Search Results (214)

Frequent extreme-weather events pose severe challenges to the secure and economical operation of power systems with high renewable energy penetration. To strengthen grid resilience against such low-probability, high-impact events while maintaining good performance under normal conditions, this paper proposes an optimal energy storage allocation method for power systems with high-wind-power penetration. We first identify two representative extreme wind power events and develop a risk assessment model that jointly quantifies load-shedding volume and transmission-line security margins. On this basis, a multi-scenario joint siting-and-sizing optimization model is formulated over typical-day and extreme-day scenarios to minimize total system cost, including annualized investment cost, operating cost, and risk cost. To solve the model efficiently, a two-stage hierarchical solution strategy is designed: the first stage determines an investment upper bound from typical-day scenarios, and the second stage optimizes storage allocation under superimposed extreme-day scenarios within this bound, thereby balancing operating economy and extreme-weather resilience. Simulation results show that the proposed method reduces loss-of-load under extreme-weather scenarios by 32.46% while increasing storage investment cost by only 0.18%, significantly enhancing system resilience and transmission-line security margins at a moderate additional cost. Full article

To address the challenge of the synergistic optimization of carbon reduction and economic operation in the integrated energy systems (IES) of industrial parks, this paper proposes an optimization scheduling model that incorporates carbon trading and supply–demand response (SDR) coordination mechanisms. This model is based on an IES coupling power-to-gas (P2G) and carbon capture and storage (CCS) technologies. First, the K-means clustering algorithm identifies three typical daily scenarios—transitional season, summer, and winter—from annual operation data. Then, we construct a synergistic optimization model that integrates a carbon trading mechanism, tiered carbon quota allocation, and SDR coordination. The model is solved via mixed-integer linear programming (MILP) to minimize total system operating costs. Systematic comparative analysis across six scenarios quantifies the incremental benefits: P2G–CCS coupling achieves a 15.2% cost reduction and 49.3% emission reduction during transitional seasons; supply–demand response contributes 3.5% cost and 5.6% emission reductions; technology synergies yield an additional 21.6 percentage points of emission reduction beyond individual contributions. The integrated system achieves 100% renewable energy utilization and optimizes peak-to-valley differences across electricity, heating, and cooling loads. Carbon price sensitivity analysis reveals three response stages—low sensitivity, rapid reduction, and saturation—with the saturation point at 200 CNY/t (28.6 USD/t), providing quantitative guidance for tiered carbon pricing design. This research provides theoretical support and practical guidance for achieving low-carbon economic operations in industrial parks. Full article

The paper discusses the economic and infrastructural challenges preventing the adoption of Electric Vehicles (EVs) in Pakistan. It focuses on key factors such as affordability, consumer preferences, and the overall readiness of the market. Based on a segment-wise comparison, the analysis reveals that four-wheeler EVs carry an initial price premium of 20 to 64 percent over internal combustion engine (ICE) vehicles, with payback periods ranging from 11 to 25 years, placing them out of reach for most middle-income consumers. In contrast, electric two- and three-wheelers—comprising more than 90 percent of registered vehicles—offer a significantly more practical and affordable pathway for mass adoption. These vehicles exhibit minimal upfront cost differences, annual operational savings exceeding PKR 62,000, and short payback periods of just 4 to 6 months, making them highly feasible in the local context. The study adopts a mixed-methods approach using national price data, vehicle registration records, and international case studies from India, Kenya, and Norway. It evaluates financing innovations such as battery leasing, concessional green loans, and carbon-credit-linked microfinance, and outlines a consumer-focused policy framework that emphasizes financial inclusion, decentralized infrastructure development, and phased implementation strategies. By aligning global lessons with Pakistan’s socioeconomic and infrastructural realities, the paper offers a scalable and inclusive roadmap for accelerating EV adoption through targeted, consumer-driven solutions. Full article

