Search Results (941)

This study addresses the multi-objective optimization of characterizing a flat glass processing plant. To assess the operational conditions required for a flat glass processing small and medium-sized enterprise (SME) to become a prosumer compatible with renewable energy community (REC) participation. This work is motivated by the presence of more than 300 SMEs in Italy, like this, where RECs represent one of the few viable strategies for achieving the European Union’s 2050 decarbonization targets. The research is carried out in two scenarios; Scenario-I includes Stage-i and Stage-ii with the mutual goal of forecasting and optimizing. Forecasting is used in Stage-i to optimize the factory load, and in Stage-ii to shift and curtail energy loads based on the forecast, considering the Italian national energy price and the regional price bands (“fasce orarie”) F1, F2, and F3. Forecasting and the indicators of environmental and social performance are the means to ensure the best energy utilization and management, as they prove that the reduction in CO₂ emissions and benefits on the community level can be both obtainable. Subsequently, the techno-economic analysis and evaluation of prosumer-readiness conditions are carried out through the optimization of industrial energy demand: three optimization objectives are assessed in this study (i) energy cost, (ii) carbon emission, and (iii) load curtailment. Four algorithms are put into effect to solve the tri-objective optimization: multi-objective particle swarm optimization (MOPSO), multi-objective ant nesting algorithm (MOANA), non-dominated sorting genetic algorithm (NSGA-II), and multi-objective grey wolf optimization (MOGWO). The algorithms are validated in Stage-ii to find the desired optimum in the cost of energy, reduce peak formation, and carbon emissions. To achieve this goal, a stochastic approach based on Monte Carlo simulations and VIKOR is used to optimally select the results. The findings show that the NSGA-II, MOPSO, and MOANA are more effective in solving the problem, while the MOGWO algorithm more quickly finds the optimal solution. Based on the defined objectives, a new configuration for the energy community is introduced, together with a community well-being index and an evaluation of the resulting benefits for the factory. In Scenario-II, the PV plants’ installation on the factory is sized, and the excess energy shared with the grid is evaluated. The Scenario-II results show that 497.184 MWh (33.9%) of energy is shared with the grid. Both results suggest how optimized industrial demand profiles improve SME participation in future RECs. Full article

Ports combine clusters of operational buildings, shared energy infrastructure, and structurally critical assets requiring coordinated management to ensure safety and efficiency. Nevertheless, existing Digital Twin (DT) frameworks for building energy management rarely integrate Structural Health Monitoring (SHM) with energy performance assessment, while port-specific implementations remain scarce. This paper presents a pre-operational energy-aware DT architecture for port building clusters, structured in a unified five-layer framework integrating three capabilities: (i) EGMS/InSAR-based SHM screening with planned in situ sensing and computer-vision inspection workflows; (ii) smart metering and measurement and verification (M&V) protocols aligned with ISO 50001/50015 and IPMVP standards; and (iii) weighted multi-criteria prioritization considering structural condition, energy saving potential, service continuity, and cost. The framework is applied to the Port of Formia (Italy), a brownfield district comprising nine buildings (3371 m²), 16 high-mast lighting towers, shore power infrastructure, and 90 kWp of planned photovoltaics. In the absence of operational metering, energy and carbon values are reported as bounded ex-ante scenario estimates, not as verified performance outcomes. The analysis estimates photovoltaic generation of 116–137 MWh/year and lighting retrofit savings of 31.5–36.8 MWh/year; the related carbon values are treated as gross grid-displacement upper bounds pending measured self-consumption and export data. A four-phase validation roadmap with quantitative acceptance criteria supports the transition from feasibility assessment to verified performance. Full article

This article aims to examine how the natural gas market evolved following the price shocks observed between 2020 and 2024, paying particular attention to market integration and the persistence of these shocks. The proposed analysis uses daily price data for the Title Transfer Facility (TTF), the main European benchmark traded on the Intercontinental Exchange and quoted in EUR/MWh, as well as Henry Hub (HH), the United States benchmark. These series are combined with a country panel on natural gas production, consumption, and gross domestic product for 59 economies, subject to data availability. The cointegration results show that TTF and HH prices moved together in 2019, but this relationship broke down in 2020 and did not return to its previous pattern in the following years. Granger causality tests point to a one-directional transmission from Henry Hub to Europe. Moreover, GARCH estimates indicate that TTF reacts almost twice as strongly to daily shocks as HH, while volatility remains persistent in both markets. Fixed-effects estimates place the TTF price elasticity of import spending close to 0.5, providing evidence consistent with a causal link between higher natural gas prices and higher domestic energy expenditure. Finally, the clustering analysis complements the econometric modeling by identifying four groups of countries defined by gas import dependency and gas intensity. This classification also offers implications for the global natural gas market since it points to the need for cluster-specific policy approaches rather than a single solution applied to every country. Full article

