For any power plant, achieving low generating costs is only half the battle as what is cheapest from an operational perspective may not be the most economically efficient in a wider sense. In fact, there may be a heavy price further down the line in terms of the impact upon human health.
This financial burden is no secret. Air pollution is responsible for a high number of hospital and emergency room visits, and has lead to an increase in rates of asthma, other pulmonary conditions, and heart attacks.
In the United States, these costs are estimated using an Environmental Benefits Mapping and Analysis Programme (BenMAP), provided by the Environmental Protection Agency (EPA). This open-source computer program calculates the number and economic value of pollution-related deaths and illnesses, and is typically used to make decisions on policy changes.
However, when running a power plant it can be difficult to see how health economics may tie in to the overall operational costs. As Athanasios Nenes of the Georgia Institute of Technology explains, ozone and fine particulate pollution is not a constant, adding an extra level of complication into the mix.
"Ozone and other pollutant concentrations vary substantially by day and by hour, depending on the emissions from local and regional sources, and also on atmospheric chemistry and atmospheric conditions such as temperature and mixing," Nenes says.
"Emissions from a specific hour of the day can have a disproportionately large impact on the pollution that forms, owing to the synergy with emissions from other hours combined with the effects of changing winds, atmospheric mixing and sunlight, or may have little. Because of this, the effect of a power plant, operating at constant capacity, on public health may vary considerably over time."
Nenes is a professor at Georgia Tech in the School of Chemistry and Biochemistry, and School of Earth and Atmospheric Sciences. Along with colleagues, he recently developed a promising new solution. The Air Pollutant Optimization model (APOM), which was described in the journal Proceedings of the National Academy of Sciences in August, combines information about power generation with real-time air quality predictions. It may enable utility companies to develop a more nuanced form of emission control.
"Our method targets the high-impact times, reducing the health impacts at a lower cost," explains Nenes. "During low-impact time periods, the operations of power plants do not need to change, minimising the impact on electricity generation cost. During high-impact times, some generation is either shifted to power plants that will have a lower effect on ozone concentrations in highly populated regions at that specific time, or generation is reduced using demand management."
The result is that ozone levels can be reduced without paying unnecessary costs. As the method is only utilised when its benefits are the greatest, and because it is managed entirely through existing system operations, power plants will not need to purchase expensive pollution control equipment, or implement pollution-saving measures on an ongoing basis.
The potential benefits of the APOM speak for themselves. Using data from the state of Georgia as a test case, researchers presented some eye-opening conclusions about savings that could have been achieved. They found that over selected months from 2004-2011, health impacts could have been reduced by $176m, while increasing generating costs by just $84m, amounting to a total cost saving of $92m.
The precise balance would be different for power systems elsewhere, depending on the type of fuel used, the degree of flexibility, and proximity of the facilities to residential areas. Even within Georgia the figures are likely to have changed significantly following the test case period – the state has since installed flue gas desulphurisation units that have reduced emission of sulphates, a key source of fine particulate matter, by up to 97%.
Considering this, the underlying principle is clear. By sifting through a huge number of possible electricity generating patterns the APOM could be used to forecast the most mathematically optimal solution for any given area.
"Based on a reduced form model generated daily from air quality forecasts, APOM can consider many ways to adjust power plant operations as the day goes on, balancing ozone impacts with power plant operation costs on an hourly basis," says Nenes. "The hourly dispatch decisions for the day could then be communicated to the power companies for application."
This ‘reduced form model’ is based on the existing template of the Congestion Mitigation and Air Quality programme (CMAQ). CMAQ is a state-of-the-art method used by the scientific community and government agencies worldwide to simulate and study air pollution formation. It is not, however, suitable for use in real-time, being too slow and complex to run the thousands of processes times required by APOM. Therefore the new model uses only the ‘sensitivities’ derived from the full system to produce predictions.
"Using a technique developed by our team called DDM-3D, we can determine from one CMAQ simulation how air quality across a region responds to emissions from power plants and other targeted sources, without repeating CMAQ simulations for each emissions scenario," Nenes explains. "This leads to a reduced form model that responds almost exactly like CMAQ to changes in emissions, but takes seconds, instead of days, to run."
The compact CMAQ will enable power plants to make necessary trade-offs, informed by essential knowledge. It would mean, for instance, that when the wind direction changes and emissions are being carried from the facility towards a residential area, the plant could run at a lower generating capacity and an alternative power unit could be fired-up.
For now, the researchers’ goal is to perform a larger US study before extending the model further afield. Power plants currently account for around a third of the total pollution, but the team would also like to examine emissions from transportation, and evaluate the impacts on ecosystems in addition to human health.
"We would like to apply this approach to countries or regions with severe air quality issues," says Nenes. "We can test the impacts of electricity system and natural gas system congestion on air quality, and better quantify the benefits and costs of renewables and other clean energy alternatives like electric cars. We can analyse and quantify the air-quality impacts of climate change policies."
These aims may sound ambitious, but examining the economics of power plants undoubtedly provides the critical first step.