Global Energy Interconnection最新文献

This paper proposes a longitudinal protection scheme utilizing empirical wavelet transform (EWT) for a through- type cophase traction direct power supply system, where both sides of a traction network line exhibit a distinctive boundary structure. This approach capitalizes on the boundary’s capacity to attenuate the high-frequency component of fault signals, resulting in a variation in the high-frequency transient energy ratio when faults occur inside or outside the line. During internal line faults, the high-frequency transient energy at the checkpoints located at both ends surpasses that of its neighboring lines. Conversely, for faults external to the line, the energy is lower compared to adjacent lines. EWT is employed to decompose the collected fault current signals, allowing access to the high-frequency transient energy. The longitudinal protection for the traction network line is established based on disparities between both ends of the traction network line and the high-frequency transient energy on either side of the boundary. Moreover, simulation verification through experimental results demonstrates the effectiveness of the proposed protection scheme across various initial fault angles, distances to faults, and fault transition resistances.

The rapid growth of the Chinese economy has fueled the expansion of power grids. Power transformers are key equipment in power grid projects, and their price changes have a significant impact on cost control. However, the prices of power transformer materials manifest as nonsmooth and nonlinear sequences. Hence, estimating the acquisition costs of power grid projects is difficult, hindering the normal operation of power engineering construction. To more accurately predict the price of power transformer materials, this study proposes a method based on complementary ensemble empirical mode decomposition (CEEMD) and gated recurrent unit (GRU) network. First, the CEEMD decomposed the price series into multiple intrinsic mode functions (IMFs). Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF. Then, an empirical wavelet transform (EWT) was applied to the aggregation sequence with a large sample entropy, and the multiple subsequences obtained from the decomposition were predicted by the GRU model. The GRU model was used to directly predict the aggregation sequences with a small sample entropy. In this study, we used authentic historical pricing data for power transformer materials to validate the proposed approach. The empirical findings demonstrated the efficacy of our method across both datasets, with mean absolute percentage errors (MAPEs) of less than 1% and 3%. This approach holds a significant reference value for future research in the field of power transformer material price prediction.

Integrated energy systems (IESs) can improve energy efficiency and reduce carbon emissions, essential for achieving peak carbon emissions and carbon neutrality. This study investigated the characteristics of the CHP model considering P2G and carbon capture systems, and a two-stage robust optimization model of the electricity-heat-gas- cold integrated energy system was developed. First, a CHP model considering the P2G and carbon capture system was established, and the electric-thermal coupling characteristics and P2G capacity constraints of the model were derived, which proved that the model could weaken the electric-thermal coupling characteristics, increase the electric power regulation range, and reduce carbon emissions. Subsequently, a two-stage robust optimal scheduling model of an IES was constructed, in which the objective function in the day-ahead scheduling stage was to minimize the start-up and shutdown costs. The objective function in the real-time scheduling stage was to minimize the equipment operating costs, carbon emission costs, wind curtailment, and solar curtailment costs, considering multiple uncertainties. Finally, after the objective function is linearized with a ψ-piecewise method, the model is solved based on the C&CG algorithm. Simulation results show that the proposed model can effectively absorb renewable energy and reduce the total cost of the system.

With the development of green data centers, a large number of Uninterruptible Power Supply (UPS) resources in Internet Data Center (IDC) are becoming idle assets owing to their low utilization rate. The revitalization of these idle UPS resources is an urgent problem that must be addressed. Based on the energy storage type of the UPS (EUPS) and using renewable sources, a solution for IDCs is proposed in this study. Subsequently, an EUPS cluster classification method based on the concept of shared mechanism niche (CSMN) was proposed to effectively solve the EUPS control problem. Accordingly, the classified EUPS aggregation unit was used to determine the optimal operation of the IDC. An IDC cost minimization optimization model was established, and the Quantum Particle Swarm Optimization (QPSO) algorithm was adopted. Finally, the economy and effectiveness of the three-tier optimization framework and model were verified through three case studies.

The penetration of new energy sources such as wind power is increasing, which consequently increases the occurrence rate of subsynchronous oscillation events. However, existing subsynchronous oscillation source-identification methods primarily analyze fixed-mode oscillations and rarely consider time-varying features, such as frequency drift, caused by the random volatility of wind farms when oscillations occur. This paper proposes a subsynchronous oscillation source-localization method that involves an enhanced short-time Fourier transform and a convolutional neural network (CNN). First, an enhanced STFT is performed to secure high-resolution time-frequency distribution (TFD) images from the measured data of the generation unit ports. Next, these TFD images are amalgamated to form a subsynchronous oscillation feature map that serves as input to the CNN to train the localization model. Ultimately, the trained CNN model realizes the online localization of subsynchronous oscillation sources. The effectiveness and accuracy of the proposed method are validated via multimachine system models simulating forced and natural oscillation events using the Power Systems Computer Aided Design platform. Test results show that the proposed method can localize subsynchronous oscillation sources online while considering unpredictable fluctuations in wind farms, thus providing a foundation for oscillation suppression in practical engineering scenarios.

