Search Results (4,379)

This study examines the determinants of apartment prices in 17 post-WWII multi-family housing estates in Ljubljana, Slovenia, constructed between 1947 and 1986. Using 1973 verified transactions from 2020 to 2025, the analysis evaluates spatial, structural, environmental, and accessibility-related variables through a combination of statistical and machine learning techniques. A hedonic price model based on ordinary least squares (OLS) demonstrates modest explanatory power (R² = 0.171), identifying local market reference prices, floor level, noise exposure, and window renovation as significant predictors. In contrast, seven machine learning models—Random Forest, XGBoost, and Gradient Boosting Machines (GBMs), including optimized versions—achieve notably higher predictive accuracy. The best-performing model, GBM with Randomized Search CV, explains 59.6% of price variability (R² = 0.5957), with minimal prediction error (MAE = 0.03). Feature importance analysis confirms the dominant role of localized price references and structural indicators, while environmental and accessibility variables contribute variably. In addition, three clustering methods (Ward, k-means, and HDBSCAN) are employed to identify typological groups of neighborhoods. While Ward’s and k-means methods consistently identify four robust clusters, HDBSCAN captures greater internal heterogeneity, suggesting five distinct groups and detecting outlier neighborhoods. The integrated approach enhances understanding of spatial housing price dynamics and supports data-driven valuation, urban policy, and regeneration strategies for post-WWII housing estates in Central and Eastern European contexts. Full article

This paper addresses the synergy between environmental and economic benefits in the green power trading market by constructing a collaborative game model for environmental rights value and electricity energy value. Based on this, a model for maximizing the benefits of flexible resource operation is proposed. Through the combination of non-cooperative and cooperative games, the conflict and synergy mechanisms of multiple stakeholders are quantified, and the Shapley value allocation rule is designed to achieve Pareto optimality. Simultaneously, considering the spatiotemporal regulation capability of flexible resources, dynamic weight adjustment, cross-period environmental rights reserve, and risk diversification strategies are proposed. Simulation results show that under the scenario of a carbon price of 50 CNY/ton (≈7.25 USD/ton) and a peak–valley electricity price difference of 0.9 CNY/kWh (≈0.13 USD/kWh), when the environmental weight coefficient α = 0.5, the total revenue reaches 6.857 × 10⁷ CNY (≈9.94 × 10⁶ USD), with environmental benefits accounting for 90%, a 15.3% reduction in carbon emission intensity, and a 1.74-fold increase in energy storage cycle utilization rate. This research provides theoretical support for green power market mechanism design and resource optimization scheduling under “dual-carbon” goals. Full article

Background/Objectives: Hip-level amputees face ambulatory challenges due to the lack of a lower limb and prosthetic hip power. Some hip-level amputees restore mobility by using a prosthesis with hip, knee, and ankle joints. Powered prosthetic joints contain an actuator that provides external flexion-extension moments to assist with movement. Powered knee and powered ankle-foot units are on the market, but no viable powered hip unit is commercially available. This research details the development of a novel powered four-bar prosthetic hip joint that can be integrated into a full-leg prosthesis. Methods: The hip joint design consisted of a four-bar linkage with a harmonic drive DC motor placed in the inferior link and an additional linkage to transfer torque from the motor to the hip center of rotation. Link lengths were determined through engineering optimization. Device strength was demonstrated with force and finite element analysis and with ISO 15032:2000 A100 static compression tests. Walking tests with a wearable hip-knee-ankle-foot prosthesis simulator, containing the novel powered hip, were conducted with three able-bodied participants. Each participant walked back and forth on a level 10 m walkway. Custom hardware and software captured joint angles. Spatiotemporal parameters were determined from video clips processed in the Kinovea software (ver. 0.9.5). Results: The powered hip passed all force and finite element checks and ISO 15032:2000 A100 static compression tests. The participants, weighing 96 ± 2 kg, achieved steady gait at 0.45 ± 0.11 m/s with the powered hip. Participant kinematic gait profiles resembled those seen in transfemoral amputee gait. Some gait asymmetries occurred between the sound and prosthetic legs. No signs of mechanical failure were seen. Most design requirements were met. Areas for powered hip improvement include hip flexion range, mechanical advantage at high hip flexion, and device mass. Conclusions: The novel powered four-bar hip provides safe level-ground walking with a full-leg prosthesis simulator and is viable for future testing with hip-level amputees. Full article

