Search Results (6,634)

Optimizing the “production–living–ecological” space (PLES) is critical for achieving the UN Sustainable Development Goals (SDGs), particularly in ecologically sensitive mountainous border regions. This study investigates the spatial patterns and dynamic evolution of PLES in Lincang City (2010–2020) to reveal the trade-offs between development and conservation. Methodologically, we proposed a coupling-coordination-based grid-level PLES identification framework. This framework integrates the coupling coordination degree model (CCDM) directly into the functional classification process at a 600 m grid scale—a resolution selected to balance the capture of spatial heterogeneity with the maintenance of functional integrity in complex terrains. Spatiotemporal dynamics were further quantified using transition matrices and a dimension-based landscape metric system. The results reveal that (a) ecological space and production–living–ecological space represent the predominant categories in the study area. During the study period, ecological space continued to decrease, while production–living space increased steadily, and other PLES categories showed only marginal variations. (b) Mutual transitions among PLES types primarily occurred among ecological space, production–ecological space, and production–living–ecological space. These transitions intensified markedly between 2015 and 2020 compared to the 2010–2015 period. (c) From 2010 to 2020, the landscape in Lincang evolved towards lower ecological risk yet higher fragmentation. High fragmentation values, often associated with grassland, cropland, and forested areas, were evenly distributed across northeastern and northwestern regions. Likewise, high landscape dominance and isolation appeared in these regions as well as in the southeast. Conversely, landscape disturbance remained relatively uniform throughout the city, with lower values detected in forested land. Full article

Battery energy storage systems (BESSs) play a vital role in contemporary smart grids, but their increasing digitalisation exposes them to sophisticated cyberattacks. Existing anomaly detection approaches typically treat sensor measurements as flat feature vectors, overlooking the intrinsic relational structure of cyber–physical systems. This work introduces an enhanced Graph Neural Network (GNN) autoencoder for unsupervised BESS anomaly detection that integrates multiscale graph construction, multi-head graph attention, manifold regularisation via latent compactness and graph smoothness, contrastive embedding shaping, and an ensemble anomaly scoring mechanism. A comprehensive evaluation across seven BESS and firmware cyberattack datasets demonstrates that the proposed method achieves near-perfect Receiver Operating Characteristic (ROC) and Precision–Recall Area Under the Curve (PR AUC) (up to 1.00 on several datasets), outperforming classical one-class models such as Isolation Forest, One-Class Support Vector Machine (One-Class SVM), and Local Outlier Factor on the most challenging scenarios. These results illustrate the strong potential of graph-informed representation learning for cybersecurity monitoring in distributed energy resource infrastructures. Full article

As distributed energy resources and nonlinear loads are integrated into power grids on a large scale, power quality issues have grown increasingly prominent, triggering a substantial rise in distribution transformer losses. Traditional approaches struggle to accurately forecast transformer losses under complex power quality conditions and lack quantitative analysis of the influence of various power quality indicators on losses. This study presents a data-driven methodology for transformer loss prediction and sensitivity analysis in such environments. First, an experimental platform is designed and built to measure transformer losses under composite power quality conditions, enabling the collection of actual measurement data when multi-source disturbances exist. Second, a high-precision loss prediction model—dubbed Newton-Raphson-Based Optimizer-Transformer-Bidirectional Long Short-Term Memory (NRBO-Transformer-BiLSTM)—is developed on the basis of an enhanced deep neural network. Finally, global sensitivity analysis methods are utilized to quantitatively evaluate the impact of different power quality indicators on transformer losses. Experimental results reveal that the proposed prediction model achieves an average error rate of less than 0.18% and a similarity coefficient of over 0.9989. Among all power quality indicators, voltage deviation has the most significant impact on transformer losses (with a sensitivity of 0.3268), followed by three-phase unbalance (sensitivity: 0.0109) and third harmonics (sensitivity: 0.0075). This research offers a theoretical foundation and technical support for enhancing the energy efficiency of distribution transformers and implementing effective power quality management. Full article

Taganrog Bay is part of the Azov Sea, which has significant environmental value. However, in recent years, anthropogenic activity and climate change have increasingly impacted this coastal system. These factors have led to increased sea salinity. These factors also contribute to abundant blooms of potentially toxic cyanobacteria. One additional method for preventing the abundant growth of cyanobacteria may be the introduction of green algae into the bay. The aim of this study was to conduct a computational experiment on the biological rehabilitation of Taganrog Bay using mathematical modeling methods. For this purpose, the authors developed and analyzed a mathematical model of phytoplankton populations. A software model was developed based on modern mathematical modeling methods. The input data for the software module included grid points for advective transport velocities, salinity, and temperature, as well as phytoplankton population and nutrient concentrations. The software module outputs three-dimensional distributions of green algae and cyanobacteria concentrations. A computational experiment on biological rehabilitation of the Taganrog Bay by introducing a suspension of green algae was conducted. Green algae and cyanobacteria concentrations were obtained over 15 and 30-day time intervals. The concentration and volume of introduced suspension were empirically determined to prevent harmful cyanobacteria growth without leading to eutrophication of the bay by green algae. Full article

