skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint

Abstract

In the traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of load forecasting technique can provide accurate prediction of load power that will happen in future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during the longer time period instead of using the snapshot of load at the time when the reconfiguration happens, and thus it can provide information to the distribution system operator (DSO) to better operate the system reconfiguration to achieve optimal solutions. Thus, this paper proposes a short-term load forecasting based approach for automatically reconfiguring distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with support vector regression (SVR) based forecaster and parallel parameters optimization. And the network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum loss at the future time. The simulation results validate and evaluate the proposed approach.

Authors:
 [1];  [1];  [1];  [1];  [1];  [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability (OE)
OSTI Identifier:
1373488
Report Number(s):
NREL/CP-5D00-67762
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2017 IEEE Power & Energy Society General Meeting (PES GM), 16-20 July 2017, Chicago, Illinois
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; short-term load forecasting; parallel parameters optimization; network reconfiguration; distribution system

Citation Formats

Jiang, Huaiguang, Ding, Fei, Zhang, Yingchen, Jiang, Huaiguang, Ding, Fei, and Zhang, Yingchen. Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint. United States: N. p., 2017. Web.
Jiang, Huaiguang, Ding, Fei, Zhang, Yingchen, Jiang, Huaiguang, Ding, Fei, & Zhang, Yingchen. Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint. United States.
Jiang, Huaiguang, Ding, Fei, Zhang, Yingchen, Jiang, Huaiguang, Ding, Fei, and Zhang, Yingchen. 2017. "Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint". United States. doi:. https://www.osti.gov/servlets/purl/1373488.
@article{osti_1373488,
title = {Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint},
author = {Jiang, Huaiguang and Ding, Fei and Zhang, Yingchen and Jiang, Huaiguang and Ding, Fei and Zhang, Yingchen},
abstractNote = {In the traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of load forecasting technique can provide accurate prediction of load power that will happen in future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during the longer time period instead of using the snapshot of load at the time when the reconfiguration happens, and thus it can provide information to the distribution system operator (DSO) to better operate the system reconfiguration to achieve optimal solutions. Thus, this paper proposes a short-term load forecasting based approach for automatically reconfiguring distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with support vector regression (SVR) based forecaster and parallel parameters optimization. And the network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum loss at the future time. The simulation results validate and evaluate the proposed approach.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2017,
month = 7
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:
  • In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operatormore » can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.« less
  • In this paper an attempt is made to forecast load using fuzzy neural network (FNN) for an integrated power system. Here, the proposed system uses a two stage FNN for a short term peak and average load forecasting (STPALF). The first stage FNN deals with the load forecasting and the second stage algorithm can be worked independently for network security. This technique is used to forecast load accurately on week days as well as holidays, weekends and some special occasions considering historical data of load and weather information and also take necessary control action for network security.
  • This paper presents the application of a neural network (NN) based short-term electric load forecast model that is being used for the energy control center of the electric utilities. This NN based short-term load forecast program has been developed with a version integrated with the electric utility`s Energy management System (EMS), as well as a PC-based stand-alone version. The model forecasts the hourly electrical load for the current day and up to seven days. A multi-layer neural network is used to provide a non-linear mapping between weather parameters and electric load. Using historical weather parameters and electric load. Using historicalmore » weather parameters (such as dry bulb temperature, relative humidity, wind velocity and light intensity), and historical hourly loads, a neural network is trained for each day type and each weather-defined season. The forecast of weather parameters can be obtained by a weather station for the forecast period. The program is capable to generate hourly weather forecast if the forecast form the weather service is partial, such as if only a few hours per day are available, or even if the maximum or minimum daily values of the temperature forecast is available. A separate NN model has also been developed for identifying seasons based on the historical weather data. This paper will discuss features of the system, the neural network models and algorithm, and a sample result of the program performance.« less
  • A novel short-term state forecasting-based optimal power flow (OPF) approach for distribution system voltage regulation is proposed in this paper. An extreme learning machine (ELM) based state forecaster is developed to accurately predict system states (voltage magnitudes and angles) in the near future. Based on the forecast system states, a dynamically weighted three-phase AC OPF problem is formulated to minimize the voltage violations with higher penalization on buses which are forecast to have higher voltage violations in the near future. By solving the proposed OPF problem, the controllable resources in the system are optimally coordinated to alleviate the potential severemore » voltage violations and improve the overall voltage profile. The proposed approach has been tested in a 12-bus distribution system and simulation results are presented to demonstrate the performance of the proposed approach.« less
  • Load forecasting in the day-ahead timescale is a critical aspect of power system operations that is used in the unit commitment process. It is also an important factor in renewable energy integration studies, where the combination of load and wind or solar forecasting techniques create the net load uncertainty that must be managed by the economic dispatch process or with suitable reserves. An understanding of that load forecasting errors that may be expected in this process can lead to better decisions about the amount of reserves necessary to compensate errors. In this work, we performed a statistical analysis of themore » day-ahead (and two-day-ahead) load forecasting errors observed in two independent system operators for a one-year period. Comparisons were made with the normal distribution commonly assumed in power system operation simulations used for renewable power integration studies. Further analysis identified time periods when the load is more likely to be under- or overforecast.« less