Dong Gu Choi

Life Cycle Energy and Greenhouse Gas Emissions for an Ethanol Production Process Based on Blue-Green Algae

Ethanol can be produced via an intracellular photosynthetic process in cyanobacteria (blue-green algae), excreted through the cell walls, collected from closed photobioreactors as a dilute ethanol-in-water solution, and purified to fuel grade ethanol. This sequence forms the basis for a biofuel production process that is currently being examined for its commercial potential. In this paper, we calculate the life cycle energy and greenhouse gas emissions for three different system scenarios for this proposed ethanol production process, using process simulations and thermodynamic calculations. The energy required for ethanol separation increases rapidly for low initial concentrations of ethanol, and, unlike other biofuel systems, there is little waste biomass available to provide process heat and electricity to offset those energy requirements. The ethanol purification process is a major consumer of energy and a significant contributor to the carbon footprint. With a lead scenario based on a natural-gas-fueled combined heat and power system to provide process electricity and extra heat and conservative assumptions around the ethanol separation process, the net life cycle energy consumption, excluding photosynthesis, ranges from 0.55 MJ/MJ(EtOH) down to 0.20 MJ/ MJ(EtOH), and the net life cycle greenhouse gas emissions range from 29.8 g CO₂e/MJ(EtOH) down to 12.3 g CO₂e/MJ(EtOH) for initial ethanol concentrations from 0.5 wt % to 5 wt %. In comparison to gasoline, these predicted values represent 67% and 87% reductions in the carbon footprint for this ethanol fuel on a energy equivalent basis. Energy consumption and greenhouse gas emissions can be further reduced via employment of higher efficiency heat exchangers in ethanol purification and/ or with use of solar thermal for some of the process heat.

Coordinated EV adoption: double-digit reductions in emissions and fuel use for

Adoption of electric vehicles (EVs) would affect the costs and sources of electricity and the United States efficiency requirements for conventional vehicles (CVs). We model EV adoption scenarios in each of six regions of the Eastern Interconnection, containing 70% of the United States population. We develop electricity system optimization models at the multidecade, day-ahead, and hour-ahead time scales, incorporating spatial wind energy modeling, endogenous modeling of CV efficiencies, projections for EV efficiencies, and projected CV and EV costs. We find two means to reduce total consumer expenditure (TCE): (i) controlling charge timing and (ii) unlinking the fuel economy regulations for CVs from EVs. Although EVs provide minimal direct GHG reductions, controlled charging provides load flexibility, lowering the cost of renewable electricity. Without EVs, a 33% renewable electricity standard (RES) would cost

40/vehicle-year.

193/vehicle-year more than the reference case (10% RES). Combining a 33% RES, EVs with controlled charging and unlinking would reduce combined electric- and vehicle-sector CO2 emissions by 27% and reduce gasoline consumption by 59% for

A long-term capacity expansion planning model for an electric power system integrating large-size renewable energy technologies

40/vehicle-year more than the reference case. Coordinating EV adoption with adoption of controlled charging, unlinked fuel economy regulations, and renewable electricity standards would provide low-cost reductions in emissions and fuel usage.

Biofuel Lifecycle Energy and Environmental Impacts: The Challenges of Co-product Allocation

Abstract The recent interest in reducing greenhouse gas emissions and the recent technical evolution of energy networks to smart grids have facilitated the integration of renewable energy technologies (RETs) into the electricity sector around the world. Although renewable energy provides substantial benefits for the climate and the economy, the large-size deployment of RETs could possibly hurt the level of power system reliability because of the RETs’ technical limitations, intermittency, and non-dispatchability. Many power system planners and operators consider this a critical problem. This paper proposes a possible solution to this problem by designing a new stochastic optimization model for the long-term capacity expansion planning of a power system explicitly incorporating the uncertainty associated with RETs, and develops its solution by using the sample average approximation method. A numerical analysis then shows the effects of the large-scale integration of RETs on not only the power system’s reliability level but also, and consequentially, its long-term capacity expansion planning. From the results of the numerical analysis, we show that our proposed model can develop a long-term capacity expansion plan that is more robust with respect to uncertain RETs and quantify the capacity the system requires to be reliable.

Increasing the likelihood of large-scale grid-enabled vehicle (GEV) penetration through appropriate design choices

Calculations of energy, greenhouse gas emissions, and other environmental impacts from biofuel production often allocate impacts between biofuel and its co-products by calculating that co-products substitute for other products. We illustrate the issues of co-product allocation with a case study of corn-derived ethanol, and show that the choice of allocation procedure and parameters can significantly influence the results. This is an open issue in environmental lifecycle assessment methodology; there are research opportunities to determine co-product allocation values by using data that relate co-product utilization to land use or market changes.

An electricity generation planning model incorporating demand response

The likelihood of widespread GEV adoption will be determined by pending vehicle, business model and policy design choices. Most designs to date have not made large-scale adoption a central design goal. We review the impacts of maximum charging rate, vehicle-to-grid (V2G) and vehicle-to-building (V2B) capabilities, and the charging business model on consumer acceptance and on the electric distribution, electric power, and overall energy systems. The analysis suggests that poor design choices can lead to significant increased cost and decreased consumer adoption. Competing technologies, such as high efficiency internal combustion engines (ICEs) and biofuels, increase the importance of GEV design choices.