A key factor when planning energy storage systems (ESS), for example for a microgrid, is to determine the expected cost savings and performance benefits provided by various ESS configurations.
Battery modelling offers a powerful way of predicting the lifetime performance and return on investment that will be provided by each ESS option.
Fuel savings are often a key factor in the choice of energy storage configuration, especially for microgrids which are often located in remote communities and rely on diesel generation, with logistical challenges around fuel delivery. However, cutting fuel consumption is just one of the purposes of battery modelling for microgrids.
Battery modelling techniques continue to evolve to better address the wider context of microgrid and renewable energy deployments. For example, simulations are now key to the project development process, as they deliver insights into renewable and storage applications ahead of deployment and help determine how much power and energy are required overall.
PRECISE MODELLING
Modelling an entire microgrid at a high level is a valuable exercise in assessing the viability of different deployments of renewable energy schemes with storage. However, when it comes to modelling the detail of these systems – such as bridging between multiple diesel generators in a large microgrid, or optimizing the set-points for operating with diesel generators in a smaller microgrid – more precise modelling is required.
High-frequency data, with granularity of no more than ten-minute intervals, is valuable. Such modelling provides insights into system operation, including diesel synchronization and cool-down times, to minimize diesel starts, maximize fuel savings and optimize battery
life.
High-level modelling is typically based on hourly data, and the granularity of ESS dispatch is correspondingly coarse. This kind of modelling 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 an island village off the US Pacific coast having a peak load of 150 kW in January. Based on this information, high-level modelling can be used to construct a typical load profile, and location-specific solar or wind data can be downloaded.
The modelling software can then quickly carry out multiple simulations to discover the optimum renewable energy power rating, along with an appropriate level of energy storage. The results illustrate fuel savings and, if sufficient inputs are provided, ROI.
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