Search Results (1,417)

To address the lack of inertia in full-power converter wind turbines and the inability of existing mechanical speed regulation technologies to achieve power smoothing without converters, this paper proposes a novel hybrid wind energy storage system integrating a Continuously Variable Transmission (CVT) and an electromechanical flywheel. This system establishes a cascaded topology featuring “CVT-based source-side speed regulation and electromechanical flywheel-based terminal power stabilization.” By utilizing the CVT for speed decoupling and introducing the flywheel via a planetary differential branch, the system retains physical inertia by eliminating large-capacity converters and overcomes the bottleneck of traditional mechanical transmissions, which struggle to balance constant frequency with stable power output. Simulation results demonstrate that the proposed system reduces the active power fluctuation range by 47.60% compared to the raw wind power capture. Moreover, the required capacity of the auxiliary motor is only about 15% of the rated power, reducing the reliance on power electronic converters by approximately 85% compared to full-power converter systems. Furthermore, during a grid voltage dip of 0.6 p.u., the system restricts rotor speed fluctuations to within 0.5%, significantly enhancing Low Voltage Ride-Through (LVRT) capability. Full article

It has become an urgent problem to deal with the uncertain influence caused by the high proportion of new energy connected to the grid and improve the consumption level of new energy in the background of the new power system. Based on the constantly updated predicted information of wind power, photovoltaic power, and load power, a multi-time-scale coordinated scheduling model for a multi-energy complementary power generation system integrated with a high proportion of new energy, including an electricity-to-hydrogen system, is proposed. The complex nonlinear factors in the operation cost of thermal power and pumped storage power generation were converted into a mixed integer linear model for solving the problem. The results show that the participation of the pumped storage units in the power grid dispatching can effectively alleviate the peak regulation and reserve pressure of the thermal power units. The electricity-to-hydrogen system has the advantages of fast power response and a wide adjustment range. Pumped storage plant, together with the electricity-to-hydrogen system, enhances the flexible adjustment ability of the power grid on the power side and the load side, respectively. The coordinated dispatch of the two can take into account the safety and economy of the power grid operation, maintain the power balance of the high-proportion new energy power generation system, and effectively reduce green power abandonment and improve the consumption level of clean energy. Full article

This paper addresses the critical challenge of multi-objective optimization in residential Home Energy Management Systems (HEMS) by proposing a novel framework based on an Improved Multi-Agent Proximal Policy Optimization (MAPPO) algorithm. The study specifically targets the low convergence efficiency of Multi-Agent Deep Reinforcement Learning (MADRL) for coupled Battery Energy Storage System (BESS) and Air Source Heat Pump (ASHP) operation. The framework synergistically integrates an action constraint projection mechanism with an economic-performance-driven dynamic learning rate modulation strategy, thereby significantly enhancing learning stability. Simulation results demonstrate that the algorithm improves training convergence speed by 35–45% compared to standard MAPPO. Economically, it delivers a cumulative cost reduction of 15.77% against rule-based baselines, outperforming both Independent Proximal Policy Optimization (IPPO) and standard MAPPO benchmarks. Furthermore, the method maximizes renewable energy utilization, achieving nearly 100% photovoltaic self-consumption under favorable conditions while ensuring robustness in extreme scenarios. Temporal analysis reveals the agents’ capacity for anticipatory decision-making, effectively learning correlations among generation, pricing, and demand to achieve seamless seasonal adaptability. These findings validate the superior performance of the proposed centralized training architecture, providing a robust solution for complex residential energy management. Full article

LoRaWAN is widely used for IoT environmental monitoring, but its lightweight security mechanisms leave the physical layer vulnerable to availability attacks such as jamming and battery-depletion. These risks are particularly critical in mission-critical environmental monitoring systems. This paper proposes a multi-attribute physical-layer authentication (PLA) framework that supports uplink legitimacy assessment by jointly exploiting radio, energy, and temporal attributes, specifically RSSI, altitude, battery_level, battery_drop_speed, event_step, and time_rank. Using publicly available Brno LoRaWAN traces, we construct a device-aware semi-synthetic dataset comprising 230,296 records from 1921 devices over 13.68 days, augmented with energy, spatial, and temporal attributes and injected with controlled jamming and battery-depletion anomalies. Five classifiers (Random Forest, Multi-Layer Perceptron, XGBoost, Logistic Regression, and K-Nearest Neighbours) are evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. The Multi-Layer Perceptron achieves the strongest detection performance (F1-score = 0.8260, AUC-ROC = 0.8953), with Random Forest performing comparably. Deployment-oriented computational profiling shows that lightweight models such as Logistic Regression and the MLP achieve near-instantaneous prediction latency (below 2 µs per sample) with minimal CPU overhead, while tree-based models incur higher training and storage costs but remain feasible for Network Server-side deployment. Full article

