The success of grid scale energy storage hinges on our ability to solve real problems economically. By mapping energy storage physics to application economics, this paper offers a technology neutral look at how energy storage can solve real problems.

A value analytics methodology was developed that combines the physics of energy storage, application power commands, and market-specific economic constructs. This approach evaluates and optimizes the value of energy storage for specific projects by providing insight into the tradeoffs between the lifecycle costs and revenues. These analytics calculate the net present value (NPV) and internal rate of return (IRR) of select energy storage assets, markets, and applications by considering these key factors:

• Duty profiles and control strategies

• Market economics and revenue streams

• Asset performance at cell, module, and system levels

• Price projections including balance of plant (BOP)

• Cycle and calendar life

• Project length and financing terms

The value analytics methodology combines three model streams. The first takes a high fidelity load profile (for example, the power output of a building, wind turbine, or solar farm), imposes a specific control strategy, and calculates revenue streams in a selected market. From this first model stream a storage power command is generated. This power command is fed into the second model stream that calculates the required size and price of a given storage type. Finally, a third model stream uses empirical life models to calculate degradation rates, replacement intervals, and maintenance costs. These are rolled up into a project specific financial analysis that forecasts project NPV and IRR.

The underlying engine for this methodology is a large performance and price database of over 100 commercial and emerging energy storage assets that spans a wide range of technologies from ultracapacitors and flywheels to lead acid and lithium-ion batteries. The physics based performance of each asset is captured as an equivalent circuit model. These models are exercised to create performance envelops that describe the rate dependent power capability as a function of the type, amount, and age of installed storage.

The energy storage value analytics described in this paper can be used to test key sensitivities. This methodology has been applied to standalone energy storage systems as well as the combination of energy storage with renewables and distributed power generation. As shown, the methodology is relevant for an even wider range of applications. Several solution maps will be shared that reveal, by market segment, the energy storage type, amount, and application that create the greatest customer value. This type of informed design and dispatch will solve real problems, create new value streams, and open new markets for grid scale energy storage.

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