Shaaban, MostafaOsman, AhmedAbdallah, Rawan Yousef Ali2022-09-082022-09-082022-0435.232-2022.19http://hdl.handle.net/11073/24099A Master of Science thesis in Electrical Engineering by Rawan Yousef Ali Abdallah entitled, “Multi-Objective Co-optimization of Power and Gas under Uncertainties with P2H embedded”, submitted in April 2022. Thesis advisors are Dr. Mostafa Shaaban and Dr. Ahmed Osman-Ahmed. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).The need for developing planning studies that detect new requirements and explore the threats and doubts associated with the long-term and large-scale investments is a hot topic in research areas. Designing new models, techniques, and simulation tools is on the rise as the interdependence between electric and natural gas systems is growing all around the world. The rapid growth in natural gas consumption by gas-fired generators and the new emerging power-to-hydrogen technology have increased the interdependency of natural gas and power systems. New challenges have been brought up to the energy system operators for the safe and economic operation of the coupled power and gas systems due to the interdependency, alongside heterogeneous uncertainties of the power system and the gas systems, including power loads, renewables, and gas loads. Uncertainties in one infrastructure could easily affect and spread to the other, increasing vulnerability and eventually resulting in cascading outages for both networks. P2H technology is the most valuable and capable solution to the vital need for large-scale energy storage systems because of the erratic nature of RES. Renewable electricity and Natural gas are widely accepted as the main technologies to transit to economic, clean, and secure energy systems worldwide. To deliver this vision; these technologies need to be investigated to work in an integrated system. This thesis proposes new approaches for the planning and operation process to co-optimize the gas and electric power systems. The proposed model aims to minimize the total operating costs of both systems considering the primary constraints, thus optimizing the operation process without jeopardizing the gas and energy supplied to customers. Further, an MINLP model is proposed for the optimal day-ahead operation of the two integrated systems. The simulation results were tested on IEEE 24-bus power system and a 20-node natural gas system. On the other hand, the proposed approach in the planning phase aims to minimize the total costs and allocate resources in the system. The proposed approach utilizes a genetic algorithm to address the uncertainty associated with each network. Simulation results show the effectiveness of the proposed approach model in minimizing the total costs.en-USMINLPGenetic AlgorithmPower-Gas NexusOptimizationPower-to-HydrogenRenewable Energy ResourcesThesis