Junyong Wu's research while affiliated with Beijing Jiaotong University and other places

... In response to the aforementioned challenges, recently, twolayer optimal scheduling models were proposed to achieve the optimal operation of MGs and power systems. Scholars worldwide have conducted extensive investigations on the conceptual modeling (Wang K. et al., 2023;Jani and Jadid, 2023;Lei et al., 2023;Luo et al., 2023), nonlinear solution algorithms (Chen C. et al., 2023;Mi et al., 2023;Wu et al., 2023), and feasibility verification (Li and Wang, 2023;Li et al., 2024) of these two-layer optimal scheduling approaches. For example, Lei et al. (Lei et al., 2023) developed a trading strategy for MGs within an intelligent DN, taking into account the influence of carbon quotas. ...

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Enhanced bi-level optimal scheduling strategy for distribution network with multi-microgrids considering source-load uncertainties

... Su et al. [26] integrated DBN and the Non-dominated Sorting Genetic Algorithm (NSGA-III) to develop a new preventive control method for a power system. Li and Wu [27] integrated DBN and active learning based on information entropy to conduct a transient stability assessment within a power grid. Zhang et al. [28] introduced an innovative framework that seamlessly incorporates DBN alongside Adaboost algorithms, aimed at achieving precise and efficient power demand forecasting. ...

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Typical Power Grid Operation Mode Generation Based on Reinforcement Learning and Deep Belief Network

... • Multiple methods of deep reinforcement learning have been applied in the field of power system optimization. For example, Double Deep Q-learning has been used in [150,151], where two NNs are used for optimization simultaneously. Another method called Soft Actor-Critic (SAC), has been used in [152], where it was combined with Multi-Agent Reinforcement Learning techniques such as DDPG to solve the OPF problem. ...

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Optimal Power Flow in Distribution Network: A Review on Problem Formulation and Optimization Methods

... However, converting the task of load prediction into multiple sub-series predictions has the following problems. On the one hand, continuing manual feature selection and constructing input feature sets based on metrics such as Pearson coefficients [10] and mutual information coefficients [11] would double the feature engineering effort; on the other hand, there is also an inherent error in the prediction of each sub-series prediction model due to the loss problem in the signal decomposition algorithm, leading to an accumulation of errors in the direct reconstruction of load predictions based on the sub-series predictions. ...

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Two-Stage Short-Term Power Load Forecasting Based on RFECV Feature Selection Algorithm and a TCN–ECA–LSTM Neural Network

... Dorrell [10] proposed a radial electrical electromagnetic excitation model that accounts for rotor eccentricity in cage induction motors. Zhang [11] theoretically investigated an electromagnetic excitation model for a motor without a load. Coupling between the dynamics and electromagnetic excitation due to dynamic and static eccentricity was analyzed. ...

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An investigation on lateral and torsional coupled vibrations of high power density PMSM rotor caused by electromagnetic excitation

... The authors of the study in [79] proposed a technique for selecting CAs for TS EPS based on DBN. The methodology consists of two parts: offline and online. ...

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Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review

... To further study the reactive power output modelling method of the SC in the UHVDC converter station, a simulation model of the SC in the UHVDC converter station is established by PSCAD/EMTDC simulation software. The SC model is an electromagnetic transient model [11,12]. ...

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Reactive Power Output Modeling of Synchronous Condenser in UHVDC Converter Station Based on Interlaced Superposition CNN-BiLSTM

... The input of CNNs, RNNs, LSTM, and GRUs usually are time series, and refs. [23][24][25][26][27] used the electrical trajectories as the inputs, which could capture the trend in power system transients and provide a reasonable system-level TSP. However, these methods do not consider the intrinsic spatial DER correlations in TSP. ...

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A Refined DER-Level Transient Stability Prediction Method Considering Time-Varying Spatial–Temporal Correlations in Microgrids

... By internalizing the external costs of carbon emissions, carbon pricing mechanisms such as carbon taxes and cap-and-trade systems incentivize reductions in greenhouse gas (GHG) emissions and foster investments in sustainable technologies and practices. Lin, Wu, and Liu (2019) explore the economic efficiency of micro energy grids (MEGs) considering Time-of-Use (TOU) gas pricing. Their study reveals that TOU pricing can significantly improve the economic efficiency of MEG operations by balancing gas supply and demand . ...

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Strategic implications of carbon pricing on global environmental sustainability and economic development: A conceptual framework

... The output space of traditional thermal power units with "strong support" will be limited, the reactive power demand of the power system will rise sharply, and the voltage stability will face great challenges [1][2][3]. In order to increase the proportion of dynamic reactive power supply, optimize the utilization rate of thermal power units, and improve the stable operation level of the power grid, the new large capacity synchronous condenser has been widely used in HVDC transmission and reception terminals in recent years, and has played an important role in suppressing the DC commutation failure and improving the voltage stability of the system [4]. Up to now, 47 new large-capacity 300 MVar synchronous market, the reactive power pricing under different operation modes of the NESC and the SCTTU is formed. ...

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Cost Analysis of Synchronous Condenser Transformed from Thermal Unit Based on LCC Theory