Study shows that wind power decreases power sector emissions
A 2011 study conducted by Argonne researchers examined the effects of introducing wind energy into the electric power system. After conducting a detailed emissions analysis based on a comprehensive model of power system operations, they found that as wind power penetration increases, pollutant emissions decrease overall due to the replacement of fossil fuels.
“Our study clearly shows that using wind to generate electricity has a discernible impact on pollution,” said Audun Botterud, who led the research team.
The study looked in detail at the effects of start-up and cycling events as a result of increasing wind power use. Wind is variable and uncertain, and can lead to fossil fuel-using plants to adjust their outputs, start up, or shut down to accommodate wind inputs.
In their analysis, the researchers looked at power system operations and resulting emissions during both start-up and operational periods, considering the impacts of emissions control technologies. Using power grid data from 2006 as a starting point, a case study on the Illinois power system was performed to simulate the impact of different wind generation levels for a 4-month period. Total emissions from all power plants in the grid for 7 pollutants were determined.
“Our analysis shows that total greenhouse gas emissions decrease with increasing wind power use, despite more start-ups and increased cycling of fossil fuel power plants,” said Botterud.
The emissions analysis was carried out in large part by students Lauren Valentino (University of Illinois at Urbana-Champaign) and Viviana Valenzuela (Georgia Institute of Technology) while they were working as DOE-funded wind interns at Argonne National Laboratory.
A key challenge in integrating wind power into the power system is to accommodate the uncertainty and variability of wind power in the scheduling and dispatch of supply and demand resources in the grid. The researchers are also focusing on the potential use of probabilistic wind power forecasts as input to the scheduling decisions. In a related study, the team showed that probabilistic forecasts can improve the performance of a power system, both in terms of cost and reliability. This work was carried out in collaboration with INESC TEC, a research institute in Portugal.
“Our results show that as the amount of renewable energy increases, power system operators will need to change their procedures to better account for the uncertainty in renewable energy. Forecasting will become increasingly important for this purpose, and probabilistic forecasts give information on the expected uncertainty in the wind power,” said Zhi Zhou, lead author of the second study.
It is important to better understand the emissions implications of a large-scale expansion of renewable energy, as reducing emissions is one of the main driving forces behind the support for these energy sources. Furthermore, new operational tools are needed to accommodate more wind power into the grid. The algorithms and models developed in this project advance the state-of-the art for forecasting and scheduling tools used in power system operations.
In the future, the researchers will continue to work on the application of probabilistic methods to efficiently address the uncertainty and variability in wind power. They are also looking at some of the same integration challenges in the context of solar energy and are starting to investigate the longer term implications of a large-scale expansion of renewable energy.
This work was funded by the U.S. Department of Energy, Energy Efficiency and Renewable Energy, Wind Power Program.
Total emissions results. The blue striped bars represent emissions resulting from normal operational periods, and the red dotted bars represent emissions resulting from start-ups. Carbon dioxide equivalent (CO2e) includes CO2, CH4, and N2O.
“System-Wide Emissions Implications of Increased Wind Power Penetration,” L. Valentino, V. Valenzuela, A. Botterud, Zhi Zhou and G. Conzelmann, Environmental Science and Technology, 2012, 46 (7), pp 4200–4206. Direct link: http://dx.doi.org/10.1021/es2038432.
“Application of Probabilistic Wind Power Forecasting in Electricity Markets,” Z. Zhou, A. Botterud, J. Wang, R. J. Bessa, H. Keko, J. Sumaili and V. Miranda, Wind Energy , in press. Direct link: http://dx.doi.org/10.1002/we.1496.