Y Chen, E Wetherley, P Yakovlev, E Berman, B Jones, D Cusworth, A Thorpe, A Ayasse, R Duren, and A Brandt. “US Oil and Gas System Emissions from Nearly One Million Aerial Site Measurements”, Nature, 627, no. 8003 (March 13, 2024): 328-34. https://doi.org/10.1038/s41586-024-07117-5.
- Shin, L, A Brandt, D Iancu, K Mach, C Field, M-J Cho, M Ng, K Chey, N Ram, T Robinson, and B Reeves. “Climate Impacts of Digital Use Supply Chains”, Environmental Research: Climate, 3, no. 1 (March 5, 2024): 015009. https://doi.org/10.1088/2752-5295/ad22eb.
- Wang, J, B Barlow, W Funk, C Robinson, A Brandt, and A Ravikumar. “Large-Scale Controlled Experiment Demonstrates Effectiveness of Methane Leak Detection and Repair Programs at Oil and Gas Facilities”, Environmental Science & Technology, 58, no. 7 (February 5, 2024). https://doi.org/10.1021/acs.est.3c09147.
- Nie, Y, X Li, Q Paletta, M Aragon, A Scott, and A Brandt. “Open-Source Sky Image Datasets for Solar Forecasting With Deep Learning: A Comprehensive Survey”, Renewable and Sustainable Energy Reviews, 189 (January 15, 2024): 113977. https://doi.org/10.1016/j.rser.2023.113977.
- Sherwin, Evan, Jeffrey Rutherford, Yuanlei Chen, Sam Aminfard, Eric Kort, Robert Jackson, and Adam Brandt. “Single-Blind Validation of Space-Based Point-Source Detection and Quantification of Onshore Methane Emissions”, Scientific Reports, 13 (March 7, 2023): 3836. https://doi.org/10.1038/s41598-023-30761-2.
- Jing, Liang, Hassan El-Houjeiri, Jean-Christophe Monfort, James Littlefield, Amjaad Al-Qahtani, Yash Dixit, Raymond Speth, Adam Brandt, Mohammad Masnadi, Heather MacLean, William Peltier, Deborah Gordon, and Joule Bergerson. “Understanding Variability in Petroleum Jet Fuel Life Cycle Greenhouse Gas Emissions to Inform Aviation Decarbonization”, Nature Communications, 13, no. 1 (December 21, 2022): 7853. https://doi.org/10.1038/s41467-022-35392-1.
- Zhang, Zhan, Evan Sherwin, Daniel Varon, and Adam Brandt. “Detecting and Quantifying Methane Emissions from Oil and Gas Production: Algorithm Development With Ground-Truth Calibration Based on Sentinel-2 Satellite Imagery”, Atmospheric Measurement Techniques, 15, no. 23 (December 13, 2022): 7155-69. https://doi.org/10.5194/amt-15-7155-2022.