Search Results (19,029)

The increasing use of integrated renewable energy sources (RESs) is undoubtedly reshaping the structure of power systems. In such conditions, achieving energy efficiency and sustainability requires the development and integration of digital solutions to manage energy flows and assets optimization. This paper aims to provide a comprehensive review of the successful integration of artificial intelligence (AI) in the energy sector, particularly in relation to the high penetration of renewable energy. The paper presents trends and potential scenarios in the digitalization of energy, along with the associated challenges. It analyzes particular applications of AI tools in strategic areas of the energy sector. Five key areas of the energy sector are identified where AI tools are applied: forecasting electricity generation from RES; forecasting demand and price fluctuations on the electricity spot market; the real-time management of energy flows and assets in active microgrids; and data processing and analyzing, and general industrial direction. The article also attempts to summarize the current status, goals, key areas, and activities in the irreversible transformation of power structures into digital intelligent ones. This digital transformation is a gradual process with consecutive steps. To improve understanding and clarity, the authors present a three-phase roadmap of AI adoption. To develop an adequate AI integration strategy, it is necessary to understand the technologies, algorithms, hierarchical structure, and connections within this structure. Accordingly, the article presents a taxonomy of the hierarchical structure of AI. The subsequent step involves the sequential construction of a digitalization model. Here, the authors consider it necessary to present a 4-layer structure model of AI energy democracy. Finally, through a comparative analysis of different types of intelligent applications for energy problem solving, guidelines are provided for successful decision making in compliance with the specified harmonized standards and protocols. Full article

Most existing studies on the cost competitiveness of self-consumption PV systems fail to jointly consider key technical, economic, and user-specific factors—such as the share of PV electricity self-consumed, energy exported or imported from the grid, and time-of-use electricity pricing—all of which significantly influence investment viability. To address these gaps, this study introduces a novel method based on a new model to calculate the unit cost of electricity consumption from the user’s perspective (C_EC, in €·kWh⁻¹). The array DC power rating is then optimally sized—assuming ideal orientation and tilt—to minimize C_EC. A self-consumption PV system is considered cost-competitive when the annualized minimized C_EC is lower than the applicable regulated electricity tariff. Colombia is selected as a case study to demonstrate the novel method due to the limited deployment and analysis of self-consumption PV systems in the country. The method is applied across residential, commercial, and industrial sectors in various locations. The resulting annualized minimized C_EC values (0.35–8.85 c€/kWh) are consistently below the corresponding regulated tariffs, demonstrating the economic viability of properly sized PV systems. The method’s adaptability to international tariff frameworks makes it a valuable tool for global application and a useful resource for policymakers and stakeholders. Full article

To enhance the market-oriented operation capability of integrated energy retailers and improve the synergy and economic efficiency of complex microgrids, this paper constructs a bi-level optimization model of “upper-level price optimization, lower-level multi-energy flow scheduling” under the background of multi-energy coupling of electricity, heat, gas, and hydrogen. The upper level optimizes electricity and heat price signals using the APSO and IGWO algorithms, while the lower level realizes coordinated multi-energy flow scheduling based on these signals. The operational performance of the two algorithms is compared across four scenarios. The results show that the scenario with multi-energy storage (Scenario 3) is the optimal adaptive scenario: the charge–discharge regulation of energy storage interacts with price guidance, and the peak-shaving and valley-filling characteristics significantly improve the system’s energy utilization efficiency. This scenario can fully unlock the value of bi-level optimization and meet the operational requirements of complex multi-energy coupling. In the algorithm comparison, the APSO algorithm presents distinct advantages, outperforming the IGWO algorithm in the precise regulation of upper-level electricity and heat prices, lower-level multi-energy flow balance, total operation cost control, and convergence stability. It provides an effective technical solution for the economic and stable operation of integrated energy systems. Full article

As the rapid evolution and expansion of Internet of Things (IoT) devices continues to accelerate, modern infrastructures face increasing cyber risks, largely driven by device inter-connectivity, limited security maturity, and interdependent attack propagation across networks. Traditional cyber insurance models often overlook these IoT-specific characteristics, relying on uniform or simplified risk assumptions that fail to capture real-world vulnerabilities. To address this gap, this paper presents a maturity-aware cyber insurance optimization framework tailored for interconnected IoT environments. The framework integrates organizational security maturity, interdependent risk propagation modeled through a modified Susceptible–Infected–Susceptible (SIS) process, and a Stackelberg game formulation that captures strategic interactions between the insurer and the defender. Through numerical studies on representative IoT topologies, we demonstrate that maturity-aware, risk-sensitive premium structures quantitatively outperform uniform pricing baselines in cost-efficiency and insurer sustainability. Specifically, our experimental results reveal that operating at an optimal intermediate maturity level ($M=3$) reduces the defender’s total expected cost by approximately $40\%$ (from $255.38$ k to $152.36$ k) compared to the baseline state ($M=1$). Furthermore, this structural hardening triggers an $88.3\%$ reduction in full-coverage insurance premiums (from $225.38$ k to $26.36$ k). In contrast, our uniform-pricing baseline exhibits reduced profitability in our experiments due to cross-subsidization effects, reinforcing the value of tiered, risk-proportional pricing for mitigating adverse-selection incentives. In summary, this work establishes a tractable, economically viable framework for cyber insurance in IoT ecosystems and provides a foundation for future extensions to richer network settings. Full article