Cystinosis is a rare lysosomal storage disorder characterized by defective cystine transport and progressive multi-organ damage, with the kidney being the primary site of pathology. In addition to the traditional perspective on lysosomal dysfunction, recent studies have demonstrated that cystinosis exerts a substantial impact on cellular energy metabolism, with a particular emphasis on oxidative pathways. Mitochondria, the central hub of ATP production, exhibit structural abnormalities, impaired oxidative phosphorylation, and increased reactive oxygen species. These factors contribute to proximal tubular cell failure and systemic complications. This review highlights the critical role of energy metabolism in cystinosis and supports the emerging idea of organelle communication. A mounting body of evidence points to a robust functional and physical association between lysosomes and mitochondria, facilitated by membrane contact sites, vesicular trafficking, and signaling networks that modulate nutrient sensing, autophagy, and redox balance. Disruption of these interactions in cystinosis leads to defective mitophagy, accumulation of damaged mitochondria, and exacerbation of oxidative stress, creating a vicious cycle of energy failure and cellular injury. A comprehensive understanding of these mechanisms has the potential to reveal novel therapeutic avenues that extend beyond the scope of cysteamine, encompassing strategies that target mitochondrial health, enhance autophagy, and restore lysosome–mitochondria communication. Full article

The development of integrated renewable energy and high-density thermal energy storage systems has been fueled by the need for environmentally friendly heating and cooling in buildings. In this paper, MiniStor, a hybrid thermochemical and phase-change material storage system, is presented. It is equipped with a heat pump, advanced electronics-enabled control, photovoltaic–thermal panels, and flat-plate solar collectors. To optimize energy flows, regulate charging and discharging cycles, and maintain operational stability under fluctuating solar irradiance and building loads, the system utilizes state-of-the-art power electronics, variable-frequency drives and modular multi-level converters. The hybrid storage is safely, reliably, and efficiently integrated with building HVAC requirements owing to a multi-layer control architecture that is implemented via Internet of Things and SCADA platforms that allow for real-time monitoring, predictive operation, and fault detection. Data from the MiniStor prototype demonstrate effective thermal–electrical coordination, controlled energy consumption, and high responsiveness to dynamic environmental and demand conditions. The findings highlight the vital role that digital control, modern electronics, and Internet of Things-enabled supervision play in connecting small, high-density thermal storage and renewable energy generation. This strategy demonstrates the promise of electronics-driven integration for next-generation renewable energy solutions and provides a scalable route toward intelligent, robust, and effective building energy systems. Full article

To mitigate the mismatch between fluctuating renewable generation and load demand in highway service area multi-microgrid systems, this paper develops a day-ahead capacity optimization model based on the coordinated operation of fixed and mobile energy storage. A ring-structured multi-microgrid architecture is established, incorporating a “one-to-many” interaction mode of mobile storage stations. A coordinated control strategy is then proposed to enable flexible power dispatch and resource sharing among microgrids. The objective function minimizes both investment and operating costs of energy storage on a day-ahead timescale, and the model is solved using an optimization approach. Case study results demonstrate that introducing mobile energy storage significantly reduces the required capacity of local fixed storage, enhances energy interconnection among microgrids, and improves overall storage utilization and system economy. Full article

Distributed energy systems (DESs) are crucial for renewable deployment, but decentralised generation substantially increases flexibility requirements. Flexibility is framed as a system property that emerges from the coordinated operation of demand, storage and dispatchable generation across multi-energy carriers. Demand response schemes and demand-side management can provide flexibility, but their effective potential is constrained by user participation. Sector-coupling strategies and energy storage systems enable temporal and cross-sector decoupling between renewable generation and demand. Electrochemical batteries are technically mature and well suited for short-term balancing, but costs and environmental impacts are significant. Power-to-Heat with heat pumps and thermal energy storage is a cost-effective solution, especially when combined with low-temperature district heating. Electric vehicles, when operated under smart-charging and vehicle-to-grid schemes, can shift large charging demands feeding energy into the grid, facing battery degradation and infrastructure costs. Power-to-Gas and Power-to-X use hydrogen and electrofuels as long-term storage but are penalised by low round-trip efficiencies and significant capital costs if power-to-power with fuel cells is applied. On the supply side, micro-CHP can provide dispatchable capacity when fuelled by renewable fuels and combined with seasonal storage. Costs and efficiencies are strongly scale-dependent, and markets, regulation, digital infrastructure and social acceptance are key enablers of flexibility. Full article

