V. Discussion
This section will start by discussing the implications of the results of this study, will offer some general comments on the concept of adding ancillary ES to the electricity grid, will reflect upon changes that could have improved this study and possibilities for future studies, and will conclude with some final thoughts on the impact of adding ES to the electricity grid will have on attempts to reduce anthropogenic climate change.
The comparison between BL and S1 allows this study to touch base with the reality of ES’s utility as a means of reducing the actual GHG emissions of the contemporary electricity grid in order to impact the causes of anthropogenic climate change. As the results show simply adding bulk ES to the contemporary electric utility grid in the model region would likely contribute to a relatively small impact on GHG emissions. Thus, while adding bulk ES to the utility grid may have a number of other tangible benefits, it will not be a sufficient step in an effort to reduce the impacts of anthropogenic climate change. However, S2, S3, and S4 indicate bulk ES can certainly play a part in such efforts.
The discovery that the net GHG emissions of BL and S1 are virtually identical is of particular interest because it detracts from the notion that the additional GHG emissions caused by inefficiencies in traditional fossil-fuel plants can be overcome through the use of bulk ES. Instead, overcoming such inefficiencies may rely solely upon the use of ancillary ES technologies (as discussed below) or other interventions. However, with the development of a model with a more detailed and sophisticated incorporation of generating inefficiencies (e.g. one that simulates generation on a plant by plant basis) might be needed to definitively conclude the matter.
The results of this study show that it is technically feasible to reduce overall and per unit GHG emissions and meet consumer electricity demand. In particular S2, S3, and S4 show 11%, 83%, and 55% respective reductions in GHG emissions compared to BL and S1. Since each of these scenarios can attribute a non-negligible portion of their GHG emissions to bulk ES capacity, the calculated reductions are likely to be less than those of any comparable study that did not consider the need for bulk ES.
The range of emissions rates in S2, S3, and S4 (with S3 the ‘nuclear future’ scenario being the lowest of them all) suggests that a move towards grid development policies and plans that focus on nuclear-powered generation (in combination with the implementation of new bulk ES technologies) could have the greatest impact on anthropogenic climate change. While this seems indisputable from the results, governments and regulatory authorities would need to take into account the other concerns surrounding nuclear-powered generators before pursuing S3 as a particular course of action.
It should also be noted that the GHG emissions values associated with the renewable generation capacity in the model is based on the values for on-shore wind turbines, which were considered the most viable type of turbine in the model region. According to Weisser (2007) off-shore wind turbines may have a lower GHG emission rate (i.e. 9-19 kgCO2e/MWh) than that of on-shore turbines (i.e. 8-30 kgCO2e/MWh). Therefore, the comparison of S3 and S4 may not be entirely straightforward for regions with significant coastal resources. Also, with regard to shorter-term planning (i.e. over the next two decades) decision makers will need to consider whether it is more technically and politically feasible to quickly set up wind and solar farms or nuclear power plants. Similarly, for longer-term planning (i.e. beyond 2030) consideration will need to be taken with regard to the limited nature of nuclear fuel sources and the ultimate disposal of spent fuel; whereas with intermittent renewable resources, neither of these issues are a concern.
From another perspective, the results show that the amount of installed bulk ES necessary to make S2, S3, and S4 viable paints a picture that also favors the nuclear future option represented by S3. The results of S2 show the need for about 36.7 GW of power releasing capability over the course of an hour, and S4 would require the installation of enough ES to release about 57.9 GW of power during an hour. Meanwhile, S3 (with significantly lower emissions) requires only about 28.7 GW of power absorbing capability from ES per hour. Although, it should be noted that some of the ES requirements could be met with ancillary ES capacity – especially in the most extreme conditions, none of these figures are insignificant in comparison to the overall generation requirements of the system (i.e. nearly 120 GW during the weekly peak).
Since S4 has more than triple the installed renewable generation capacity of S2 but does not require triple the amount of installed ES capability, it can be concluded there is likely to be a diminishing requirement for ES at higher percentages of intermittent generation capacity. However, since S2 and S3 have comparable amounts of intermittent generation capacity, the fact that S2 has only about 72 GW of installed non-intermittent capacity where S3 has about 80 GW of such capacity indicates (not surprisingly) the amount of ES required is inversely proportionate to the amount of non-intermittent generation capacity. However, it is not entirely clear from these results which (if either) factor has a greater impact. Likewise, predicting the correct amount of required ES may be a complex algorithm that accounts for both factors as well as their relation to electricity demand.
Since this model amalgamates the generic use of bulk ES, it only calculates the so-called direct GHG emissions associated with ES. Other GHG emissions (e.g. those associated with construction, maintenance, and demolition) would be highly dependent upon the specific types of bulk ES technology that are installed. Based on Weisser’s (2007) suggestion that LCA GHG emissions could vary by an order of magnitude, the importance of understanding the specific amount of ES required to be installed during any particular grid development strategy becomes more important if attempts are made to include full LCA GHG emission information. Thus, for conscientious planners the difference between needing to install 28.7 GW as opposed to 57.9 GW of ES maybe a pivotal factor in choosing a strategy aimed at decarbonizing an electric utility grid that reaches beyond simple financial considerations.
In the interest of ensuring that this study was applicable to real world situations, the amount of generation capacity by fuel type was closely matched with that of a the PJM Interconnection in the USA (see section III.2. for details). Thus, in order to measure the applicability of this study, the rate of GHG emissions simulated in the model region can be compared to the official rate of GHG emissions declared by the PJM for 2009. According to PJM (2009) the annual average rate of GHG emissions for 2009 was 1,137 lbs. CO2e/MWh (or 517 kgCO2e/MWh). Furthermore, the range for the average rate of GHG emissions varied monthly in 2009 from a rate of 484-561 kgCO2e/MWh. Curiously, this matches up with the lower end of the range of the simulated rate of emissions in BL, which was 557 kgCO2e/MWh. Of course, the PJM rates are well below the upper end of the range of the simulated rate of emissions in BL, which was 761 kgCO2e/MWh.
