In this chapter, it was shown that there is a disparity between whole system benefits and market rewarded benefits. The former approach is particularly relevant to a policy maker whereas the latter is of interest to a private investor seeking a positive return. Previous studies investigating storage value can be broadly categorized as those in which storage has a profit maximising function and those whereby profit maximisation is a secondary objective. From previous studies investigating storage value, even lesser emphasis was placed on storage operations; rather the focus has been on the revenues storage generated. The optimal charging and discharging patterns required to maximise revenues in GB markets is still unknown and investigated in Chapters 5 and 6.
In the profit maximising category, the findings support the conclusion that profitability is very specific to a market as a wide variability of revenues was shown to exist. In GB, the value of pure arbitrage revenues is not known and hence merits further investigation. A number of studies have also pointed towards reserve service providing substantial revenues and in some cases even exceeding arbitrage revenues. The type of reserve varied across studies, and similar to the case for arbitrage, these results cannot be generalised, hence requiring an evaluation within the GB market context.
In studies where storage profitability is a secondary function, overwhelmingly, the primary objective related to some form of wind generation support. This would include wind output smoothing, conversion of wind to baseload power, wind output curtailment reduction and deferring transmission investment.
A review of the modelling approaches for storage value studies was also undertaken; the majority of models were optimisation models with a larger number of MILP models compared to LP models. While each optimisation model had subtle variations relating to the additional of a cost feature such as start- up costs or self-discharge, essentially the models were very similar. In two cases, the models did not optimize revenues despite described as optimisation models, highlighting the need for the distinction between a profit generating regime and a profit maximising one.
In section 2.4.2, the choice of an optimisation horizon remains unclear as choices made in the existing literature appear arbitrary. Since these horizons have a direct impact on revenue potential, there is value in investigating the most beneficial optimisation horizons taking into account computational efficiency as well, explained further in Chapter 4.
Inherently, by building a model, assumptions about foresight and bidding strategies have to be made; In the literature, foresight has been dealt with in a number of ways. Techniques applied included backcasting, forecasting, merit order cost functions and fixed dispatch. There is an emerging pattern from these studies, whereby merit order cost functions are preferred in relatively more distant future scenarios whereas backcasting and forecasting approaches are predominantly applied in near future scenarios.
A growing number of studies are also adopting a co-optimisation approach whereby revenues are optimally aggregated. This aggregation generates higher revenues compared to the case where storage participates in a single market. Co-optimisation models have been chosen due to their ability to aggregate revenues; however, additional benefits or drawbacks of co-optimisation model have not been explored further and hence are investigated in this thesis.
Revenues, in general, were seen to be sensitive to storage parameters such as efficiency, power and energy capacity. However, conflicting studies were also found, especially in relation to the impact of efficiency on revenues and the impact of energy capacity under imperfect foresight. More specifically, while efficiency was generally found to be a strong driver for revenues, this is not always true as section 2.5 showed. Similarly, there is a conflicting evidence surrounding the impact of imperfect foresight on revenues with varying energy capacities, explained earlier in section 2.6. The impact of storage parameters on revenues are explicitly investigated in Chapter 5 and 6. Additionally these conflicting results from previous studies are explicitly addressed in these chapters.
From the studies explored, there were no clear preference for the optimisation horizons chosen. Since these determine the arbitrage trading opportunities available, several optimisations are investigated in this thesis, to assess their impact on revenues.
There is some merit in investigating storage value with a technology neutral approach as the focus can be shifted onto the market revenues. Ultimately, however, these revenues support specific projects and hence several studies have also compared the economic performance of storage technologies. Conventional storage technologies such as PHES and CAES were largely superior to the relatively less mature ones such as VRB. In order to place these revenues into perspective, some technology specific capital and operational cost parameters are taken into consideration to determine the NPV of these technologies. Furthermore, the power to energy ratio factor is adjusted to investigate the potential profitability of these technologies with different power to energy configurations.
The value of energy storage under high wind penetration scenario has been extensively explored in the previous studies. However, these studies investigated cases where storage is coupled with wind for avoid curtailment, output smoothing rather than investigate how increased wind generation may affect the markets and hence affect the potential revenues for storage. This approach is important especially for cases where storage is owned and operated independently, drawing revenues from market mechanisms. Therefore, the potential impacts of wind on the market revenues for energy storage is investigated in Chapter 7.
The value of the optimally combined revenue streams has been investigated previously in other markets such as the US and in Denmark, however remains relatively unexplored in GB markets. Furthermore, there has been little focus on the storage operations required to achieve such optimal revenues and whether there are other benefits to a co-optimisation approach. The next chapter describes these market mechanisms available to a storage owner and how they contribute to storage revenues.