Extending Battery Life with Control Strategies: An Interview with Jonathan Schulte

Coordinating the control of multiple energy resources in a microgrid system for optimal efficiency is complex. It’s even more challenging while attempting to reduce operational costs. The intricacy of this process has fueled Jonathan Schulte’s exploration of microgrid control methods and shaped his expertise. 

Schulte, an energy data engineer for AMMP Technologies, specializes in strategies to increase the sustainability of off-grid systems. His research explores the ways in which control methods—specifically those informed by forecast-based algorithms—can extend battery life and decrease operational costs. 

He will discuss the ability of dynamic control algorithms to reduce operational expenses in off-grid energy systems and will demonstrate their efficacy through use cases in Nigeria and Tanzania at the 8th annual HOMER International Microgrid Conference in October. 

Using dynamic control algorithms, Schulte and his team have developed a method for reducing the average state of charge of lithium-based battery systems and decreasing the time the battery spends in a fully charged state—both of which lead to longer battery lifetimes without impacting the system performance. We recently spoke to him about these insightful findings and are pleased to share his perspectives with readers of Microgrid News.

Microgrid News (MN): What type of control strategies have you found most successful? What control strategies would you recommend specifically for off-grid systems?

Jonathan Schulte (JS): In my research, I focused on control strategies for off-grid systems with lithium-ion batteries and lead-acid batteries, either in combination with a genset or stand-alone. I found that this strategy is most effective for systems with a lithium-ion battery and no generator. The goal here is to keep the battery’s state-of-charge (SOC) between 40% and 80%, as a lithium-ion battery ages slower between this threshold. This can be accomplished by not fully charging the battery or by delaying charging. This is similar to what Apple is doing for the iPhone with iOS 13 and later. In comparison to phones, an empty battery in an off-grid energy system can be a disaster, as it could result in a power outage. This is why our algorithms include various safety measurements to prevent any avoidable situations with completely empty batteries.

MN: How does using historical data in forecast-based algorithms help prolong the lifespan of a lithium-ion battery?

JS: The historical data helps us to predict how much energy will be generated and consumed in the off-grid system. The consumption forecasting is based on the historic consumption data of the system. Here our algorithm showed strength in predicting recurring weekly events (e.g. the shop is always closed on Sunday and consequently consumes less electricity). The energy generation prediction is based on historic data and weather forecasts. In combination, the consumption and generation forecasting helps us to keep the batteries in desired state-of-charge ranges without increasing the risk of an empty battery. 

MN: How can these control strategies impact operational costs?

JS: Our algorithms can extend the lifetime of the battery. This is crucial, as batteries are often the main cost driver of PV off-grid systems. Furthermore, shipping off-grid equipment to many Sub-Saharan African sites is still a long and expensive process. Therefore, any solution to reduce the number of battery changes is desirable. 

MN: The case studies that you plan to highlight at the HOMER International Microgrid Conference are sited in Africa. Are these strategies site-specific or applicable to deployments elsewhere?

 JS: Yes, the solution focuses on off-grid solutions, which are very common in Sub-Saharan Africa, but also in other remote areas across the world. 

MN: What advice would you offer a project developer researching control strategies?

JS: There is no perfect energy management strategy for EVERY system. Depending on the typology and application, different solutions are applicable. Before you build the system, think about your expected consumption curve and even more important how far and how often the consumption could deviate from this curve. System design softwares like HOMER is a great help to find a good control strategy based on that. Once your system is up and running, monitoring softwares like AMMP help you to understand if your control strategy works as expected or if you need to adjust. This feedback loop is also very helpful when you are planning to build other similar systems. 

Readers are invited to attend the HOMER International Microgrid Conference free of charge by registering at microgridconference.com. Jonathan Schulte will present, The Potential of Dynamic Control Algorithms to Reduce OPEX in Off-Grid Energy Systems during a session dedicated to Controls: The Key to Sustainable Microgrids, on October 15, 2020, from 1:00 pm to 3:00 pm. 

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