Optimization of energy systems
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 has applied optimization techniques to two problems: optimizing carbon capture and storage technologies and modeling the transition to oil substitutes within an optimization framework.
Carbon dioxide capture and storage optimization
Our group uses optimization to improve the design and operation of carbon dioxide capture and storage (CCS) technologies, with a particular focus on integrating CCS technologies into future electricity systems with high fractions of intermittent renewable energy penetration.
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 focuses on building optimization models of integrated CCS and renewable facilities. 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.
R.S. Middleton, A.R. Brandt (2012). Using Infrastructure Optimization to Reduce Greenhouse Gas Emissions from Oil Sands Extraction and Processing. Environmental Science & Technology. DOI: 10.1021/es3035895
Nemet, G., A.R. Brandt (2011). Willingness to pay for a climate backstop: Liquid fuel producers and direct CO2 air capture. The Energy Journal 33(1): 53-81. DOI:10.5547/ISSN0195-6574-EJ-Vol33-No1-3
Brandt, A.R. (2011) ROMEO model documentation: The Regional Optimization Model for Environmental impacts from Oil Substitutes Model (version 2.0) [PDF]
Kang, C.A., A.R. Brandt, L. Durlofsky (2011) Optimal operation of an integrated energy system including fossil fuel power generation, CO2 capture and wind. Energy 36(2011): 6806-6820. DOI:10.1016/j.energy.2011.10.015
Brandt A.R., R.J. Plevin and A.E. Farrell (2010). Dynamics of the oil transition: Modeling capacity, depletion, and emissions. Energy. 35(7): 2852-2860. DOI:10.1016/j.energy.2010.03.014
Brandt, A.R. (2009) Greenhouse gas emissions from oil substitutes: Dynamics, resources, and systems behavior. Stanford Energy Seminar. [PDF]