Search Results (186)

Germany’s energy transition to a higher share of renewable energy sources (RESs) is characterized by decentralization, with citizens, cooperatives, SMEs, and municipalities playing a central role. As of early 2025, private individuals own a significant share of renewable energy installations, particularly PV panels, which corresponds to approximately half of the total installed PV power. This trend is driven by physical, technological, and societal factors. Technological advances in battery storage and sector coupling are expected to further decentralize the energy system. Thereby, the electrification of mobility, particularly through electric vehicles (EVs), offers significant storage potential and grid-balancing capabilities via bidirectional charging, although it also introduces challenges, especially for distribution grids during peak loads. Within this work we present a detailed digital twin of the entire distribution grid of the rural German municipality of Wüstenrot. Using grid operator data and transformer measurements, we evaluate strategic expansion scenarios for electromobility, PV and heat pumps based on existing infrastructure and predicted growth in both public and private sectors. A core focus is the intelligent integration of EV charging infrastructure to avoid local overloads and to optimise grid utilisation. Thereby municipally planned and privately driven expansion scenarios are compared, and grid bottlenecks are identified, proposing solutions through charge load management and targeted infrastructure upgrades. This study of Wüstenrot’s low-voltage grid reveals substantial capacity reserves for future integration of heat pumps, electric vehicles (EVs), and photovoltaic systems, supporting the shift to a sustainable energy system. While full-scale expansion would require significant infrastructure investment, mainly due to widespread EV adoption, simple measures like temporary charge load reduction could cut grid stress by up to 51%. Additionally, it is shown that bidirectional charging offers further relief and potential income for EV owners. Full article

The increasing deployment of islanded microgrids in disaster-prone and infrastructure-constrained regions has elevated the importance of resilient energy storage systems capable of supporting autonomous operation. Grid-forming energy storage (GFES) units—designed to provide frequency reference, voltage regulation, and black-start capabilities—are emerging as critical assets for maintaining both energy adequacy and dynamic stability in isolated environments. However, conventional storage planning models fail to capture the interplay between uncertain renewable generation, time-coupled operational constraints, and control-oriented performance metrics such as virtual inertia and voltage ride-through. To address this gap, this paper proposes a novel distributionally robust optimization (DRO) framework that jointly optimizes the siting and sizing of GFES under renewable and load uncertainty. The model is grounded in Wasserstein-metric DRO, allowing worst-case expectation minimization over an ambiguity set constructed from empirical historical data. A multi-period convex formulation is developed that incorporates energy balance, degradation cost, state-of-charge dynamics, black-start reserve margins, and stability-aware constraints. Frequency sensitivity and voltage compliance metrics are explicitly embedded into the optimization, enabling control-aware dispatch and resilience-informed placement of storage assets. A tractable reformulation is achieved using strong duality and solved via a nested column-and-constraint generation algorithm. The framework is validated on a modified IEEE 33-bus distribution network with high PV penetration and heterogeneous demand profiles. Case study results demonstrate that the proposed model reduces worst-case blackout duration by 17.4%, improves voltage recovery speed by 12.9%, and achieves 22.3% higher SoC utilization efficiency compared to deterministic and stochastic baselines. Furthermore, sensitivity analyses reveal that GFES deployment naturally concentrates at nodes with high dynamic control leverage, confirming the effectiveness of the control-informed robust design. This work provides a scalable, data-driven planning tool for resilient microgrid development in the face of deep temporal and structural uncertainty. Full article

