Development of a novel platform for the optimal design, sizing and energy management of grid-integrated hybrid photovoltaic-hydrogen energy systems.

Atteya, Ayatte

Abstract

With the World moving towards the Net-Zero energy transition, there is an increasing demand to scale-up the investment in renewable energy systems. To enable the increased integration of intermittent renewables such as wind and solar energies, while exploiting their full potential, adequate and sustainable energy storage systems are required. Hydrogen (H2) energy storage systems, given their eco-friendliness, fast response and versatility, are seen as potential solutions for mitigating the renewable energy intermittency. This thesis presents new strategies for optimising the design, sizing, and energy management of grid-integrated hybrid renewable-hydrogen energy systems. This thesis key contribution to the knowledge lies in developing a novel precise dynamic system model that allows the accurate sizing and real-world dynamic simulation of hybrid Photovoltaic-Hydrogen (PV-H2) energy systems. This novel model captures the electrochemical dynamic behaviour of the individual system components in response to changes in operating conditions. Further contribution to the knowledge included developing real-word system-sizing optimisation models through integrating the developed novel model with both single-objective and multi-objective particle swarm optimisation algorithms. This integration allows optimising the hybrid system sizing while considering its real-world dynamic operation. The developed optimisation models respectively offer optimal system sizing for minimising the levelized cost of energy (LCOE) solely and for simultaneously minimising the LCOE along with the carbon footprint. A benchmark comparison versus the commercially available HOMER software is conducted to highlight the developed optimisation models’ added privileges over HOMER. Finally, an innovative platform that encapsulates all the developed novel methodologies is presented to assist decision-makers in designing, optimally sizing, and simulating the real-world dynamic behaviour of grid-integrated hybrid PV-H2 energy systems prior implementation. This developed platform enables bridging critical gaps in existing software through modelling the system real-world dynamic behaviour, enabling the selection of customized decarbonisation levels, and allowing multi-objective optimisation for multi-prospect investment decisions. Key findings reveal that, compared to a generic system model that neglects the real-world dynamics of the system components, the developed novel precise dynamic model significantly allowed reducing the size of the H2 storage tank required by 72.5%, thus eliminating oversizing costs. The application of the developed single-objective optimisation model on the grid integrated case-study building has enabled achieving 31.54% reduction in the LCOE reaching 0.3697 £/kWh with a grid dependency of 53%, outperforming HOMER which resulted in a LCOE of 0.3976 £/kWh and a grid dependency of 62.78%. The multi-objective optimisation model has enabled further reduction in the grid dependency to 43%, while maintaining the LCOE at slightly higher reasonable value of 0.4117 £/kWh. Notably, it can be seen that for nearly the same LCOE as HOMER, the multi-objective optimisation model significantly lowered the grid dependency, thus providing much greener solution at no additional costs.

Citation

ATTEYA, A. 2025. Development of a novel platform for the optimal design, sizing and energy management of grid-integrated hybrid photovoltaic-hydrogen energy systems. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2935027

• Thesis Type: Thesis
• Deposit Date: Jul 23, 2025
• Publicly Available Date: May 31, 2025
• DOI: https://doi.org/10.48526/rgu-wt-2935027
• Keywords: Hybrid renewable-hydrogen energy systems; hybrid PV-H2 energy systems; electrolyser; fuel cells; grid-connected buildings energy transition; precise dynamic modelling; particle swarm optimisation; decision-support
• Public URL: https://rgu-repository.worktribe.com/output/2935027
• Award Date: May 31, 2025