Medium and long term load forecasting method for distribution network with high penetration DGs

Zhao, Mingxin; Liu, Wei; Su, Jianhui; Zhao, Lijun; Dong, Xiaojing

doi:10.1109/ciced.2014.6991746

Cited by 15 publications

(3 citation statements)

References 3 publications

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“…As an important part of planning and construction, medium-and long-term load forecasting of distribution network plays an important role in network architecture optimization, selection of new energy access point, distribution transformer overload warning and other scenarios. Therefore, it is necessary to carry out in-depth research on medium-and long-term load forecasting of distribution network (Zhao et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Medium—And Long-Term Load Forecasting Method for Group Objects Based on the Image Representation Learning

et al. 2021

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Medium-and long-term load forecasting in the distribution network has important guiding significance for overload warning of distribution transformer, transformation of distribution network and other scenarios. However, there are many constraints in the forecasting process. For example, there are many predict objects, the data sample size of a single predict object is small, and the long term load trend is not obvious. The forecasting method based on neural network is difficult to model due to lack of data, and the forecasting method based on time sequence law commonly used in engineering is highly subjective, which is not effective. Aiming at the above problems, this paper takes distribution transformer as the research object and proposes a medium-and long-term load forecasting method for group objects based on Image Representation Learning (IRL). Firstly, the data of distribution transformer is preprocessed in order to restore the load variation in natural state. And then, the load forecasting process is decoupled into two parts: the load trend forecasting of the next year and numerical forecasting of the load change rate. Secondly, the load images covering annual and inter-annual data change information are constructed. Meanwhile, an Image Representation Learning forecasting model based on convolutional neural network, which will use to predict the load development trend, is obtained by using load images for training; And according to the data shape, the group classification of the data in different periods are carried out to train the corresponding group objects forecasting model of each group. Based on the forecasting data and the load trend forecasting result, the group forecasting model corresponding to the forecasting data can be selected to realize the numerical forecasting of load change rate. Due to the large number of predict objects, this paper introduces the evaluation index of group forecasting to measure the forecasting effect of different methods. Finally, the experimental results show that, compared with the existing distribution transformer forecasting methods, the method proposed in this paper has a better overall forecasting effect, and provides a new idea and solution for the medium-and long-term intelligent load forecasting of the distribution network.

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Section: Introductionmentioning

confidence: 99%

Medium—And Long-Term Load Forecasting Method for Group Objects Based on the Image Representation Learning

et al. 2021

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“…The distribution network planning is carried out on the basis of load forecasting and substation site selection and capacity, which has a direct and significant impact on the economics and reliability of distribution network construction and operation [1]- [3]. Distribution network planning requires the combination of geographic information to determine the spatial layout of the line, contact object selection, load division and other issues.…”

Section: Introductionmentioning

confidence: 99%

Practical Automatic Planning for MV Distribution Network Considering Complementation of Load Characteristic and Power Supply Unit Partitioning

Liu

Wang

et al. 2020

IEEE Access

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A new practical automatic planning method for MV distribution network considering complementation of load characteristic and division of power supply is presented to solve the problems of large network size and low equipment utilization ratio caused by the lack of consideration of complementation of load characteristic in distribution network planning. First, based on the existing research results on load clustering and grid planning, the concepts of MV distribution network feeder block and power supply unit are redefined and the technical framework of MV distribution network planning considering division of power supply is designed. Then, the evaluation indexes of feeder block partitioning and power supply unit partitioning considering complementation of load characteristic are set up, and rotation center line distance weighting alternate location algorithm is proposed to divide the feeder block. Further, the MV distribution network planning model based on power supply unit partitioning is proposed, which takes the minimum value of the sum of interruption cost based on reliability index and line integrated cost of the planning year. Combining ant colony algorithm, automatic planning algorithm is proposed to get the combinatorial solution of trunk line and branch line. Finally, the scientificalness and practicability of the model and method in this paper was verified through the case study. INDEX TERMS Load characteristics, block partition, power supply unit, automatic planning.

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“…Accurate load forecasting can start and stop a power system generator set both economically and reasonably, and plays an important role in maintaining the security and stability of the power grid operation, maintaining the normal production and life of society, and effectively reducing the generation costs [5]. Load forecasting can be divided into four categories; super short-term, short-term, medium-term, and long-term based on different purposes, and their time ranges of forecasting are within 1 h in the future, one day or a week, one month to one year, and 3 to 5 years in the future, respectively [6][7][8]. This paper mainly makes a short-term load forecasting of user electricity load.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior

Guo

2016

Algorithms

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Abstract:The smart meter is an important part of the smart grid, and in order to take full advantage of smart meter data, this paper mines the electricity behaviors of smart meter users to improve the accuracy of load forecasting. First, the typical day loads of users are calculated separately according to different date types (ordinary workdays, day before holidays, holidays). Second, the similarity between user electricity behaviors is mined and the user electricity loads are clustered to classify the users with similar behaviors into the same cluster. Finally, the load forecasting model based on the Online Sequential Extreme Learning Machine (OS-ELM) is applied to different clusters to conduct load forecasting and the load forecast is summed to obtain the system load. In order to prove the validity of the proposed method, we performed simulation experiments on the MATLAB platform using smart meter data from the Ireland electric power cooperation. The experimental results show that the proposed method is able to mine the user electricity behaviors deeply, improve the accuracy of load forecasting by the reasonable clustering of users, and reveal the relationship between forecasting accuracy and cluster numbers.

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Medium and long term load forecasting method for distribution network with high penetration DGs

Cited by 15 publications

References 3 publications

Medium—And Long-Term Load Forecasting Method for Group Objects Based on the Image Representation Learning

Medium—And Long-Term Load Forecasting Method for Group Objects Based on the Image Representation Learning

Practical Automatic Planning for MV Distribution Network Considering Complementation of Load Characteristic and Power Supply Unit Partitioning

Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior

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