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The increasing penetration of renewable energy sources reduces system inertia and introduces significant challenges for maintaining frequency stability in modern power grids. Battery Energy Storage Systems (BESS) have emerged as an effective solution for mitigating frequency deviations; however, existing studies typically recommend relocating BESS to the bus that is electrically furthest from the Center of Inertia (COI) to maximize frequency support. This paper investigates an alternative operational strategy in which the BESS remains co-located with the renewable energy source. A methodology combining COI-based electrical distance analysis and an artificial intelligence (AI)-driven dispatch framework is proposed to evaluate optimal BESS utilization without physical relocation. The AI model generates generator dispatch scenarios that are evaluated through dynamic simulations to assess the resulting system frequency nadir following disturbances. The proposed approach is validated using a modified IEEE nine-bus power system model. Simulation results demonstrate that, under specific generator dispatch conditions, maintaining the BESS at the renewable energy bus can achieve frequency-nadir performance comparable to relocating the BESS to the furthest bus from the COI. The analysis further identifies critical generator output ranges that influence frequency stability under different BESS placement scenarios. These findings suggest that optimized dispatch strategies can reduce the need for costly infrastructure relocation while maintaining effective frequency support in low-inertia power systems. Full article

Hydropower capacity in the United States currently stands at approximately 103 GW, and there are remaining water resources that could help meet the rapidly increasing demand for electricity and ancillary grid services. Existing dams that do not generate power, known as non-powered dams, are a near-term solution to enhance the contribution of hydropower to the US power grid. However, there are thousands of such sites, and the lack of detailed information about their specific characteristics and associated costs presents significant challenges for stakeholders. This study addresses these challenges by developing a reference site model to evaluate the potential costs of hydropower at non-powered dams using currently available technologies. An application of the model reveals a wide range of estimates for capacity, capital costs, levelized cost of electricity, and cost components. While many sites are competitive with current technologies, the majority would need cost-reducing innovations to be viable. Despite the limited available information, the model offers valuable insights into the relative competitiveness of hydropower projects at non-powered dams. The simulation results highlight the need for continued technological advancements in hydropower and provide a basis for evaluating the benefits of new innovations. Full article

With the large-scale integration of renewable energy sources, grid-forming (GFM) converters with inherent voltage and frequency support capabilities have attracted significant attention. However, due to the limited overcurrent withstand capability of power electronic devices, the stable operation of GFM converters under grid faults such as grid voltage sags remains a critical challenge. To address this issue, this paper systematically investigates the mechanisms of power angle instability and overcurrent generation during grid faults by a unified equivalent impedance model. Based on this analysis, a comprehensive control strategy that simultaneously considers power angle stability and overcurrent suppression is proposed. By introducing an adaptive improved observer control (AIOC), the active power reference is adaptively adjusted to enhance the power angle stability of the system. Meanwhile, the voltage reference is dynamically regulated to effectively limit the fault current while enhancing the voltage support capability. Finally, comprehensive theoretical analysis and experimental validation are provided. The experimental results demonstrate that the proposed strategy is capable of ensuring power angle stability and limits the overcurrent to within 1.5 p.u. Meanwhile, the voltage magnitude is increased by approximately 6%. The results demonstrate the robustness and adaptability of the proposed method under various conditions. Full article

