Search Results (326)

With the continuous increase in the penetration of single-phase microgrids in low-voltage distribution networks (LVDNs), the phase asymmetry of source–load distribution has made the problem of three-phase imbalance increasingly prominent. To address this issue, this paper proposes an active–reactive power coordinated optimization model for distribution network–microgrid clusters considering three-phase imbalance mitigation. The model is formulated within a master–slave game framework: in the upper level, the distribution network acts as the leader, formulating time-of-use prices for active and reactive power based on day-ahead forecast data with the objective of minimizing operating costs. These price signals guide the flexible loads and photovoltaic (PV) inverters of the lower-level microgrids to participate in mitigating three-phase imbalance. In the lower level, each microgrid responds as the follower, minimizing its own operating cost by determining internal scheduling strategies and power exchange schemes with the distribution network. Finally, the resulting leader–follower game problem is transformed into a unified constrained model through strong duality theory and formulated as a mixed-integer second-order cone programming (MISOCP) problem, which is efficiently solved using the commercial solver Gurobi. Simulation results demonstrate that the proposed model fully exploits the reactive power compensation potential of PV inverters, significantly reducing the degree of three-phase imbalance. The maximum three-phase voltage unbalance factor decreases from 3.98% to 1.43%, corresponding to an overall reduction of 25.87%. The proposed coordinated optimization model achieves three-phase imbalance mitigation by leveraging existing resources without the need for additional control equipment, thereby enhancing power quality in the distribution network while ensuring economic efficiency of system operation. Full article

In DC microgrids, the combination of pulsed loads and renewable energy sources significantly impairs system stability, especially in highly dynamic operating environments. The resilience and reaction time of conventional proportional–integral (PI) controllers are often inadequate when managing the nonlinear dynamics of hybrid energy storage systems. This research suggests a frequency-decomposed fractional-order control strategy for stabilizing DC microgrids with solar, batteries, and supercapacitors. The control architecture divides system disturbances into low- and high-frequency components, assigning high-frequency compensation to the ultracapacitor (UC) and low-frequency regulation to the battery, while a fractional-order controller (FOC) enhances dynamic responsiveness and stability margins. The proposed approach is implemented and assessed in MATLAB/Simulink (version R2023a) using comparison simulations against a conventional PI-based control scheme under scenarios like pulsed load disturbances and fluctuations in renewable generation. Grey Wolf Optimizer (GWO), a metaheuristic optimization procedure, has been used to tune the parameters of the FOPI controller. The obtained results using the same conditions were compared using an optimal fractional-order PI controller (FOPI) and a conventional PI controller. The microgrid with the best FOPI controller was found to perform better than the one with the PI controller. Consequently, the objective function is reduced by 80% with the proposed optimal FOPI controller. The findings demonstrate that the proposed method significantly enhances DC bus voltage management, reduces overshoot and settling time, and lessens battery stress by effectively coordinating power sharing with the supercapacitor. Also, the robustness of the proposed controller against parameters variations has been proven. Full article

The concept of synergistic water resources, water environment, water ecology remediation, and restoration (3WRR) is essential for addressing the interlinked challenges of water scarcity, pollution, and ecological degradation. An intelligent platform of remediation and restoration project optimization was developed, integrating multi-source data fusion, a coupled air–land–water model, and dynamic decision optimization to support 3WRR in river basins. Applied to the Jinshan Lake Basin (JLB) in China’s Greater Bay Area, the platform assessed 894 scenarios encompassing diverse remediation and restoration plans, including point/non-point source reduction, sediment dredging, recycled water reuse, ecological water replenishment, and sluice gate control, accounting for inter-annual meteorological variability. The results reveal that source control alone (95% reduction in point and non-point loads) leads to limited improvement, achieving less than 2% compliance with Class IV water quality standards in tributaries. Integrated engineering–ecological interventions, combining sediment dredging with high-flow replenishment from the Xizhijiang River (26.1 m³/s), increases compliance days of Class IV water quality standards by 10–51 days. Concerning the lake plans, including sluice regulation and large-volume water exchange, the lake area met the Class IV standard for COD, NH₃-N, and TP by over 90%. The platform’s multi-objective optimization framework highlights that coordinated, multi-scale interventions substantially outperform isolated strategies in both effectiveness and sustainability. These findings provide a replicable and data-driven paradigm for 3WRR implementation in complex river–lake systems. The platform’s application and promotion in other watersheds worldwide will serve to enable the low-cost and high-efficiency management of watershed water environments. Full article

