With the rapid growth of renewable energy deployment, multi-microgrid systems have become crucial for improving energy flexibility and reliability. However, challenges such as renewable intermittency and energy imbalance necessitate more advanced storage solutions. This paper proposes a shared energy storage (SES) allocation and scheduling strategy tailored for multi-microgrid environments. A bi-level optimization framework based on Mixed-Integer Linear Programming (MILP) is developed, aiming to simultaneously minimise the annual investment cost of SES and the operational costs of the microgrid cluster. The model incorporates renewable energy utilisation constraints and performs cost-benefit analysis under varying utilisation rates. Case studies demonstrate that the proposed approach significantly reduces wind and solar curtailment, enhances renewable energy consumption, and improves overall economic performance. The results validate the model's effectiveness and provide practical insights for the integrated planning and operation of SES in multi-microgrid systems.

In the context of high proportion of new energy integration and high proportion of power electronic equipment application, due to the grid connection of new energy generation through inverters with different control characteristics, the power system exhibits a regulatory form with the coexistence of grid-following (GFL) and grid-forming (GFM), thus forming a heterogeneous inverter paralleled system (HIPS). Its multi-time scale characteristics and complex interactions pose severe challenges to the stability of the power system. To address this issue, this paper first constructs a complete linear time-periodic (LTP) model of HIPS, and then analyzes the influence of key stabilizing factors on system stability. It provides a theoretical basis for suppressing oscillations, optimizing control parameters, and ensuring the reliable operation of different types of inverters in the power grid.

As distributed generation becomes more prevalent in the electric power system, it is essential to analyze the generation mix and associated technologies to ensure resilient and stable grid operation. In particular, the increased share of grid-following versus grid-forming generation can pose serious grid stability issues and needs to be carefully evaluated. This study employs the Modelica open-source modelling framework to conduct dynamic simulations on a medium-voltage distribution network model where demand is primarily served by local distributed generation. In order to increase scalability, the complexity of the distribution network model is reduced to an equivalent reduced-order model that preserves the dynamic behavior of the original detailed system. Various penetration scenarios of conventional grid-following converters versus distributed synchronous generators are investigated. The scenarios are examined during sudden load disruptions of varying magnitudes, similar to observed events such as the 2021 Texas power crisis or the 2003 East Coast blackout. The results reveal that, in scenarios with a high penetration of grid-following converters, grid stability is significantly compromised. In particular, penetrations greater than 80% lead to disconnection of all converters during severe load increases.

In order to achieve higher power generation efficiency, modern wind turbine blades are showing a clear trend towards larger size. The length of largest wind turbine blade has already exceeded 140 meters. The structural and aeroelastic response of long flexible wind turbine blade becomes nonlinear because of geometric nonlinearity from the large deformation, which will cause time-frequency vibration characteristics and pose challenges to the safety of wind turbine blade. To study the nonlinear characteristics of structural and aeroelastic response of large wind turbine blade, wavelet time-frequency analysis will be carried out based on the structural response from geometrically exact beam theory model (GEBT) and aeroelastic response from NREL FAST8 for IEA 22MW wind turbine. From the time-frequency analysis result, it can be found that the dominant frequency is changing periodically under large deflection condition, and the nonlinear structural coupling between torsion and deflection exhibit an obvious time-frequency characteristics.

Grid-forming inverters (GFIs) have emerged as key enablers for establishing and stabilizing power grid voltage in inverter-dominated grids. International activities increasingly emphasize verification-oriented specifications for GFIs to support secure integration. The recent FNN note on grid-forming inverters provides structured requirements and test considerations to define and prove grid-forming behavior. However, while core properties and current limiting are addressed, it offers limited guidance for transition process between current-limiting and normal operation. This work investigates a representative GFI employing a voltage phasor-based limiting mechanism. Based on the FNN note on grid-forming behavior, we performed further experimental tests transitioning the GFI between current-limiting and normal grid-forming operation. The test framework was extended to include fault ride-through (FRT) combined with islanding, which reveals the voltage source characteristics of the GFI during limiting conditions. The employed current-limiting approach demonstrates adequate transition performance across these test scenarios. Even though, further investigations and finetuning have been identified as necessary to achieve optimised release from current limiting mode.

