Active distribution network type identification method of high proportion new energy power system based on source-load matching

Shi, Qijia; Yang, Peng; Tang, Bo; Lin, Jie; Gao, Yu; Muyeen, S. M.

doi:10.1016/j.ijepes.2023.109411

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…With the construction of a new power system and the gradual development of finegrained power load management [1][2][3], the diversified load short-term prediction technology for the demand side of the power system plays an important role in accurate management decision-making for diversified stations and load types [4]. Fine-grained management of power load [5] refers to the real-time monitoring and analysis, regulation, and optimization of power load to achieve accurate management of the power load, effectively improving the efficiency and stability of the power system and reducing energy consumption and pollution emissions, which is the trend and direction of the development of the modern power industry [6].…”

Section: Introductionmentioning

confidence: 99%

Demand-Side Electricity Load Forecasting Based on Time-Series Decomposition Combined with Kernel Extreme Learning Machine Improved by Sparrow Algorithm

Sun,

Lin,

Pan

et al. 2023

Energies

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With the rapid development of new power systems, power usage stations are becoming more diverse and complex. Fine-grained management of demand-side power load has become increasingly crucial. To address the accurate load forecasting needs for various demand-side power consumption types and provide data support for load management in diverse stations, this study proposes a load sequence noise reduction method. Initially, wavelet noise reduction is performed on the multiple types of load sequences collected by the power system. Subsequently, the northern goshawk optimization is employed to optimize the parameters of variational mode decomposition, ensuring the selection of the most suitable modal decomposition parameters for different load sequences. Next, the SSA–KELM model is employed to independently predict each sub-modal component. The predicted values for each sub-modal component are then aggregated to yield short-term load prediction results. The proposed load forecasting method has been verified using actual data collected from various types of power terminals. A comparison with popular load forecasting methods demonstrates the proposed method’s higher prediction accuracy and versatility. The average prediction results of load data in industrial stations can reach RMSE = 0.0098, MAE = 0.0078, MAPE = 1.3897%, and R2 = 0.9949. This method can be effectively applied to short-term load forecasting in multiple types of power stations, providing a reliable basis for accurate demand-side power load management and decision-making.

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Section: Introductionmentioning

confidence: 99%

Demand-Side Electricity Load Forecasting Based on Time-Series Decomposition Combined with Kernel Extreme Learning Machine Improved by Sparrow Algorithm

Sun,

Lin,

Pan

et al. 2023

Energies

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“…The planning approach proposed in this paper does not evaluate the effectiveness of source-load matching, which cannot assess the suitability of constructing an EHMES in the studied region. Future research could consider establishing a set of source-load matching indicators to quantify the matching effects, like the study in [46]. This effort would facilitate the preliminary selection of areas suitable for EHMES construction based on the wind and solar resources of the area.…”

mentioning

confidence: 99%

Optimal Capacity Planning of Green Electricity-Based Industrial Electricity-Hydrogen Multi-Energy System Considering Variable Unit Cost Sequence

Xia,

Zou,

Wang

2024

Sustainability

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Utilizing renewable energy sources (RESs), such as wind and solar, to convert electrical energy into hydrogen energy can promote the accommodation of green electricity. This paper proposes an optimal capacity planning approach for an industrial electricity-hydrogen multi-energy system (EHMES) aimed to achieve the local utilization of RES and facilitate the transition to carbon reduction in industrial settings. The proposed approach models the EHMES equipment in detail and divides the system’s investment and operation into producer and consumer sides with energy trading for effective integration. Through this effort, the specialized management for different operators and seamless incorporation of RES into industrial users can be achieved. In addition, the variations in investment and operating costs of equipment across different installed capacities are considered to ensure a practical alignment with real-world scenarios. By conducting a detailed case study, the influence of various factors on the capacity configuration outcomes within an EHMES is analyzed. The results demonstrate that the proposed method can effectively address the capacity configuration of equipment within EHMES based on the local accommodation of RES and variable unit cost sequence. Wind power serves as the primary source of green electricity in the system. Energy storage acts as crucial equipment for enhancing the utilization rate of RES.

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Day-ahead Dispatch Strategy for Distribution Network Based on Hierarchical Control

Zhang,

Liu,

Chen

et al. 2024

2024 IEEE 7th International Electrical and Energy Conference (CIEEC)

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Active distribution network type identification method of high proportion new energy power system based on source-load matching

Cited by 4 publications

References 25 publications

Demand-Side Electricity Load Forecasting Based on Time-Series Decomposition Combined with Kernel Extreme Learning Machine Improved by Sparrow Algorithm

Demand-Side Electricity Load Forecasting Based on Time-Series Decomposition Combined with Kernel Extreme Learning Machine Improved by Sparrow Algorithm

Optimal Capacity Planning of Green Electricity-Based Industrial Electricity-Hydrogen Multi-Energy System Considering Variable Unit Cost Sequence

Day-ahead Dispatch Strategy for Distribution Network Based on Hierarchical Control

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