The large-scale integration of wind and solar power introduces significant volatility into electricity markets, posing challenges for accurate day-ahead price forecasting for generation companies. This paper proposes a hybrid forecasting model, CEEMD-SE-IBA-LSTM, based on hyperparameter adaptation to improve prediction accuracy. First, a similar-day selection method integrating Random Forest and an Improved Grey Ideal Value approximation identifies the most relevant historical days. Second, Complete Ensemble Empirical Mode Decomposition with Sample Entropy (CEEMD-SE) decomposes and reconstructs the price series into stable components. Third, an Improved Bat Algorithm (IBA), incorporating differential evolution and adaptive weighting, is developed to optimize two key LSTM hyperparameters: the number of hidden layer neurons, which is treated as a model architecture hyperparameter, and the learning rate, which is treated as a training hyperparameter. The number of LSTM layers and the number of training epochs are kept fixed as model settings to ensure reproducibility. Using data from the US PJM market, the proposed model is validated against six benchmarks. The results show that CEEMD-SE-IBA-LSTM achieves superior performance, with a Mean Absolute Percentage Error (MAPE) of 3.73%, a Root Mean Square Error (RMSE) of 3.57 $/MWh, and a Mean Absolute Error (MAE) of 1.95 $/MWh. The method provides accurate price trends, offering effective decision support for new energy enterprises in price bidding to enhance revenue. Full article

Weak grid infrastructure and the absence of flexible storage are among the principal barriers to reliable, low-carbon energy access in sub-Saharan transmission systems. This paper proposes a hierarchical multi-objective framework for the optimal siting and sizing of battery energy storage systems (BESSs), applied to the 130-bus Mali transmission network within the EMERGE project. The upper level employs NSGA-II to simultaneously maximize daily price arbitrage revenue and minimize active power losses; the lower level solves a network-constrained DC optimal power flow with thermal branch limits enforced as hard linear inequalities via the Power Transfer Distribution Factor (PTDF) matrix. Over 500 generations, the framework identifies Bus 91 (SIRAKORO II, 150 kV) as the dominant storage location, achieving a maximum daily revenue of approximately €10,033 at a marginal loss increment of $6.7\times {10}^{-3}$ MWh. The resulting Pareto front gives Mali system planners a quantitative tool for trading off private investment returns against grid-level environmental impact, demonstrating that rigorous network-constrained BESS planning is technically tractable and economically viable in the resource-constrained context of sub-Saharan energy transitions. Full article

With the rapid expansion of artificial intelligence infrastructure and cloud computing services, data centres are evolving from rigid electricity loads into flexible resources capable of contributing to renewable energy integration, grid regulation and cross-regional computing power allocation. Addressing the shortcomings in existing research regarding the differences between various types of computing tasks, the mechanisms of green migration under network constraints, and the characterisation of curtailment risks for renewable energy, this paper proposes a risk-aware collaborative optimisation and scheduling method for a power–computing–network–storage coupled system across remote data centres. Firstly, a hierarchical model of multi-type computing tasks is constructed, classifying data centre loads into fixed real-time tasks, online inference tasks, long-duration AI training tasks, and opportunistic elastic tasks, to characterise the differences between these tasks in terms of latency, time-shift, migration, and completion volume constraints. Secondly, a graph-attention-inspired green affinity prior is proposed, mapping grid topological distance, renewable energy availability, data centre PUE, and energy storage regulation capacity into interpretable migration signals, thereby guiding flexible computing power to migrate towards nodes with abundant green electricity and favourable grid support conditions. Subsequently, we introduce the CVaR metric to quantify the tail risk of renewable energy curtailment, establishing a multi-scenario stochastic linear optimisation model that incorporates DC power flow, unit output, renewable energy utilisation, campus energy storage, task SLAs, and cross-node migration constraints. A 24 h simulation based on the IEEE 10-machine, 39-node system demonstrates that the proposed method can reduce the expected curtailment volume from 176.939 MWh to 0 MWh, lower the CVaR curtailment risk from 694.085 MWh to 0 MWh, and increase the proportion of green computing power by 9.283 percentage points compared to the fixed-load baseline, whilst improving the five-tier collaborative score by 4.885 points. Full article