The energy transition represents a complex, multi-level system subject to profound uncertainty and recurrent shocks. Current policy design approaches predominantly rely on static optimization frameworks (centralized, calculative models that presume stable conditions and predictable technological trajectories). Yet evidence from the 2021–2023 energy crisis in Europe, coupled with structural challenges in market liberalization and renewable integration, demonstrates persistent challenges in policy implementation. Price interventions affect competitive dynamics; subsidies influence technology selection; capacity mechanisms create coordination tensions; and rigid tariff structures create misalignments with evolving grid needs. This paper argues that these recurrent policy tensions stem not from implementation gaps, but from an inadequate theoretical foundation: the treatment of energy systems as optimizable rather than as complex, adaptive systems operating under Knight–Mises uncertainty and Huerta de Soto dynamic efficiency. This work explores an alternative framework grounded in dynamic efficiency, complex–uncertain systems, decentralized incentives, and adaptive governance (international–domestic, public–private, etc.). This review uses the theoretical and methodological framework of the Heterodox Synthesis, an alternative to the Neoclassical Synthesis. There is a reinterpretation of some insights from Knight and Mises (uncertainty), Hayek (distributed knowledge), Huerta de Soto (dynamic efficiency) and contemporary complexity economics into operational criteria applicable to energy policy design: (1) robustness to deep uncertainty; (2) preservation of price signals and risk-bearing mechanisms; (3) alignment of incentives across distributed actors; (4) institutional adaptability; and (5) minimization of ex post policy corrections. Through illustrative application to four critical policy instruments (price caps, renewable subsidies, capacity mechanisms, and network tariff design), it is shown how this framework identifies systematic tensions and consequences that conventional analysis overlooks. The contribution is exploratory in a bootstrap way: theoretical, by integrating classical and contemporary economics into energy governance; methodological, by operationalizing dynamic efficiency into evaluable criteria distinct from existing adaptive governance frameworks; and sectorial, by providing policymakers and regulators with diagnostic tools for assessing design robustness in conditions of deep uncertainty and rapid transition. According to this review, improved energy policy design under uncertainty is not achieved through more sophisticated optimization (in a calculative way), but through institutional architectures that preserve creative and adaptive learning, maintain distributed decision-making capacity, and remain functional when assumptions prove incorrect or not well-known. Full article

The increasing demand for energy and the continued reliance on fossil fuels pose important environmental and social challenges, particularly for rural and isolated communities in developing countries that lack reliable access to the grid. Gravitational water vortex turbines (GWVT) are a run-of-river technology for low-head and moderate-flow sites that can provide decentralized electricity without the construction of large reservoirs. The expected environmental impacts are lower; nevertheless, to increase acceptance by the community, there is a necessity to identify and analyze the potential environmental impacts of GWVT in all its life-cycle phases (installation, operation, maintenance, and dismantling). The present study applies the Conesa cause–effect matrix to identify, classify, and analyze the potential environmental impacts associated with GWVT phases. Key identified impacts include removal of vegetation coverage and site disturbance (−32), sediment dynamics alterations (−39), formation of a depleted stretch (−45), accidental releases of hazardous maintenance products (−42), and remobilization of retained sediments (−46). These impacts can produce habitat alteration and fragmentation and loss of ecological connectivity. The relevant significance of energy generation that can have multiple benefits in the local communities was also identified. Primary mitigation measures include the incorporation of environmental flows in the design, sediment management, and strict protocols for hazardous materials. The findings underscore the necessity to conduct site-specific baseline surveys to preserve environmental, socio-economic, and cultural conditions in the local ecosystem and communities. Full article

High-dimensional global optimization and microgrid economic scheduling problems are often dominated by nonlinear search landscapes, strong coupling among decision variables, and stringent operational constraints, which severely limit the effectiveness of conventional metaheuristic approaches. In response to these challenges, this study presents a multi-strategy cooperative optimization framework derived from stock exchange trading principles, referred to as MESETO. The proposed method departs from the single-path evolutionary process of the standard SETO algorithm by introducing a diversified strategy collaboration mechanism that enables the dynamic adjustment of search behaviors throughout the optimization process. Multiple complementary update strategies are jointly employed to balance global exploration and local exploitation, while an adaptive probability regulation scheme continuously reallocates computational effort toward strategies that demonstrate superior performance. In addition, a solution validation mechanism is incorporated to prevent population degradation by rejecting ineffective evolutionary moves, thereby enhancing convergence stability. Extensive numerical experiments conducted on the CEC2017 and CEC2022 benchmark suites across different dimensional configurations demonstrate that MESETO consistently achieves improved solution accuracy, faster convergence, and stronger robustness compared with several representative state-of-the-art metaheuristic algorithms. Furthermore, the applicability of the proposed optimizer is verified through a 24 h microgrid economic scheduling case that integrates renewable energy sources, energy storage systems, dispatchable generators, and grid interaction. Simulation results confirm that MESETO effectively reduces operational costs while maintaining stable and efficient scheduling performance. Overall, the results indicate that MESETO constitutes a reliable and efficient optimization framework for solving complex global optimization problems and practical energy management applications. Full article