This paper develops a unified analytical framework for pricing discretely sampled volatility-average swaps under the 4/2 stochastic volatility model. The model accommodates a broad range of volatility dynamics by combining affine and inverse-affine components in the instantaneous volatility specification, thereby unifying and extending the structural features of the classical Heston and 3/2 stochastic volatility models. Closed-form expressions for the conditional complex moments of the asset price are derived and serve as the fundamental building blocks for obtaining explicit analytical pricing formulas for volatility-average swaps under discrete sampling. The validity of the proposed pricing formulas is rigorously established within the admissible parameter space of the model. Extensive numerical experiments verify the accuracy and computational efficiency of the analytical results when compared with Monte Carlo simulations. The numerical analysis further reveals that discretely sampled volatility swap prices converge to their continuous-time counterparts in a manner that may be monotonic or non-monotonic, depending on the interaction between the volatility and inverse-volatility components of the 4/2 model, thereby emphasizing the importance of sampling effects in volatility derivative valuation. A detailed sensitivity analysis demonstrates how variations in the parameters governing the volatility and inverse-volatility components influence the fair strike prices, underscoring the structural flexibility of the 4/2 stochastic volatility model. Overall, the proposed framework provides an analytically tractable and computationally efficient approach for pricing volatility-linked derivatives under discrete sampling, offering valuable insights for both theoretical research and practical applications in volatility markets. Full article

Modern financial systems do not exist in isolation but form part of a complex global network of interconnected financial systems. This globalization of financial systems significantly increases the risk of contagion in financial markets, impacting asset prices and other important economic factors, including interest rates and market volatility. This phenomenon informs not only investors’ investment strategies but also the prices of contingent claims. In this article, we present a derivative pricing model in an incomplete and globalized financial market. To appreciate the dynamics and impact of some important market factors, particularly default risks due to contagion, we consider two different financial markets with defaultable assets: in one market, we consider a stock whose price process follows a Heston stochastic volatility model, and in the other, a stock that follows a Hawkes-type jump diffusion model whose intensity is subjected to external systemic shocks. In both markets, we derive an indifference price for a contingent claim that is subject to the risk of default and show the impacts the investor’s risk aversion and external shocks on the price of the contingent claim. Full article

Background: This study revisits whether oil revenue windfalls translate into higher socio-economic welfare in oil-exporting economies and explains why oil price booms often fail to generate sustained gains in real GDP per capita. Methods: Using annual data for ten oil-exporting countries over 1990–2024, we estimate country-specific ARDL/ECM models under a unified specification. The dependent variable is log real GDP per capita, explained by log real oil prices, the log share of government expenditure in GDP, population growth, and world GDP growth, with political and devaluation dummies where relevant. Results: Cointegration and significant error correction terms hold for most exporters, but adjustment speeds differ sharply. Long-run oil price elasticities are heterogeneous: strongly positive in Qatar, weak or insignificant in several cases (including Azerbaijan), and negative in a post-rentier pattern (UAE/Oman). Fiscal and demographic channels emerge as systematic constraints: government expenditure shares are often negatively associated with long-run welfare, and population growth typically reduces GDP per capita. World GDP growth is generally positive but uneven in significance. Conclusions: Resource use is conditional: welfare outcomes depend on fiscal regimes, demographic pressures, and structural transformation rather than windfall size alone. Full article

This study integrates the SOR framework with signalling theory, centring on information disclosure transparency as the core construct, to systematically examine its direct and indirect effects on consumers’ purchase hesitation. It specifically investigates the mediating roles of seller uncertainty and product uncertainty, whilst also testing the moderating effects of product price, type, and attributes. The research employs PLS-SEM in conjunction with the PROCESS Macro for empirical validation, drawing on 814 valid responses collected from online consumers in Taiwan. The principal findings indicate the following: (1) information disclosure transparency exerts a significant negative direct effect on purchase hesitation (B = −0.582, p < 0.001); (2) both seller uncertainty (indirect effect = −0.061) and product uncertainty (indirect effect = −0.060) exhibit partial mediation; (3) the model demonstrates strong predictive relevance for purchase hesitation (Q² = 0.486), underscoring its robust explanatory power in consumer decision-making processes; and (4) product price, type, and attributes significantly moderate the relationships between information disclosure transparency and the two uncertainty constructs. By extending signalling theory—originally developed in traditional markets—to the digital consumption context, this study provides empirical support for the signalling efficacy of information disclosure. It thereby offers an alternative theoretical lens for analysing consumer behaviour in online environments. Full article