Energy storage will play an important role in the new power system with a high penetration of renewable energy due to its flexibility. Large-scale energy storage can participate in electricity market clearing, and knowing how to make more profits through bidding strategies in various types of electricity markets is crucial for encouraging its market participation. This paper considers differentiated bidding parameters for energy storage in a two-stage market with wind power integration, and transforms the market clearing process, which is represented by a two-stage bi-level model, into a single-level model using Karush–Kuhn–Tucker conditions. Nonlinear terms are addressed using binary expansion and the big-M method to facilitate the model solution. Numerical verification is conducted on the modified IEEE RTS-24 and 118-bus systems. The results show that compared to bidding as a price-taker and with marginal cost, the proposed mothod can bring a 16.73% and 13.02% increase in total market revenue, respectively. The case studies of systems with different scales verify the effectiveness and scalability of the proposed method. Full article

The Australian native foods, despite high phytochemical composition, are severely underutilized in research and on the commercial market. One of these plants is the Davidson plum (Davidsonia jerseyana), a nutrient-dense and sustainable food ingredient. The study aimed to develop functional fruit sorbets incorporating freeze-dried Davidson plum powder (0–20% w/w) and evaluate their physicochemical, antioxidant, and sensory properties. Sorbets were created using strawberry, raspberry, pomegranate, and Davidson plum bases and analyzed for nutritional content, color, melting rate, texture, and antioxidant capacity (Total Phenolic Content (TPC), Total Flavonoid Content (TFC), Ferric Reducing Antioxidant Power (FRAP), Cupric Reducing Antioxidant Capacity (CUPRAC), 2,2-Diphenyl-1-picrylhydrazyl (Radical Scavenging Assay (DPPH)), total proanthocyanin and anthocyanin content. Sensory evaluation was also conducted using a semi-trained panel. The results showed that increasing Davidson plum concentration led to higher antioxidant activity and slower melting rates. Sorbets containing 10% and 15% Davidson plum demonstrated the highest levels of phenolic and flavonoid compounds. However, sensory analysis indicated that sorbets with 5% and 10% Davidson plum, particularly those made with a strawberry base were the most acceptable in terms of flavour, texture, and overall appeal. These findings suggest that incorporating Davidson plum into frozen desserts, especially at lower concentrations, can enhance both the functional and sensory qualities of sorbets while offering potential health benefits. Full article

Smart monitoring of local electricity systems (LESs) with sources based on renewable energy resources (RESs) from the point of view of the requirements of the functions of an intelligent system are hardware and software systems that can solve the tasks of both analysis (optimization) and synthesis (design, planning, control). The article considers the following: a functional scheme of smart monitoring of LESs, describing its main components and scope of application; an assessment of the state of the processes and the state of the equipment of generators and loads; dynamic pricing and a dynamic assessment of the state of use of primary fuel and/or current costs of generators; economic efficiency of generator operation and loads; an assessment of environmental acceptability, in particular, the volume of CO₂ emissions; provides demand-side management, managing maximum energy consumption; a forecast of system development; an assessment of mutual flows of electricity; system resistance to disturbances; a forecast of metrological indicators, potential opportunities for generating RESs (wind power plants, solar power plants, etc.); an assessment of current costs; the state of electromagnetic compatibility of system elements and operation of electricity storage devices; and ensures work on local electricity markets. The application of smart monitoring in the formation of tariffs on local energy markets for transactive energy systems is shown by conducting a combined comprehensive assessment of the energy produced by each individual power source with graphs of the dependence of costs on the generated power. Algorithms for the comprehensive assessment of the cost of electricity production in a transactive system for calculating planned costs are developed, and the calculation of the cost of production per 1 kW is also presented. A visualization of the results of applying this algorithm is presented. Full article

As the installed power of wind turbines increases, new challenges for the implementation of wind energy in the national power system emerge. Several hours of high energy productivity from wind turbines, together with the periodic occurrence of relatively low energy consumption (at a national scale), sometimes result in the need to stop their operation and, much more often, result in very low revenues for electricity. One of the ways to reduce these phenomena, from a technical and economic point of view, is to use energy storage. However, managing such energy storage poses many challenges due to the unpredictably different duration of favorable and unfavorable wind conditions. Based on historical data on wind turbine energy generation and market data on electricity prices, the impact of using an energy storage with an effective capacity of 2.4 MWh (total 4 MWh) with a maximum charging and discharging power (set parameter) of 1.2 MW in cooperation with a wind turbine (capacity 3 MW) was analyzed. Using simulation methods for energy production and price data from 34,964 h (4 years), the potential additional revenue for the energy storage installed at the wind turbine was calculated. The developed model considered various values: minimum charging power, maximum charging power; and as elements of price signals: price averaging period, level of price deviation from the average electricity price. Full article