Nanoparticle superlattices, periodic assemblies of nanoscale building blocks, offer opportunities to tailor mechanical behavior through controlled lattice geometry and interparticle interactions. Here, classical molecular dynamics simulations were performed to investigate the compressive responses of copper nanoparticle superlattices with face-centered cubic (FCC), hexagonal close-packed (HCP), body-centered cubic (BCC), and simple cubic (SC) arrangements, as well as disordered assemblies. The flow stresses span 0.5–1.5 GPa. Among the studied configurations, the FCC and HCP superlattices exhibit the highest strengths (~1.5 GPa), followed by the disordered assembly (~1.0 GPa) and the SC structure (~0.8 GPa), while the BCC superlattice exhibits the lowest strength (~0.5 GPa), characterized by pronounced stress drops and recoveries resulting from interfacial sliding. Atomic-scale analyses reveal that plastic deformation is governed by two coupled geometric factors: (i) the number of interparticle contact patches, controlling the density of dislocation sources, and (ii) their orientation relative to the loading axis, which dictates stress transmission and slip activation. A combined parameter integrating particle coordination number and contact orientation is proposed to rationalize the structure-dependent strength, providing mechanistic insight into the deformation physics of metallic nanoparticle assemblies. Full article

Although extracellular vesicles (EVs) from mesenchymal stem cells have shown potential in skin wound repair, the diversity of EV sources and the optimization of delivery systems still need further exploration. This study is the first to demonstrate that extracellular vesicles from chemically induced mammary epithelial cells (CiMECs-EVs) possess distinct skin wound repair activity. To enhance the therapeutic efficacy of CiMECs-EVs and optimize their delivery efficiency, we innovatively combined them with a chitosan hydrogel to construct a composite repair system (CiMECs-EVs-chitosan hydrogel, CMECG). This system was then applied to a rat skin wound model. The results showed that CMECG significantly promoted the proliferation and migration of fibroblasts and mammary epithelial cells (MECs). In animal experiments, the relative wound closure efficiency of the control group was approximately 70% on day 14, while that of the CMECG group (loaded with 200 μg CiMECs-Exo) was enhanced to 90%, markedly accelerating the wound healing process. Histological analysis indicated that this system could effectively restore the structural continuity of various skin layers and significantly promote the synthesis and remodeling of collagen at the wound site. Mechanistically, the wound healing effect of CiMECs-EVs is closely associated with the endogenous miRNAs they encapsulate. These miRNAs can coordinately regulate cell proliferation, migration, and angiogenesis, modulate the inflammatory microenvironment, and inhibit excessive scar formation—thus regulating the entire repair process. This process involves multiple wound healing-related signaling pathways, including MAPK, PI3K-Akt, FoxO, TGF-β, and JAK-STAT. In summary, this study successfully constructed a novel EV-chitosan hydrogel repair system. This system is expected to provide an effective and innovative EV-based therapeutic strategy for the clinical treatment of skin wound repair. Full article

In response to the challenges faced by high-energy-consuming enterprises in utilizing renewable energy and implementing low-carbon operations, this paper proposes a multi-objective optimization strategy based on source–storage–load collaborative scheduling. The strategy establishes a refined model of aluminum electrolysis load, thoroughly considering the coupling relationship between temperature, production output, and power consumption. Additionally, it develops a dynamic coupling model between multi-functional crane loads and aluminum electrolysis production to reveal the influence mechanism of auxiliary equipment on the main production process. Based on this foundation, this paper constructs a multi-objective optimization model that targets the minimization of operating costs, the minimization of carbon emissions, and the maximization of the renewable energy consumption rate. An improved heuristic intelligent optimization algorithm is employed to solve the model. The simulation results demonstrate that, under a renewable energy penetration of 67.8%, the proposed multi-objective optimization strategy achieves a maximum reduction in carbon emissions of 1677.35 t and an increase in renewable energy consumption rate of 12.11%, compared to the conventional single-objective economic optimization approach, while ensuring the stability of aluminum electrolysis production. Furthermore, when the renewable energy penetration is increased to 76.2%, the maximum reduction in carbon emissions reaches 8260.97 t, and the renewable energy consumption rate is improved by 18.86%. Full article

