The present thesis addresses the increasing complexity of modern energy systems, driven by the need for decarbonisation, improved efficiency, and the integration of renewable energy sources. The maritime sector and isolated energy systems are of particular interest, due to their high operational constraints, limited infrastructure, and strong dependence on fossil fuels. In such contexts, the implementation of advanced Energy Management Systems (EMS) is imperative to ensure the optimal coordination of multiple energy carriers. These include electricity, heat, fuels, and emerging vectors such as hydrogen and freshwater. In order to address these challenges, this work proposes the development of the Multi-Energy Dispatch Optimiser (MEDO), a flexible and modular optimisation framework based on Mixed-Integer Linear Programming (MILP). The framework has been developed to address complex dispatch optimisation problems by achieving a balance between modelling accuracy, computational efficiency, and robustness. Nonlinear system behaviours are approximated through the use of piecewise linearisation techniques. This enables the representation of detailed physical processes while preserving global optimality and scalability. A thoroughgoing comparative analysis of optimisation methodologies is conducted, demonstrating that the mixed-integer linear programming (MILP) approach outperforms non-linear and heuristic approaches in terms of convergence reliability, solution optimality, and computational performance, particularly for large-scale and deterministic dispatch problems. The development of advanced component-level models is predicated on the preceding foundation. This development encompasses degradation-aware formulations for Battery Energy Storage Systems (BES) and flexible operational models for conventional power generation units, including gas turbines (GT) and combined cycle gas turbines (CCGT). The MEDO framework has been applied to a variety of real-world case studies in order to demonstrate its versatility and effectiveness. In the maritime domain, the tool is employed to optimise the onboard energy systems of a cruise ship. This results in fuel consumption, operational costs, and CO2 emissions being reduced by approximately 5%, which corresponds to annual savings of around 1,450 tons of CO2. Concurrently, the framework is being applied to isolated island energy systems, where it supports the integration of renewable energy sources, energy storage, desalination processes, and hydrogen production, enhancing system flexibility and reducing reliance on imported fuels. The present thesis establishes a robust and adaptable link between optimisation theory and practical engineering applications. This link provides the reader with a tool for the techno-economic analysis and optimal operation of complex multi-energy systems. The proposed MEDO framework is set to contribute to the development of next-generation EMS solutions, thereby supporting the transition towards more sustainable, efficient, and resilient energy infrastructures.
MEDO (Multi-Energy Dispatch Optimiser): A MILP-Based Optimisation Framework for Energy Management in the Maritime Sector and Isolated Islands
VASYLYEV, ANDRIY
2026-05-22
Abstract
The present thesis addresses the increasing complexity of modern energy systems, driven by the need for decarbonisation, improved efficiency, and the integration of renewable energy sources. The maritime sector and isolated energy systems are of particular interest, due to their high operational constraints, limited infrastructure, and strong dependence on fossil fuels. In such contexts, the implementation of advanced Energy Management Systems (EMS) is imperative to ensure the optimal coordination of multiple energy carriers. These include electricity, heat, fuels, and emerging vectors such as hydrogen and freshwater. In order to address these challenges, this work proposes the development of the Multi-Energy Dispatch Optimiser (MEDO), a flexible and modular optimisation framework based on Mixed-Integer Linear Programming (MILP). The framework has been developed to address complex dispatch optimisation problems by achieving a balance between modelling accuracy, computational efficiency, and robustness. Nonlinear system behaviours are approximated through the use of piecewise linearisation techniques. This enables the representation of detailed physical processes while preserving global optimality and scalability. A thoroughgoing comparative analysis of optimisation methodologies is conducted, demonstrating that the mixed-integer linear programming (MILP) approach outperforms non-linear and heuristic approaches in terms of convergence reliability, solution optimality, and computational performance, particularly for large-scale and deterministic dispatch problems. The development of advanced component-level models is predicated on the preceding foundation. This development encompasses degradation-aware formulations for Battery Energy Storage Systems (BES) and flexible operational models for conventional power generation units, including gas turbines (GT) and combined cycle gas turbines (CCGT). The MEDO framework has been applied to a variety of real-world case studies in order to demonstrate its versatility and effectiveness. In the maritime domain, the tool is employed to optimise the onboard energy systems of a cruise ship. This results in fuel consumption, operational costs, and CO2 emissions being reduced by approximately 5%, which corresponds to annual savings of around 1,450 tons of CO2. Concurrently, the framework is being applied to isolated island energy systems, where it supports the integration of renewable energy sources, energy storage, desalination processes, and hydrogen production, enhancing system flexibility and reducing reliance on imported fuels. The present thesis establishes a robust and adaptable link between optimisation theory and practical engineering applications. This link provides the reader with a tool for the techno-economic analysis and optimal operation of complex multi-energy systems. The proposed MEDO framework is set to contribute to the development of next-generation EMS solutions, thereby supporting the transition towards more sustainable, efficient, and resilient energy infrastructures.
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