Global Energy Interconnection最新文献

The premise and basis of load modeling are substation load composition inquiries and cluster analyses. However, the traditional kernel fuzzy C-means (KFCM) algorithm is limited by artificial clustering number selection and its convergence to local optimal solutions. To overcome these limitations, an improved KFCM algorithm with adaptive optimal clustering number selection is proposed in this paper. This algorithm optimizes the KFCM algorithm by combining the powerful global search ability of genetic algorithm and the robust local search ability of simulated annealing algorithm. The improved KFCM algorithm adaptively determines the ideal number of clusters using the clustering evaluation index ratio. Compared with the traditional KFCM algorithm, the enhanced KFCM algorithm has robust clustering and comprehensive abilities, enabling the efficient convergence to the global optimal solution

Cooperation in energy systems is no longer limited to the distribution of electricity, and more attention is paid to the trading of green certificates (GCs). This paper proposed a cooperative method for photovoltaic (PV) and electric-to- hydrogen (EH) trading, including GC trading under risk management. First, a novel PV and EH model is established and the cooperation mechanism is analyzed. Meanwhile, PV and EH models were risk-controlled using the conditional value at risk to reduce the impact of the uncertainty of PV electricity and EH loads. Then, the PV-EH cooperative model was established based on cooperative game theory; this was then divided into two subproblems of “cooperative benefit maximization” and “transaction payment negotiation,” and the above two subproblems were solved distributively by alternating direction multiplier method (ADMM). Only energy transactions and price negotiations were conducted between the PV and EH, which can protect the privacy and confidentiality of each entity. Finally, the effectiveness of the cooperation model was verified using a practical engineering case. The simulation results show that the cooperation of the PV-EH can significantly improve the operational efficiency of each entity and the overall efficiency of the cooperation and realize the efficient redistribution of electricity and GC.

With the construction of new power systems, the power grid has become extremely large, with an increasing proportion of new energy and AC/DC hybrid connections. The dynamic characteristics and fault patterns of the power grid are complex; additionally, power grid control is difficult, operation risks are high, and the task of fault handling is arduous. Traditional power-grid fault handling relies primarily on human experience. The difference in and lack of knowledge reserve of control personnel restrict the accuracy and timeliness of fault handling. Therefore, this mode of operation is no longer suitable for the requirements of new systems. Based on the multi-source heterogeneous data of power grid dispatch, this paper proposes a joint entity–relationship extraction method for power-grid dispatch fault processing based on a pre-trained model, constructs a knowledge graph of power-grid dispatch fault processing and designs, and develops a fault-processing auxiliary decision-making system based on the knowledge graph. It was applied to study a provincial dispatch control center, and it effectively improved the accident processing ability and intelligent level of accident management and control of the power grid.

The high overlap of participants in the carbon emissions trading and electricity markets couples the operations of the two markets. The carbon emission cost (CEC) of coal-fired units becomes part of the power generation cost through market coupling. The accuracy of CEC calculation affects the clearing capacity of coal-fired units in the electric power market. Study of carbon–electricity market interaction and CEC calculations is still in its initial stages. This study analyzes the impact of carbon emissions trading and compliance on the operation of the electric power market and defines the cost transmission mode between the carbon emissions trading and electric power markets. A long-period interactive operation simulation mechanism for the carbon–electricity market is established, and operation and trading models of the carbon emissions trading market and electric power market are established. A daily rolling estimation method for the CEC of coal- fired units is proposed, along with the CEC per unit electric quantity of the coal-fired units. The feasibility and effectiveness of the proposed method are verified through an example simulation, and the factors influencing the CEC are analyzed.

A robust low-carbon economic optimal scheduling method that considers source-load uncertainty and hydrogen energy utilization is developed. The proposed method overcomes the challenge of source-load random fluctuations in integrated energy systems (IESs) in the operation scheduling problem of integrated energy production units (IEPUs). First, to solve the problem of inaccurate prediction of renewable energy output, an improved robust kernel density estimation method is proposed to construct a data-driven uncertainty output set of renewable energy sources statistically and build a typical scenario of load uncertainty using stochastic scenario reduction. Subsequently, to resolve the problem of insufficient utilization of hydrogen energy in existing IEPUs, a robust low-carbon economic optimal scheduling model of the source-load interaction of an IES with a hydrogen energy system is established. The system considers the further utilization of energy using hydrogen energy coupling equipment (such as hydrogen storage devices and fuel cells) and the comprehensive demand response of load-side schedulable resources. The simulation results show that the proposed robust stochastic optimization model driven by data can effectively reduce carbon dioxide emissions, improve the source-load interaction of the IES, realize the efficient use of hydrogen energy, and improve system robustness.