Cancer is a major global health concern, affecting nearly 20 million individuals annually, according to the International Agency for Research on Cancer (IARC). There are some unconventional and conventional treatments for cancer. Typically, they span a wide spectrum of conventional and advanced therapeutic approaches, such as photodynamic therapy (PDT). This has long been valued for its non-invasive, targeted, and minimally toxic approach in the management of cancer. More importantly, PDT results in fewer operative and post-operative major complications, faster recovery times, reduced operating time, and saved costs. There are two types of photosensitizers in PDT, including synthetics (e.g., hematoporphyrin derivative, photofrin II, verteporfin) and natural (e.g., Hypocrellin A (HA) and Hypocrellin B (HB)). Nine electronic databases—WanFang Data, PubMed, ScienceDirect, Scopus, Web of Science, Springer Link, SciFinder, and the China National Knowledge Infrastructure (CNKI)—were systematically searched for this review, covering the literature published within the past 20 to 30 years (time range), without language restrictions. Studies were included if they were identified using the keywords Hypocrellin A, Hypocrellin B, photodynamic therapy, and cancer (inclusion criteria). All eligible papers were collected, critically analyzed, and summarized. Duplicate records were excluded during the screening process (exclusion criteria). HA and HB, derived from the fungus Hypocrella bambusae, offer a natural alternative with lower toxicity. However, these compounds are still in the in vitro or in vivo, and must meet rigorous standards for “quality”, “safety”, “efficacy”, “pharmacokinetics”, as well as “regulatory compliance” before entering clinical trials. “Curcumin” is a successful PS for traditional Chinese medicine used in PDT during clinical study and it is used as a benchmark for HB. Currently, scientists are paying attention to “nanotechnology” that enhances hypocrellin’s properties in PDT for achieving clinical goals, but further investigations are required. Full article

This study explores the decarbonization of the district heating network in Riva del Garda. The existing system (baseline) was modeled in EnergyPLAN, and future configurations were optimized using a Multi-Objective Evolutionary Algorithm (MOEA) to minimize both CO₂ emissions and annual costs. Nine decision variables were assessed under defined boundary conditions to generate alternative future scenarios grouped into five types. In Type A, a large deep geothermal cogeneration plant combined with a small biomass boiler achieved the only zero-emission solution, with lower annual costs than the baseline but high capital needs. Excluding deep geothermal cogeneration (Type B) led to dominance of the biomass boiler and waste heat recovery from the Alto Garda Power (AGP) plant; full decarbonization remained possible only with extensive biomass use at a higher cost. Removing biomass (Type C), the solar thermal plant, and the shallow geothermal heat pump enabled deep but costly decarbonization, including grid electricity dependence. Types D and E, dominated, respectively, by shallow geothermal heat pump and electric boiler, provided moderate emission reductions and further increase in costs. Across all types, thermal storage improved operational flexibility. These analyses were also extended to assess potential district heating network expansions within Riva del Garda and into the neighboring municipality of Arco. Full article

Under the global imperative for climate action and sustainable development, accelerating the transition towards high-penetration renewable energy systems remains a universal priority, central to achieving the United Nations Sustainable Development Goals. However, the inherent uncertainty and volatility of renewables such as wind and solar PV pose fundamental challenges to power system stability and flexibility worldwide. These challenges, if unaddressed, could significantly hinder the reliable and sustainable integration of clean energy on a global scale. While pumped storage hydropower (PSH) represents a mature, large-scale solution for enhancing system regulation capabilities, existing planning methodologies frequently suffer from critical limitations. These included oversimplified scenario representations—particularly the inadequate consideration of escalating extreme weather events under climate change—and computational inefficiencies in solving large-scale stochastic optimization models. These shortcomings ultimately constrained the practical value of such approaches for advancing sustainable energy planning and building climate-resilient power infrastructures globally. To address these issues, this paper proposed a bi-level stochastic planning method integrating scenario optimization and improved Benders decomposition. Specifically, an integrated framework combining affinity propagation clustering and isolation forest algorithms was developed to generate a comprehensive scenario set that covered both typical and anomalous operating days, thereby capturing a wider range of system uncertainties. A two-layer stochastic optimization model was established, aiming to minimize total investment and operational costs while ensuring system reliability and renewable integration. The upper layer determined PSH capacity, while the lower layer simulated multi-scenario system operations. To efficiently solve the model, the Benders decomposition algorithm was enhanced through the introduction of a heuristic feasible cut generation mechanism, which strengthened subproblem feasibility and accelerated convergence. Simulation results demonstrated that the proposed method achieved a 96.7% annual renewable energy integration rate and completely avoided load shedding events with minimal investment cost, verifying its effectiveness, economic efficiency, and enhanced adaptability to diverse operational scenarios. Full article