The heat transfer and flow characteristics of TiO₂/R123 nano-organic working fluid are investigated and compared with that of R123. A three-dimensional numerical model of the smooth circular tube with a diameter of 10 mm and a length of 1 m is established, and the thermodynamic properties of the nano-organic working fluids are rectified with the volume of fluid model. The grid independence validation is conducted, and the simulation results from three models (the k-ε model, the realizable k-ε model, and the Reynolds Stress Model) are evaluated against experimental data. When using the TiO₂/R123 nano-organic working fluid, the error between the simulation and experimental results is 6.1%. The flow field distribution is examined, and the effect of mass flux on heat transfer coefficient and pressure drop is discussed. Results demonstrated that the inclusion of TiO₂ nanoparticles significantly enhances heat transfer performance. At a 0.1 wt% nanoparticle concentration, the heat transfer coefficient increases by 23.2%, reaching a range of 1430.11 to 2647.25 W/(m²·K), compared to pure R123. However, this improvement in heat transfer performance is accompanied by an increase in flow resistance, with the flow resistance coefficient rising from 0.0353 to 0.0571. Additionally, pressure drops increase by up to 18.7%. Full article

With growing community awareness of greenhouse gas emissions and their environmental consequences, distributed rooftop photovoltaic (PV) systems have emerged as a sustainable energy alternative in residential settings. However, the high penetration of these systems without effective operational strategies poses significant challenges for local distribution grids. Specifically, the estimation of surplus energy production from these systems, closely linked to complex outdoor weather conditions and seasonal fluctuations, often lacks an accurate forecasting approach to effectively capture the temporal dynamics of system output during peak periods. In response, this study proposes a recurrent neural network (RNN)- based forecasting framework to predict rooftop PV surplus in the context of micro-residential communities over time horizons not exceeding 48 h. The framework includes standard RNN, long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and gated recurrent unit (GRU) networks. In this context, the study employed estimated surplus energy datasets from six single-family detached houses, along with weather-related variables and seasonal patterns, to evaluate the framework’s effectiveness. Results demonstrated the significant effectiveness of all framework models in forecasting surplus energy across seasonal scenarios, with low MAPE values of up to 3.02% and 3.59% over 24-h and 48-h horizons, respectively. Simultaneously, BiLSTM models consistently demonstrated a higher capacity to capture surplus energy fluctuations during peak periods than their counterparts. Overall, the developed data-driven framework demonstrates potential to enable short-term smart energy scheduling in micro-residential communities, supporting electric vehicle charging from single-family detached houses through efficient rooftop PV systems. It also provides decision-making insights for evaluating renewable energy contributions in the residential sector. Full article

With the increasing penetration of renewable energy and electric vehicles (EVs), virtual power plants (VPPs) have become a key mechanism for coordinating distributed energy resources and flexible loads to participate in electricity markets. However, the uncertainties of renewable generation and EV user behavior pose significant challenges to bidding strategies and real-time execution. This study proposes a two-stage optimal bidding strategy for VPPs by integrating vehicle-to-grid (V2G) technology. An aggregated EV schedulable-capacity model is established to characterize the time-varying charging and discharging capability boundaries of the EV fleet. A unified day-ahead and real-time optimization framework is further developed to ensure coordinated bidding and scheduling. Case studies on a modified IEEE-33 bus system demonstrate that the proposed strategy significantly enhances renewable energy utilization and market revenues, validating the effectiveness of coordinated V2G operation and multi-type flexible load control. Full article

Driven by energy transition strategies, distributed resources are being extensively integrated into the distribution network (DN). However, sufficient coordination among these resources remains challenging due to their diverse ownership structures. To address this, a hybrid game-theoretic economic scheduling method for the distribution network based on grid–storage–load interaction is proposed. A two-layer game framework, “distribution network–shared energy storage–microgrid alliance (MGA)”, is established to enable coordinated utilization of flexible resources across the grid, storage, and load sides. The upper-layer distribution network determines time-of-use electricity prices to guide the energy strategies of storage and microgrid alliance. The lower-layer agents engage in a two-stage interaction: Stage 1, multiple microgrids (MGs) form an alliance to lease shared energy storage to smooth net-load profiles. The shared energy storage operator (SESO) then utilizes its surplus capacity to assist the distribution network in peak shaving, thereby maximizing its own revenue. Stage 2, the alliance facilitates mutual power support and implements demand response (DR), reducing its energy costs and assisting the system in peak shaving and valley filling. Case analysis demonstrates that, compared to baseline without coordination, the proposed method reduces the distribution network’s electricity procurement cost by 11.28% and lowers the system’s net load peak-to-valley difference rate by 56.53%. Full article

