Lina Montuori

Port decarbonisation requires integrated energy solutions capable of reducing fossil-fuel dependence while maintaining safety, reliability, and operational continuity. This paper presents a machine-learning-based energy management tool designed to aggregate and disaggregate energy loads according to port operational activities. The tool—modular and designed for phased deployment—integrates onshore power supply (OPS), photovoltaics (PV), vertical-axis wind turbines (VAWTs) and battery energy storage (BESS), and forecasts energy demand under different cargo-handling scenarios. It is applied to the Port of Klaipėda (Lithuania) through four capacity configurations, from a 2024 baseline (S0) to a 2035 low-carbon configuration (S3), each calibrated with real port data and evaluated under a common 2025 operational year so that the technology portfolio is isolated from inter-annual variability. Relative to the baseline, the 2035 configuration raises the renewable fraction from 0.6% to 19.4%, drops annual energy cost by 22.2% (from €8441k to €6571k) and CO₂ emissions by 18.4% (from 9679 to 7899 tCO₂/yr), and increases OPS utilisation from 0.4% to 10.6%. Random Forest demand forecasting achieves an MAE of 600.6 kW (MAPE 8.1%), improving on a persistence baseline by 25.7%, although its low R² keeps the forecasting layer to planning support rather than validated real-time control. The results establish the tool’s viability as a strategic platform for port energy transition, providing integration criteria and a scalable roadmap towards net-zero operations.

According to Global Waste Management Outlook 2024, Urban Solid Waste will reach 3.8 billion tons by 2050 highlighting the need to explore new management measures in order to avoid the estimated annual cost of €562.79 billion. Specifically, in 2022, according to Unite Nations “Food Waste Index Report 2024”, the organic fraction wasted accounted for 1.05 billion tons of food in the retail, food service and households’ sectors combined. In this framework, the present work is a feasibility study of a smart and automated equipment for treatment of Food Waste to subsequently produce compost to be used as fertilizer for adjacent green areas or agriculture. Furthermore, a novel methodology has been proposed to implement the Food Waste treatment equipment as energy self-sufficient system based on renewable energy sources and capable to treat different types of Urban Solid Waste. Finally, CAPEX and OPEX costs of the Municipal Solid Waste management systems most widely used in Europe are analyzed throughout the analysis of the Levelized Cost of Waste Treatment.

This research proposes an optimization model for the scheduling of power generation expansion in countries with high hydrological variability, to be implemented in the Colombian system for the 2027–2041 period. The method combines a Vector Autoregressive (VAR) model, evaluated using daily series of inflows, demand, and exports for 2010–2024, with a two-stage stochastic optimization model. Unlike previous methods, the model maintains the contemporaneous covariance calculated when creating the planning scenarios. Hydrological inflows cause demand (p = 0.0003), which reflects the possible effect of the El Niño–Southern Oscillation (ENSO). Their contemporaneous correlation of −0.1018 comes from the dry months, which coincide with the months of higher consumption. The main contribution of this research lies in measuring the cost of disregarding this dependence. This is performed using two similar optimization problems that differ only in whether they maintain the covariance. In the base case, the plan based on the independence hypothesis adds 45% more capacity, replaces wind power with solar power, and requires an additional 950 million dollars compared with the scenario that maintains the correlation, representing 1.97% of the system’s optimal cost.

