Abstract
The present work proposes a two-stage optimization approach for flexibility services provided by battery energy storage systems (BESS) in distribution networks with photovoltaic (PV) generation and electric vehicles (EV). The considered flexibility services include reserve allocation and voltage regulation to support network operation. The first stage optimizes the day-ahead (DA) scheduling of distributed BESS to minimize overall costs, including energy, BESS usage, and reserve, while accounting for stochastic variations in load, PV generation, and EV penetration. The second stage simulates the real-time (RT) operation of the electrical distribution network, evaluating system behavior under different scenarios based on DA decisions. A coordinated control strategy is applied, integrating DA scheduling with network voltage levels. Deviations between BESS outputs in DA and RT stages are fed back into a new DA run to adjust outputs and reduce costs. Results on a medium-voltage distribution system with 157 nodes (based on a reduced version of the EPRI CKT5 feeder) demonstrate that the proposed scenario-based model provides feasible solutions under uncertainty, with BESS playing a key role while strictly adhering to planned operational modes from DA to RT, as typically enforced in energy market participation.

Abstract
Seaport cranes operate continuously and consume large amounts of energy while aiming to minimise containerships' berthing time. Although previous studies have contributed to addressing the crane scheduling problem, most have focused exclusively on loading time, often overlooking the aspect of energy consumption. Furthermore, crane activity is typically modelled in a simplified manner-commonly assuming a fixed cycle duration or constant energy usage when handling a container-without accounting for the impact of variable container masses. In this study, an energy-aware quay crane scheduling formulation for container terminals is proposed, highlighting the importance of integrating an energy model into the scheduling problem. The optimisation problem is formulated as a Mixed Integer Linear Programming (MILP) model. The objective is to minimise total energy costs by reordering the sequence in which containers are handled, while respecting precedence constraints defined by the ship's stowage plan. Two solution methods-a MILP approach solved using CPLEX and a genetic algorithm (GA)-are compared. The results indicate that, for larger containerships, the genetic algorithm provides a more efficient solution method. Moreover, incorporating detailed energy consumption models for electric cranes may significantly reduce energy costs during containership handling operations.

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.