Dissertation Proposal: Energy Storage

Hello Reader,

As I promised in my previous post, this post includes part of my dissertation proposal. I have decided to spare everyone the mundane details surrounding my time table and contingency plans. I hope this post gives you a good sense of the need for studying electrical energy storage.
----------------------------------------

SUMMARY

In the context of global climate change, energy consumed to generate electricity for regional electric utility grids plays a significant role. The need for a comprehensive simulation focused on optimizing greenhouse gas emissions on a contemporary, developed electric utility grid through the use of viable, large-scale energy storage is established. A methodology for developing such a simulation and a schedule for producing a study are proposed. Contingencies to potential problems are also addressed.

I.    INTRODUCTION

As developed countries seek to modernize their electric utility grid, whether for the sake of cost savings, the environment, energy security, grid stability, or some combination thereof, many utility companies have started to implement or consider the use of large-scale energy storage (ES) systems to meet present and future demand. Careful consideration of present and future grid scenarios and issues such as inefficiencies in traditional non-renewable energy generation (Dell and Rand 2001), the intermittency of renewable energy sources (Pickard et al 2009), the increased use of distributed generation technologies (Bayod-Rújula 2009), and the introduction of plug-in electric vehicles (EVs) (Verhaegh et al 2010) provide a number of opportunities to implement energy management ES systems that reduce the overall greenhouse gas (GHG) emissions of a grid thereby lessening the region’s impact on anthropogenic climate change.

II.    JUSTIFICATION

Sims et al (2007) explain that energy use currently accounts for 70 percent of global GHG emissions and of this 40 percent is used to produce electricity. Furthermore, approximately two-thirds of electricity is generated through the combustion of fossil fuels (i.e. coal, lignites, natural gas, and oil), which creates direct GHG emissions (Sims et al 2007). As a result, comprehensive attempts to mitigate anthropogenic climate change will likely involve addressing electricity generation and use in some form or another.

Due to the contemporary structure of electric grids, electricity must be generated at the time of use, which causes inefficiencies that exacerbate the associated impact on anthropogenic climate change. Even everyday conditions such as diurnal fluctuations in demand are a source of avoidable emissions (Dell and Rand 2001). The inclusion of ES systems on a grid can decouple electricity supply from demand, reducing the impact of such inefficiencies (Chen et al 2009, Dell and Rand 2001).

Chen et al (2009) indicate diurnal and annual demand fluctuations not only cause generation inefficiencies but also require that generation capacity be over-built to meet peak demand that may only last a few hours each year. With sufficient ES capacity to meet such peak-demand the construction of additional primary generation capacity can be delayed or avoided (Dell and Rand 2001).

Dell and Rand (2001) suggest that peak-shaving and load-leveling with ES can reduce the need to maintain plants in “spinning reserve” (i.e. generating electricity at sub-nominal values) to avoid a short-term shutdown. When fossil fuel plants are operating in spinning reserve, the GHG emissions per kWh of generation is greater than emissions during optimal generation (Voorspools and D’haeseleer 2000). Instead, plants can be maintained at optimal generation levels by running at a constant or near-constant rate (load-leveling or peak-shaving respectively), charging ES systems during low-demand and allowing ES systems to meet demand during peak conditions (Chen et al 2009).

In addition to the limitations of traditional energy production, ES appears to play an even greater role in future development plans and attempts to mitigate climate change. Perhaps most significantly, as developed nations look to integrate emission-free renewable energy technologies with intermittent generation into their energy portfolio (e.g. NCSC 2009), the integration of ES systems on the utility grid may be not only desirable but necessary for the practical and economic viability of large-scale implementation (e.g. Pickard et al 2009, Aguado et al 2009, Benitez et al 2008). Additionally, Voorspools et al (2000) has suggested that studies analyzing GHG emissions associated with “emission-free” technologies need to take into account indirect emission embedded in construction in addition to the direct emissions from the fuel cycle, which is traditionally the limit of the scope of energy generation analyses.

Bayod-Rújula (2009) and Verhaegh et al (2010) suggest that the future of the electricity grid in developed countries will likely involve increased distributed (non-centralized) generation and/or the wide-scale use of EVs and residential heat-pumps, which may vastly alter the nature of the contemporary diurnal supply and demand cycles. In the US in particular mass production of EVs seems imminent within the next several years (Woody and Krauss 2010). Thus, earlier studies that have not consider these developments will need to be reexamined or taken with caution.

Chen et al (2009) have thoroughly explored the state of ES technologies in the present and near-future. Their analysis roughly divides ES technologies into two categories (see TABLE J1), those that are useful for power quality management (capable of making short-term, high power, low energy interventions) and those that are useful for energy management (capable of mediating variations in supply and demand). Though power quality management ES technologies have a definite role to play to play in the future stability of the electricity grid (e.g. Shayeghi et al 2009 and Hartikainen et al 2007), their likely contributions seem difficult to quantify in an absolute manner. Therefore, it seems more reasonable and useful to focus on energy management ES and the tangible effects it could have on GHG emissions in the near-term. Of course, focusing on the near-term means that some technologies are not yet viable on a commercial (large-) scale (Chen et al 2009). In fact without even considering cost limitations, only pumped-hydro power, compressed-air, and certain types of batteries and flow-batteries have examples of successfully developed MW-scale systems capable of operating for multiple hours.

Voorspools and D’haeseleer (2000) stress the need for a simulation tool, stating: “Since it would be impractical to constantly monitor the instantaneous composition of the power system and to calculate (or measure) the corresponding emissions… [f]or studies or scenarios carried out for future or hypothetical developments, monitoring is not even an option and, hence, a simulation tool is essential.” They also highlight the need for “instantaneous” rather than “linear” emission approximations (i.e. not using daily or annual average figures) to accurately determine GHG emissions under varying demand-supply scenarios (Voorspools and D’haeseleer 2000). Despite the need for a sufficiently resolved time-scale, the duration need not be exceptionally long. For example, Verhaegh et al (2010) elected to focus on one week periods during different seasons (e.g. winter and summer), which suggests that this is a reasonable approach to avoid simulating an entire year’s worth of data.

Finally, while a number of energy simulation studies have examined some combination of traditional generation, intermittent generation, and ES systems, the majority of recent studies appear to have optimized their results for financial gain (e.g. Aguado et al 2009, Benitez et al 2008, and Crampes and Moreaux 2010); however, optimizing a system for financial gain will likely result in inefficiencies with regard to GHG emissions (Voorspools and D’haeseleer 2000). Thus, with the issue of global climate change in mind, there is a distinct need to consider situations optimized to reduce GHG emissions.

III.    OBJECTIVE

The objective of this study is to assess the potential near-term impact on greenhouse gas emissions of a large-scale implementation of energy storage systems on an electric utility grid in a region with a fully developed grid system. To achieve this objective, technical data will be collected and a simulation will be developed.

<---sections omitted here--->

VII.    OUTCOME

This study should fill a significant gap in the literature by combining ES with a variety plausible demand and supply scenarios and focusing on GHG emissions optimization rather than pure fiscal optimization. The final results of this study should offer insight regarding the degree to which the addition of energy storage on an already-developed, though evolving electricity grid can enable GHG emissions reductions. These results should be particularly valuable as utility companies and regulators evaluate their options to meet and develop current and near-term energy portfolio standards.

For a list of references please see the accompanying post.