According to new research, energy suppliers are underestimating the long-term effects that climate change is likely to have on future power demands. The research was published in the September edition of the academic journal Risk Analysis in September 2018. Sayanti Mukherjee, assistant professor of industrial and systems engineering at Buffalo, led the research team of scientists from the University of Buffalo and Purdue University. The current findings of the team suggest that traditional energy demand models are both unreliable and imprecise.
Existing models problems
At present, the vast bulk of climate scientists anticipate that temperatures worldwide will rise throughout the course of the 21st century. As a result, this is very likely to increase the demand for electricity as more people’s switch to air-conditioning in order to keep cool.
At present, one of the most common energy modelling systems used to predict future power demand is called MARKAL and named after MARKet and ALlocation. Unfortunately, it does not factor in climate change variability.
Another model often used is the National Energy Modelling System (NEMS), and like MARKAL fails to factor in climate change. In addition, it is limited to heating and cooling degree-days. A heating degree-day is defined as a day when the average temperature is above 65 degrees Fahrenheit (18 degrees Celsius). A cooling degree-day is when the average temperature is below 65 degrees.
Trouble is there are various ways to measure heating and cooling degree-days;, they are often calculated by adding the day’s highest temperature to the day’s lowest temperature and then dividing the sum by two. For instance, a high of 78° and a low of 60 °, results in an average temperature of 68°.
Nevertheless, the research team leader Sayanti Mukherjee, says, “The main weakness of this model is that it does not consider time. For instance, it could be 76° for 23 hours in just 60° for just one hour. But, the average temperature for the day would still be recorded at 68°. In addition, Sayanti Mukherjee says, “Choice of the accurate balance point temperature is highly contentious, and there is no consensus from the research community of how to best select it.”
The researchers developed a better way to predict future energy demands using a pair of more exact predictors — mean dew point temperature and extreme maximum temperature.
“Existing energy demand models haven’t kept pace with our increasing knowledge of how the climate is changing,” observes Sayanti Mukherjee, assistant professor of industrial and systems engineering at Buffalo, said in a news release. He adds, “This is troublesome because it could lead to supply inadequacy risks that cause more power outages, which can affect everything from national security and the digital economy to public health and the environment.”
The point is, forecasting how much electricity demand will rise, will depend on which model you use.
A new model
Because of these problems found with models that are more traditional for forecasting future, demands, the joint team of scientists at the University of Buffalo and Purdue University, developed a new forecasting model for energy demand. In this model, the team of researchers combined new climate prediction factors with energy, weather data, and social economic data. To test the new model they try to predict future power demand across the American state of Ohio. They found climate variability closely affected energy demand by the residential sector.
In addition, the new model demonstrated a moderate rise in the average dew point temperature that could boost energy demand by 20% in the residential sector and 14% in the industrial sector.
By comparison, the Public Utility Commission of Ohio (PUCO), which does not consider climate change in its models, predicts residential demand increases of less than 4% up to 2033.
It is similar in the commercial sector, where the researchers say demand could increase to 14%. Again, PUCO’s forecasts are lower, 3.2%. The industrial sector is less sensitive to temperature fluctuation; but researchers say the demand could still exceed projections.
During the winter months, variations between the models are less significant. That is due, in part, to the low percentage (22.6%) of Ohio residents who heat their homes using electricity.
What is clear observes Roshanak Nateghi, assistant professor of industrial and environmental engineering at Purdue, “The availability of public data in the energy sector, combined with advances in algorithmic modelling, has enabled us to go beyond existing approaches that often exhibit poor predictive performance. As a result, we’re able to better identify the nexus between energy demand and climate change, and assess future supply inadequacy risks.”
While the study is restricted to Ohio, researchers say the model can be applied to other states. To communicate results, the researchers used heat maps, which provide an immediate visual summary of the data represented by colours. The idea, they say, is to better inform decision makers with accurate and easy to understand information.
At present, fossil fuel supplies 2/3 of the United States power supply. The findings suggest that unless coal and natural gas generation are phased out in favour of such renewable power generation technologies like wind, solar and hydro. It is likely that there will be an increased demand for electricity due to increasing carbon emissions caused by failing to phase out traditional generation sources such as coal and natural gas.
The research was financed in part by the Purdue Climate Change Research Centre and the National Science Foundation.