To address the operational challenges of industrial microgrids under different weather conditions, this study proposes an optimal dispatch strategy for energy storage systems under representative weather scenarios. Photovoltaic (PV) power generation is first forecast using a Light Gradient Boosting Machine (LightGBM) model, while the load input is prepared based on recent historical demand patterns, and the forecasting performance is evaluated under representative sunny and cloudy scenarios. A mathematical microgrid model incorporating PV generation, battery energy storage, load demand, and grid interaction is then established, in which the total operating cost is minimized subject to time-of-use electricity pricing, battery degradation, and state-of-charge (SOC) constraints. Based on this formulation, an optimization-based day-ahead scheduling strategy is implemented. Under the selected representative sunny and cloudy conditions, the proposed method reduced the daily operating cost by 19.93% and 4.44%, respectively. Over seven representative days, the average cost reduction rate reached 12.54%, thereby confirming its economic effectiveness under representative weather scenarios. Full article

In the era of renewable dominated grids, integration of dynamic loads such as EV charging stations have increased the operational challenges in multifolds, particularly in DC microgrids (DC MGs). Traditional battery-dominated grid energy management strategies (EMSs) are often not capable of handling fast transients due to the limitations of battery electrochemistry. To overcome this limitation, a hierarchical hybrid energy management strategy is proposed that uses the combination of data-driven and metaheuristic algorithms. The designed optimization framework consists of particle swarm optimization (PSO) and a neural network (NN) implemented in the central controller of a 4-bus ringmain DC MG. An efficient decoupling of fast and slow storage dynamics is performed, where the supercapacitor (SC) is optimized using the NN and the battery is optimized using PSO. This selective optimization reduces the computational overhead on the PSO making it more feasible for real-time implementation. The designed hybrid PSO-Neural EMS framework is initially designed on MATLAB and further validated on a real-time hardware setup. Robustness of the control scheme is verified with various case studies, such as renewable intermittency, dynamic loading and partial shading scenarios. An effective optimization of the SC in both transient and heavy load scenarios are observed. LabVIEW interfacing is used for MODBUS-based interaction with PV emulators and DC-DC converters. Full article

BCI biosensors enable continuous monitoring of neural activity, but existing systems face challenges in scalability, latency, and reliable integration with cloud infrastructure. This work presents a cloud-aware, real-time cognitive grid architecture for multimodal BCI biosensors, validated at the system level through a full physical prototype. The system integrates the BioAmp EXG Pill for signal acquisition with an RP2040 microcontroller for local preprocessing using edge-resident TinyML deployment for on-device feature/inference feasibility coupled with environmental context sensors to augment signal context for downstream analytics talking to the external world via Wi-Fi/4G connectivity. A tiered data pipeline was implemented: SD card buffering for raw signals, Redis for near-real-time streaming, PostgreSQL for structured analytics, and AWS S3 with Glacier for long-term archival. End-to-end validation demonstrated consistent edge-level inference with bounded latency, while cloud-assisted telemetry and analytics exhibited variable transmission and processing delays consistent with cellular connectivity and serverless execution characteristics; packet loss remained below 5%. Visualization was achieved through Python 3.10 using Matplotlib GUI, Grafana 10.2.3 dashboards, and on-device LCD displays. Hybrid deployment strategies—local development, simulated cloud testing, and limited cloud usage for benchmark capture—enabled cost-efficient validation while preserving architectural fidelity and latency observability. The results establish a scalable, modular, and energy-efficient biosensor framework, providing a foundation for advanced analytics and translational BCI applications to be explored in subsequent work, with explicit consideration of both edge-resident TinyML inference and cloud-based machine learning workflows. Full article