Large-scale renewable energy bases in desert regions face challenges of unstable output and inefficient utilization due to the fluctuating nature of wind and solar power. To address these issues, this study proposes an optimization model for shared hybrid electricity–hydrogen energy storage across multiple micro-energy systems. The model minimizes the total investment and operation cost under electricity–hydrogen coupling and system balance constraints, and an improved Shapley value method is introduced to ensure fair cost allocation among participants. A case study based on a desert renewable base shows that the proposed shared configuration reduces the total annualized cost by 10.36% and increases renewable energy utilization by 12.19% compared with independent electrical storage systems. These results demonstrate that shared hybrid storage can effectively enhance energy utilization and cost efficiency in large-scale renewable energy bases, providing a feasible approach for integrated power–hydrogen energy management. Full article

This work proposes a modeling framework based on artificial neural networks (ANN) for the integrated design and dynamic optimization of renewable electrodialysis (ED) systems considering water demand uncertainty, using a first-principles ED model as the data source for the development of the ANN. The optimization goal is to identify the optimal photovoltaic (PV) and battery (BAT) capacities and the optimal time-varying ED voltage and flow profiles during the batch process, considering an uncertain distribution of potential water demand for each batch over an annual operating horizon. This is achieved by minimizing the annual capital and operating costs of the renewable ED-PV-BAT system. The ANN model demonstrated excellent predictive capabilities that closely matched the data generated by the ED model, with ± 3.5–9.6% and ± 2.0–4.8% error margins in the prediction intervals at a 95% confidence level. The optimal design resulting from dynamic optimization exhibited a lower cost than the design attained from the steady-state optimization, as the batch time and energy consumption were 50% and 17% lower, respectively. For this design, the energy consumption and nitrate concentration predicted by the ANN were only 0.31% and 1.2% different from the ED model predictions, without any effects on the predicted costs and batch times. Full article

Remote island regions often depend on isolated power grids dominated by small-scale thermal power plants. Decarbonizing these systems is challenging due to limited interconnection capacity and variable renewable output, highlighting the need for flexible resource balance. This study develops an optimization model that minimizes system costs and CO₂ emissions by integrating variable renewable energy and Vehicle-to-Grid (V2G) while considering the minimum-output constraints of thermal power generation. The model is applied to Tanegashima Island, Japan. The results demonstrate that all optimized scenarios reduced the cost and emissions compared with the baseline. In the cost-minimizing scenario, the total annual cost decreased from 2.81 to 2.46 billion yen, while CO₂ emissions decreased from 56.5 to 44.4 kt. In the CO₂-minimizing scenario, V2G further reduced emissions to 43.8 kt at a lower cost (2.54 billion yen) than the system without V2G. However, renewable curtailment remained high due to the minimum-output constraint of thermal generators. These findings confirm that while V2G is a cost-effective, distributed flexibility resource, it cannot fully eliminate renewable curtailment under current operational limits. Enhanced coordination, behavioral engagement, and complementary measures—such as relaxing thermal constraints and expanding storage—are required to unlock its full potential in isolated power systems. Full article

The rural community of L’Anse au Loup in southern Labrador depends on a long-distance transmission link to Hydro-Québec for its electricity supply, with diesel generation as backup during outages. This dependence raises electricity costs, exposes the community to supply disruptions, and limits control over local energy security. This study evaluates the feasibility of a solar–wind hybrid energy system to reduce imported electricity and improve supply reliability. A detailed site assessment identified a 50-hectare area north of the community as suitable for system installation, offering adequate space and minimal land-use conflict. Using Hybrid Optimization of Multiple Energy Resources (HOMER Pro 3.18.3) software, the analysis modeled local load data, renewable resource profiles, and financial parameters to determine the optimal grid-connected configuration. The optimized design installs 19.25 MW of photovoltaic (PV) and 4.62 MW of wind capacity, supported by inverters and maximum power point tracking (MPPT) to ensure stable operation. Simulations show that the hybrid system supplies about 70% of annual demand, cuts greenhouse gas emissions by more than 95% compared with conventional generation, and lowers long-term energy costs. The results confirm that the proposed configuration can strengthen local energy security and provide a replicable framework for other remote and coastal communities in Newfoundland and Labrador pursuing decarbonization. Full article