The power Internet of Things (IoT) is a significant trend in technology and a requirement for national strategic development. With the deepening digital transformation of the power grid, China’s power system has initially built a power IoT architecture comprising a perception, network, and platform application layer. However, owing to the structural complexity of the power system, the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment, diverse IoT protocol access methods, high concurrency of network communications, and weak data security protection. To address these issues, this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT, comprising cloud, pipe, edge, and terminal parts. It further reviews and analyzes the key technologies involved in the power IoT, such as the unified management of the physical model, high concurrent access, multi-protocol access, multi-source heterogeneous data storage management, and data security control, to provide a more flexible, efficient, secure, and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

To facilitate the coordinated and large-scale participation of residential flexible loads in demand response (DR), a load aggregator (LA) can integrate these loads for scheduling. In this study, a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR. First, based on the operational characteristics of flexible loads such as electric vehicles, air conditioners, and dishwashers, their DR participation, the base to calculate the compensation price to users, was determined by considering these loads as virtual energy storage. It was quantified based on the state of virtual energy storage during each time slot. Second, flexible loads were clustered using the K-means algorithm, considering the typical operational and behavioral characteristics as the cluster centroid. Finally, the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load. The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys, thereby improving the load management performance.

This paper presents a peer-to-peer community cost optimization approach based on a single-prosumer energy management system. Its objective is to optimize energy costs for prosumers in the community by enhancing the consumption efficiency. This study was conducted along two main axes. The first axis focuses on designing a digital twin for a residential community microgrid platform. This phase involves data collection, cleaning, exploration, and interpretation. Moreover, it includes replicating the functionality of the real platform and validating the results. The second axis involves the development of a novel approach that incorporates two distinct prosumer behaviors within the same community microgrid, while maintaining the concept of peer-to-peer energy trading. Prosumers without storage utilize their individual PV systems to fulfill their energy requirements and inject excess energy into a local microgrid. Meanwhile, a single prosumer with a storage system actively engages in energy exchange to maximize the community’s profit. This is achieved by optimizing battery usage using a cost optimization solution. The proposed solution is validated using the developed digital twin.

Insulation failure significantly contributes to the unpredictable shutdown of power equipment. Compared to the partial discharge and high-frequency (HF) injection methods, the HF common-mode (CM) leakage current method offers a non-intrusive and highly sensitive alternative. However, the detection of HF CM currents is susceptible to interference from differential-mode (DM) currents, which exhibit high-amplitude and multifrequency components during normal operation. To address this challenge, this paper proposes a double-ring current sensor based on the principle of magnetic shielding for inverter-fed machine winding insulation monitoring. The inner ring harnesses the magnetic aggregation effect to isolate the DM current magnetic field, whereas the outer ring serves as the magnetic core of the Rogowski current sensor, enabling HF CM current monitoring. First, the magnetic field distributions of the CM and DM currents were analyzed. Then, a correlation between the sensor parameters and signal-to-noise ratio of the target HF CM current was established. Finally, an experimental study was conducted on a 3-kW PMSM for verification. The results indicate that the proposed double-ring HF CM sensor can effectively mitigate DM current interference. Compared to a single-ring sensor, a reduction of approximately 40% in the DM component was achieved, which significantly enhanced the precision of online insulation monitoring.

With the expansion and implementation of rural revitalization strategies, there is a constant need for new energy sources for the construction of new townships. Consequently, integrated energy systems with the interconnection and interaction of multiple energy sources are developing rapidly. Biomass energy, a renewable green energy source with low pollution and wide distribution, has significant application potential in integrated energy systems. Considering the application of biomass energy in townships, this study established an integrated biomass energy system and proposed a model to optimize its operation. Lowest economic cost and highest clean energy utilization rate were considered as the objective functions. In addition, a plan was suggested to adjust the heat-electricity ratio based on the characteristics of the combined heat and power of the biomass. Finally, a simulation analysis conducted for a town in China was discussed, demonstrating that the construction of a township integrated-energy system and the use of biomass can significantly reduce operating costs and improve the energy utilization rate. Moreover, by adjusting the heat-electricity ratio, the economic cost was further reduced by 6.70%, whereas the clean energy utilization rate was increased by 5.14%.