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Considering the high-randomness and the low-economic-benefit characteristics of the offshore wind-power-integrated multi-microgrid, a two-stage optimal scheduling method considering the uncertain power of source and load is proposed to improve the operation profits of offshore wind-power-integrated multi-microgrid. The proposed two-stage optimal scheduling method consists of a day-ahead stage and an hour-ahead stage. In the day-ahead stage, the proposed method is based on the forecast data of the wind power and the load demand, which considers the distribution characteristics of the prediction errors. A stochastic optimization model is established to determine the unit committee of the diesel generators and the state-of-charge of the battery storages, so as to maximize the expected daily operation income. A deterministic optimization model is established based on the decisions from the day-ahead optimization relying on the hour-ahead forecast data of the wind power output and load demand. By optimizing the power of the diesel generators, wind turbines and battery energy storages, the operation income of each hour is maximized. Finally, a simulation model is established to verify the proposed method based on the prediction data of sources and loads in wind-power-integrated multi-microgrid. The simulation results show that compared with the conventional schedule strategies, the proposed two-stage optimal scheduling method can achieve a higher income and a higher overall consumption rate of the wind power.

The dependence of wind power on the natural environment leads to volatility, which can cause hidden dangers to the safe and stable operation of the power grid. In this work, a deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected prediction network is proposed for the short-term prediction issue of wind power generation, and the deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected network is compared with five algorithms including long short-term memory network and NasNet. The dataset was collected in Natal. The six algorithms employed predicted the value of wind power for the coming day. Among all, the deep learning-based GoogLeNet embedded no-pooling dimension fully-connected network achieved the optimal prediction results and evaluation metrics. The percentage reduction of each metric value from the second smallest long short-term memory network for the deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected network is 27.0% for mean absolute error, 27.2% for mean absolute percentage error, 34.8% for mean squared error, 19.9% for root mean square error and 21.6% for symmetric mean absolute percentage error. [ABSTRACT FROM AUTHOR]

With the rise in the proportion of renewable energy and energy storage in modern power systems, the volatility of renewable energy and the increasing demand for loads pose a significant risk of congestion in transmission lines. Along with transmission congestion, prolonged heavy loads on transmission lines increase equipment failure rates, leading to a range of issues within the power system. This study proposes a scene clustering method for power system scheduling by leveraging the net load related with the load and renewable energy power outputs. Subsequently, a scheduling model and line load evaluation indexes were developed to analyze the line load rate of power systems with different renewable energy proportions. The simulation results indicate that the utilization rate of lines, the fluctuation rate of line load, the maximum line load, and heavy line load time increase as the installed proportion of renewable energy increases. Finally, a penalty term for heavy loads was incorporated into the objective function and methods of rescheduling and planning energy storage considering the heavy load penalty function are proposed. A case study validated the significant improvements in load management, achieving a reduction in heavy load time by approximately 30% and reducing transmission congestion by 20% under high-renewable-energy-penetration scenarios. These results illustrate the effectiveness of the heavy load cost function in enhancing system resilience and optimizing load distribution. [ABSTRACT FROM AUTHOR]

Battery Energy Storage Systems (BESSs) play a vital role in modern power grids by optimally dispatching energy according to the price signal. This paper proposes a reinforcement learning-based model that optimizes BESS scheduling with the proposed Q-learning algorithm combined with an epsilon-greedy strategy. The proposed epsilon-greedy strategy-based Q-learning algorithm can efficiently manage energy dispatching under uncertain price signals and multi-day operations without retraining. Simulations are conducted under different scenarios, considering electricity price fluctuations and battery aging conditions. Results show that the proposed algorithm demonstrates enhanced economic returns and adaptability compared to traditional methods, providing a practical solution for intelligent BESS scheduling that supports grid stability and the efficient use of renewable energy. [ABSTRACT FROM AUTHOR]

As the proportion of renewable energy in power system continues to increase, that power system will face the risk of a multi-time-scale supply and demand imbalance. The rational planning of energy storage facilities can achieve a dynamic time–delay balance between power system supply and demand. Based on this, and in order to realize the location and capacity optimization determination of multiple types of energy storage in power system, this paper proposes a collaborative optimization planning framework for multiple types of energy storage. The proposed planning framework is modelled as a two-stage MILP model based on scenarios via the stochastic optimization method. In the first stage, investment decisions are made for two types of energy storage: battery energy storage (short term) and hydrogen energy storage (long term). In the second stage, power system operation simulation is conducted based on typical scenarios. Finally, the progressive hedging (PH) algorithm is applied to realize the efficient solving of the proposed model. A modified IEEE 39-bus test system is used to verify the validity of the proposed multiple types of energy storage collaborative optimization planning model and PH algorithm. [ABSTRACT FROM AUTHOR]

With the increasing integration of stochastic sources and loads, ensuring the flexibility of AC/DC hybrid distribution networks has become a pressing challenge. This paper aims to enhance the operational flexibility of AC/DC hybrid distribution networks by proposing a flexibility-oriented optimization framework that addresses the growing uncertainties. Notably, a comprehensive evaluation method for operational flexibility assessment is first established. Based on this, this paper further proposes a flexibility-oriented operation optimization model using the distributionally robust chance-constrained (DRCC) method. A customized solution method utilizing second-order cone relaxation and sample average approximation (SAA) is also introduced. The results of case studies indicate that the flexibility of AC/DC hybrid distribution networks is enhanced through sharing energy storage among multiple feeders, adaptive reactive power regulation using soft open points (SOPs) and static var compensators (SVCs), and power transfer between feeders via SOPs. [ABSTRACT FROM AUTHOR]

