Using Software to Model a Battery’s Lifetime Performance

When exploring energy storage opportunities, grid operators should keep battery modeling top-of-mind.

As the market for energy storage systems expands, microgrids in remote communities have become an attractive opportunity for the deployment of renewable generation with storage. Because these communities generally rely on diesel generation, and often have logistical challenges for fuel delivery, fuel savings is a fundamental objective.

Reducing fuel consumption in microgrids is just one of many targets obtainable with battery modeling. Battery modeling is a known technique, yet it continues to evolve to better address the demands of our digital society. These simulations are critical to the project development process, as they deliver insights into energy storage applications ahead of deployment. They also assist with determining how much power and energy is required. 

Coupling High-Level Modeling with Complex Simulations

High-level modeling of an entire microgrid is an excellent exercise to check the viability of renewables with storage. One example of the tools that can be used is a commercial software suite derived by the National Renewable Energy Laboratory. Such modeling is typically based on hourly data and the granularity of energy storage system dispatch is correspondingly coarse.

 

Saft’s Matlab-Simulink models paired with HOMER Pro Software offer a robust assessment of energy storage system capabilities.

 

High-level modeling is feasible, even with minimal data input. For example, an initial model of a microgrid can be constructed with minimal inputs, such as the coordinates of a village in Northern Canada having a peak load of 130kW in January. With this information, high-level modeling builds a typical load profile and offers the ability to download location-specific solar or wind resources. The software then quickly performs multiple simulations to optimize the renewable energy power rating, along with an appropriate level of energy storage. The results illustrate fuel savings and, if sufficient inputs are detailed, return on investment (ROI).

When it comes to modeling the detail of these systems, such as bridging between multiple diesels in a large microgrid, or optimizing the set points for operating with diesels in a smaller microgrid, more precise modeling is required. High frequency data, at least as granular as every 10 minutes, is valuable. This modeling provides insights into system operation, including diesel synchronization and cool-down times, to minimize diesel starts, maximize fuel savings and optimize battery life.

Precise modeling requires more detailed inputs and time to optimize the dispatch methodology. Thus, coupling high-level with precise modeling offers a cohesive, informed glimpse into an energy storage system to facilitate evaluation of the viability of a project, as well as a detailed strategy required to ensure project success.

Data to Consider for Battery Modeling

The data requirements for modeling microgrids are relatively simple, including load, renewable resource, diesel configuration, and information on any dispatchable loads, such as electric water heaters. The next step beyond microgrids is to weak grids such as islands, where energy storage can play a critical role for grid stabilization, addressing both the variability of renewables and other disruptions, such as generator trips.

 

Saft’s megawatt scale Li-ion containerized energy storage systems for grids and renewable energy sources smooths intermittent generation and ramp rates, as well as

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