Search Results (688)

Taxi fleets operate intensively, accumulate high annual mileage, and contribute disproportionately to urban greenhouse gas emissions, making them attractive candidates for transport electrification. Despite growing interest in electric mobility, integrated evaluations of the environmental, energy, and economic implications of taxi fleet electrification remain limited, particularly for medium-sized cities in Türkiye. This research examines the replacement of the commercial taxi fleet operating in the central districts of Denizli with battery electric vehicles (BEVs) through a framework that integrates operational emission estimation, electricity demand, charging infrastructure, and life-cycle cost analysis (LCCA). Operational emissions were quantified using the IPCC Tier 1 fuel-based approach together with a distance-based consistency check. Under the adopted assumptions, electrification reduced annual operational CO₂ emissions by 4217.48 tCO₂ (57.9%). The electrified fleet required 18.36 MWh of electricity per day (6.70 GWh annually), while estimated peak charging demand varied between 1.22 MW and 5.57 MW, depending on the charging strategy. Economic evaluation showed a positive net present value (NPV) of 1.32 million TL per vehicle, an internal rate of return (IRR) of 80.86%, and a discounted payback period (DPP) of 1.37 years. Although profitability varied with energy prices, vehicle costs, and annual mileage, fleet electrification remained economically feasible across all scenarios considered. These results suggest that electrifying commercial taxi fleets can support urban decarbonization while remaining financially attractive when accompanied by appropriate charging infrastructure and coordinated transport planning. Full article

The integration of photovoltaic generation, battery storage, electric vehicles, smart appliances, and dynamic electricity pricing has made residential energy management a challenging real-time optimization problem. Conventional demand-side management methods often depend on fixed rules and are less effective under uncertain solar generation, changing tariffs, and variable user demand. To address this issue, this paper proposes a Proximal Policy Optimization-based deep reinforcement learning framework for smart home energy management. The proposed PPO controller learns adaptive scheduling decisions using real-time PV output, electricity price, battery state of charge, EV charging status, and appliance operating conditions. The controller coordinates shiftable, controllable, and non-shiftable loads while reducing electricity cost and maintaining user comfort. The proposed method is compared with DDPG and TRPO. Simulation results show that PPO reduces the average daily energy cost by 4.7% compared with TRPO and 8.3% compared with DDPG. The results confirm that PPO is an effective and stable approach for real-time residential demand-side management. Full article

To alleviate renewable energy curtailment and the high operating costs arising from the temporal and spatial mismatch of distributed generation, this paper develops a cross-park dispatch optimization approach for power systems under the joint participation of carbon trading and green certificate trading (GCT). The proposed approach aims to improve system flexibility and economic performance in coordinated multi-park operation. Specifically, adjustable resources in different parks are dispatched in a coordinated manner, and the total comprehensive operating cost is taken as the optimization objective. In addition, the Alternating Direction Method of Multipliers (ADMM) is adopted to determine inter-park electricity trading prices and exchanged power in a distributed framework. Furthermore, an asymmetric bargaining model is introduced to distribute the cooperative benefits, ensuring a balance between fairness and incentive compatibility. Simulation results demonstrate that inter-park electricity interaction reduces generation costs by 5.29%. The integration of carbon and green certificate trading further reduces costs by 7.4%. After asymmetric bargaining-based benefit allocation, the operating costs of parks with higher contributions decrease by up to 10.34%. The results conclude that the proposed strategy effectively leverages the complementary advantages of multi-park resources and optimizes the synergy between carbon markets, green certificate markets, and physical dispatch. Full article