Accurate forecasting of hourly electricity consumption is critical for smart grid management, energy market operations, national policy planning, and—particularly for import-dependent economies such as Türkiye—energy security. This study presents, to the best of the authors’ knowledge, the first systematic benchmark of four state-of-the-art time series architectures—TimesNet, PatchTST, iTransformer, and Temporal Fusion Transformer (TFT)—conducted specifically on a national-scale Turkish multivariate energy dataset from the Energy Exchange Istanbul (EPİAŞ), covering 72,322 hourly observations across 15 generation, consumption, and market-clearing price variables from January 2018 to April 2026. While benchmark studies of Transformer-based architectures exist on general time-series datasets, no prior work has applied this specific combination of architectures to the EPİAŞ dataset under unified experimental conditions with an explicit energy-security interpretation. All models were trained under standardized preprocessing (StandardScaler), a 24 h lookback window, and systematic hyperparameter optimization. Experimental results demonstrate that iTransformer achieves the best predictive performance (MAE = 521.34 MWh, RMSE = 748.12 MWh, R² = 0.9881, MAPE = 1.34%), followed by TFT (R² = 0.9863) and PatchTST (R² = 0.9844). TimesNet, while the most computationally efficient, achieves an R² of 0.9791. Beyond predictive benchmarking, this study situates the findings within Türkiye’s energy security agenda: the dataset captures fossil fuel dependency, the growing share of domestic renewables, and market-clearing price dynamics shaped by geopolitical shocks, including the Russo–Ukrainian war and evolving EU–Türkiye energy relations. Comprehensive analysis of model architectures, attention mechanisms, temporal feature importance, and computational efficiency is provided. These findings establish a rigorous baseline for deploying modern sequence models in large-scale, real-time national energy forecasting systems that serve both market-efficiency and strategic-energy-autonomy objectives. The results specifically highlight how high-fidelity forecasting can serve as a risk-mitigation tool against geopolitical supply disruptions by quantifying the impact of domestic renewable integration. Full article

Decarbonizing heavy-duty road transport requires comparing zero-emission options to guide infrastructure investments along strategic corridors. This study develops a scenario-based techno-economic model to evaluate hydrogen fuel cell trucks (HFCTs) and battery electric trucks supported by dynamic wireless power transfer (DWPT) on a 100 km segment of Italy’s A4 motorway in 2030 and 2050 scenarios. The framework integrates traffic flows, vehicle archetypes, infrastructure sizing, and end-to-end energy chains (power-to-hydrogen-to-wheel for hydrogen and grid-to-wheel for WPT) to estimate capital and operating costs, efficiencies, and energy demand. Results show that hydrogen refueling infrastructure requires lower initial investment (approximately €60 million CAPEX and €20 million annual OPEX) than wireless charging systems (€80 million CAPEX and €15 million OPEX). However, WPT achieves significantly higher grid-to-wheel efficiency (96% vs. 62%) and lower per-vehicle energy demand (18 MWh/year vs. 25 MWh/year). These findings highlight a fundamental trade-off: hydrogen solutions offer operational flexibility and are better suited to long-haul or low-density contexts, while WPT systems are more efficient and become increasingly competitive in high-traffic corridors with high infrastructure utilization. Overall, the results suggest that no single technology universally dominates and that optimal deployment depends on traffic density, infrastructure usage, and system integration. A combined implementation of hydrogen and wireless charging technologies may provide the most effective pathway to balance efficiency, flexibility, and cost in future heavy-duty transport systems. Full article

This paper investigates the energy recovery potential of LNG cold energy using cryogenic binary cycles. The thermoeconomic performance of single-, two- and three-stage Organic Rankine Cycle (ORC) configurations across different working fluids and LNG regasification capacities has been evaluated. The analysis shows that ORC-based LNG cold energy power units achieve specific net power outputs of 45–55 kW/(kg_LNG/s) for single-stage, 74–83 kW/(kg_LNG/s) for two-stage, and 79–88 kW/(kg_LNG/s) for three-stage configurations. The corresponding net energy efficiencies are 6.6–7.5%, 10.1–11.2% and 10.8–12.0%, respectively, while the exergy efficiencies are 15.9–17.6%, 22.9–25.3%, and 24.3–26.8%, respectively. Two-stage systems achieve the lowest costs: a levelized cost of electricity (LCOE) of 80–105 €/MWh and a specific investment cost (SIC) of 6000–8300 €/kW. For most of the evaluated working fluids, the power gain from a third stage does not justify the increase in equipment costs. Among the evaluated working fluids, R32, R41 and R161 achieve the best economic performance, while carbonyl sulfide (COS), R32 and R161 achieve the best thermodynamic performance. The highest net power, 12.5 MW, is achieved with COS, whereas the lowest LCOE (80 €/MWh) and SIC (6000 €/kW) are obtained with R32, all for an LNG regasification capacity of 700,000 Sm³/h. Full article

