Skip to main content Skip to secondary navigation

Energy systems optimization

Main content start
Energy System research diagram
Figure 1. System diagram of integrated carbon dioxide capture and storage system. Source: Kang et al. 2011.

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.



*P.G. Brodrick, A.R. Brandt., L.J. Durlofsky. Optimal design and operation of integrated solar combined cycles under emissions intensity constraintsApplied 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


*Brodrick, P.G., A.R. Brandt., L.J. Durlofsky. Operational optimization of an integrated solar combined cycle under practical time-dependent constraintsEnergy. DOI: 10.1016/

*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 plantEnergy. DOI:10.1016/


*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 techniqueApplied energy. DOI: 10.1016/j.apenergy.2016.07.062 


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/

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 stationInternational Journal of Greenhouse Gas Control. DOI: 10.1016/j.ijggc.2015.11.023 


Kang, C.A., Brandt, A.R., Durlofsky, L.J. (2014) Optimizing heat integration in a flexible coal-natural gas power station with CO2 captureInternational Journal of Greenhouse Gas Control. DOI: 10.1016/j.ijggc.2014.09.019