Machine learning (ML) is becoming a key enabler in building energy management systems (BEMS), yet most existing reviews focus on simulations and fail to reflect the realities of real-world deployment. In response to this limitation, the present work aims to present a systematic review dedicated entirely to experimental, field-tested applications of ML in BEMS, covering systems such as Heating, Ventilation & Air-conditioning (HVAC), Renewable Energy Systems (RES), Energy Storage Systems (ESS), Ground Heat Pumps (GHP), Domestic Hot Water (DHW), Electric Vehicle Charging (EVCS), and Lighting Systems (LS). A total of 73 real-world deployments are analyzed, featuring techniques like Model Predictive Control (MPC), Artificial Neural Networks (ANNs), Reinforcement Learning (RL), Fuzzy Logic Control (FLC), metaheuristics, and hybrid approaches. In order to cover both methodological and practical aspects, and properly identify trends and potential challenges in the field, current review uses a unified framework: On the methodological side, it examines key-attributes such as algorithm design, agent architectures, data requirements, baselines, and performance metrics. From a practical standpoint, the study focuses on building typologies, deployment architectures, zones scalability, climate, location, and experimental duration. In this context, the current effort offers a holistic overview of the scientific landscape, outlining key trends and challenges in real-world machine learning applications for BEMS research. By focusing exclusively on real-world implementations, this study offers an evidence-based understanding of the strengths, limitations, and future potential of ML in building energy control—providing actionable insights for researchers, practitioners, and policymakers working toward smarter, grid-responsive buildings. Findings reveal a maturing field with clear trends: MPC remains the most deployment-ready, ANNs provide efficient forecasting capabilities, RL is gaining traction through safer offline–online learning strategies, FLC offers simplicity and interpretability, and hybrid methods show strong performance in multi-energy setups. Full article

The rapid growth and seasonal availability of agricultural materials, such as straws, stalks, husks, shells, and processing wastes, present both a disposal challenge and an opportunity for renewable fuel production. Solar-assisted thermochemical conversion, such as solar-driven pyrolysis, gasification, and hydrothermal routes, provides a pathway to produce bio-oils, syngas, and upgraded chars with substantially reduced fossil energy inputs compared to conventional thermal systems. Recent experimental research and plant-level techno-economic studies suggest that integrating concentrated solar thermal (CSP) collectors, falling particle receivers, or solar microwave hybrid heating with thermochemical reactors can reduce fossil auxiliary energy demand and enhance life-cycle greenhouse gas (GHG) performance. The primary challenges are operational intermittency and the capital costs of solar collectors. Alongside, machine learning (ML) and AI tools (surrogate models, Bayesian optimization, physics-informed neural networks) are accelerating feedstock screening, process control, and multi-objective optimization, significantly reducing experimental burden and improving the predictability of yields and emissions. This review presents recent experimental, modeling, and techno-economic literature to propose a unified classification of feedstocks, solar-integration modes, and AI roles. It reveals urgent research needs for standardized AI-ready datasets, long-term field demonstrations with thermal storage (e.g., integrating PCM), hybrid physics-ML models for interpretability, and region-specific TEA/LCA frameworks, which are most strongly recommended. Data’s reporting metrics and a reproducible dataset template are provided to accelerate translation from laboratory research to farm-level deployment. Full article

This paper proposes a unified state-of-health (SoH) predictive energy management and adaptive grid-forming (GFM) control framework for battery energy storage systems, addressing the conflict between battery lifetime degradation and dynamic stability under grid-support operation. A composite degradation model is incorporated into a multi-timescale EMS to anticipate aging trends, while real-time virtual inertia, damping, and impedance are adjusted according to instantaneous SoH. Simulation results demonstrate that, compared with conventional non-SoH-aware control, the proposed method reduces transient overshoot by up to 32%, shortens settling time by 25–40%, and lowers peak battery current stress by 12–23% under aged (60% SoH) conditions. Moreover, the unified framework maintains consistent damping performance across different aging stages, whereas traditional approaches exhibit significant degradation. These quantitative improvements confirm that jointly embedding SoH prediction into both dispatch scheduling and GFM control can effectively enhance transient performance while extending battery lifetime. Full article