Amid accelerating climate change, climate-related hazards—such as floods, wildfires, hurricanes, and sea-level rise—increasingly drive land transformations and pose growing risks to housing markets by affecting property valuations, insurance availability, mortgage performance, and broader financial stability. This review synthesizes recent progress in two distinct domains and their linkage: (1) assessing climate-related financial risks in housing markets, and (2) applying AI-driven remote sensing for hazard detection and land transformation monitoring. While both areas have advanced significantly, important limitations remain. Existing housing finance studies often rely on static models and coarse spatial data, lacking integration with real-time environmental information, thereby reducing their predictive power and policy relevance. In parallel, remote sensing studies using AI primarily focus on detecting physical hazards and land surface changes, yet rarely connect these spatial transformations to financial outcomes. To address these gaps, this review proposes an integrative framework that combines AI-enhanced remote sensing technologies with financial econometric modeling to improve the accuracy, timeliness, and policy relevance of climate-related risk assessment in housing markets. By bridging environmental hazard data—including land-based indicators of exposure and damage—with financial indicators, the framework enables more granular, dynamic, and equitable assessments than conventional approaches. Nonetheless, its implementation faces technical and institutional barriers, including spatial and temporal mismatches between datasets, fragmented regulatory and behavioral inputs, and the limitations of current single-task AI models, which often lack transparency. Overcoming these challenges will require innovation in AI modeling, improved data-sharing infrastructures, and stronger cross-disciplinary collaboration. Full article

Against the backdrop of global low-carbon transition, the integrated development of electricity and carbon markets demands higher efficiency in the optimal operation of virtual power plants (VPPs) and distribution networks, yet conventional trading mechanisms face limitations such as inadequate recognition of differentiated contributions and inequitable benefit allocation. To address these challenges, this paper proposes a collaborative optimal trading mechanism for VPP clusters and distribution networks in an electricity–carbon coupled market environment by first establishing a joint operation framework to systematically coordinate multi-agent interactions, then developing a bi-level optimization model where the upper level formulates peer-to-peer (P2P) trading plans for electrical energy and carbon allowances through cooperative gaming among VPPs while the lower level optimizes distribution network power flow and feeds back the electro-carbon comprehensive price (EACP). By introducing an asymmetric Nash bargaining model for fair benefit distribution and employing the Alternating Direction Method of Multipliers (ADMM) for efficient computation, case studies demonstrate that the proposed method overcomes traditional models’ shortcomings in contribution evaluation and profit allocation, achieving 2794.8 units in cost savings for VPP clusters while enhancing cooperation stability and ensuring secure, economical distribution network operation, thereby providing a universal technical pathway for the synergistic advancement of global electricity and carbon markets. Full article

Demand-side flexible resources in building energy systems hold significant potential for enhancing grid reliability and operational efficiency. However, their effective coordination remains challenging due to the complexity of modeling and aggregating heterogeneous loads. To address this, this paper proposes a feasible region aggregation and coordination method for load aggregators based on a GNMTL-LSTM-Zonotope framework. A Gradient Normalized Multi-Task Learning Long Short-Term Memory (GNMTL-LSTM) model is developed to forecast the power trajectories of diverse flexible resources, including air-conditioning systems, energy storage units, and diesel generators. Using these predictions and associated uncertainty bounds, dynamic feasible regions for individual resources are constructed with Zonotope structures. To enable scalable aggregation, a Minkowski sum-based method is applied to merge the feasible regions of multiple resources efficiently. Additionally, a directionally weighted Zonotope refinement strategy is introduced, leveraging time-varying flexibility revenues from energy and reserve markets to enhance approximation accuracy during high-value periods. Case studies based on real-world office building data from Shandong Province validate the effectiveness, modeling precision, and economic responsiveness of the proposed method. The results demonstrate that the framework enables fine-grained coordination of flexible loads and enhances their adaptability to market signals. This study is the first to integrate GNMTL-LSTM forecasting with market-oriented Zonotope modeling for heterogeneous demand-side resources, enabling simultaneous improvements in dynamic accuracy, computational scalability, and economic responsiveness. Full article

The BRICS countries have been increasingly prioritizing electricity transition as a crucial step towards achieving sustainable growth, energy security, and mitigating climate change. As major emerging economies, the BRICS nations will play a significant role in the global energy landscape since their transition to renewable energy sources holds a significant implication for global energy markets and environmental sustainability. This study investigates the impact of the renewable energy transition on economic growth in BRICS nations from 1990 to 2023, employing a panel NARDL, DOLS, and FMOLS models. This study investigates the relationship between disaggregated renewable energy sources and economic growth. The findings show that renewable energy’s impact on economic growth varies across countries and depends on the type of renewable energy source. Specifically, hydropower, and wind power are found to have significant positive impacts on economic growth in some BRICS countries, while other renewables and trade openness have insignificant impacts. To foster economic growth and the expansion of renewable energy, it is essential for policymakers to focus on investments in hydropower and wind energy. Furthermore, they should encourage trade liberalization, as well as nuclear power development, and enhance regional collaboration. This study offers significant contributions to the current body of literature on the renewable energy–economic growth nexus, supplying crucial insights for both policymakers and researchers. Full article