Background/Objectives: The advent of immune checkpoint inhibitors (ICIs) and targeted therapies has revolutionized advanced cutaneous melanoma care, shifting it from an acutely fatal illness to a chronic, manageable condition. While extending survival, these modern therapies introduce a new spectrum of persistent immune-related adverse events (irAEs) and long-term toxicities. This study aims to explore the “Toxicity-HRQoL Paradox” to comprehensively evaluate the true price of survival across the melanoma treatment continuum. Methods: This narrative review, based on a systematic database search, synthesizes quantitative patient-reported outcomes, health-related quality of life (HRQoL) trajectories, and health-economic data. It evaluates the impact of surgical, regional, and modern systemic therapies utilizing data derived from validated instruments, including the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30), EuroQol 5 Dimensions (EQ-5D), Functional Assessment of Cancer Therapy-Melanoma (FACT-M), and comprehensive score for financial toxicity (COST). Results: Historically, extensive locoregional surgeries and high-dose interferons caused profound, debilitating drops in physical and overall QoL. Today, modern systemic therapies are associated with high rates of severe (Grade ≥ 3) acute toxicities and irreversible chronic conditions, such as permanent endocrinopathies. Despite this, global HRQoL frequently remains stable or even improves. This “toxicity-HRQoL paradox” occurs because the profound psychological relief of durable disease control effectively offsets the physical symptom burden. Furthermore, neoadjuvant ICI approaches demonstrate superior long-term HRQoL by enabling tailored surgical de-escalation. Ultimately, extended survivorship unmasks hidden burdens, revealing high prevalences of fear of cancer recurrence (FCR) (81–86%) and substantial financial toxicity. Conclusions: Despite the severe physical toxicities of modern melanoma treatments, the psychological benefit of survival largely preserves overall HRQoL. However, optimizing the modern survivorship experience requires the widespread adoption of melanoma-specific HRQoL tools and proactive, multidisciplinary care models to manage chronic toxicities, psychosocial distress, and financial burdens. Full article

European energy markets have experienced significant instability as a result of consecutive global systemic shocks, particularly the COVID-19 pandemic and the geopolitical conflict in Ukraine. This paper analyses the development of crude oil and natural gas prices between 2019 and 2024 and discusses their implications for sustainable logistics, energy security and supply chain resilience in Europe. The study is based on secondary data from internationally recognized sources, including the International Energy Agency, OPEC, Eurostat, the European Council, the World Bank and the U.S. Energy Information Administration. An event-based comparative approach supported by descriptive price-change calculations was applied to distinguish between the pandemic-related demand shock and the geopolitical supply-side shock after 2022. The results show that crude oil prices declined from approximately 64 USD/barrel in 2019 to 41 USD/barrel in 2020, representing a decrease of about 3f5.9%, mainly in connection with reduced mobility, lower transport activity and industrial slowdown during the COVID-19 pandemic. In contrast, crude oil prices increased to approximately 100 USD/barrel in 2022, representing an increase of about 143.9% compared to 2020, coinciding with geopolitical uncertainty and supply-side pressures. The European natural gas market appeared particularly vulnerable to the 2022 crisis because of supplier dependence, pipeline infrastructure constraints and reduced Russian gas flows. EU natural gas demand declined by 55 billion m³, or 13%, in 2022, indicating the effect of high prices, energy savings and crisis adaptation. The findings suggest that crude oil shocks are mainly related to transport costs and freight rates, while natural gas shocks may influence energy-intensive production, warehousing, cold chains and broader supply chain stability. The study highlights the need for energy diversification, renewable and low-carbon energy development, energy efficiency and more resilient logistics strategies. Full article

Most Transmission System Operators (TSOs) rely on seasonally static line rating models based on extreme weather conditions to determine the transmission capacity of power lines. These conservative rating approaches constrain grid capacity, limiting the integration of new renewable energy sources and delaying the transition to a more sustainable power system. Furthermore, they restrict cross-border transmission capacity between market zones, leading to “false” congestion and unnecessary market splitting. Market splitting can result in economic losses for market participants due to price differences between market zones and the potential curtailment of renewable generation. The adoption of Dynamic Line Rating (DLR) models can help avoid the need for new transmission infrastructure, reduce market splitting and false congestion, and mitigate line degradation in a cost-effective manner. The OptiRES.Lines tool integrates several DLR models, enabling their simulation and visualization through a Geographic Information System (GIS) interface. These dynamic rating models are combined with an Optimal Power Flow (OPF) model to assess: (1) the long-term potential for integrating new power plants at different grid locations; (2) the available cross-border transmission capacity between market zones; and (3) short-term grid congestion. The tool was tested in two regions of Portugal and demonstrated a significant increase in the grid’s capacity to accommodate additional renewable generation, thereby contributing to a more sustainable power system. The results showed that, although DLR alone indicated an increase in transmission line capacity during approximately 70% of the analysed period, the inclusion of OPF analysis revealed that DLR reduced line loading factors during 95% of the time analysed, highlighting its broader system-level benefits. Full article

