Abstract
The increasing energy demand in seaport operations, driven by electrification and decarbonisation targets, requires enhanced tools for operational planning and flexibility management. This paper proposes a novel centralised Energy Management System designed for seaports, which, unlike previous approaches that mainly focused on cost minimisation jointly optimises Battery Energy Storage System scheduling, energy and reserve market participation, and carbon-intensity reduction. A key contribution of this work is the integration of CO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_2$$\end{document} emission forecasts and day-ahead market data into a multi-objective formulation, allowing the Energy Management System not only to minimise operational costs but also to reduce indirect emissions. Additionally, a Traffic Light system is proposed to support operators' decision-making by providing actionable flexibility guidelines. A case study based on real-world data from the Port of Sines shows that this method achieves at least an 17% reduction on an annual basis compared to baseline operations, while ensuring cost efficiency. Results highlight the Energy Management System's potential as a decision-support tool for port authorities seeking to align operational efficiency with sustainability goals.

Abstract
Maritime transport faces increasing decarbonisation requirements, placing new demands on port energy systems. Yet most existing studies analyse isolated components or short time horizons, limiting their usefulness for long-term planning. This work develops a holistic, least-cost optimisation model of the Port of Sines energy system using OSeMOSYS, integrating electricity and fuel consumption across port operations and fuel-management processes from 2020 to 2050.The study evaluates alternative technology pathways and policy measures, including carbon taxation, national emission-reduction targets, and the adoption of an innovative ocean-going vessel fleet. Results show that electrification, driven by onshore power supply and renewable expansion, is the most cost-effective decarbonisation route, while its performance depends on local generation capacity and the carbon intensity of the electricity mix. Policy mechanisms and fleet innovation further influence the timing and depth of emissions reductions. Overall, the model provides a replicable framework to support strategic port decarbonisation planning.

Abstract
The lack of open-source platforms capable of integrated operational modeling and multi-scenario decarbonization analysis, often hinders data-driven decision-making in the maritime sector. To address this gap, this paper presents an open-source, multi-agent, discrete-event simulator capable of accurately forecasting the energy consumption associated with the diverse assets and activities within a container terminal. The tool's modular architecture enables transparent evaluations of operational strategies and decarbonization alternatives by allowing users to systematically modify inputs or alter embedded energy modules. The tool's capabilities were validated through a case study of a medium-sized Portuguese container terminal. For this particular port, findings indicate that installing three onshore power supply (OPS) units and fully electrifying the internal truck fleet yields the most substantial emission reductions. However, these interventions result in a two-fold increase in daily electricity demand, potentially straining grid capacity. This finding underscores that the effectiveness of terminal electrification as a decarbonization strategy ultimately depends on a simultaneous transition to a decarbonized and secure energy supply.

Abstract
Renewable Energy Communities (RECs) are a promising driver for energy system decarbonization, engaging end consumers in energy markets, promoting local renewable generation and affordability, and enhancing community resilience. This paper presents a methodology for planning REC by optimally sizing distributed energy resources (DERs), including conventional resources such as photovoltaic (PV) systems and battery energy storage systems (BESSs), but also power-to-hydrogen (P2H) systems composed of an electrolyzer (EC), hydrogen tank, and fuel cell (FC). A mixed-integer linear programming (MILP) model is used to minimize DER investments and REC operational costs, using a reduced input data set based on clustering techniques to decrease complexity and improve computational efficiency. Then, a detailed MILP operation model is used to assess the REC performance with the newly sized DERs under different scenarios using conventional indicators such as the net present value (NPV) and the payback period. A sensitivity analysis is also conducted on electricity costs, EC costs and the hydrogen selling price for an external combined heat and power (CHP) client. Results show that integrating P2H systems increases NPV and provides new revenue opportunities, thereby reinforcing their role as a complementary technology to PV and BESS within RECs.