The synergistic utilization of post-mining spaces and geothermal energy through underground seasonal thermal energy storage (USTES) provides a promising pathway for sustainable heating and the low-carbon redevelopment of mining regions. To advance the thermal management and reveal the thermo-hydraulic evolution patterns within these repurposed environments, this study proposes an integrated approach that utilizes post-mining roadways as heat storage reservoirs, within the scope of a single idealized case study. A comprehensive USTES heating system model was established to systematically evaluate operational characteristics and environmental impacts under diverse conditions assuming homogeneous rock properties and idealized thermal boundaries. Results demonstrate that the surrounding ground temperature and the low thermal conductivity of the rock mass contribute to limiting heat dissipation and maintaining stable seasonal storage performance. For a roadway with a 20,000 m³ water storage capacity and an optimal 3900 m² solar collector area, the system successfully satisfies the thermal demand of 30,000 m² of building area. The configuration achieves 1239 MWh of cumulative heat storage over a 245-day cycle, maintaining a direct heating-to-heat-pump-upgraded heating ratio of 1.02. Furthermore, the implementation of variable-frequency thermal management strategies demonstrates remarkable economic and environmental superiority, yielding a 35.8% cost reduction compared to coal-fired heating, an overall energy saving rate of 77.5% relative to electric heating systems and a 13.5% decrease in CO₂ emissions relative to gas-fired systems. This research provides fundamental design parameters for the synergistic exploitation of mineral and geothermal resources, advancing the development of green heating and the sustainable utilization of post-mining spaces. Full article

The efficiency of anaerobic digestion (AD) of lignocellulosic biomass is strongly determined by biomass yield, chemical composition, and bioavailability, all of which undergo substantial seasonal variation. However, integrated analyses linking these factors with AD performance, process kinetics, and energy-economic efficiency remain limited. This study aimed to evaluate the effect of seasonal variability in the chemical composition of Helianthus annuus biomass on AD efficiency from a technological and economic perspective. The novelty of this study lies in integrating seasonal changes in biomass composition with AD kinetics, CH₄ productivity per hectare, and CHP techno-economic performance to identify the optimal harvest window for Helianthus annuus. The experiments were conducted using biomass harvested from June to December. The results showed significant (p < 0.05) variability in biomass properties, including a progressive increase in lignocellulosic fractions over the growing season, with neutral detergent fiber (NDF) increasing from 30.58 ± 1.8 to 66.58 ± 3.1% TS and acid detergent lignin (ADL) from 5.13 ± 0.5 to 10.35 ± 0.9% TS, accompanied by a decline in substrate bioavailability. The maximum CH₄ yield of 258 ± 13 mL/g VS was obtained in August, with a process rate of 29.0 ± 3.4 mL/g VS·d and the highest utilization of methane potential, reaching 62.5 ± 3.8% (BMP_CH4/TBMP). Correlation and regression analyses indicated that ADL and NDF were the strongest empirical predictors of AD performance within the analyzed dataset, showing a negative association with both CH₄ production yield and kinetics (R² up to 0.86), whereas reducing sugars had a stimulatory effect. Multiple regression models showed high predictive performance, with R² = 0.889 for BMP_CH4. The highest energy and economic efficiency was achieved in summer. In August, CH₄ production reached 3214 ± 596 m³/ha, corresponding to 11.2 ± 2.1 MWh/ha of electricity and a net result of 1559 ± 417 EUR/ha. Increased lignification in the later part of the season led to reduced process efficiency and a deterioration of the economic balance. From a practical perspective, these results demonstrate that harvest scheduling should be based on the trade-off between biomass quantity and biodegradability rather than on biomass yield alone. Full article

Hydrogen (H₂) is regarded as a promising option for sustainable energy systems; however, its large-scale use in electricity supply remains limited. This study develops a stochastic network optimization model to examine the applicability of H₂-based electricity generation. The proposed Hydrogen Supply Chain (HSC) model evaluates cost and emission performance under uncertainty by considering disaster conditions, transmission losses, depreciation, and the time value of money. The Marmara Region of Türkiye is divided into 24 grid nodes, and a single-period model for 2023 is solved using Mixed-Integer Linear Programming (MILP). The HSC is allowed to meet 10–40% of electricity demand and to replace collapsed grid lines by supplying critical public centers (CPCs) during disasters. The results show that the HSC can meet 24.82% of demand, although at costs approximately 3.9 times higher than power grid (PG) electricity, while producing 3.44 MtCO₂/year compared to 65.96 MtCO₂/year from the PG. The model is then extended to a multi-period structure (2023–2053) and solved by Variable Neighborhood Search (VNS). Over time, H₂ costs decline, and their share rises from 19% to 35%, while electricity costs decrease from 408 USD/MWh to 170 USD/MWh. These findings suggest that H₂-based electricity supply can support long-term sustainability and resilience objectives in regional energy planning. Full article