This paper focuses on the evaluation of the operational effect of a pumped storage plant in a new power system. An evaluation index system is established by selecting key indicators from the four benefit dimensions of system economy, low carbon, flexibility, and reliability. The evaluation criteria are based on the values of indexes for pumped storage plants that have already been put into operation. Using this method, the operational effect of pumped storage plants with different installed capacities, regulation durations, and conversion efficiencies are comprehensively evaluated and analyzed. The calculation results show that the operation effect of a pumped storage plant with high regulation performance and high comprehensive conversion efficiency is better, indicating that the established index system and evaluation method can comprehensively and truly reflect the positive benefits brought by a pumped storage plant to a new power system. This study can provide a practical reference for the early planning and decision making of pumped storage in a new power system. [ABSTRACT FROM AUTHOR]

Energy loss in perovskite grain boundaries (GBs) is a primary limitation toward high‐efficiency perovskite solar cells (PSCs). Two critical strategies to address this issue are high‐quality crystallization and passivation of GBs. However, the established methods are generally carried out discretely due to the complicated mechanisms of grain growth and defect formation. In this study, a combined method is proposed by introducing 3,4,5‐Trifluoroaniline iodide (TFAI) into the perovskite precursor. The TFAI triggers the union of nano‐sized colloids into microclusters and facilitates the complete phase transition of α‐FAPbI3 at room temperature. The controlled chemical reactivity and strong steric hindrance effect enable the fixed location of TFAI and suppress defects at GBs. This combination of well‐crystallized perovskite grains and effectively passivated GBs leads to an improvement in the open circuit voltage (Voc) of PSCs from 1.08 V to 1.17 V, which is one of the highest recorded Voc without interface modification. The TFAI‐incorporated device achieved a champion PCE of 24.81%. The device maintained a steady power output near its maximum power output point, showing almost no decay over 280 h testing without pre‐processing. [ABSTRACT FROM AUTHOR]

As the adoption of renewable energy sources grows, ensuring a stable power balance across various time frames has become a central challenge for modern power systems. In line with the "dual carbon" objectives and the seamless integration of renewable energy sources, harnessing the advantages of various energy storage resources and coordinating the operation of long-term and short-term storage have become pivotal directions for future energy storage deployment. To address the complexities arising from the coupling of different time scales in optimizing energy storage capacity, this paper proposes a method for energy storage planning that accounts for power imbalance risks across multiple time scales. Initially, the Seasonal and Trend decomposition using the Loess (STL) decomposition method is utilized to temporally decouple actual operational data. Subsequently, power balance computations are performed based on the obtained data at various time scales to optimize the allocation of different types of energy storage capacities and assess the associated imbalance risks. Finally, the effectiveness of the proposed approach is validated through hourly applications using real-world data from a province in southern China over recent years. [ABSTRACT FROM AUTHOR]

In order to study the operating characteristics of variable speed constant frequency wind turbine under different working conditions and the monitoring system of wind turbine. In this paper, the simulation model of each component system of wind turbine is established by MATLAB/Simulink module, and the influence law of different wind speed and ground fault types on the output power of wind turbine is studied. The active power of wind turbines under different short-circuit fault types is compared. At the same time, in order to realize real-time monitoring of wind turbine speed and output power, an online monitoring system for wind turbine operation based on industrial Internet of Things is proposed, and the composition and operation characteristics of this remote monitoring system are given. The practical application shows that the on-line monitoring system can accurately and remotely monitor the running status of the wind turbine and avoid the unstable running of the wind turbine. The research conclusions of this paper can provide reference for the design and construction of wind turbines and the operation of connecting to the power grid. [ABSTRACT FROM AUTHOR]

The hydropower group is a kind of weak AC system while hydropower asynchronous interconnected with main network through VSC-HVDC (Voltage Source Converter based High Voltage Direct Current). Thus, VSC-HVDC should be taken part into the system voltage and frequency control, which can enhance the transient stability of power grid while reduce the conventional control costs at the same time. The transfer function of turbine governor is introduced to expound the principle of hydraulic turbine unit participating in DC power support. Different kinds of DC power modulation have been compared which show the difference of power angle recovery after fault between VSC-HVDC and LCC-HVDC (Line Commutated Converter based High Voltage Direct Current) power support, thus the modulation priority of different kinds of DC in the emergency control should be taken into consideration. The DC power support quantity calculation method after fault is proposed in this paper. The emergency coordination control strategy can be formulated by considering with both priority and DC power support quantity under different fault. [ABSTRACT FROM AUTHOR]

In this letter, an improved real-time, short-term voltage stability monitoring method is introduced. The impact of voltage magnitude oscillation on the calculation of the Lyapunov exponent is analyzed, and a phase rectification method to eliminate the negative influence of oscillation is proposed. The simulation work was conducted on the provincial power grid at Guangdong in China. Based on our simulation results, the proposed method is expected to improve the effectiveness of short-term voltage stability monitoring. [ABSTRACT FROM PUBLISHER]