Hybrid renewable energy systems (HRESs) are being incorporated and evaluated within seaports to realize efficiencies, reduce dependence on grid electricity, and reduce operating costs. The paper adopts a genetic algorithm (GA)-based optimization framework to assess four energy management scenarios that embed wind turbines (WTs), photovoltaic energy (PV), an energy storage system (ESS), and an energy management system (EMS). The scenarios were developed based on different levels of renewable energy integration, energy storage utilization, and grid dependency to optimize cost and sustainability while reflecting the actual port energy scenario as the base case. Integrating HRES, ESS, and EMS reduced the port’s levelized cost of energy (LCOE) by up to 54%, with the most optimized system (Scenario 3) achieving a 53% reduction while enhancing energy stability, minimizing grid reliance, and maximizing renewable energy utilization. The findings show that the HRES configuration provides better cost, sustainability, and resiliency than the conventional grid-tied system. The unique proposed EMS takes it a step further, optimizing not just the energy flow but also the cost, making the overall system more efficient—and less costly—for the user. ESS complements energy storage and keeps it functional and reliable while EMS makes it completely functional by devising ways to reduce costs and enhance efficiency. The study presents the technical and economic viability of HRES as an economic and operational smart port infrastructure through its cost-effective integration of renewable energy sources. The results reinforce the move from conventional to sustainable autonomous port energy systems and lay the groundwork for forthcoming studies of DR-enhanced port energy management schemes. While prior studies have explored renewable energy integration within ports, many lack a unified, empirically validated framework that considers HRES, ESS, and EMS within real-world port operations. This research addresses this gap by developing an optimization-driven approach that assesses the techno-economic feasibility of port energy systems while incorporating real-time data and advanced control strategies. This study was conducted to enhance port infrastructure and evaluate the impact of HRES, ESS, and EMS on port sustainability and autonomy. By bridging the gap between theoretical modeling and practical implementation, it offers a scalable and adaptable solution for improving cost efficiency and energy resilience in port operations.

Ports are vital nodes in global trade networks but are also significant contributors to greenhouse gas emissions. Their transition toward sustainable, nearly zero-energy operations require comprehensive and structured strategies. This study proposes a practical and scalable framework to support the energy decarbonization of ports through the phased integration of hybrid renewable energy systems (HRES) and energy storage systems (ESS). Emphasizing a systems-level approach, the framework addresses key aspects such as energy demand assessment, resource potential evaluation, HRES configuration, and ESS sizing, while incorporating load characterization protocols and decision-making thresholds for technology deployment. Special consideration is given to economic performance, particularly the minimization of the Levelized Cost of Energy (LCOE), alongside efforts to meet energy autonomy and operational resilience targets. In parallel, the framework integrates digital tools, including smart grid infrastructure and digital shadow technologies, to enable real-time system monitoring, simulation, and long-term optimization. It also embeds mechanisms for regulatory compliance and continuous adaptation to evolving standards. To validate its applicability, the framework is demonstrated using a representative case study based on a generic port profile. The example illustrates the transition process from conventional energy models to a sustainable port ecosystem, confirming the framework’s potential as a decision-making tool for port authorities, engineers, and policymakers aiming to achieve effective, compliant, and future-proof energy transitions in maritime infrastructure.

Energy-intensive industries deploying hybrid renewable energy systems require performance monitoring frameworks that evolve with phased system implementation. This paper introduces the performance and sustainability framework, a simulation-grounded evolution of the sustainability balanced scorecard for longitudinal assessment of renewable energy infrastructure. The framework requires that key performance indicators derive from validated techno-economic simulations, that assessment is repeated at temporal checkpoints corresponding to physical system changes, and that each balanced scorecard perspective includes at least one environmental or circular-economy indicator. The framework is demonstrated in a cheese manufacturing facility in Crete, Greece, where a 38 kW cheese whey biomass generator, 72.2 kW photovoltaic system, and 10 kW wind turbine are deployed over five years. Annual HOMER Pro re-simulations are combined with weighted SWOT scoring to track technical, economic, environmental, and organisational performance. By Year 5, the system achieves an 88.7% electrical renewable fraction, 60.0% gross-operational CO₂-eq reduction, 0.1148 EUR/kWh levelised cost of energy, and 22.3% internal rate of return. The longitudinal trajectory also reveals declining delivered thermal renewable contribution and cheese whey utilisation, exposing operational trade-offs that single-point scorecard assessments would miss. Applicability of the PSF to community-scale governance under ISO 37101:2016 and to renewable energy communities under Directive (EU) 2018/2001 is examined exclusively as a conceptual scaling framework for future research. The present empirical demonstration is restricted to a single-facility case study, and no community-level stakeholder data are collected or analysed.

