Decision Support System for Battery Energy Supply Systems

Abstract

The adoption of Battery Energy Storage Systems BESS into modern energy infrastructure is expanding on a rapid pace, necessitating effecient decision support system to help in the selection among the Commmercial On The Shelves (COTS) of BESS components. This research presents the front end and backend design of a DSS tailored for the purpose of BESS component selection. The system leverages advanced algorithms and model-driven approach to assess the critical factors influencing the choice of battery pack, power conversion system, power electronics, and thermal management solutions. The DSS utilizes a hybrid framework including multi-criteria and networking problems, considering parameters such as cost, performance and geometry.

Using users’ requirements and preferences helps in mass customizaion processes where it will allow manufacturers to understand its customer needs. The selection algorithm of the DSS helps decision-makers to choose among wide range of COTS of each component of the BESS. The output of the DSS is a a ranked list of feasible configurations solutions. The solutions are presented mainly based on their performance in the decisoin criteria while making account to the relative importance of each critereon. This work is conducted to help decision-maker's in making educated decisions and offer them the capability to make tradeoffs in their choices. Furthermore, using the system leads to cost, resource, time, and effort savings

This research contributes in advancing BESS technology immerging by providing a comprehensive methodology for the selection of the most suitable components, offering energy professionals and decision-makers a valuable tool to enhance the efficiency and sustainability of energy storage projects based on their customers needs. The DSS represents a significant step towards the optimization of BESS deployment, aligning with the global transition towards a cleaner and more reliable energy infrastructure.

Date of Award	19 Dec 2023
Original language	American English
Supervisor	Andrei Sleptchenko (Supervisor)