The power flow problem is one of the most challenging tasks in power systems, affecting both generation cost and energy quality. Optimal power flow (OPF) further complicates this task by requiring the optimal adjustment of system variables and parameters. This paper adapts the Modified Effective Butterfly Optimizer (MEBO) to solve multi-objective optimal power flow (MOOPF) problems with the contribution of optimized weighting using multiple Pareto archives. MEBO is an advanced optimization algorithm that utilizes population reduction and parameter learning to guide subsequent searches for unconstrained problems. The proposed technique has been tested on IEEE 30 and 57 bus test systems, and the results have been compared with existing methods reported in the literature. In the paper, four single-objective functions, namely generator cost, active power loss, fuel emission, and voltage deviation, are used to construct four multi-objective (MO) problems: cost–loss, cost–voltage, cost-emission, and emission–loss. For the cost-emission case, the proposed MEBO achieved compromised solutions of 791.1951 $/h fuel cost with 0.10873 ton/h emission and 801.8172 $/h fuel cost with 0.10044 ton/h emission under different Pareto-based optimization metrics. In the emission–loss case, the algorithm obtained 0.20539 ton/h emission with 3.1403 MW/h power loss, demonstrating the effectiveness of the proposed approach in balancing conflicting objectives. The Pareto curves of MEBO in achieving MO problems are presented, along with the suggested compromised solutions acquired from the literature. In the literature, this is the first application of MEBO for solving MOOPF problems. The results demonstrate that MEBO performs better than most other alternatives; this shows potential for further improvements with respect to the MOOPF problem. Full article

The decarbonisation of the building sector represents a key challenge for the European energy transition, particularly in the heating segment, which is still largely dependent on fossil fuels. In this context, data centres (DCs) offer a promising opportunity as local sources of recoverable waste heat. This study investigates the use of data centre waste heat for building heating through a comparative annual energy analysis applied to two building typologies in a Mediterranean climate (Italy): a residential building and a school. Three scenarios are considered: non-integrated scenario S0 (data centre with its own cooling system and buildings with gas-fired boilers), non-integrated scenario S1 (data centre with its own cooling system and buildings with air-to-water heat pumps), and integrated scenario S2 (data centre cooling system coupled with the buildings through waste heat recovery and heat pump technology). A theoretical 300 kW data centre was considered as the waste heat source. The integrated scenario significantly improves system performance. In the residential case, the seasonal COP increases from 2.15 to 4.50, reducing electricity consumption from 289.5 MWh to 128.9 MWh. In the school case, the COP increases from 2.51 to 8.00, with electricity consumption decreasing from 161.3 MWh to 49.1 MWh. These improvements lead to reductions in non-renewable primary energy demand of up to 63% and 79% for the residential and school buildings, respectively, compared to the baseline scenario. The results demonstrate that data centres can act as decentralised thermal sources, supporting the transition towards low-carbon and Nearly Zero-Energy Buildings. Full article

This study presents a comprehensive life cycle assessment (LCA) of the modernization of an existing 460 MW coal-fired power unit into a hybrid system incorporating a high-temperature gas-cooled reactor (HTGR). The analysis was conducted from a cradle-to-grave perspective using a functional unit of 1 MWh of net electricity, based on the ecoinvent 3.9 database and the ReCiPe 2016 Midpoint method. The results indicate that the modernized system achieves a global warming potential (GWP) of 18.2 g CO₂-eq/kWh, representing a 93.5% reduction compared to a supercritical coal-fired unit. The largest contribution to the total environmental burden is associated with the upstream uranium supply chain, accounting for approximately 42% of GWP. In contrast, the operational phase exhibits a negative contribution due to the application of environmental credits resulting from the avoidance of emissions related to coal combustion. The findings also confirm a significant improvement in resource efficiency, including reduced primary energy demand and waste generation compared to the reference system. Sensitivity analysis demonstrated the robustness of the results with respect to variations in key economic and thermodynamic parameters, particularly CAPEX (capital expenditures) and operating temperature. Overall, the results suggest that hybrid retrofitting of coal-fired power plants with HTGR technology may serve as a viable transitional pathway supporting the decarbonization of the Polish energy sector. Full article