Amid rising load volatility and uncertainty, demand-side resources with regulation capabilities are increasingly engaged at scale in ancillary service markets, facilitating sustainable peak load mitigation and alleviating grid stress while reducing reliance on carbon-intensive peaking plants. This study examines the integration of electric vehicles (EVs) in peak regulation, proposing a multi-stage operational strategy framework grounded in the analysis of EV power and energy response constraints to promote both economic efficiency and environmental sustainability. The model holistically accounts for temporal charging and discharging behaviors under diverse incentive mechanisms, incorporating user response heterogeneity alongside multi-period market peak regulation demands while supporting clean transportation adoption. An optimization model is formulated to maximize aggregator revenue while enhancing grid sustainability and is solved via MATLAB(2021b) and CPLEX(20.1.0). The simulation outcomes reveal that the discharge-based demand response (DBDR) strategy elevates aggregator revenue by 42.6% and enhances peak regulation margins by 19.2% relative to the conventional charge-based demand response (CBDR). The hybridization of CBDR and DBDR yields a threefold revenue increase and a 28.7% improvement in peak regulation capacity, underscoring the efficacy of a joint-response approach in augmenting economic returns, grid flexibility, and sustainable energy management. Full article

With the deep integration of electricity and carbon trading markets, distribution networks are facing growing operational stress and a shortage of flexible resources under high penetration of renewable energy. This paper proposes a three-layer coordinated planning model for Source–Grid–Load–Storage (SGLS) systems, considering electricity–carbon coupling and flexibility supply–demand balance. The model incorporates a dynamic pricing mechanism that links carbon pricing and time-of-use electricity tariffs, and integrates multi-source flexible resources—such as wind, photovoltaic (PV), conventional generators, energy storage systems (ESS), and controllable loads—to quantify the system’s flexibility capacity. A hierarchical structure encompassing “decision–planning–operation” is designed to achieve coordinated optimization of resource allocation, cost minimization, and operational efficiency. To improve the model’s computational efficiency and convergence performance, an improved adaptive particle swarm optimization (IAPSO) algorithm is developed which integrates dynamic inertia weight adjustment, adaptive acceleration factors, and Gaussian mutation. Simulation studies conducted on the IEEE 33-bus distribution system demonstrate that the proposed model outperforms conventional approaches in terms of operational economy, carbon emission reduction, system flexibility, and renewable energy accommodation. The approach provides effective support for the coordinated deployment of diverse resources in next-generation power systems. Full article

The number of electric vehicles is constantly increasing in Europe and around the world. Providing a reliable charging infrastructure for the se vehicles is a major challenge for distribution grid operators. Off-grid microgrids have become a promising solution to this challenge, using renewable energy sources such as solar power to meet the demand in a sustainable way. This paper presents a practical study of a solar-powered microgrid operating at a university campus in Ilmenau, Germany, aimed at supporting electric vehicle (EV) charging at public workplaces. The system includes eight charging stations and utilizes renewable energy to reduce grid dependency. Statistical methods, including distribution functions, medians, and mean values, were applied to classify and evaluate the dataset to analyze energy generation and variable load patterns, as well as system performance. The results show that the Ilmenau microgrid can meet EV charging demand during the warm season but underperform during the cold season. An economic analysis determined costs of EUR 0.58/kWh based on pre-2020 component prices and EUR 0.46/kWh based on 2025 market prices. The calculated annual cost per employee is EUR 308.29 over a 20-year period. Increasing energy storage was found to be neither cost-effective nor operationally beneficial. The scalability of the microgrid to larger workplaces is investigated, and recommendations for system improvements are provided. Full article

This article considers various aspects of the functioning of electric power systems (EPSs) with a high proportion of available renewable energy sources (RES). In the absence of sufficient sources of basic generation in the EPS, new ways to eliminate possible consumer load jumps in the form of power reserves will be required. Based on the studies carried out in the Baltic States’ energy systems, it follows that the best way to ensure stable and safe operation of power plants in these conditions is to use energy storage devices, namely, a battery energy storage system (BESS). The BESS battery system will be able to provide reserves in a more economical way than most power plants that use organic fuels. A model for the distribution of production capabilities of an electric power producer with specified energy characteristics in market conditions is proposed. The practical implementation of the model makes it possible to obtain the initial data for creating characteristics of price proposals in the formation of a market for power reserves. The implementation of the model is illustrated for a concrete example. Full article