Traditional distribution network planning relies on the professional knowledge of planners, especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors. The inherent laws reflected by the historical data of the distribution network are ignored, which affects the objectivity of the planning scheme. In this study, to improve the efficiency and accuracy of distribution network planning, the characteristics of distribution network data were extracted using a data-mining technique, and correlation knowledge of existing problems in the network was obtained. A data-mining model based on correlation rules was established. The inputs of the model were the electrical characteristic indices screened using the gray correlation method. The Apriori algorithm was used to extract correlation knowledge from the operational data of the distribution network and obtain strong correlation rules. Degree of promotion and chi-square tests were used to verify the rationality of the strong correlation rules of the model output. In this study, the correlation relationship between heavy load or overload problems of distribution network feeders in different regions and related characteristic indices was determined, and the confidence of the correlation rules was obtained. These results can provide an effective basis for the formulation of a distribution network planning scheme.

Current power systems face significant challenges in supporting large-scale access to new energy sources, and the potential of existing flexible resources needs to be fully explored from the power supply, grid, and customer perspectives. This paper proposes a multi-objective electricity consumption optimization strategy considering the correlation between equipment and electricity consumption. It constructs a multi-objective electricity consumption optimization model that considers the correlation between equipment and electricity consumption to maximize economy and comfort. The results show that the proposed method can accurately assess the potential for electricity consumption optimization and obtain an optimal multi-objective electricity consumption strategy based on customers’ actual electricity consumption demand.

Aiming at reducing the difficulty of cooling the interior of high-density motors, this study proposed the placement of a water cold plate cooling structure between the axial laminations of the motor stator. The effect of the cooling water flow, thickness of the plate, and motor loss density on the cooling effect of the water cold plate were studied. To compare the cooling performance of water cold plate and outer spiral water jacket cooling structures, a high-speed permanent magnet motor with a high loss density was used to establish two motor models with the two cooling structures. Consequently, the cooling effects of the two models were analyzed using the finite element method under the same loss density, coolant flow, and main dimensions. The results were as follows. (1) The maximum and average temperatures of the water cold plate structure were reduced by 25.5% and 30.5%, respectively, compared to that of the outer spiral water jacket motor; .(2) Compared with the outer spiral water jacket structure, the water cold plate structure can reduce the overall mass and volume of the motor. Considering a 100 kW high-speed permanent magnet motor as an example, a water cold plate cooling system was designed, and the temperature distribution is analyzed, with the result indicating that the cooling structure satisfied the cooling requirements of the high loss density motor.

In this study, a novel non-intrusive temperature rise fault-identification method for a distribution cabinet based on tensor block-matching is proposed. Two-stage data repair is used to reconstruct the temperature-field information to support the demand for temperature rise fault-identification of non-intrusive distribution cabinets. In the coarse-repair stage, this method is based on the outside temperature information of the distribution cabinet, using tensor block-matching technology to search for an appropriate tensor block in the temperature-field tensor dictionary, filling the target space area from the outside to the inside, and realizing the reconstruction of the three-dimensional temperature field inside the distribution cabinet. In the fine-repair stage, tensor super-resolution technology is used to fill the temperature field obtained from coarse repair to realize the smoothing of the temperature-field information inside the distribution cabinet. Non-intrusive temperature rise fault-identification is realized by setting clustering rules and temperature thresholds to compare the location of the heat source with the location of the distribution cabinet components. The simulation results show that the temperature- field reconstruction error is reduced by 82.42% compared with the traditional technology, and the temperature rise fault- identification accuracy is greater than 86%, verifying the feasibility and effectiveness of the temperature-field reconstruction and temperature rise fault-identification.

The time-of-use (TOU) strategy can effectively improve the energy consumption mode of customers, reduce the peak-valley difference of load curve, and optimize the allocation of energy resources. This study presents an Optimal guidance mechanism of the flexible load based on strategies of direct load control and time-of-use. First, this study proposes a period partitioning model, which is based on a moving boundary technique with constraint factors, and the Dunn Validity Index (DVI) is used as the objective to solve the period partitioning. Second, a control strategy for the curtailable flexible load is investigated, and a TOU strategy is utilized for further modifying load curve. Third, a price demand response strategy for adjusting transferable load is proposed in this paper. Finally, through the case study analysis of typical daily flexible load curve, the efficiency and correctness of the proposed method and model are validated and proved.