7 Lessons learned from merchant energy storage projects

This blog post is part 2 in a 2-part series on merchant storage.

Recently, we published the first part of this blog series, "Unravelling the complexity of merchant energy storage projects" which compared typical fossil fuel power plants to energy storage operations in merchant markets. This post will discuss considerations for evaluating merchant energy storage projects and offer suggestions to manage risks associated with the uncertainty of revenue projections.

Merchant storage is complicated, but not impossible
As the storage market evolves, we see projects with an increasing proportion of their revenue expected to come from the merchant market rather than firm contracts. Just a few years ago it was the opposite. This changing risk appetite has forced developers and lenders to better understand the underpinning drivers and risks associated with merchant storage revenue. The suggestions below represent the top lessons learned from evaluating merchant storage projects and offer guidance for best practices to consider when evaluating merchant storage risk.

  1. Create low, base, and high scenarios around future price volatility, not future average price levels, to account for long-term price forecast error.
    Average price levels are important, but low, base, and high revenue scenarios should focus on changing market dynamics (e.g. varying amounts of renewable penetration — read more in our 2019 Energy Transition Outlook report) that result in different levels of volatility rather than scenarios with a range of low, medium, and high average electricity prices.
  2. Consider imperfect storage dispatch to account for day-ahead price forecast error.
    Once price forecasts are established for a scenario (see above), the storage system must be simulated to operate under those market conditions to determine revenue. In practice, day-ahead price forecasts are imperfect – actual operational revenue will be less than computer simulated outputs that assumed perfect knowledge of future prices. Adjustments for day-ahead price forecast error should be taken into account when incorporating revenue simulation results into financial models.
  3. Ensure operations do not violate battery warranties.
    Unbridled merchant revenue maximization could result in battery charging and discharging operations that violate cell and system warranties and reduce the useful life of the system. While this may be a strategy to consider, the impacts on the useful life of the system should be well understood to evaluate the tradeoffs associated with more conservative operational strategies. Figure 1 illustrates the impact on battery energy capacity retention from different cycling regimes as modeled by DNV’s tool BatteryXT.
    20200420 Blog figure 1 Capacity retention curves 770x450pxl
    Figure 1: Capacity retention curves from various cycling regimes for a typical lithium ion battery[1]
  4. Storage system sizing is complex with many tradeoffs.
    Sizing must be considered both in terms of MWs installed and the ratio of MWh to MW. Longer duration systems (higher MWh to MW ratio) tend to have greater flexibility to provide services and are less prone to day-ahead forecast error due the higher number of daily operational hours.
    Longer duration products tend to have lower unit costs in terms of USD/kWh but higher overall costs in absolute terms versus shorter duration products. Merchant revenue tends to increase with discharge duration (for a given MW size) but with diminishing returns. Net present values (NPV) do not always increase with duration (see Figure 2).[2]
    20200420 Blog figure 2 Illustrative merchant revenue 770x385
    Figure 2: Illustrative merchant revenue from a storage project across a range of prices and durations (10 yr life)  
    Revenue projections over a project life are impacted by capacity degradation. As capacity degrades, the system’s effective duration decreases. Project developers typically decide between oversizing a project upfront and allowing it to degrade at a relatively known rate or sizing more conservatively upfront and planning for capacity augmentation investments periodically throughout the project life. Some system providers offer capacity guarantees as part of a service agreement as an alternative option to actively managing degradation. Choosing among these options often has to do with developer or owner risk tolerance and a perspective on future battery prices. Sizing is also often determined by contracts, interconnection rules and limitations, space constraints, renewable charging restrictions, financing limitations, and storage pricing, among other criteria. Hence, it is often difficult to identify the perfect size for a given project.
  5. Pairing with renewables does not always maximize storage value.
    Dedicated renewable (wind and solar) charging imposes costs on projects by restricting charging to periods when the renewables are generating and by reducing the net output of the renewable asset (due to battery efficiency losses). The tradeoffs should be quantified when considering renewable paired merchant storage projects versus standalone storage. Batteries that are DC connected to solar charging facilities may have discharging limitations during periods of time when the solar is producing due to constraints of the shared inverter.
  6. Know that policy uncertainty is a certainty.
    Fortunately, energy storage is an incredibly flexible asset. Within its design and operational constraints, storage operations can be modified with software updates to reflect new market rules. This is not to say storage is shielded from all policy changes, simply that there is wiggle room as conditions change, with longer duration systems typically offering more flexibility.
  7. Greenhouse gas (GHG) emissions from storage are determined by operational strategies.
    The pressure on lenders to invest in projects that mitigate climate change is likely to increase the scrutiny placed on the emissions associated with storage operations, emission impacts which are largely ignored today. Measuring GHG impacts of storage operations is not as straightforward as it is for wind and solar assets. Storage emission impacts can be quantified by utilizing marginal grid emission rates at the time of charging and discharging (rather than average emission rates) to determine which grid emissions a storage project increases (when charging) and displaces (when discharging). DNV can utilize marginal emission data from organizations such as WattTime to calculate net emissions from storage dispatch.[3] Furthermore, emission signals can be utilized by storage operators to modify dispatch to minimize emissions. We expect this metric to become an increasingly important consideration for investors in all types of storage projects and a critical factor for storage systems that operate in markets with carbon prices.

Merchant storage projects are complex, but the risks can be measured and managed with appropriate planning and analysis. Merchant markets are evolving quickly all over the world, many being intentionally designed to incorporate more energy storage. DNV will continue to support its clients in the development of these projects and is encouraged by the innovative work taking place in this dynamic segment.

If you have any questions or comments, please feel free to reach out to us here. Read Part 1 of this series on merchant storage.

[1] A cycle is defined as a kWh discharged per kWh installed. For example, a 10 kWh battery operating at 2 cycles per day would discharge 20 kWh each day. Useful life varies significantly by cell type. Generally, DNV assumes a battery will reach the end of its useful life when its capacity is between 60% and 70% of its initial capacity. Typical warranties guarantee capacity to about 70% of initial capacity.

[2] Note that the results in Figure 2 represent only a single example and are not meant to be representative of all merchant projects.

[3] https://www.watttime.org/

4/20/2020 3:00:00 PM

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Brian Warshay

Brian Warshay

Head of Section, Energy Storage Engineering