The thermal decomposition of natural gas into low-emission hydrogen is a relatively new process that can act as a bridge technology toward a decarbonized hydrogen economy. Hydrogen produced via the thermal decomposition of natural gas has no direct CO2 emissions and generates solid carbon as a commercially valuable co-product. This chapter provides a review of the thermochemical fundamentals, reactor configurations (thermal, plasma, catalytic, and electrified), and catalyst systems (Fe, Ni, carbon-based) governing methane pyrolysis, assessing the technology’s current readiness level against large-scale deployment requirements. A central focus is placed on the strategic advantage of utilizing existing natural gas transmission and distribution infrastructure. Furthermore, the chapter discusses the lifecycle emission profiles of turquoise hydrogen, including the valorization of solid carbon in the rubber, electronics, construction, and agriculture (biochar) sectors. Consequently, regulatory and certification challenges, including carbon accounting standards for solid sequestration, are also discussed. As main findings, the levelized cost of hydrogen (LCOH) from methane pyrolysis has been calculated to range from USD 1.30 to USD 1.47 per kilogram for highly integrated designs under low gas price conditions, with the possibility of falling below USD 0.50 per kilogram when taking into account battery-grade carbon co-product revenues. Conclusions highlight that turquoise hydrogen can offer a strategically viable, near-term decarbonization lever for hard-to-abate industrial sectors, particularly in regions with well-developed natural gas infrastructure and accessible solid carbon markets.

This paper presents a case study of a Home Energy Management System (HEMS) integrating photovoltaic (PV) generation, battery energy storage (BES), thermal storage, and a heat pump in a single-family household operating under a dynamic electricity tariff. The analysis is based on real operational data and focuses on system performance under varying solar generation conditions. The results show that during sunny days, the battery storage absorbs the entire surplus PV generation until reaching full capacity, i.e., 10 kWh, effectively preventing curtailment and maximizing self-consumption. On days with limited solar production, the system actively utilizes the available storage capacity by shifting energy use in time and, when economically justified, temporarily charging the battery from the grid during low-price periods. This strategy reduces electricity purchases during peak-price hours and stabilizes household energy costs. For the analyzed case, daily PV generation self-consumption exceeded 70% on high-generation days, while the application of storage-based load shifting under dynamic tariffs reduced daily electricity costs by up to 30% compared to a fixed-rate tariff. The study confirms that the economic and operational performance of residential energy systems under dynamic pricing depends primarily on adaptive storage control rather than on PV capacity alone, highlighting the central role of battery energy storage in year-round energy optimization.

For nations heavily dependent on fossil-fuel exports, hydrogen is emerging as a promising solution to reduce carbon emissions while preserving economic stability and promoting countries’ energy independence. This research study examines hydrogen potential as a renewable energy source to facilitate the transition toward a sustainable economy with a special focus on Middle East and North Africa (MENA) countries. The analysis delves into policy frameworks, technological advancements, and infrastructure adaptations to build a reliable green hydrogen supply chain for a scalable and bankable future. The role played by other renewable energies like solar and wind, together with the risk related to the high demand for water resources to achieve the green hydrogen transition, has also been assessed. Furthermore, key challenges have been highlighted, including the repurposing of the existing pipelines into the energy networks, public–private partnerships to secure investment, and legislation requirements to encourage the adoption of novel hydrogen applications. In order to do that, a SWOT-PESTEL analysis has been carried out to identify the main decarbonization strategies for achieving a replicable framework. Moreover, a multi-criteria decision analysis was performed, applying 11 indicators across supply-side (e.g., solar/wind potential, LCOE, and water stress), demand-pull/logistics (e.g., maritime connectivity, steel production, and LNG export capacity), and risk/regulation dimensions (e.g., governance effectiveness, regulatory quality, and fossil rent dependence). The Analytic Hierarchy Process (AHP) was used for weighting, the entropy method for weighting variability (hybrid 50/50 combined weights), min–max normalization for costs, 5% Winsorization for outliers, and TOPSIS for aggregation following OECD-JRC composite indicator guidelines. Results have been validated through a multiple scenario analysis (base, supply-led, and risk-aware) and sensitivity testing via Dirichlet bootstrapping (5000 iterations) with ±20% weight perturbations. Six countries of the MENA region have been studied. The multi-criteria decision analysis outcomes rank Egypt (composite score 0.518), Algeria (0.482), and Oman (0.479) as the most suitable countries for large-scale green hydrogen and ammonia production/export, while Saudi Arabia, Qatar, and Kuwait achieved lower supply scores in the base case due to higher perceived risks.