The large-scale adoption of electric vehicles has created an urgent need for the orderly management of charging loads in residential communities. While existing research on community-based orderly charging architectures and control strategies primarily focuses on connected charging piles (CPs) equipped with remote power control functions. However, in practical scenarios, most residential communities still rely on unconnected charging piles (UCPs) that lack remote communication capabilities, making it difficult to practically deploy many intelligent orderly architectures and control strategies that rely on communication with charging piles. Therefore, this paper proposes a non-intrusive orderly charging architecture tailored for UCPs. This architecture does not require modifying the hardware of UCPs; instead, it introduces pile-end management units (PMUs) to interact with users for orderly charging, thereby facilitating easier deployment and promotion. Based on this architecture, an optimized control strategy using the GD-SA (greedy-simulated annealing) algorithm for orderly charging is constructed, which considers the dual constraints of transformer capacity and charging demand. Case studies on a typical community in Tianjin, China, demonstrate that with the proposed order charging architecture and strategy, when users fully accept the orderly charging approach, the peak load can be reduced by over 17% compared to uncontrolled charging scenarios. Additionally, the effectiveness of the method has been validated through sensitivity analysis of user acceptance, stress scenario testing, and statistical analysis with a 95% confidence interval. Finally, this paper summarizes the practical value potential of supporting UCPs in achieving orderly charging, while also pointing out the limitations of the current research and identifying directions for further in-depth exploration. Full article

To address the problem of selecting an “appropriate” charging station for emergency charging during the journey of electric vehicles, this paper proposes a basic architecture of an intelligent charging navigation system composed of the power system, traffic system, charging stations, and on-board navigation terminals. The concept of a charging time window is introduced into a “reservation-based charging + consumption” service model for electric vehicle charging prediction. On this basis, a dynamic dispatching model based on a rolling time axis is designed, enabling the charging process of users to be freed from the constraints of queuing time and time-dependent charging service fees. Case simulations show that intelligent charging navigation for electric vehicles based on reservation charging service can effectively improve the users’ charging experience while taking into account both the operating state of the power grid and the benefits of charging station operators. Full article

Despite the ongoing electromobility revolution in urban areas, fleet managers still prefer combustion engines over electric vehicles. Fleet electrification can deliver tangible benefits not only for the urban environment but also for the company itself. However, this requires a robust economic and technical analysis approach. This study assesses the technical and economic viability of integrating electric vehicle (EV) fleets into the Turkish manual frequency recovery reserve (mFRR) market. Using a life-cycle costing (LCC) framework, three operational scenarios are modeled: Baseline (leased EVs without V2G), V2G+ (leased EVs with aggregator-based mFRR), and High Utilization (owned EVs with full V2G integration and increased rental activity). The baseline scenario assumes a net cost of USD 142,500 over 10 years, excluding revenue share. V2G+ reduces this amount to USD 137,000, generating an annual income of approximately USD 4400 from its share of the frequency reserve. A high utilization scenario, combining V2G with ownership and higher rental income, reduces the net LCC to USD 125,500 and generates over USD 12,000 annually, reaching breakeven around year 7. Sensitivity analyses show that the financial profitability of the system is significantly influenced by EV purchase prices, aggregator fees, mFRR capacity payments, and vehicle utilization rates. Adding a 30–50% solar-powered charging enclosure further reduces operating costs by up to USD 21,500, demonstrating the synergistic potential of integrating V2G and distributed photovoltaics. These results influence not only the priorities for electrifying the urban vehicle fleet, but also smart city regulations in the area of energy management, through the development of bidirectional charging standards and pilot implementation of V2G in emerging markets such as Turkey. Full article

This work presents a novel wireless charging system for unmanned aerial vehicles (UAVs), which employs conical-shaped coils that also function as landing gear. By integrating electromagnetic simulation, circuit modeling, and system-level evaluation, we introduce an innovative coil design that enhances wireless power transfer (WPT) efficiency while reducing misalignment sensitivity. The conical geometry naturally facilitates mechanical alignment upon drone landing, thereby improving inductive coupling. High-frequency simulations were carried out to optimize the coil parameters and evaluate the link efficiency at 6.78 MHz, an ISM-designated frequency. Our experimental testing confirmed that the proposed conical coil achieves high power transfer efficiency (up to 94%) under practical conditions, validating the effectiveness of the geometry. The characteristics of the designed coil make it highly suitable for use with Class EF amplifiers operating in the same frequency range; however, detailed amplifier hardware implementation and efficiency characterization were beyond the scope of this study and are reserved for future work. The results demonstrate the potential of the proposed system for deployment in UAV field applications such as surveillance, delivery, and remote sensing. Full article