Unplanned islanding and off-grid issues of photovoltaic (PV) power stations caused by tie-line faults have seriously undermined the power supply reliability and operational stability of PV plants. Furthermore, it takes a relatively long time to restore normal operation after an off-grid event, leading to substantial power losses. To address this problem, this paper proposes a tie-line fault ride-through control strategy based on the coordinated control of on-site energy storage units. After a fault on the tie-line occurs, the control mode of PV inverters is switched to achieve source–load balance, and the control mode of energy storage inverters is switched to VF control mode, which supports the stability of voltage and frequency in the islanded system. Subsequently, the strategy coordinates with the tie-line recloser device to perform synchronous checking and grid reconnection. Simulation results show that, for transient tie-line faults, the proposed method can achieve stable control of the islanded system and grid reconnection within 2 s after a fault on the tie-line occurs. It successfully realizes fault ride-through within the operation time limit of anti-islanding protection, effectively preventing the PV plant from disconnecting from the grid. Finally, a connection scheme for the control strategy of a typical PV plant is presented, providing technical reference for on-site engineering. Full article

With the integration of a large number of flexible distributed resources, microgrids have become an important form for supporting the coordinated operation of power sources, grids, loads, and energy storage. The flexibility provided by the point of common coupling is also a crucial regulating resource in power systems. However, due to the complex network constraints within microgrids, such as voltage security and branch capacity limitations, the flexibility of distributed resources cannot be fully reflected at the point of common coupling. Moreover, the flexibility that can be provided externally by different network reconfiguration strategies shows significant differences. Therefore, this paper focuses on optimizing reconfiguration strategies to enhance grid-connected flexibility. Firstly, the representation methods of grid-connected power flexibility and voltage regulation flexibility based on aggregation are introduced. Next, a two-stage robust optimization model aimed at maximizing grid-connected power flexibility is constructed, which comprehensively considers the aggregation of distributed resource flexibility and reconfiguration constraints. The objective is to maximize the grid-connected power flexibility of the small-micro parks. In the first stage of the model, the topology of the small-micro parks is optimized, and the maximum flexibility of all distributed resources is aggregated at the PCC. In the second stage, the feasibility of the solution for the PCC flexible operation range obtained in the first stage is verified. Subsequently, based on strong duality theory and using the column-and-constraint generation algorithm, the model is effectively solved. Case studies show that the proposed method can fully exploit the flexibility of distributed resources through reconfiguration, thereby significantly enhancing the power flexibility and voltage support capability of the small-micro parks network at the PCC. Full article

One of the main challenges faced by DC microgrid (DCMG) is their low inertia, which leads to rapid and significant voltage fluctuations during load or generation changes. These fluctuations can negatively impact sensitive loads and protection devices. Previous studies have addressed this by enabling battery converters to mimic the behavior of synchronous generators (SGs), but this approach becomes ineffective when the converters or batteries reach their current or energy limits, leading to a loss of inertia and potential system instability. In interconnected multi-microgrid (MMG) systems, the presence of multiple batteries offers the potential to enhance system inertia, provided there is a coordinated control strategy. This research introduces a hierarchical control method that combines decentralized and centralized approaches. Decentralized control allows individual converters to emulate SG behavior, while the centralized control uses Internet of Things (IoT) technology to enable real-time coordination among all Energy Storage Units (ESUs). This coordination improves inertia across the DCMMG system, enhances energy management, and strengthens overall system stability. IoT integration ensures real-time data exchange, monitoring, and collaborative decision-making. The proposed scheme is validated through MATLAB simulations, with results confirming its effectiveness in improving inertial response and supporting the integration of renewable energy sources within DCMMGs. Full article