The large-scale integration of distributed photovoltaics (DPV) and their inherent uncertainties have significantly increased the operational risks of distribution networks. Moreover, frequent outages caused by extreme events further impose substantial losses on these networks, highlighting the urgent need to enhance their disaster resilience and load-supply capabilities. To address these challenges, this paper proposes an energy storage allocation method that simultaneously considers economic performance and comprehensive vulnerability. First, a vulnerability assessment framework for distribution networks is established from both pre-disaster and post-disaster perspectives. In the pre-disaster stage, an improved electrical betweenness index, voltage deviation index, and network-balance index are employed to identify weak lines and nodes. In the post-disaster stage, based on the identified weak components, two types of scenarios, namely random line failures and worst-case failures, are constructed to emulate extreme events, and an enhanced network supply efficiency index is developed to quantitatively evaluate the network’s recovery capability. Subsequently, a multi-objective optimal allocation model for energy storage is formulated with economic cost and comprehensive vulnerability as objective functions, and an Enhanced Beluga Whale Optimization algorithm is adopted to obtain the optimal siting and sizing of energy storage systems. Case studies on an improved IEEE 33-bus distribution system show that, compared with the no-ESS scheme, the proposed plan yields about a 66.4% reduction in network loss cost, around 22% improvement in average voltage deviation, and a roughly 10% reduction in the comprehensive vulnerability index under normal operation. Under random and targeted line outage scenarios, the proposed scheme also achieves the highest area under curve and average network effectiveness indices and the lowest performance volatility among the benchmark strategies. These results demonstrate that, for the tested IEEE 33-bus system, the vulnerability-driven ESS planning framework can markedly enhance both economic efficiency and resilience to extreme events. Full article

Aiming at multi-agent interest demands and environmental benefits, a distributionally robust game-theoretic optimization algorithm based on a green certificate–carbon trading mechanism is proposed for uncertain microgrids. At first, correlated wind–solar scenarios are generated using Kernel Density Estimation and copula theory and the probability distribution ambiguity set is constructed combining 1-norm and $\infty$-norm metrics. Subsequently, with gas turbines, renewable energy power producers, and an energy storage unit as game participants, a two-stage distributionally robust game-theoretic optimization scheduling model is established for microgrids considering wind and solar correlation. The algorithm is constructed by integrating a non-cooperative dynamic game with complete information and distributionally robust optimization. It minimizes a linear objective subject to linear matrix inequality (LMI) constraints and adopts the column and constraint generation (C&CG) algorithm to determine the optimal output for each device within the microgrid to enhance its overall system performance. This method ultimately yields a scheduling solution that achieves both equilibrium among multiple stakeholders’ interests and robustness. The simulation result verifies the effectiveness of the proposed method. Full article

With the increasing penetration of distributed energy resources (DERs) and the rapid development of active distribution networks, the traditional centrally controlled operation mode can no longer meet the flexibility and autonomy requirements under the multi-dimensional coupling of sources, networks, loads, and storage. To achieve regional energy self-balancing and autonomous operation, this paper proposes a partitioned configuration method for energy storage systems (ESSs) in energy-autonomous distribution networks based on autonomous unit division. First, the concept and hierarchical structure of the energy-autonomous distribution network and its autonomous units are clarified, identifying autonomous units as the fundamental carriers of the network’s autonomy. Then, following the principle of “tight coupling within units and loose coupling between units,” a comprehensive indicator system for autonomous unit division is constructed from three aspects: electrical modularity, active power balance, and reactive power balance. An improved genetic algorithm is applied to optimize the division results. Furthermore, based on the obtained division, an ESS partitioned configuration model is developed with the objective of minimizing the total cost, considering the investment and operation costs of ESSs, power purchase cost from the main grid, PV curtailment losses, and network loss cost. The model is solved using the CPLEX solver. Finally, a case study on a typical multi-substation, multi-feeder distribution network verifies the effectiveness of the proposed approach. The results demonstrate that the proposed model effectively improves voltage quality while reducing the total cost by 20.89%, ensuring optimal economic performance of storage configuration and enhancing the autonomy of EADNs. Full article

The photovoltaic–energy storage–direct current–flexibility (PEDF) system provides an integrated pathway for low-carbon and intelligent building energy management by combining on-site PV generation, electrical storage, DC distribution, and flexible load control. This paper reviews recent advances in these four modules and synthesizes quantified benefits reported in real-world deployments. Building-scale systems typically integrate 20–150 kW PV and achieve ~10–18% energy-efficiency gains enabled by DC distribution. Industrial-park deployments scale to 500 kW–5 MW, with renewable self-consumption often exceeding 50% and CO₂ emissions reductions of ~40–50%. Community-level setups commonly report 10–15% efficiency gains and annual CO₂ reductions on the order of tens to hundreds of tons. Key barriers to large-scale adoption are also discussed, including multi-energy coordination complexity, high upfront costs and uncertain business models, limited user engagement, and gaps in interoperability standards and supportive policies. Finally, we outline research and deployment priorities toward open and interoperable PEDF architectures that support cross-sector integration and accelerate the transition toward carbon-neutral (and potentially carbon-negative) built environments. Full article