The mechanization of wheat harvesting in Egypt is a critical step towards enhancing food security. This study evaluated the operational performance, grain loss, and economic viability of four wheat harvesting systems for the ‘Sakha 95’ variety in the Nile Delta. To evaluate and rank the different systems based on multiple criteria, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was employed. A Randomized Complete Block Design (RCBD) with three replicates was used to test three self-propelled combine harvesters (Claas [4.2 m], Field-King [2.0 m], Daedong [1.4 m]) alongside one semi-mechanized system (reaper–binder + stationary thresher). The TOPSIS analysis identified the Field King combine as the most recommended system (Rank 1), providing the optimal balance between operational efficiency and cost. It achieved the lowest direct harvesting cost (3386.66 EGP ha⁻¹) with a minimal grain loss of only 0.05%. The Claas combine secured Rank 2. While it reached the highest effective field capacity (1.18 ha h⁻¹) and near-total grain recovery (0.005% loss), its ranking was influenced by its high initial purchase price and fuel consumption. The reaper–binder system (Rank 3) and Daedong combine (Rank 4) followed. Despite having the highest operational cost (7371.42 EGP ha⁻¹) and higher grain losses (0.72%), the reaper–binder remains a scientifically justified choice for integrated crop-livestock systems, as its ability to produce ready-to-use “soft straw” provides a net economic advantage for smallholders. The study concludes that while large combines are ideal for the “New Lands,” mid-sized units like the Field King are best suited for scaling through cooperatives in fragmented landscapes. Full article

This paper examines the relationship between traditional gold (XAU) and its tokenized counterparts (PAXG and XAUT), providing an empirical assessment of how digital representations of real-world assets align with their underlying benchmarks. Using multi-year time series data, the study evaluates price deviations, tracking accuracy, correlations, and volatility across both weekday-only and 24/7 trading datasets, incorporating weekend effects and crypto-market microstructure. Results show that both tokenized assets exhibit strong long-term alignment with XAU, while short-term divergences arise from continuous crypto trading, liquidity fragmentation, and issuer-specific design features, with XAUT consistently tracking spot gold more closely than PAXG. Building on this analysis, the paper examines the role of tokenized gold within dynamic, smart contract-driven crypto portfolios that also include BTC, ETH, and cash. Portfolio simulations demonstrate that adaptive rebalancing strategies materially improve risk-adjusted performance, with XAUT serving as a stabilizing anchor and cash enabling rapid, automated repositioning during volatility spikes. The findings offer a dual contribution: they clarify the fidelity and market behavior of tokenized gold and provide evidence of its practical utility within automated, on-chain portfolio management, highlighting both its strengths and structural limitations in emerging digital financial systems. Full article

Achieving sustainability in prefabricated construction necessitates a balance between operational efficiency and stringent environmental constraints. However, cascading rework chains triggered by assembly defects frequently disrupt this equilibrium. Existing literature predominantly addresses this dynamic through reactive rescheduling, thereby largely overlooking the potential of proactive topological interception. To bridge this gap, this study proposes a proactive bi-level scheduling framework that mathematically integrates strategic quality inspection planning with operational low-carbon project execution. Specifically, a Generalized Total Cost (GTC) model is formulated to internalize multi-objective trade-offs—including time, cost, and carbon emissions—into a unified financial metric through market-based shadow prices. This framework is operationalized through a novel bi-level Hybrid Evolutionary Algorithm (H-TS-CDBO). By combining the global exploration capabilities of Chaotic Dung Beetle Optimization with the local refinement mechanisms of Tabu Search, the proposed solver is specifically engineered to navigate the topological ruggedness induced by proactive inspection interventions. Empirical benchmarking validates the computational robustness of the solver, while an illustrative case study substantiates a critical managerial paradigm shift from “passive remediation” to “active prevention”: compared to traditional methods, a marginal preventive investment of 5.4% functions as an effective containment mechanism, yielding a 40.8% net reduction in the GTC. Furthermore, a sensitivity analysis regarding varying static carbon tax rates simulates algorithmic adaptation under diverse regulatory intensity thresholds, delineating an actionable pathway for project managers to achieve lean, low-carbon synergy amidst evolving regulatory pressures. Full article

