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Energy Storage Cost-effectiveness Evaluation

E3 has worked with the CPUC, CEC and stakeholders to develop cost-effectiveness methodologies and reports for energy efficiency, distributed generation, demand response, permanent load shifting and Title 24 building standards. In collaboration with several clients, E3 has extended this expertise to model the cost-effectiveness of energy storage technologies and applications. E3's expertise and modeling of energy storage has supported multiple EPRI reports on energy storage, the CPUC/Joint IOU Study of Permanent Load Shifting, financial analyses for technology vendors and project developers, and a white paper titled "Utility Scale Energy Storage and the Need for Flexible Capacity Metrics" comparing the cost effectiveness of storage and traditional fossil resources.

E3's energy storage models are developed in Analytica. The use of modules and nodes makes the modeling approach and flow visibly transparent to the user. Formulas, along with a list of their inputs and outputs, are easily viewed and understood. Both a downloadable free player and a web interface allows anyone to use and view the full model. These features support the transparency and accessibility that is fundamental to all public analysis performed by E3.

Here we provide an example of an energy storage cost-effectiveness analysis using the Analytica Optimizer and Analytica Cloud Player, which provides the web based interface to the model. The user can select from among several pre-run cases to quickly show the model inputs and results. In addition, the user can modify inputs and run the model. However, optimizing the hourly dispatch across several benefit categories may take from 2 to as much as 8 minutes (the desktop version runs more quickly) depending on the technology and benefits selected. The demonstration version includes only half of the approximately 20 benefits included in the full model.

Eight pre-loaded cases are immediately viewable without re-running the model. Three example storage technologies, Compressed Air Energy Storage (CAES), Li-ion and Sodium Sulfur (NaS) have been modeled using historical 2010 CAISO prices. In addition, for comparison purposes, an LM 6000 Combustion Turbine (CT) is run through exactly the same model as the energy storage technologies. All three storage technologies and the LM 6000 have also been run through a 2020 scenario. The 2020 scenario uses prices produced by the PLEXOS production simulation model used in the CPUC and CAISO Long Term Procurement Proceeding (LTPP) process, and include a Load Following market.

The model results include:

  • A levelized cost of energy, in $/kWh and $/kW-Yr.
  • The residual capacity value (Total costs minus revenues earned in the energy and AS markets)
  • A comparison of the present value costs and benefits over the useful life of the technology
  • A chart of the daily revenues by market
  • A hourly chart of the energy storage level
  • The capacity factor of the resource (energy discharged/(nameplate capacity *8760)


"Utility Scale Energy Storage and the Need for Flexible Capacity Metrics"


This model is presented only as an example and proof of concept of the energy storage cost-effectiveness analysis possible using these models in Analytica. The results shown in this demo do not represent the opinion or forecast of E3 or any of its clients regarding the economic potential for a particular technology or energy storage in general and should not be cited in any manner. The model inputs and features have been streamlined significantly for ease of use and simplicity in this demo version. Market rules and technology characteristics are evolving rapidly and many aspects of the models are continuously being developed and validated. Energy and Ancillary Service prices are actual historical prices or results from PLEXOS production simulation. The energy storage technology costs, however are illustrative only and do not represent actual vendor quotes or price forecasts.