The increasing penetration of distributed energy resources and diverse load characteristics in interconnected multi-microgrid systems creates significant challenges for coordinated energy management and optimal resource planning. This study proposes a multi-objective optimization framework for the simultaneous sizing of photovoltaic (PV) systems and battery energy storage systems (BESSs), combined with coordinated energy management and bidirectional power exchange among residential, commercial, and industrial microgrids connected to the IEEE 33-bus distribution network. The framework incorporates 24 h load profiles, photovoltaic generation, time-of-use electricity pricing, and distribution network operational constraints. A Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is employed to simultaneously minimize the total daily cost, network power losses, and grid dependency while satisfying the voltage, feeder loading, and battery state-of-charge constraints. The economic objective combines the equivalent daily investment costs of PV and BESSs with daily operating costs using a capital recovery factor (CRF)-based formulation to ensure dimensional consistency. The best compromise solution is selected using the minimum normalized Euclidean distance to the ideal Pareto solution. Simulation results demonstrate that the proposed framework achieves a total daily cost of $13,412.52/day, total network power losses of 4179.1 kW, and grid dependency of 3262.28 kWh while maintaining all operational constraints within acceptable limits. Furthermore, coordinated PV–BESS operation improves voltage regulation, reduces feeder loading and network power losses, enhances renewable energy utilization, and decreases the reliance on the utility grid. The results demonstrate that the proposed framework provides an effective techno-economic approach for the coordinated planning and operation of interconnected multi-microgrid systems with high renewable energy penetration. Full article

Carbon price instability within the EU Emissions Trading System (EU ETS) is associated with financial stress that propagates across green finance markets, yet the system-level dynamics linking carbon allowance instruments, clean energy equities, green bonds, and oil volatility remain insufficiently characterized over the turbulent 2021–2026 period. This paper applies the DCC-GARCH $R^2$ decomposed connectedness framework to five exchange-traded funds and one volatility index spanning the principal channels through which EU ETS regulatory shocks propagate to financial markets, and derives a novel Connectedness-Based Hierarchy Index (CBHI) that translates the transmitter–receiver hierarchy into a time-varying portfolio desirability index. Five key findings emerge. First, the carbon allowance futures ETF (KRBN) and the Paris-aligned equity ETF (CARB) form a near-closed systemic bloc within this asset universe: bilateral connectedness reaches $0.867$, with to and from directional connectedness values both approaching 80%. Second, this co-transmitter structure is highly contingent on the joint inclusion of both instruments; excluding CARB raises KRBN’s CBHI from $0.251$ to $22.204$, reclassifying it as a structural diversifier and reducing the mean Total Connectedness Index (TCI) from 52.18% to 30.81%. Third, system-wide connectedness averages 52.18% but surges to nearly 73% during EU ETS regulatory crises. Fourth, connectedness-aware portfolios outperform the minimum-variance benchmark across all risk-adjusted metrics, yielding an annualized Sharpe ratio of $0.148$ vs. $-1.066$ ($T=1035$ observations, daily rebalancing, zero transaction costs); this improvement is driven by reallocation toward crude oil volatility (OVX), the sole non-ETF and most peripheral instrument (to = 5.55%, $\mathrm{CBHI}=14.310$), which the CBHI identifies as the system’s dominant structural diversifier and whose weight rises from 0.4% in the benchmark to 31.6%. Fifth, the CBHI uncovers a structural tension in Paris-aligned mandates: CARB records the second-lowest CBHI ($0.254$), indicating that climate alignment and systemic risk minimization are partially conflicting objectives. Full article

Energy-efficient coordination of robotic swarms requires effective integration of task scheduling, motion planning, and communication management, particularly in resource-constrained environments where computation and wireless communication compete for limited energy resources. Existing multi-robot approaches typically address these concerns in separate stages: task-allocation methods (e.g., market- and auction-based schemes) price assignments by distance, and computation-offloading methods decide execution placement after a route has been fixed. This paper’s specific contribution is to fold the execution-placement decision (local computation versus offloading to a peer) into the edge-relaxation step of an A* path search, using a composite cost whose communication term is derived from the instantaneous neighborhood of each node; routing and compute placement are therefore co-optimized within a single search rather than in decoupled stages. The framework is evaluated in simulation with a swarm of 25 robots against two decoupled baselines: a path-only planner that ignores workload and communication costs, and a workload-only scheduler that ignores travel and communication costs. Across 20 randomized trials, the proposed heuristic reduces total swarm energy consumption by approximately 22% relative to the path-only baseline and 9% relative to the workload-only baseline, shortens average task completion time by roughly 20%, and lowers the load imbalance factor from 6.7 (path-only) and 3.2 (workload-only) to 1.9. We report these gains for the tested configurations and delimit their scope: the search retains the asymptotic complexity of standard A*, but path optimality does not extend to the compute-placement decisions, which are locally greedy, and all results are obtained in simulation rather than on hardware. Full article

