The stochastic nature of distributed generation from renewable energy sources has created problems in the power distribution system. Most of these problems can be solved by the deployment of battery energy storage systems (BESSs) along with the increasing penetration of distributed resources and the control of these BESSs. For example, with BESSs, fluctuation mitigation, peak power shaving, ramp-rate control, and maintaining the power balance can be achieved. However, the integration of BESSs may result in significant energy loss due to the round trip (charging-discharging) energy conversion efficiency of the BESS, depending on the operating conditions of the power electronic converter and battery modules. Therefore, it becomes crucial to consider energy efficiency in BESSs while allowing fast charging/discharging and limiting degradation in batteries and converters.
In this project, a self-optimised converter system, comprises interconnected converter modules with an intelligent control system, will be developed for grid-scale BESS charging/discharging. The control system will employ Machine Learning with module shedding and topology adjustment. The objective function will utilise predefined efficiency maps of converter modules and batteries to minimise power conversion losses, charging/discharging cost, and degradation in batteries and converters. The coefficients of the cost function will be adapted in real time based on user and network preferences. The inherent self-optimisation capability will avoid operation at low-efficiency regions and provide a robust fault-tolerant operation that ensures the overall integrity of battery charging/discharging.
Newcastle University is committed to being a fully inclusive Global University which actively recruits, supports and retains colleagues from all sectors of society. We value diversity as well as celebrate, support and thrive on the contributions of all our employees and the communities they represent. We are proud to be an equal opportunities employer and encourage applications from everybody, regardless of race, sex, ethnicity, religion, nationality, sexual orientation, age, disability, gender identity, marital status/civil partnership, pregnancy and maternity, as well as being open to flexible working practices.
Application enquiries:
Dr Mohammed Elgendy, [Email Address Removed], Staff Profile - School of Engineering - Newcastle University (ncl.ac.uk)
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