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Mobile Energy Storage Systems for Benefit Maximization in Resilient Smart Grids
Alshaal, Dima
Alshaal, Dima
Date
2025-04
Author
Advisor
Type
Thesis
Degree
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35.232-2025.51a Dima Alshaal.pdf
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Description
A Master of Science thesis in Electrical Engineering by Dima Alshaal entitled, “Mobile Energy Storage Systems for Benefit Maximization in Resilient Smart Grids”, submitted in April 2025. Thesis advisor is Dr. Mostafa Shabaan and thesis co-advisor is Dr. Mahmoud Ibrahim. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).
Abstract
modernization of electrical grids, driven by the increasing integration of renewable energy resources, presents significant challenges in maintaining grid stability and reliability. Additionally, the frequency and intensity of natural disasters have increased in recent years due to climate change, further complicating efforts to ensure continuous and resilient power supply. Due to these factors, the implementation of energy storage systems has become essential to enhance grid resilience and support reliable energy delivery. In this context, Mobile Energy Storage Systems (MESS) are explored as a versatile and transporTable solution, capable of connecting to the grid at specific substations to provide a range of critical utility services. These services include load leveling, load shifting, minimizing losses, engaging in energy arbitrage, enhancing overall system reliability and resilience. With the growing need for reliable and cost-effective electricity, the optimal deployment of MESS is becoming increasingly critical. This work presents a hybrid optimization algorithm designed to improve the sizing, storage type selection, and allocation of MESS for multi-service applications, significantly enhancing system performance, reducing outage impacts, and optimizing energy arbitrage. The methodology integrates a dynamic MES model that considers capacity and lifespan constraints with a comprehensive network power flow model, which captures load variation and market price fluctuations. Additionally, the approach includes optimizing repair crew routes, ensuring efficient response to system failures and minimizing downtime. Given the complexity of this mixed-integer nonlinear programming (MINLP) problem, a hybrid technique is employed, combining a Genetic Algorithm for optimal sizing and type selection with mathematical optimization for precise allocation and operational scheduling. This hybrid approach enables more reliable and cost-effective MES management, addressing both operational and economic objectives in energy storage applications. Simulation results on a typical distribution network indicate that the optimized solution for the MESS is a configuration of 32 lithium-ion units, that achieved an energy loss reduction by up to 5.8%, an improvement in system reliability, reflecting in reduced yearly costs of interruptions by 25%, and reduction in the yearly cost of interruptions due to disasters by 9.5%. These results underline the potential financial and operational benefits of deploying MESS in modern electrical networks.