The rapid construction and commissioning of battery energy storage system (BESS) installations, both standalone and combined with photovoltaic power plants (PVPPs), is rapidly reshaping the energy market. Mapping these latest iterations in the energy infrastructure allows for a detailed analysis of the effects they have on the grid, in correlation with the already abundant operational PPV. This paper will provide a list of all BESS installations commissioned between 1 January and 30 September 2025. Taking into consideration their grid-connection power, and respective battery capacity, along with their geographical location and co-located (or lack thereof) PVPPs, the following-up analysis aims to answer several key questions: how do these installations compare to one another in terms of power, capacity and distribution across Bulgaria; how do they affect the availability of electric power from PVPP, co-located or not, to the end consumers; and how does that shift in availability affect the profits, both for the BESS and PVPP owners, based on the shifting price of electricity? Full article

This study critically examines the sectoral dynamics of renewable energy (RE) adoption across the EU-27 from 1990 to 2023, addressing the persistent gap between electricity generation and end-use sectors. Utilizing Eurostat energy balance data, the research employs a robust multi-methodological framework. We apply the Logarithmic Mean Divisia Index (LMDI) decomposition to isolate driving factors, and the Self-Organizing Maps (SOM) of Kohonen to cluster countries with similar transition structures. Furthermore, the Method of Moments Quantile Regression (MMQR) is used to estimate heterogeneous drivers across the distribution of RE shares. The empirical findings reveal a sharp dichotomy: while the share of renewables in the electricity generation mix (RES-E-Renewable Energy Share in Electricity) reached approximately 53.8% in leading member states, the aggregated share in the transport sector (RES-T) remains significantly lower at 9.1%. This distinction highlights that while power generation is decarbonizing rapidly, end-use electrification lags behind. The MMQR analysis indicates that economic growth drives renewable adoption more effectively in countries with already high renewable shares (upper quantiles) due to established market mechanisms and grid flexibility. Conversely, in lower-quantile countries, regulatory stability and direct infrastructure investment prove more critical than market-based incentives, highlighting the need for differentiated policy instruments. While EU policy milestones (RED I–III-) align with progress in power generation, they have failed to accelerate transitions in lagging sectors. This study concludes that achieving climate neutrality requires moving beyond aggregate targets to implement distinct, sector-specific interventions that address the unique structural barriers in transport and thermal applications. Full article

When forest fires occur, timely detection and initial attack are critical for fire prevention. This study focuses on optimizing the cruise path of Unmanned Aerial Vehicles (UAVs) from the perspective of initial attack. It aims to maximize coverage of regions where initial attack success rates are low, shorten the time taken to detect fires, and, in turn, boost detection effectiveness and the initial attack success. In this paper, a path planning strategy, Improved Multi-Objective Crested Porcupine Optimizer (IMOCPO), is proposed. This strategy employs a weighted sum approach to formulate a composite objective function that balances global search and local optimization capabilities, considering practical requirements such as UAV endurance and uneven distribution of risk areas, thus enhancing adaptability in complex forest environments. The weight selection is justified through systematic grid search and validated by sensitivity analysis. The proposed strategy was compared and evaluated with a related strategy using four metrics: high-risk coverage rate, grid coverage rate, Average Distance Risk (ADR), and Average Grid Risk (AGR). Results show that the proposed path planning strategy performs better in these metrics. This study provides an effective solution for optimizing UAV cruise strategies in forest fire monitoring and has practical significance for improving the intelligence of forest fire prevention. Full article

With the rapid development of the Internet of Things (IOT), 5G, and modern power systems, demand-side loads are becoming increasingly observable and remotely controllable, which enables demand-side flexibility to participate more actively in grid dispatch and emergency support. Under extreme rainstorm conditions, however, component failure risk rises and the availability and dispatchability of demand-side flexibility can change rapidly. This paper proposes a risk-aware emergency regulation framework that translates rainstorm information into actionable multi-load aggregation decisions for urban power systems. First, demand-side resources are quantified using four response attributes, including response speed, response capacity, maximum response duration, and response reliability, to enable a consistent characterization of heterogeneous flexibility. Second, a backpropagation (BP) neural network is trained on long-term real-world meteorological observations and corresponding reliability outcomes to estimate regional- or line-level fault probabilities from four rainstorm drivers: wind speed, rainfall intensity, lightning warning level, and ambient temperature. The inferred probabilities are mapped onto the IEEE 30-bus benchmark to identify high-risk areas or lines and define spatial priorities for emergency response. Third, guided by these risk signals, a two-level coordination model is formulated for a load aggregator (LA) to schedule building air conditioning loads, distributed photovoltaics, and electric vehicles through incentive-based participation, and the resulting optimization problem is solved using an adaptive genetic algorithm. Case studies verify that the proposed strategy can coordinate heterogeneous resources to meet emergency regulation requirements and improve the aggregator–user economic trade-off compared with single-resource participation. The proposed method provides a practical pathway for risk-informed emergency regulation under rainstorm conditions. Full article