Addressing the problems of wind power’s anti-peak regulation characteristics, increasing system peak regulation difficulty, and wind power uncertainty causing frequency deviation leading to power imbalance, this paper considers the peak shaving and valley filling function and frequency regulation characteristics of energy storage, establishing a day-ahead and intraday coordinated two-stage optimization scheduling model for research. Stage 1 establishes a deterministic wind power prediction model based on time series Autoregressive Integrated Moving Average (ARIMA), adopts dynamic peak-valley identification method to divide energy storage operation periods, designs energy storage peak regulation working interval and reserves frequency regulation capacity, and establishes a day-ahead 24 h optimization model with minimum cost as the objective to determine the basic output of each power source and the charging and discharging plan of energy storage participating in peak regulation. Stage 2 still takes the minimum cost as the objective, based on the output of each power source determined in Stage 1, adopts Monte Carlo scenario generation and improved scenario reduction technology to model wind power uncertainty. On one hand, it considers how energy storage improves wind power system inertia support to ensure the initial rate of change of frequency meets requirements. On the other hand, considering energy storage reserve capacity responding to frequency deviation, it introduces dynamic power flow theory, where wind, thermal, load, and storage resources share unbalanced power proportionally based on their frequency characteristic coefficients, establishing an intraday real-time scheduling scheme that satisfies the initial rate of change of frequency and steady-state frequency deviation constraints. The study employs improved chaotic mapping and an adaptive weight Particle Swarm Optimization (PSO) algorithm to solve the two-stage optimization model and finally takes the improved IEEE 14-node system as an example to verify the proposed scheme through simulation. Results demonstrate that the proposed method improves the system net load peak-valley difference by 35.9%, controls frequency deviation within ±0.2 Hz range, and reduces generation cost by 7.2%. The proposed optimization scheduling model has high engineering application value. Full article

The demand for sustainable energy generation and storage methods has become inevitable. As a result, numerous sectors are investing in research focused on energy harvesting (EH) techniques. In this context, a promising area involves integrating piezoelectric materials into unmanned aerial vehicles (UAVs)—an application that enables electrical energy generation from the kinetic energies produced during flight. This article aims to use polyvinylidene fluoride (PVDF) piezoelectric transducers coupled to an EH power management unit (LTC3588-1) to convert and store electrical energy generated by wind from the propellers and motor vibration. Methodologically, the motor and transducers are characterized, a model is developed using LTSpice^®, and experimental validation of the performance of this coupling is carried out for output voltages (V_out) of 1.8 V, 2.5 V, 3.3 V, and 3.6 V. With a motor rotation speed of 3975 rpm, the transducers generated a voltage amplitude of 17.3 V, enabling the capacitor coupled to the EH power management unit—adjusted to the highest V_out—to be charged in approximately 162 s. Thus, this study demonstrated the feasibility of using PVDF as a piezoelectric nanogenerator in UAVs, enabling onboard electronic circuits and sensors to be powered while reserving the battery solely for propulsion, thereby increasing flight autonomy. Full article

Energy policies and economies of scale have promoted the expansion of renewable energy sources, leading to the displacement of conventional generation units and a consequent reduction in system inertia. Low inertia amplifies frequency deviations in response to generation–load imbalances, increasing the risk of load shedding and service interruptions. To address this issue, this paper proposes a coordinated control strategy based on neuro-fuzzy networks, applied to a hybrid energy storage system (HESS) composed of batteries and supercapacitors. The controller is designed to simultaneously emulate virtual inertia and implement virtual droop control, thereby improving frequency stability and reducing reliance on spinning reserve. Additionally, a state-of-charge (SOC) management layer is integrated to prevent battery operation in critical zones, mitigating degradation and extending battery lifespan. The neuro-fuzzy controller dynamically coordinates the power exchange both among the energy storage technologies (batteries and supercapacitors) and between the HESS and the conventional generation unit, enabling a smooth and efficient transition in response to power imbalances. The proposed strategy was validated through simulations in MATLAB R2022b using a two-area power system model with parameters sourced from the literature and validated references. System performance was evaluated using standard frequency response metrics, including performance indicators (ITSE, ISE, ITAE and IAE) and the frequency nadir, demonstrating the effectiveness of the approach in enhancing frequency regulation and ensuring the operational safety of the energy storage system. Full article