The rapid growth of distributed solar energy, such as rooftop photovoltaics (PVs), has revolutionized conventional power systems into more distributed networks, enabling end-users to engage in and trade within the energy market. Maximizing the benefits of rooftop PV panels for residential end-users, including increased renewable energy use and reduced reliance on the utility grid, remains an essential challenge in conventional centralized markets. Moreover, reducing energy consumption may lead to increased peak demand, decreased self-consumption, reduced system flexibility, and reduced grid stability. Therefore, this study presents a transactive energy market framework that integrates home energy management systems (HEMSs) with multi-objective optimization and an aggregator-based, distributed peer-to-peer (P2P) trading strategy to increase rooftop PV utilization and reduce grid dependency within an intra-residential community. The HEMS is structured to integrate rooftop PV production, battery energy storage systems, and smart appliances to offer flexibility through demand response programs in balancing supply and demand by scheduling appliances during periods of rooftop PV production and lower grid prices. Multi-objective (i.e., minimizing energy consumption cost and peak load) optimization problems are solved using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) by achieving a Pareto-optimal solution. To validate the reliability and optimality of the NSGA-II results, the same problem formulation is solved using a mixed-integer linear programming approach. Moreover, a Strategic Double Auction with Dynamic Pricing (SDA-DP) strategy is proposed to support P2P trading among consumers and prosumers and thereafter compared with a rule-based zero-intelligence strategy with market-matching rules to analyze the trading performance of the proposed SDA-DP. The results of this comparative analysis (for 10 households, year-long simulation with 15 min time resolution) demonstrate that compared to the baseline case, integrating NSGA-II optimization with SDA-DP trading significantly enhances rooftop PV utilization by 35.11%, reduces grid dependency by 34.04%, and reduces electricity consumption costs by 30.53%, with savings of €1.93 to €6.67 for a single day after participating in the proposed P2P market. Full article

Transportation remains one of the sectors with the highest GHG emissions in urban areas, forming around a third of household footprints in affluent countries like the Nordics and being the main source of particulate matter emissions in urban areas around the world. This study focuses on the Reykjavík Capital Area in Iceland, which is known for its car-centricity and where modal shift remains a major challenge. The study examines barriers to modal shift to understand why Reykjavík residents are reluctant to change their transport modes away from private cars. The study uses softGIS survey data gathered in 2025 of 1801 respondents. The results show that mobility remains car-dominated, with even regular public and active-mode users owning a car for running errands. The main barriers for switching to public or active modes include long travel distances, high travel time need, an unreliable public transport system, and difficulties running errands. Slight differences emerged between native and non-native residents’ barriers, with the latter being more likely to be impacted by price and connectivity issues. The study further recognizes the potential impact of climate awareness and education, as people with a stronger belief in individual impact on climate were less likely to find these aspects to be a barrier. Full article

Herbicides have been pivotal tools but decreasing their use is currently a political and societal priority to minimize the risk for human health and the environment and to hinder the evolution of herbicide resistance. A 3-year experiment was conducted to compare three weed management strategies in winter wheat fields in northern Italy: (1) sole chemical control, (2) sole mechanical control, or (3) their combination. Agronomic and economic performances of the three strategies were assessed. Large variability of weed presence and crop yield was observed across the three years. Higher weed biomass was observed in the mechanical management, while the lowest weed presence and cost for weed control was estimated for the chemical management. Conversely, no differences were observed across the three management strategies in terms of crop yield or net return. The results confirmed that herbicides are currently the most cost-effective control tools, but the continuous variation in prices and costs can modify this situation. Thus, the economic assessment should be periodically updated to remain valid. However, the lack of differences between managements in terms of wheat grain yield or net profit suggested that mechanical or combined weed control can be sustainable alternatives for wheat production in northern Italy. Nevertheless, to ensure the long-term sustainability of weed management strategies with low or no herbicide use, a more holistic approach should be considered, involving a diversified set of control tactics arranged throughout the whole crop rotation. Full article

This paper investigates the integration of on-site photovoltaic (PV) systems in the industrial sector under a zero feed-in configuration, where all generated electricity is consumed locally without export to the grid. The analysis follows the current Greek regulatory framework and uses real operating data from an insulation materials manufacturing plant. Twelve months of measured electricity demand were combined with Typical Meteorological Year (TMY) solar data to simulate PV systems of 500, 1000, 1500, and 2000 kWp. Annual PV production ranges from approximately 739 MWh (500 kWp) to 2970 MWh (2000 kWp), and it is all fully self-consumed by the factory due to its high and continuous load. However, given the plant’s large annual electricity use, the PV systems offset 1.0–2.8% of total consumption. The avoided grid purchases correspond to 40–160 MWh/year of net energy savings, delivering positive Net Present Value (NPV) when electricity tariffs exceed EUR 0.15/kWh. The results confirm that zero feed-in PV deployment is technically feasible and economically attractive for industrial facilities facing high electricity prices, while also enhancing sustainability by reducing dependency on the public grid. Full article