A well-performing road network is essential for modern society. But any road is nothing without its users—cyclists, drivers, pedestrians. Road network cannot be managed without knowing who the roads serve. The gaps in this knowledge lead to decisions that hinder efficiency, equality, and sustainability. This is why monitoring traffic is imperative for road management. However, traditional short-term traffic counting methods fail to provide full coverage at a reasonable cost. This study assessed the economic feasibility of drone-enabled traffic monitoring systems across Estonian urban environments through comparative spatial and economic analysis. Hexagonal tessellation was applied to 255 urban locations, identifying 47,530 monitoring points across 4077 grid cells. Economic modeling compared traditional counting costs with drone-based systems utilizing ultralight drones and nomadic 5G infrastructure. Monte Carlo simulation evaluated robustness under varying operational intensities from 30 to 180 days annually. Analysis identified an 8-point density threshold for economic viability, substantially lower than previously reported requirements. Operational intensity emerged as the critical determinant: minimal operations (30 days) proved viable for 9.0% of locations, while semi-continuous deployment (180 days) expanded viability to 81.6%. The findings demonstrate that drone-based monitoring achieves 60–80% cost reductions compared to traditional methods while maintaining equivalent accuracy (95–100% detection rates for vehicles, cyclists, and pedestrians), presenting an economically superior alternative for 67% of Estonian urban areas, with viability extending to lower-density locations through increased operational utilization. Full article

In response to China’s “Dual Carbon” goals, this paper proposes a Transmission Network Expansion Planning (TNEP) model that explicitly incorporates the operational flexibility of Virtual Power Plants (VPPs). Unlike conventional approaches that focus mainly on transmission investment, the proposed method accounts for the aggregated dispatchable capability of VPPs, providing a more accurate representation of distributed resources. The VPP aggregation model is characterized by the inclusion of electric vehicles, which act not only as load-side demand but also as flexible energy storage units through vehicle-to-grid interaction. By coordinating EV charging/discharging with photovoltaics, wind generation, and other distributed resources, the VPP significantly enhances system flexibility and provides essential support for grid operation. The vertex search method is employed to delineate the boundary of the VPP’s dispatchable feasible region, from which an equivalent model is established to capture its charging, discharging, and energy storage characteristics. This model is then integrated into the TNEP framework, which minimizes the comprehensive cost, including annualized line investment and the operational costs of both the VPP and the power grid. The resulting non-convex optimization problem is solved using the Quantum Particle Swarm Optimization (QPSO) algorithm. A case study based on the Garver-6 bus and Garver-18 bus systems demonstrates the effectiveness of the approach. The results show that, compared with traditional planning methods, strategically located VPPs can save up to 6.65% in investment costs. This VPP-integrated TNEP scheme enhances system flexibility, improves economic efficiency, and strengthens operational security by smoothing load profiles and optimizing power flows, thereby offering a more reliable and sustainable planning solution. Full article