The increasing penetration of photovoltaic (PV) generation in power systems is progressively displacing traditional synchronous generators, leading to a significant reduction in the system’s equivalent inertia. This decline undermines the system’s ability to withstand rapid frequency variations, adversely affecting its dynamic stability. In this context, battery energy storage systems (BESS) have emerged as a viable alternative for providing synthetic inertia and enhancing the system’s response to frequency disturbances. This paper proposes the design and implementation of an adaptive fuzzy logic controller aimed at frequency regulation in PV-BESS systems. The controller uses frequency deviation (Δf), rate of change of frequency (ROCOF), and battery state of charge (SOC) as input variables, with the objective of improving the system’s response to frequency variations. The controller’s performance was evaluated through simulations conducted in the MATLAB environment, considering various operating conditions and disturbance scenarios. The results demonstrate that the proposed controller achieves the lowest maximum frequency deviation across all analyzed scenarios when the initial SOC is 50%, outperforming other comparative methods. Finally, compliance with primary frequency regulation (PFR) was verified in accordance with the Technical Procedure PR-21 related to spinning reserve, issued by the Peruvian Committee for Economic Operation of the System. Full article

Energy storage technology is crucial for addressing the intermittency of renewable energy sources and plays a key role in power systems and electronic devices. In the field of energy storage systems, multivalent vanadium-based oxides have attracted widespread attention. Among these, vanadium dioxide (VO₂) is distinguished by its key advantages, including high theoretical capacity, low cost, and strong structural designability. The diverse crystalline structures and plentiful natural reserves of VO₂ offer a favorable foundation for facilitating charge transfer and regulating storage behavior during energy storage processes. This mini review provides an overview of the latest progress in VO₂-based materials for energy storage applications, specifically highlighting their roles in lithium-ion batteries, zinc-ion batteries, photoassisted batteries, and supercapacitors. Particular attention is given to their electrochemical properties, structural integrity, and prospects for development. Additionally, it explores future development directions to offer theoretical insights and strategic guidance for ongoing research and industrial application of VO₂. Full article

Introduction: Postoperative atrial fibrillation (POAF) is a common complication after heart surgery, adversely impacting clinical outcomes and healthcare costs. Little is known about the dynamics of nucleotide metabolism associated with the development of POAF at a ventricular level. We conducted a post hoc trial analysis to investigate the changes in ventricular adenine nucleotides and the clinical predictors associated with the development of AF. Methods: Using data from a randomised trial, we analysed ATP/ADP, ATP/AMP, and energy charges in left and right ventricular biopsies of patients who developed AF compared to non-AF patients. A logistic regression model was used to understand the predictors associated with the development of atrial fibrillation in this cohort. Results: We analysed adenine nucleotide levels available in 88 patients who underwent coronary artery bypass grafting (CABG) (n = 65) and aortic valve replacement (AVR) (n = 23), out of which 27 (31%) developed a new onset of AF. Seventeen (43.4%) patients in the CABG group and ten (26.15%) in the AVR group developed AF. The patients who developed postoperative AF had longer cross-clamp times for CABG (p = 0.013) and AVR (p = 0.002). The most significant predictors for AF development were age (p = 0.003) and cross-clamp time (p = 0.012). In patients undergoing CABG who developed AF, we found a significant drop in post-reperfusion ATP/ADP and ATP/AMP ratios compared to pre-reperfusion. This was not significant for the patients who underwent AVR. Furthermore, the patients who underwent CABG and developed AF had higher pre- and post-reperfusion ATP/ADP ratios and energy charges than non-AF patients, suggesting a higher reserve of cardiac nucleotides. Conclusions: The development of postoperative atrial fibrillation is associated with intraoperative changes in the ventricular adenine nucleotide metabolism of patients undergoing CABG. In the clinical analysis, age and cross-clamp time were significant predictors of AF development. Full article

