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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.

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.

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.

In light of the increasing hydrogen permeability in distribution networks as a means to cope with extreme events and improve network resilience, this paper introduces a novel strategy for enhancing power distribution network resilience. It outlines a comprehensive approach that focuses on dispatching hydrogen storage (HS) and hydrogen vehicle (HV) within hydrogen penetrated distribution systems (HPDS), segmenting the strategy into pre‐disaster and post‐disaster stages. Firstly, in the pre‐disaster stage, models for HS and HVs are established to gather operational data and facilitate rapid post‐disaster response, alongside a coupled electric grid and road network model for optimising HV routing and dispatch. Subsequently, the post‐disaster stage focuses on a scheduling model that aims to minimise load power losses and economic costs, balancing immediate power support with cost‐effectiveness through detailed analysis of HS and HV dispatch strategies. Finally, this paper demonstrates the effectiveness of this strategy via a case study, highlighting significant improvements in network resilience and recovery and underscoring the potential of hydrogen technologies in enhancing infrastructure resilience.

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. A combined method is proposed by introducing 3,4,5‐Trifluoroaniline iodide (TFAI) into the perovskite precursor. The TFAI induces a complete phase transition of α‐FAPbI3 at room temperature and serves as a passivation agent at grain boundaries. The TFAI‐modified device achieves over 24% PCE and maintains steady power output for 380 h with almost no performance decay.

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.

The energy storage system (ESS) is a promising technology to address issues caused by the large‐scale deployment of renewable energy. Deploying ESS is a business decision that requires potential revenue assessment. Current value assessment methods focus on the energy storage owner or the electricity utility. The system value of the ESS needs to be fully considered to gain a broad understanding of benefits across the whole power system. Thus, this study proposes a system value assessment method of grid‐integrated ESS to quantify the total system value‐avoided cost based on an improved DC power flow model considering transmission losses. Four typical applications (production cost saving, upgrade deferral, environmental benefit, and transmission loss saving) are chosen to represent the system value of the ESS across the whole power system. In addition, the co‐optimisation model considering the “source‐grid‐storage” coordination operation and battery capacity degradation, is proposed for peak shaving and frequency regulation based on the Chinese power market rule. The proposed method is tested in simulation and experimental studies.

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.

The sub-synchronous oscillation (SSO) which occurred in direct-driven wind generators (DDWG) based wind farms became a research hotspot. The SSO excited the shaft torsional vibration of thermal generators and caused trip failures, leading to economic losses and stabilization problems. However, the existing phasor-based oscillation source locating methods cannot be applied to the SSO since the power system quasi-steady-state assumption was not satisfied in the SSO scenarios. Thus, a novel locating method for SSO sources was urgently required. To fill this gap, this paper proposed an improved method for locating nonlinear sub-synchronous oscillation sources based on Energy Supply on Port (ESP) and bicoherence. The ESP was calculated with the instantaneous values of measured voltages and currents at some predefined network interfaces, i.e., the ports. The tendency of the ESP indicated the transient energy injection from a certain subsystem into the rest of the network during a designated period, which can help figure out the oscillation source of the SSO. After oscillation source localization, the nonlinear index and its threshold were proposed by defining the bicoherence coefficient in consideration of the nonlinear oscillation characteristics. According to the power-quality standard and the comparison of the bicoherence with the threshold, the nonlinearity of the oscillation source was examined. The feasibility and effectiveness of the proposed method were proved in the case study which simulated the oscillation scene of the accident in Hami.

With the large-scale integration of wind and solar energy into the power grid, the power system is facing uncertainty challenges in multiple links, such as source, grid, and load. How to efficiently dispatch flexible resources, such as energy storage, has become an urgent problem to be solved. To this end, this paper considers the correlation between new energy stations due to natural conditions, uses Vine-Copula theory to describe the correlation characteristics of the output of multiple new energy stations, and proposes a wind solar new energy output scenario generation method based on Vine-Copula theory; Then, to develop the optimal scheduling and operation plan, considering the goal of minimizing operating costs within a scheduling cycle, combined with the scenario of output of wind and solar energy, an optimization and scheduling model for wind-solar-thermal-storage power system operation of multiple energy stations was constructed; On this basis, considering the difficulty in obtaining the probability distribution of load uncertainty, a risk-averse model and a risk-seeking model based on information Gap Decision Theory (IGDT) were constructed, and a multi energy station power system operation optimization scheduling method based on correlation-IGDT was proposed. By setting risk strategies and risk deviation factors, the power system operation scheduling scheme under this strategy can be obtained. Simulation experiments were conducted based on an improved IEEE39 node system for verification, and the results showed that compared to traditional methods that do not consider correlation, this method can reduce thermal power costs by 0.63% and energy storage costs by 10.56%. Meanwhile, Monte Carlo sampling analysis shows that the model has good accuracy and stability within the range of load disturbances. Further analysis shows that under the risk avoidance strategy, the maximum power variation of thermal power is controlled at 284 MW, with an average of 172 MW; while under the risk acceptance strategy, the maximum variation is 198 MW, with an average of 127 MW, significantly improving the system’s adaptability and operational efficiency to uncertain environments. The main contribution of this article is to integrate the modeling of new energy correlation with information gap decision-making and construct a power system scheduling optimization framework for multiple uncertain factors, which has good promotion value and practical application potential.