Background: Pediatric odontogenic infections pose significant diagnostic challenges, particularly in distinguishing between cellulitis and abscess. Accurate differentiation is crucial for guiding appropriate management—antibiotics alone for cellulitis versus surgical incision and drainage (I&D) for an abscess—but can be difficult without specialized expertise or advanced imaging. Objective: We aimed to evaluate the diagnostic accuracy of handheld point-of-care ultrasound (POCUS; Philips Lumify), utilized by non-specialist clinicians, in differentiating cellulitis from abscess in pediatric odontogenic infections. A secondary objective was to assess its impact on reducing hospital admissions and emergency department (ED) burden. Methods: This prospective cohort study involved 111 pediatric patients (aged 1–17 years) presenting with maxillofacial odontogenic infections to a tertiary care academic medical center. Following clinical evaluations, handheld POCUS assessments were performed by trained non-specialist clinicians. Findings from I&D or clinical resolution with antibiotics served as the reference standard. Ninety cases were included in the final diagnostic accuracy analysis after 21 exclusions. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy with 95% confidence intervals (CIs) were calculated. Hospital admission trends were compared before (2017–2021) and after POCUS implementation (January 2022–April 2025). Interpretation should consider potential verification bias from the asymmetric reference standard (I&D for abscess vs. clinical resolution for cellulitis). Results: Handheld POCUS exhibited a sensitivity of 72.97% (95% CI: 57.02–84.60%), specificity of 73.58% (95% CI: 60.42–83.56%), PPV of 65.85% (95% CI: 50.55–78.44%), NPV of 79.59% (95% CI: 66.36–88.52%), and overall accuracy of 73.33% (95% CI: 63.38–81.38%). Following POCUS implementation, the annualized hospital admission rate for pediatric facial odontogenic infections decreased from 60.0 to 19.5 admissions/year; rate ratio (RR) = 0.33 (95% CI: 0.25–0.42), p < 0.001 (Poisson regression with log-offset for period length). Conclusions: Handheld POCUS, operated by non-specialist clinicians after a defined training protocol, was associated with a lower annualized admission rate and demonstrated moderate diagnostic accuracy. Its adoption was associated with a notable reduction in hospitalizations, suggesting its potential for alleviating ED overcrowding, reducing healthcare costs, and minimizing pediatric stress. Wider adoption, supported by standardized training, could enhance healthcare efficiency and quality in managing this common pediatric condition. Full article

This study explored the optimization of green hydrogen production via seawater electrolysis powered by a hybrid photovoltaic (PV)-wind system in KwaZulu-Natal, South Africa. A Box–Behnken Design (BBD), adapted from Response Surface Methodology (RSM), was utilized to address the synergistic effect of key operational factors on the integration of renewable energy for green hydrogen production and its economic viability. Addressing critical gaps in renewable energy integration, the research evaluated the feasibility of direct seawater electrolysis and hybrid renewable systems, alongside their techno-economic viability, to support South Africa’s transition from a coal-dependent energy system. Key variables, including electrolyzer efficiency, wind and PV capacity, and financial parameters, were analyzed to optimize performance metrics such as the Levelized Cost of Hydrogen (LCOH), Net Present Cost (NPC), and annual hydrogen production. At 95% confidence level with regression coefficient (R² > 0.99) and statistical significance (p < 0.05), optimal conditions of electricity efficiency of 95%, a wind-turbine capacity of 4960 kW, a capital investment of $40,001, operational costs of $40,000 per year, a project lifetime of 29 years, a nominal discount rate of 8.9%, and a generic PV capacity of 29 kW resulted in a predictive LCOH of 0.124$/kg H₂ with a yearly production of 355,071 kg. Within the scope of this study, with the goal of minimizing the cost of production, the lowest LCOH observed can be attributed to the architecture of the power ratios (Wind/PV cells) at high energy efficiency (95%) without the cost of desalination of the seawater, energy storage and transportation. Electrolyzer efficiency emerged as the most influential factor, while financial parameters significantly affected the cost-related responses. The findings underscore the technical and economic viability of hybrid renewable-powered seawater electrolysis as a sustainable pathway for South Africa’s transition away from coal-based energy systems. Full article

This paper proposes an innovative hybrid optimization framework for the optimal installation and operation of thyristor-switched capacitors (TSCs) within medium-voltage distribution networks, targeting both energy losses reduction and cost efficiency. The core of the approach combines the exploratory capabilities of the atan-sinc optimization algorithm (ASOA), a recent metaheuristic inspired by mathematical functions, with the local refinement power of the IPOPT solver within a master–slave architecture. This integrated method addresses the inherent complexity of a multi-objective, mixed-integer nonlinear programming problem that seeks to balance conflicting goals: minimizing annual system losses and investment costs. Extensive testing on IEEE 33- and 69-bus systems under fixed and dynamic reactive power injection scenarios demonstrates that our framework consistently delivers superior solutions when compared to traditional and state-of-the-art algorithms. Notably, the variable operation case yields energy savings of up to 12%, translating into annual monetary gains exceeding USD 1000 in comparison with the fixed support scenario.The solutions produce well-distributed Pareto fronts that illustrate valuable trade-offs, allowing system planners to make informed decisions. The findings confirm that the proposed strategy constitutes a scalable, and robust tool for reactive power planning, supporting the deployment of smarter and more resilient distribution systems. Full article