The increasing share of variable renewable energy sources (RES) in power systems leads to growing challenges related to grid balancing and the management of periodic electricity surpluses. One potential pathway for utilizing these surpluses is their conversion into synthetic fuels via Power-to-X technologies. This study analyzes the technical and economic potential of surplus renewable electricity utilization for the production of green hydrogen and synthetic fuels, using Poland and Spain as representative case studies of power systems with low and high RES penetration, respectively. An original methodology based on hourly power system data was developed to identify technically feasible surplus electricity volumes, accounting for changes in renewable and conventional generation, minimum renewable energy share thresholds, and a minimum two-hour continuous operation requirement. The analysis quantifies both instantaneous and usable surplus energy on an annual basis and evaluates the resulting capacity factors of Power-to-X installations. The results show that the annual usable surplus energy amounts to approximately 886 GWh in Poland and 2329 GWh in Spain, corresponding to maximum capacity factors of about 27% and 50%, respectively. Based on these surpluses and assuming low-cost electricity during surplus periods (10 EUR/MWh), the levelized cost of green hydrogen was estimated at 4.1 EUR/kg in Poland and 2.18 EUR/kg in Spain. The resulting production costs of green methanol reach approximately 739 EUR/Mg for Poland and 378 EUR/Mg for Spain after accounting for avoided CO₂ emissions. The findings indicate that surplus-based Power-to-X systems can play a meaningful role in integrating high shares of renewable energy, particularly in power systems with high and stable RES penetration. However, their contribution remains strongly constrained by surplus availability, temporal continuity, and system-specific characteristics.

Numerous models exist for estimating the specific energy yield of wind turbines, typically relying on meteorological wind speed data and turbine characteristics. However, the applicability and accuracy of these models must be validated against real-world data, particularly concerning the conditions specific to Poland. The primary objective of this study was to verify the accuracy of existing wind energy yield models for onshore wind turbine installations in Polish conditions. The study was conducted in two parts. First, the compliance of wind speed data derived from two global reanalysis databases (ERA5 and MERRA) was analyzed against actual hourly measurements. These measurements were collected from nacelle-mounted sensors at the hub height of six operational turbines (two 3 MW and four 0.8 MW units) at a wind farm site over the course of 2019. Second, a computational model for the specific energy yield was verified using the same on-site measurements, incorporating data on turbine configuration, location, and the ERA5/MERRA inputs. A significant discrepancy was observed: wind speeds measured directly on the higher-capacity turbines (3 MW) were consistently higher than those reported in the ERA5 and MERRA databases. This difference is attributed to the fact that the coarse grid resolution of global databases does not capture the precise, optimized placement of turbines at sites specifically selected for high wind potential, often considering local topography. Despite this initial wind speed variance, the subsequent verification of the energy yield model showed satisfactory agreement with real production data. The relative mean bias error (rMBE) was found to be below 8% for the ERA5 input paired with the 3 MW turbine data and below 12% for the MERRA input paired with the 0.8 MW turbine data. The findings confirm that while global reanalysis databases may underestimate local wind speeds due to generalized grid resolution, the tested energy yield model provides satisfactory results for wind turbine planning in Poland.

This paper presents threats emerging from the rise in implementation of emerging technologies, such as smart inverters, on everyday human life and their impact on sustainability. Focusing on household photovoltaic systems, this study provides a detailed analysis of the energy production and the trends in the photovoltaic market. A specific application on household photovoltaic systems located in Poland has been carried out in order to assess the potential danger arising from cyberattacks. As the number of distributed energy resources {XE “distributed energy resources”} on the grid increases, the need for smart inverter functionality has grown. In this framework, the present work includes a review of past cyberattacks and their repercussions on technologic developments of smart inverters and on society in general. Additionally, measures to ensure that smart inverter capabilities can be fully realized and to prevent cyberattacks are also discussed. Through the simulation of multiple experimental scenarios, remaining existing threats to the penetration of smart technologies aimed at enhancing grid reliability are identified. The results achieved provide strategic tools to address cybersecurity issues, including those of photovoltaic installations, as well as to promote countries’ energy independence.