This discrepancy could be related to a number of factors. For instance, it could indicate that PJM has relatively new (and efficient) generation technologies compared to the studies discussed by Weisser (2007). Of course, another explanation is that the cumulative effect of all of the subtle differences between the model region and the real PJM territory (e.g. the difference between the actual consumption patterns and the sinusoidal simulation) have resulted in an over estimation of GHG emissions. However, it seems likely that the discrepancy indicates the figures PJM used to calculate the results displayed in their report do not include full LCA GHG emissions but rather simple direct GHG emissions, which would explain a bias in which the figures in this study should be higher than those in the PJM report. Thus, even if the final figures are not interchangeable on a 1 to 1 basis with the PJM report, the real world results (i.e. the total actual GHG emissions released into the atmosphere) associated with operating the PJM electric utility grid as a whole should follow a trend similar to the results of this study if bulk ES technologies are installed and the installed generation capacity shifts in a manner similar to one of the three scenarios S2, S3, or S4.
As mentioned in section II.2., the installation of ancillary ES (or the use of plug-in EVs) could hypothetically reduce or eliminate the need for traditional generation plants to be operated in spinning reserve or frequency regulation modes. In so doing the actual GHG emissions associated with S2, S3, and S4 (especially those of S2 and S4 which have higher percentages of fossil fuel powered plants in operation) would be lowered below those shown in the results. Furthermore, it is likely that ancillary ES, which tends to have higher roundtrip cycle efficiencies than bulk ES technologies, would not have as many direct GHG emissions associated with its use. The one caveat to such a suggestion being that the use of ancillary ES, which tends to have a much more rapid self-discharge rate than bulk ES technologies, would need to be carefully calibrated and coordinated to ensure that unnecessary losses are not regularly experienced on the system.
More specifically – assuming that the transportation system in the USA continues to rely heavily upon the use of personal cars and that the current research, development, and deployment trends for plug-in EVs of the past few years continue – the use of plug-in EVs seems at least superficially favorable compared to typical ancillary ES from perspective of reducing the overall GHG emissions of the USA. Even though the current direct GHG emissions from the electric utility grid may be higher than the current direct emissions from petroleum-fueled vehicles. The opportunity to symbiotically eliminate direct GHG emissions from the transportation sector, reduce additional GHG emissions caused by inefficient electricity generation during ancillary generation, and creating a grid that is better prepared to cope with increased intermittent renewable generation capacity appears to be a win-win-win scenario. However, in order to fully validate this notion a comprehensive LCA study of the GHG emissions (as opposed to a simple direct emission comparison) associated with plug-in EVs must be performed.
Before making any final conclusions, it is prudent to reflect upon the possibility of redoing, improving, or building upon the findings of this study. First and foremost, the attempt to survey utilities and ES manufacturers, which was not fruitful, could have been completely omitted. It was initially attempted to secure some original data. However, even the most professional and tenacious attempts to encourage participants to respond apparently could not overcome the barriers to a high response rate (e.g. a lack of prestige or name recognition, a short timeframe, and a lack of direct incentives). In retrospect, even a 100% response rate would likely not have provided any more insight into the current state of ES technological development (let alone that of the near future) than the supplementary sources upon which this study relied. One suggestion for future studies with similar goals would be to skip the formal survey process and rely more heavily upon academic articles and an investigative strategy (i.e. searching for publically available information such as users manuals or notes from intra-industry conferences). Employing such a strategy would have saved several weeks of time spent on survey creation, distribution, and collection. This time would have been spent more effectively on model development and running an increased number of trial runs.
Specifically, an increased number of trial runs would have allowed a better understanding of how incrementally increasing and decreasing various types of generation technologies and/or incrementally improving the roundtrip cycle efficiency of ES technologies impacts GHG emissions. Also, observing the results pertaining to the percentage of the net GHG emissions attributable to the use of bulk ES technology begs the question: is there a maximum percentage of the GHG emissions that can be attributed to the use of ES technology? An initial hypothesis is that the potential attributable percentage would prove to display an asymptotic nature related to the roundtrip cycle efficiency of ES (i.e. the attributable percentage would not exceed the percentage of roundtrip losses incurred by the ES technology).
Additionally, a more sophisticated model that is based on the aggregated electricity generation of hundreds or thousands of individual plants would have allowed for a better understanding of the nature of spinning reserve and frequency regulation inefficiencies in traditional plants. Thus, a greater level of confidence could be assumed about similarities in the results showing that BL and S1 are so similar. However, to produce such a model the computing power of Microsoft Excel would likely not be sufficient, so another software base for the model would be required.
Finally, based on all of the background research and the results of the computer simulation performed in this study, it has been concluded that the inclusion of bulk and ancillary energy storage on electric utility grids over the next two decades would benefit attempts to mitigate the causes of anthropogenic climate change by reducing the overall greenhouse gas emissions released by electric utility grid operators. However, such an effort to include energy storage on an electric utility grid will only result in significant benefits if accompanied by a shift in generation capacity away from fossil fuels and toward nuclear and/or renewable generating capacity. In so doing, electric utility operators will be able to mitigate the causes of anthropogenic climate change without adversely impacting the habits of contemporary electricity consumers in developed countries.
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