Electric vehicles (EVs), electric buses (EBs), and battery energy storage system (BESS), as both controllable power sources and load, play a great role in providing flexibility for the power grid, especially with the increased renewable energy penetration. However, there is still a lack of studies on EVs’ pricing strategy as well as the EBs-charging piles matching problem. To address these issues, a multi-objective optimal operation model is presented to achieve the lowest load fluctuation level, minimum electricity cost, and maximum discharging benefit. An improved load boundary prediction method (ICDLBPM) and a novel pricing strategy are proposed. In addition, reduction in the number of EBs charging piles would not only impact normal operation of EBs, but also even lead to load flexibility decline. Thus a handling method of the EBs-charging piles matching problem is presented. Several case studies were conducted on a regional distribution network comprising 100 EVs, 30 EBs, and 20 BESS units. The developed model and methodology demonstrate superior performance, improving load smoothness by 45.78% and reducing electricity costs by 19.73%. Furthermore, its effectiveness is also validated in a large-scale system, where it achieves additional reductions of 39.31% in load fluctuation and 62.45% in total electricity cost. Full article

The increasing penetration of wind energy has led to reduced system inertia and heightened sensitivity to dynamic disturbances in modern power systems. This paper proposes a Newton–Raphson-Based Optimizer (NRBO) for tuning proportional, integral, and feedforward gains of a grid-forming converter applied to a wind energy conversion system operating in a low-inertia environment. The study considers an aggregated wind farm modeled as a single equivalent DFIG-based wind turbine connected to an infinite bus, with detailed dynamic representations of the converter control loops, synchronous generator dynamics, and network interactions formulated in the dq reference frame. The grid-forming converter operates in a grid-connected mode, regulating voltage and active–reactive power exchange. The NRBO algorithm is employed to optimize a composite objective function defined in terms of voltage deviation and active–reactive power mismatches. Performance is evaluated under two representative scenarios: small-signal disturbances induced by wind torque variations and short-duration symmetrical voltage disturbances of 20 ms. Comparative results demonstrate that NRBO achieves lower objective values, faster transient recovery, and reduced oscillatory behavior compared with Differential Evolution, Particle Swarm Optimization, Philosophical Proposition Optimizer, and Exponential Distribution Optimization. Statistical analyses over multiple independent runs confirm the robustness and consistency of NRBO through significantly reduced performance dispersion. The findings indicate that the proposed optimization framework provides an effective simulation-based approach for enhancing the transient performance of grid-forming wind energy converters in low-inertia systems, with potential relevance for supporting stable operation under increased renewable penetration. Improving the reliability and controllability of wind-dominated power grids enhances the delivery of cost-effective, cleaner, and more resilient energy systems, aiding in expanding sustainable electricity access in alignment with SDG7. Full article

Building resilient and sustainable energy systems is a critical challenge for disaster-prone regions in the Global South. This study investigates the energy management of a networked microgrid (NMG) system on Lombok Island, Indonesia, a region frequently exposed to natural disasters (NDs) and characterized by vulnerable grid infrastructure. A multi-objective optimization framework is developed to jointly minimize operational costs, load-not-served, and environmental impacts under both normal and abnormal operating conditions. The proposed strategy employs the Multi-objective JAYA (MJAYA) algorithm to coordinate photovoltaic generation, diesel generators, battery energy storage systems, and inter-microgrid power exchanges within a 20 kV distribution network. Using real load, generation, and electricity price data, we evaluate the NMG’s performance under five representative fault scenarios that emulate ND-induced outages, including grid disconnection and loss of inter-microgrid links. Results show that the interconnected NMG structure significantly enhances system resilience, reducing load-not-served from 366.3 kWh in fully isolated operation to only 31.7 kWh when interconnections remain intact. These findings highlight the critical role of cooperative microgrid networks in strengthening community-level energy resilience in vulnerable regions. The proposed framework offers a practical decision-support tool for planners and governments seeking to enhance energy security and advance sustainable development in disaster-affected areas. Full article