A lime–sand island–reef formation has a dual structure consisting of an overlying loose or weakly consolidated coral sand (CS) layer and an underlying reef limestone layer. The coral sand layer is the sole carrier of the underground freshwater lens in the lime–sand island–reef, and it differs in terms of its hydraulic properties from common terrigenous quartz sand (QS). This study investigated the mechanism of freshwater lens formation, dominated by solute dispersion, combining multi-scale experiments and numerical simulations (GMS) to reveal the control mechanisms behind the dispersion properties of coral sand and their role in freshwater lens formation. Firstly, the dispersion test and microscopic characterization revealed the key differences in coral sand in terms of its roundness, roughness, particle charge, and surface hydrophilicity. Accordingly, a hierarchical conversion model for the coral sand–quartz sand coefficient of dispersion (COD) was established (R² > 0.99). Further, combining this with numerical simulation in GMS revealed that the response pattern of the coefficient of dispersion to key parameters of freshwater lens development is as follows: freshwater appearance time > steady-state freshwater body thickness > steady-state freshwater reserve > lens stabilization time. These results clarify the development mechanism and formation process behind freshwater lenses on island reefs, from the micro to the macro scale, and provide a scientific basis for optimizing the protection of freshwater resources in coral islands and guiding the construction of artificial islands. Full article

Although lithium-ion batteries are considered an ideal postulant for renewable energy harvesting, storage and applications, these batteries show promising performance; however, at the same time, these harvesting devices suffer from some major limitations, including scarce lithium resources, high cost, toxicity and safety concerns. Potassium ion batteries (PIBs) can be proven a favorable alternative to metal ion batteries because of their widespread potassium reserves, low costs and enhanced protection against sparks. In this study, DFT simulations were employed using the B3LYP/6-311⁺⁺g(d p) method to explore the application of graphene and its doped variants (N,B,P-graphene) as potential anode materials for PIBs. Various key parameters such as adsorption energy, Gibbs free energy, molecular orbital energies, non-covalent interactions, cell voltage, electron density distribution and density of states were computed as a means to evaluate the suitability of materials for PIB applications. Among the four structures, nitrogen- and phosphorus-doped graphene exhibited negative Gibbs free energy values of −0.020056 and −0.021117 hartree, indicating the thermodynamic favorability of charge transfer processes. Doping graphene with nitrogen and phosphorus decreases the HOMO-LUMO gap energy, facilitating efficient ion storage and charge transport. The doping of nitrogen and phosphorus increases the cell voltage from −1.05 V to 0.54 V and 0.57 V, respectively, while boron doping decreases the cell voltage. The cell voltage produced by graphene and its doped variants in potassium ion batteries has the following order: P-graphene (0.57 V) > N-graphene (0.54 V) > graphene (−1.05 V) > B-graphene (−1.54 V). This study illustrates how nitrogen- and phosphorus-doped graphene can be used as a propitious anode electrode for PIBs. Full article

The development of electric mobility offers new perspectives in the energy sector and improves resource efficiency and sustainability. This paper proposes a new strategy for synchronizing the energy requirements of home, commercial, and vehicle mobility, with a focus on the batteries of electric cars. In particular, this paper describes the coordination between a battery management algorithm that optimally assigns its capacity so that at least a part is reserved for mobility and a vehicle-to-building (V2B) service algorithm that uses a share of EV battery energy to improve user participation in renewable energy exploitation at home and at work. The system offers the user the choice of always maintaining a minimum charge for mobility or providing more flexible use of energy for business needs while maintaining established vehicle autonomy. Suitable management at home and at work allows always charging the vehicle to the required level of charge with renewable power excess, highlighting how the cooperation of home and work charging may provide novel frameworks for a smarter and more sustainable integration of electric mobility, reducing energy consumption and providing more effective energy management. The effectiveness of the proposed solution is demonstrated in a realistic configuration with real data and an experimental setup. Full article