Opinion, Berkeley Blogs

A modern equation for energy

By Santiago Miret


The global energy landscape continues to change as more and more renewable energy sources and diversified energy systems become a substantial component of the energy infrastructures across the world. Given the onset of these new energy systems, the overarching return of diverse energy sources will become a more and more important factor in the future design of the global energy mix.

One important factor of that equation is the economic return, which determines how much profit investors can expect to make on a given energy technology. Economic return drives many energy developments, as shown by the recent boom in the residential solar industry across the US and the EU that has been motivated by falling solar module prices and innovative business models.

[caption id="" align="alignnone" width="461"] Installed Solar Capacity Across the World - Source: The Guardian[/caption]

Yet, as energy systems across the world continue to develop in diverse directions, including the surge of microgrids and novel storage technologies to aid the development of renewable based energy infrastructures, another metric of return may become more relevant when making future energy decisions. This metric is the named the Energy Return on Energy Invested, or EROEI for short.

The concept of the metric is fairly simple: The EROEI factor equals the Energy Output of a given energy source, divided by the Energy Input required to produce the usable energy. In a gallon of gasoline, for example, the energy output would be the energy extracted from burning that gallon in the motor of a car, whereas the energy input would contain the energy required to extract the crude oil from the well, the energy used in the refining process of oil to gasoline, the energy consumed in the transportation of the oil to the refinery and the transportation of the gasoline to the car, as well as other miscellaneous energy cost that along the way. A good EROEI ensures that a given energy sources will produce more energy over its lifetime than was required to make it functional, making EROEI an important metric.

The EROEI calculations of many fossil fuels and further traditional energy sources often involve relatively straightforward energy outputs, with some uncertainties on the energy input side. However, the EROEI of newer energy sources, especially wind and solar power, involve many uncertainties in both the energy output and energy input calculations, which can lead to many diverging numbers for various technologies.

Moreover, geography becomes a critical factor in the EROEI of many renewable energy technologies, as the location largely influences the energy output of renewables: A solar module in the Arizona dessert, for example, will produce more energy over its lifetime than a solar module on a rooftop in Massachusetts. Additionally, renewable energy technologies often require energy-storage mechanisms to function effectively within a larger system, such as in a microgrid, which further adds to the energy input costs required to make the larger system functional.

Given the novelty of many storage technologies, the energy costs of many of these systems still remain uncertain. However, current storage technologies, as outlined here, normally have substantial energy input costs associated with them, leading to unfavorable EROEI for many renewable + storage energy systems.

Due to the factors outlined above, many renewable energy systems tend to have unfavorable EROEI factors when compared to more traditional energy sources, such as fossil fuels, hydropower and nuclear energy. A recent study by Weißbach et al. outlines some EROEI for many energy sources in Germany. The figure below summarizes their essential findings:

Weißbach et al. calculated that the economic threshold for an EROEI to be a viable energy source in a developed economy, such as Germany, should be ~7 to maintain the energy requirements of the country and its economy. As seen from the graph above, many new renewable energy technologies fail to meet this requirement, especially when combined with storage system (referred to as the "buffered" state in the figure). While other studies have obtained differing EROEI values for the various energy technologies, the general agreement suggests that renewable energy technologies have significantly lower EROEI than traditional energy sources, suggesting that an energy infrastructure solely based on solar and wind energy sources is unfeasible with current technologies.

However, an energy system of renewable-energy technologies, combined with traditional carbon neutral sources with high EROEI (such as hydropower and nuclear energy), are already being developed: France, a country that has traditionally used nuclear power to supply its energy demands, is moving to a more and more carbon neutral system, and the province of Ontario, Canada, has been combining its hydropower capacity with increasing solar and wind power installations to enable an energy infrastructure without coal as an energy carrier.

The EROEI metric should be taken as a caution that the current energy challenge is extraordinarily complex. Additionally, future energy infrastructures that are both environmentally and economically sustainable will require substantial innovations leading to smart and resourceful systems that can integrate newer energy sources as significant carriers.

However, technological advancement of renewable energy sources, as well as energy-storage technologies are not the only way in which the return on energy for the system can be enhanced. Demand-response systems, which try to minimize energy loss and thereby reduce buffering requirements, will also help increase EROEI values of modern energy systems. In the end, it will most likely be the interactions of various systems within the energy sphere that will yield the most innovative and also most viable solutions.

Cross-posted from BERC Blog, published online by the Berkeley Energy & Resources Exchange, a network of UC Berkeley scholars and industry professionals.