This study investigates the impact of monitoring infrastructure characteristics (specifically sensor penetration and measurement accuracy) on the effectiveness of voltage regulation and congestion management within distribution networks. As distribution system operators transition toward active management, the integration of Distributed renewable Generation (DG) and demand response introduces significant physical and cyber-physical uncertainties. To address these challenges, a smart grid service framework has been employed to optimize flexibility resources from aggregated users and DG inverters through a genetic algorithm. The framework was tested on the IEEE European Low Voltage Test Feeder across various scenarios defined by distributed monitoring systems’ penetration and their measurement accuracy. Results show that sensor penetration has a dominant impact: increasing monitoring coverage from 0% to 100% raises the percentage of cases with fewer than one residual congestion from 46.2% to 91.9% (sensors with an accuracy class of 2%), reaching 97.9% with an accuracy class of 0.5%, while voltage violations are eliminated under full monitoring. These findings suggest that widespread sensor deployment, with a suitable measurement accuracy, is a fundamental prerequisite for reliable and efficient smart grid operation. Full article

The electricity supply system faces major challenges. The physical and social vulnerability of the monoculture of hierarchical, centralized systems urgently requires radical transformation of their organizational structures as well as their infrastructures. These transformations to low carbon are often characterized as ‘decentralization’. However, decentralization is a process that only signifies a move away from centralized models. This does not necessarily result in a decentralized architecture, but rather a model in which the dominance of ‘commercial private’ combined with ‘monopolistic public’ is replaced by cooperation and community. The research question is: what will be the design of future electricity grids after the transformation? The integration of distributed renewable resources and the growing need for resilience requires great diversity and flexibility from socio-technical smart grids. These involve digitization, enabling the transformation of power grids into networks of clustered, self-healing microgrids with distributed energy systems: generation, storage, transmission, demand response, and internal energy management. Several fundamentals of Common Pool Resources theory (Ostrom) on the analysis of sustainable management of natural resources are reviewed on their relevance: the Socio-Ecological System framework, distinct property regimes, the Polycentricity concept, and the Institutional Analysis and Development (IAD) framework. The transformation leads to ‘distributed’ rather than ’decentralized’ models. Governance no longer takes place from a single control point, but from many, spread across multiple levels, similar to ecosystems. End users play a key role and become partly coproducing prosumers. Governance is polycentric rather than decentral. The IAD provides as its most important condition that, at the legislative level, there must be minimum recognition of the right of ‘renewable energy communities’ to organize themselves as microgrids. This is immediately the biggest social acceptance challenge, as the current monoculture incorporates several lock-ins: incumbent powerful actors, centralized hierarchical control legislation, and obstructive market conditions, including taxing systems. Full article

The rapid incorporation of solar energy (PV) systems into electrical grids has increased the demand for accurate short-term forecasts to ensure stability and improve processes. Although hybrid artificial intelligence (AI) models are increasingly being suggested to address this challenge, there is a lack of systematic compilation of their structures, effectiveness, and readiness for use in real-world applications. This paper provides a detailed analysis of 58 peer-reviewed articles (2020–2025) focused on hybrid models for short-term (1–24 h) solar photovoltaic power forecasting. We propose an innovative classification that groups hybrids into four categories: AI-AI (28%), AI with optimization (21%), decomposition-based (17%), and image-based (7%). Our research indicates that weather conditions (34%) and historical photovoltaic energy records (32%) are the most frequent inputs, and that optimized hybrids and those using decomposition achieve the best balance between effectiveness and computational efficiency. From a geographical perspective, the study focuses mainly on the United States (29%) and China (22%), suggesting that more extensive climate validation is crucial. Essentially, we have identified ongoing obstacles to implementation, such as high computational costs, data quality issues, and gaps in interpretation. In addition, we present a plan for future research focusing on hybrid architectures that are lightweight, understandable, and interactive with the grid. This analysis provides a thorough assessment of the current landscape and a strategic framework to guide the creation of operational forecasting systems capable of supporting highly solar-integrated grids. Full article