The large-scale popularisation of electric vehicles (EVs) is confronted by two major challenges. Firstly, there is the need to reduce the carbon emissions of the transport sector. Secondly, there are fluctuations in grid load. This study proposes an experimental simulation of public data in the California region of the United States. The simulation is under the dual action of a reinforcement learning algorithm that optimises charging scheduling strategies in real time and a framework transaction mechanism that combines blockchain technology to build a transparent and trusted dynamic smart charging network. The experimental results demonstrate the efficacy of the framework in balancing the load on the power grid, enhancing the efficiency of distributed charging, and reducing costs and increasing efficiency (reducing carbon emissions by approximately 5%–15% in comparison with conventional scheduling) while ensuring data ownership. This study provides a robust theoretical foundation for the utilisation of reinforcement learning algorithms and blockchain technology to advance the transformation of intelligent transportation and ESG goals.

As part of the transition to an energy system with 100% renewable generation, renewable energy power plants must increasingly take over responsibility for system stability. A key element in ensuring system stability is grid forming control of generation and storage units. In Germany, scenarios predict a significant expansion of renewable energy generation and storage systems in distribution networks. Integration of grid-forming systems into distribution networks raises several open questions, among them are power system oscillations and temporary island operation: Grid forming inverters may unintentionally lead to oscillation issues like those found in traditional transmission systems. A study reveals that as the level of virtual inertia in the distribution grid increases, these oscillations may become more pronounced which highlights the importance of implementing effective damping measures. Temporary island operation: In principle, grid-forming inverters enable the stable continued operation of critical sub-grids after a fault-related disconnection from the supply grid. Both disconnection and interception of the grid, as well as resynchronization and reconnection of the sub-grid with active generators and consumers are simulated in a series of laboratory tests and the basic suitability of this technology and the necessary requirements for this application are explained.

This paper proposes an analysis of power distribution between onshore terminals during output fluctuations of offshore wind farms, considering power interchange from wind farms and transmission stability.

With the high penetration of renewable energy in modern power systems, the deployment of grid-forming converters (GFMCs) has increased significantly, owing to their inherent capabilities of voltage and frequency support. However, the high-order nonlinearity and mode-switching dynamics of GFMCs with current limiters pose enormous challenges to the modelling of multi-GFMC systems under faults. To address this problem, this paper investigates the aggregated modelling of multi-GFMC systems under current saturation caused by grid faults. By analyzing the key factors that affect the similarities of power angle curves among individuals, a coherency criterion is proposed to divide the converter group into distinct coherent groups. Under the premise of ensuring similar power dynamics among converters, the equivalent parameters of the coherent groups are calculated, and an equivalent aggregated model for the Multi-GFMC system under current saturation is established. Finally, a simulation model of 12 GFMCs integrated into the IEEE 39-bus power grid is developed in the MATLAB/Simulink platform to validate the accuracy of the proposed equivalent aggregated model.

In response to the demands of low-carbon economic operation and high-quality energy utilization for park-level integrated energy system clusters (PIES-C), this paper proposes a tri-stage collaborative optimization strategy for PIES-C based on an exergy-carbon spatiotemporal coupling pricing mechanism. In the first stage, a multi-objective collaborative optimization model for park-level PIES-C is established, categorizing energy interaction roles based on differences in energy purchase and sale. The second stage innovatively proposes an exergy-carbon spatiotemporal coupling pricing mechanism led by power-selling parks, which incorporates exergy value and carbon emission constraints to guide power-purchasing parks in optimizing their energy consumption behaviours. The third stage employs an improved Non-dominated Sorting Genetic Algorithm III (NSGA-III) to identify high-quality Pareto optimal solutions that balance the energy efficiency, low-carbon, and economic objectives of PIES-C. Within a typical daily operational cycle, this strategy achieves a 13.1% reduction in total system economic costs, a 10.2% decrease in carbon emission intensity, and an 8.7% improvement in energy conversion exergy efficiency. These results validate its engineering applicability in PIES-C and its significant contribution to the realization of energy-saving and low-carbon objectives.