To address the challenge of coordinating economic cost control and low-carbon objectives in microgrid scheduling, while overcoming the performance limitations of the traditional Zebra Optimization Algorithm (ZOA) in complex problems, this paper proposes a Synergistic Zebra Optimization Algorithm (SZOA) and integrates it with innovative management concepts to enhance the microgrid scheduling process. The SZOA incorporates three core strategies: a multi-population cooperative search mechanism to strengthen global exploration, a vertical crossover–mutation strategy to meet high-dimensional scheduling requirements, and a leader-guided boundary control strategy to ensure variable feasibility. These strategies not only improve algorithmic performance but also provide technical support for innovative management in microgrid scheduling. Extensive experiments on the CEC2017 (d = 30) and CEC2022 (d = 10, 20) benchmark sets demonstrate that the SZOA achieves higher optimization accuracy and stability compared with those of nine state-of-the-art algorithms, including IAGWO and EWOA. Friedman tests further confirm its superiority, with the best average rankings of 1.20 for CEC2017 and 1.08/1.25 for CEC2022 (d = 10, 20). To validate practical applicability, the SZOA is applied to grid-connected microgrid scheduling, where the system model integrates renewable energy sources such as photovoltaic (PV) generation and wind turbines (WT); controllable sources including fuel cells (FC), microturbines (MT), and gas engines (GS); a battery (BT) storage unit; and the main grid. The optimization problem is formulated as a bi-objective model minimizing both economic costs—including fuel, operation, pollutant treatment, main-grid interactions, and imbalance penalties—and carbon emissions, subject to constraints on generation limits and storage state-of-charge safety ranges. Simulation results based on typical daily data from Guangdong, China, show that the optimized microgrid achieves a minimum operating cost of USD 5165.96, an average cost of USD 6853.07, and a standard deviation of only USD 448.53, consistently outperforming all comparison algorithms across economic indicators. Meanwhile, the SZOA dynamically coordinates power outputs: during the daytime, it maximizes PV utilization (with peak output near 35 kW) and WT contribution (30–40 kW), while reducing reliance on fossil-based units such as FC and MT; at night, BT discharges (−20 to −30 kW) to cover load deficits, thereby lowering fossil fuel consumption and pollutant emissions. Overall, the SZOA effectively realizes the synergy of “economic efficiency and low-carbon operation”, offering a reliable and practical technical solution for innovative management and sustainable operation of microgrid scheduling. Full article

The increasing penetration of renewable energy presents substantial challenges to frequency stability, particularly in low-inertia microgrids. This study introduces an agent-based microgrid model that integrates generators, loads, an energy storage system (ESS), and renewable sources, mathematically formalized through the discrete-event system specification (DEVS) to ensure both structural clarity and extensibility. To dynamically simulate power system behavior, the model incorporates multiple control strategies—including ESS scheduling, automatic generation control (AGC), predictive AGC, and grid-forming (GFM) inverter control—each posed as an mathematically defined control problem. Simulations on the IEEE 13-bus system demonstrates that the coordinated operation of ESS, GFM, and the proposed strategies markedly enhances frequency stability, reducing frequency peaks by 1.14, 1.14, and 0.72 Hz, and shortening the average recovery time by 9.05, 0.15, and 2.58 min, respectively. Collectively, the model provides a systematic representation of grid behavior and frequency regulation mechanisms under high renewable penetration, and establishes a rigorous mathematical framework for advancing microgrid research. Full article

With the rapid development of renewable energy and the continuous pursuit of efficient energy utilization, distributed photovoltaic power generation has been widely used in village-level microgrids. As a key platform connecting distributed photovoltaics with users, energy storage systems play an important role in alleviating the imbalance between supply and demand in VMG. However, current energy storage systems rely heavily on lithium batteries, and their frequent charging and discharging processes lead to rapid lifespan decay. To solve this problem, this study proposes a hybrid energy storage system combining supercapacitors and lithium batteries for VMG, and designs a hybrid energy storage scheduling strategy to coordinate the “source–load–storage” resources in the microgrid, effectively cope with power supply fluctuations and slow down the life degradation of lithium batteries. In order to give full play to the active support ability of supercapacitors in suppressing grid voltage and frequency fluctuations, the scheduling optimization goal is set to maximize the sum of the virtual inertia time constants of the supercapacitor. In addition, in order to efficiently solve the high-complexity model, the reason for choosing the snow goose algorithm is that compared with the traditional mathematical programming methods, which are difficult to deal with large-scale uncertain systems, particle swarm optimization, and other meta-heuristic algorithms have insufficient convergence stability in complex nonlinear problems, SGA can balance global exploration and local development capabilities by simulating the migration behavior of snow geese. By improving the convergence effect of SGA and constructing a multi-objective SGA, the effectiveness of the new algorithm, strategy and model is finally verified through three cases, and the loss is reduced by 58.09%, VMG carbon emissions are reduced by 45.56%, and the loss of lithium battery is reduced by 40.49% after active support optimization, and the virtual energy inertia obtained by VMG from supercapacitors during the scheduling cycle reaches a total of 0.1931 s. Full article