In highway aerial imagery, complex weather conditions such as rain, fog, snow, and low illumination often lead to severe appearance degradation and feature loss of vehicle targets, posing significant challenges for vehicle detection. Existing research faces two major challenges: first, the lack of large-scale, high-quality annotated datasets tailored for complex weather scenarios; second, the difficulty traditional detectors encounter in effectively extracting feature information and performing multi-scale feature fusion under conditions of severe feature degradation and dense distribution of small objects. To address these issues, this paper investigates both data construction and algorithm design. Firstly, a Complex Weather Highway Vehicle Dataset (CWHVD) is established to provide a benchmark for related research. Secondly, a Feature-Enhanced Grid-Based Feature Fusion Complex-Weather Vehicle Detection Network (FGCV-Det) is proposed. A wavelet transform-based Feature Enhancement Module (FEWT) is introduced at the input stage to strengthen edge and texture representation. In the backbone, Adaptive Pinwheel Convolution (APConv) and a C3K2-HD module based on Hidden State Mixer-Based State Space Duality (HSM-SSD) are employed to enhance semantic modeling. Furthermore, a Complex Weather Grid Feature Pyramid Network (CWG-FPN) is designed to achieve weighted cross-scale fusion. The FGCV-Det significantly outperforms YOLO11s on CWHVD, achieving 63.4% precision, 48.6% recall, 51.7% mAP50, and 28.2% mAP50:95. It also generalizes well, reaching 47.1% and 49.6% mAP50 on VisDrone2019 and UAVDT, respectively, surpassing baseline and mainstream detectors, demonstrating strong robustness and generalization capability. Full article

The growing integration of renewable energy sources in grid-connected microgrids (MG) has made it increasingly challenging to attain the most cost-effective and emission-efficient power dispatch in the face of uncertainty. This study addresses the scheduling problem of MG under utility-induced demand side load participation level for residential areas. Our research overcomes the constraints of conventional techniques by utilizing quantum-inspired particle swarm optimization (QPSO) to improve the operational efficiency and resilience of MG’s. In this study, a three-stage stochastic framework is proposed to address the optimal energy scheduling of MGs while taking economic and emission aspects into account. Using real-time meteorological data, five Cases were investigated and simulated using MATLAB/Simulink. Without the involvement of load participation, MG’s producing units in first Case, had carbon emissions of 797.110 kg and an operating cost of 267.10 €. Similar to this, the impact of demand side on the MG was evaluated in the remaining Cases. According to the simulation results, the fifth Case, which has optimal DGs scheduling, is the suggested way to improve MGs efficiency and provide a dependable power supply with low operating costs, emission reduction, and convergence features. This study not only demonstrates the practicality of QPSO algorithms but also paves the way for more resilient, efficient, and sustainable energy systems. Full article

The adoption of electric vehicles (EVs) is a promising strategy for reducing emissions and promoting sustainable mobility. The increasing adoption of EVs has created a demand for efficient and sustainable charging infrastructure. The integration of rooftop solar-powered EV charging stations into distribution networks is a promising solution for reducing carbon emissions and improving grid efficiency. This integration also introduces challenges, such as power quality issues, grid instability, and the impact of environmental factors on solar generation. This study proposes a novel system that integrates a smart control algorithm for a central battery management system (CBMS) with advanced bidirectional DC-DC converters for optimised power distribution. Unlike existing systems that focus on individual components, this study combines real-time environmental monitoring with adaptive power management algorithms to handle variations in generation owing to solar irradiance, temperature, and shading, and ensure maximum power harvesting. This study also presents the role of the DC-to-DC converter integrated with a smart charging control and CBMS in smart grid-enabled EV charging station. The proposed system was validated using MATLAB 2025b Simulink simulations. This study demonstrates an improvement in overall grid stability and highlights the potential of DC-DC converter technologies for smart grid applications and decarbonisation efforts. Full article