This study investigates how smallholder farmers and vendors in Piracicaba, Brazil, navigate the tensions between standardized spatial governance and heterogeneous livelihood strategies in the rural–urban transition. Based on 25 semi-structured interviews with participants in municipal open-air markets (varejões), we analyze how policies implemented by the Municipal Secretariat of Agriculture and Food Supply (SEMA), including rigid price controls, mandatory MEI registration, and inflexible infrastructure rules, clash with the realities of organic, family-based, and mixed production systems. The findings reveal that SEMA’s one-size-fits-all approach fails to account for real production costs, quality differentiation, and informal cooperation, thereby penalizing innovation and marginalizing vulnerable producers. Rather than passive recipients, vendors actively resist and reshape governance through income diversification, informal cooperatives, direct sales, and ethical redefinitions of agricultural work. We argue that effective spatial governance must move beyond technical standardization and instead recognize productive heterogeneity and local agency as central to sustainable rural–urban transitions. This Brazilian case offers a critical counterpoint to top-down models and underscores the need for flexible, participatory, and context-sensitive policy design. Full article

This paper proposes a bi-level game-theoretic framework for coordinating large-scale electric vehicles (EVs) in regional power grid deep peak regulation under a demand response mechanism. The upper-level model formulates a non-cooperative game among the deep peak regulation market operator (DPRMO), electric vehicle aggregators (EVAs), and distributed generation units to optimize electricity pricing and scheduling strategies. The lower-level model adopts an evolutionary game based on the logit protocol to describe the bounded rational decision-making process of EV users in charging and discharging strategy selection. Through iterative interaction between the two layers, a Nash equilibrium of the overall system is achieved. Case studies demonstrate that the proposed method effectively improves system performance and economic efficiency. The total operating cost is reduced to 278,642.53 CNY, while the renewable energy utilization rate reaches 96.73%. The peak–valley load difference is reduced to 12,480.52 kW, indicating significant load-smoothing capability. In addition, EV participation generates 12,684.37 CNY in net benefit, while the profits of the distributed energy supplier and EV aggregator reach 94,836.71 CNY and 30,118.52 CNY, respectively. Furthermore, the consumer surplus of flexible loads increases to 417,863.29 CNY, reflecting enhanced demand-side participation. Comparative results show that the proposed bi-level game framework outperforms benchmark strategies in terms of economic cost reduction, renewable energy accommodation, and peak regulation capability. The results verify the effectiveness of the proposed coordinated optimization approach for multi-stakeholder energy systems with large-scale EV integration. Full article

This study evaluates the economic efficiency of the timber-production function across 24 forest stands in Czech Republic, representing monocultures, low-diversity mixed stands, mixed stands, and structurally differentiated stands, in the context of the profound changes that have affected forestry in the Czech Republic in recent years. Bark beetle outbreaks, climatic extremes, and the degradation of Norway spruce monocultures have increased concerns about their long-term production reliability and economic stability, highlighting the need to identify more resilient and sustainable management approaches. Mixed and structurally diversified stands, owing to their species diversity and higher ecological stability, represent a potential alternative; however, their management and economic assessment require more complex planning and interpretation. The study analyses the volume production of selected stands, timber market prices by assortments and tree species recalculated on a per-hectare basis and compares silvicultural and harvesting costs. Economic efficiency is expressed using the cost coefficient (Kn) and the efficiency coefficient (Ke), which quantify both direct production costs and the economic return of individual stand types. Results show that monoculture stands, especially those with a high share of valuable assortments, achieved the highest economic efficiency under the applied static cost–revenue assessment. This finding reflects the observed assortment structure, realized timber prices, and selected management costs. In the broader Central European forestry context, however, previous studies indicate that even-aged conifer monocultures may be more exposed to biotic and abiotic disturbance risks, which can affect their long-term production reliability and economic stability. Stands with higher species and structural diversity exhibit an economic profile that differs substantially from that of monocultures. Based on aggregated price and cost inputs for the reference period 2020–2024, low-diversity mixed and mixed stands reach intermediate values of cost intensity and efficiency, whereas structurally differentiated stands display the highest cost intensity and the lowest efficiency. Monocultures, by contrast, achieve the highest economic efficiency, primarily due to a greater share of high-quality timber assortments (classes I–III). Diversified stand structures (mixed and structurally differentiated stands) broaden the assortment composition and produce a more even distribution of monetization across quality classes. Diversification, therefore, did not maximize immediate economic efficiency in the static assessment; rather, it was associated with broader assortment composition and a less concentrated revenue structure across quality classes. Full article