Against the backdrop of an intensifying global climate change and energy crisis, energy system decarbonization constitutes a primary sector for carbon mitigation. Integrated Energy Systems (IES) of district heating systems (DHS), a critical component of district energy networks (DEN), enable energy cascade utilization and enhance renewable energy integration efficiency when coupled with incremental distribution networks (IDN). However, retrofitting coupled systems necessitates significant capital investment and sustained operational expenditures. To evaluate the economic and environmental benefits of system retrofitting and assess cross-sector coordinated optimization potential, this study develops a multi-objective optimization framework for IES transition planning of DHS. Using an operational DHS energy station as a case study, we establish multi-scenario retrofitting strategies and operational protocols with comprehensive feasibility assessments, incorporating sensitivity analysis of cross-sector optimization potential while evaluating how varying electricity-to-heat load ratios affect optimization performance. Results demonstrate that intelligent operation optimization is essential for coordinating multi-equipment operations and maximizing energy conservation. Significant long-term economic and carbon mitigation potential remains untapped in ground source heat pumps and combined cooling, heating, and power (CCHP) systems. Coordinated optimization with campus incremental distribution networks further enhances energy cascade utilization in urban energy systems. Full article

This paper proposes a novel hybrid control strategy integrating a Finite Control Set Model Predictive Controller (FCS-MPC) with a universal droop controller (UDC) for effective load power sharing in inverter-fed microgrids. Traditional droop-based methods, though widely adopted for their simplicity and decentralized nature, suffer from limitations such as steady-state inaccuracies and poor transient response, particularly under mismatched impedance conditions. To overcome these drawbacks, the proposed scheme incorporates detailed modeling of inverter and source dynamics within the predictive controller to enhance accuracy, stability, and response speed. The UDC complements the predictive framework by ensuring coordination among inverters with different impedance characteristics. Simulation results under various load disturbances demonstrate that the proposed approach significantly outperforms conventional PI-based droop control in terms of voltage and frequency regulation, transient stability, and balanced power sharing. The performance is further validated through real-time simulations, affirming the scheme’s potential for practical deployment in dynamic microgrid environments. Full article

This study investigates the primary frequency regulation dynamic characteristics of industrial steam extraction turbine units under deep peak regulation conditions. A high-fidelity integrated dynamic model was established, incorporating the governor system, steam turbine with extraction modules, and interconnected pipeline dynamics. Through comparative simulations and experimental validation, the model demonstrates high accuracy in replicating real-unit responses to frequency disturbances. For the power grid system in this study, the frequency disturbance mainly comes from three aspects: first, the power imbalance formed by the random mutation of the load side and the intermittence of new energy power generation; second, transformation of the energy structure directly reduces the available frequency modulation resources; third, the system-equivalent inertia collapse effect caused by the integration of high permeability new energy; the rotational inertia provided by the traditional synchronous unit is significantly reduced. In the cogeneration unit and its control system in Guangxi involved in this article, key findings reveal that increased peak regulation depth (30~50% rated power) exacerbates nonlinear fluctuations. This is due to boiler combustion stability thresholds and steam pressure variations. Key parameters—dead band, power limit, and droop coefficient—have coupled effects on performance. Specifically, too much dead band (>0.10 Hz) reduces sensitivity; likewise, too high a power limit (>4.44%) leads to overshoot and slow recovery. The robustness of parameter configurations is further validated under source-load random-intermittent coupling disturbances, highlighting enhanced anti-interference capability. By constructing a coordinated control model of primary frequency modulation, the regulation strategy of boiler and steam turbine linkage is studied, and the optimization interval of frequency modulation dead zone, adjustment coefficient, and frequency modulation limit parameters are quantified. Based on the sensitivity theory, the dynamic influence mechanism of the key control parameters in the main module is analyzed, and the degree of influence of each parameter on the frequency modulation performance is clarified. This research provides theoretical guidance for optimizing frequency regulation strategies in coal-fired units integrated with renewable energy systems. Full article