Smart-port decarbonisation requires operations-research decision support under day-ahead uncertainty. We present a rolling-horizon Model Predictive Control Energy Management System, formulated as a Mixed-Integer Linear Program with five forecast streams, and benchmark it against a deterministic rule-based controller on an identical configuration. A full-year proof-of-concept at the Port of Ancona (8760 hourly steps over the 2024 Italian Day-Ahead Market, 6.5 MWp PV, 1.0 MWh BESS) combines realised 2024 market, photovoltaic and auxiliary-demand series with a post-AFIR projected cold-ironing demand—the dominant load—and is therefore an operational proof-of-concept rather than a fully metered baseline. The principal MPC outcome is structural: anticipatory dispatch raises the mean BESS state of charge from 13.6% to 46.0% and cuts residence at the minimum SoC from 81% to 6% of hours. The forecasting layer attains sub-7% sMAPE on cold-ironing-loaded demand and 9–18% on the remaining streams (seasonal MASE₂₄ ≤ 0.74 on demand and price streams). At the relay-constrained 0.08 C pilot, the realised savings is 0.44% (€14,463 yr⁻¹; 95% moving-block bootstrap CI [€12,842, €15,742]); benchmarked against an enhanced rule-based controller that is itself permitted price-threshold grid charging, the residual value of predictive optimisation is €5652 yr⁻¹ (0.17%), with the remainder of the gap being the value of enabling grid charging. A C-rate sweep shows the savings doubling to 0.93% at 0.5 C, and a direct 20 MWh/±10 MW simulation yields a €0.57 M yr⁻¹ gross arbitrage savings whose net value, after a realistic battery-degradation penalty, is substantially smaller. Controller-level operational CO₂ rises marginally (+6.2 t, +0.13%), an effect distinct from—and dwarfed by—the system-level cold-ironing decarbonisation. The framework is reproducible in open-source Python (PuLP/HiGHS) from the actual data and is portable to other single-node smart city energy hubs.

Supplying shore-side electricity to ships at berth, a practice known as cold ironing, removes the emissions of their auxiliary engines, yet the resulting electricity demand is large, highly seasonal and hard to serve economically from the grid at the small and medium-sized ports that make up most of the European network. This study asks how to meet that demand affordably and cleanly. It develops a smart-sizing and energy-management framework for a grid-connected hybrid renewable energy system that jointly optimizes solar photovoltaic and wind capacity together with a combined battery-and-hydrogen storage envelope. An energy-conserving stochastic reconstruction of the hourly cold-ironing demand is embedded within a genetic algorithm that minimizes the levelized cost of energy and the carbon footprint, and the system is operated by a transparent, priority-based controller. On a full year of real operational data from a Mediterranean port, the optimizer selects 380 kilowatts of photovoltaic capacity and a 2064 kilowatt-hour, battery-dominated storage envelope, reaching a renewable penetration equal to 76 percent of annual demand, with 57 percent of demand met without the grid. Relative to grid-only cold ironing it lowers the levelized cost of energy by about 10 percent on a screening basis, before life-cycle costs bring it to roughly grid parity, while cutting greenhouse-gas emissions by 45 percent; emissions fall 72 percent relative to auxiliary engines. Storage capacity, not oversized renewable generation, proves decisive for deep decarbonization, and battery storage dominates the cost-optimal design for this diurnal load. The framework gives port operators a transferable, data-driven decision-support tool.