Heavy-duty freight transport remains one of the most difficult sectors to decarbonize due to its high energy demand, long-distance operation, and strong dependence on diesel fuel. In Chile, more than 90% of heavy trucks operate with diesel engines, contributing significantly to greenhouse gas emissions and local air pollutants. At the same time, Chile has favorable conditions for the development of green hydrogen due to its world-class solar and wind resources. This study presents a technical and economic assessment of green hydrogen fuel cell trucks recently introduced in Chile, comparing their operational performance with conventional diesel freight trucks. A techno-economic framework based on total cost of ownership, fuel consumption, operational range, fleet utilization, and hydrogen price scenarios was developed using information reported in public studies and official Chilean strategic documents. The results indicate that hydrogen trucks are technically suitable for long-haul and intensive-duty operations due to their rapid refueling capability and high operational autonomy. However, economic competitiveness remains strongly dependent on hydrogen price, fleet scale, infrastructure utilization, and vehicle capital cost. Under current market conditions, diesel trucks preserve cost advantages, while hydrogen trucks become increasingly competitive as hydrogen prices approach long-term target values and annual mileage increases. Chile’s renewable resource base positions the country as a strategic candidate for early adoption in mining, logistics corridors, and captive fleets. The study concludes that hydrogen freight transport can become a realistic decarbonization pathway if accompanied by targeted public policies, infrastructure deployment, and industrial scale-up. Full article

With the strategic advancement of energy structure transformation and the implementation of carbon peaking and carbon neutrality goals, the Integrated Energy System (IES) has become a core research direction owing to its superior performance in multi-energy complementation, operational efficiency, and low-carbon emission characteristics. Nevertheless, existing studies reveal that the optimal operation of IES still faces significant challenges, including the high complexity of multi-energy coupling, supply–demand imbalance caused by renewable energy penetration, and insufficient exploitation of demand-side flexibility. As a core measure of demand-side management, demand response (DR) provides an effective approach to motivate users to adjust power load via price incentives or direct load control. DR can effectively smooth load profiles, improve resource utilization, and boost the consumption level of renewable energy. To meet the operational demands of modern IES, this paper establishes a security-constrained economic dispatch model embedded with multi-level demand response mechanisms. The proposed framework is divided into four key modules: First, a price-based demand response strategy is developed to dynamically guide users in regulating multi-energy consumption behaviors. Second, electric vehicles (EVs) are considered flexible demand-side resources with unique response characteristics. An aggregated EV charging–discharging model is established to suppress power fluctuations and support high proportions of renewable energy integration. Third, to precisely calculate the overall operating cost of IES, a combined economic evaluation index integrating time-of-use tariff and Levelized Cost of Electricity is adopted. It maintains a balance between amortized long-term generation investment and short-term operational expenditure, and coordinates the economic benefits and operational reliability of the whole system. Finally, numerical simulations are performed on a coupled test system comprising an IEEE 33-bus distribution network and a 20-node natural gas network. Simulation results verify that the proposed co-optimization model can effectively reduce total system operating costs and greatly improve the local assumption of fluctuating renewable energy. Full article

This discussion paper addresses the financial and regulatory challenges of implementing Full Water Cost recovery in urban systems with high Non-Revenue Water under climate variability. Using a pipe network in Kozani, Greece, as a case study, the analysis quantifies Direct, Environmental, and Resource Costs across the water cycle. A novel indicator, the Minimum Charge Difference (MCD), reveals distortions caused by fixed charges. The framework integrates pricing, demand elasticity, and hydraulic management. Additionally, a per capita tariff approach is introduced under revenue neutrality, enabling evaluation of distributional impacts and revealing inequities of connection-based pricing, providing a practical tool for socially fair tariff design and policy implementation. Full article