load features. Numerical testing of the presented method demonstrates ... Index Terms-- Bus load distribution factor, Decoupled Extended. Kalman Filter, Neural ...
An Efficient Approach for Short-Term Substation Load Forecasting Xiaorong Sun, Peter B. Luh, Fellow, IEEE
Kwok W. Cheung, Senior Member, IEEE
Laurent D. Michel, Stephen Corbo Electrical and Computer Engineering Computer Science Engineering University of Connecticut Storrs, CT, 06269
Wei Guan, Member, IEEE, Kenneth Chung R&D at Alstom Grid Inc. Redmond, WA, 98052
Abstract-
Load forecasting methods for a large geographical
area such as New England are widely established.
However,
substation load forecasting is much more difficult, since load patterns of substations are more irregular than that of a large system. In addition, considering the large number of substations in an area, computation time is also an important issue when forecast all the substations together.
In this paper, an efficient
approach is presented for short-term load forecasting of all substations within a given system. The key idea is the addressed load pattern similarities analysis between substations and zone which is upper grid than substation.
If load patterns of
substations are similar to that of zonal load, forecasting of these substations can be directly obtained from proportion of the zonal forecasting results.
For those substations whose load
patterns are different from that of the zonal load, artificial neural network is used to capture the complicated substation load features.
Numerical testing of the presented method
demonstrates the effectiveness of our method based on 23 substations within two zones.
Index Terms--
Bus load distribution factor, Decoupled Extended
Kalman Filter, Neural networks, Short term substation load forecasting.
I.
INTRODUCTION
Short-term substation load forecasting provides an approach for managing and analyzing small area load. It is of great significance for the planning and operation of distribution systems. For example, system operators can control power transformer loading on substations based on the forecasting information [1]. However, efficient short term substation load forecasting is difficult due to the complicated load features and the large number of substations within one area. Substation load forecasting is much more difficult than forecasting of a large system load since substation load patterns can be much more different than a large system load. In addition, considering the large number of substations in This work is supported by a grant from Alstom Grid. The views expressed in this paper are solely those of the authors and do not necessarily represent those of Alstom Grid.
978-1-4799-1303-9/13/$31.00 ©2013 IEEE
one area, computation time is also an important issue when forecast the substations altogether. Methods for short term load forecasting and small area load forecasting will be reviewed in Section II. In this paper, an efficient approach is developed for short-term substation load forecasting. The key idea is that if the load pattern of a substation is similar to that of the zonal load, with zonal load being properly forecasted, this substation load can be obtained from proportion of zonal load forecasting results. However, for those substations whose patterns are different from the zonal load pattern, proportion from zonal load may not work. With these in mind, difficulties thus arise. How to identify the load pattern similarity between substation and zone? How to properly obtain the proportion from zonal load forecasting for substations whose load patterns are similar to that of zonal load? And how to deal with the rest of substations whose load patterns are much different from that of zonal load? Substation load analysis and substation differentiation are described in Section III. Within an area, substation load patterns could be different from those of others even they share the similar weather information. Due to the large number of substations, it is time consuming and impractical to forecast each substation via individual model. Hence, differentiation of substations based on the similarity of their load patterns with zonal load patterns is presented. In order to forecast those substations whose load patterns are similar to that of zonal load, proportion of zonal loaf forecasting results will simplify the forecasting for all substations. Section IV introduces Bus Load Distribution Factor (BLDF) to forecast substation whose load shape is similar to that of zonal load. Zonal level outputs from Decoupled Extended Kalman Filter (DEKF) based Neural Network are then proportionally distributed into substation forecasting by BLDF. However, for the rest of the substations, BLDF cannot provide a proper forecasting. Load affecting factors are required to be considered and fed into the neural networks to capture the complicated load features of these substations In Section V, the method is tested with two examples. Example 1 illustrates the effects of DEKFNN for zonal load forecasting based on ISO-New England data. Example 2
demonstrates the efficiency of substation differentiation for substation load forecasting based on the data from 23 substations within two zones. II.
LITERATURE REVIEW
Load forecasting methods developed for a large power system can be separated into regression models and artificial neural networks. Regression models such as Autoregressive Integrated Moving Average (ARIMA) [2] and time series method [3]. Regression models assume the functional forms that describe relationships between load and affecting factors. Coefficients of the functions are estimated based on historical data. Artificial neural network is widely used since it can strongly approximate the nonlinear function between load and affecting factors through learning historical data to adjust weights of networks [4]. Extended Kalman filter (EKF) as a learning algorithm has been used for the training of neural network by treating the weights as the state [5]. Decoupled EKF, which is simplified from EKF, can reduce the computation time by ignoring some dependency of weights [5]. A Neural network-based market clearing prices forecaster with Decoupled EKF is presented in [6]. Other research introduced the modifications of neural network and input selection to improve forecasting accuracy. Wavelet Neural Network-based very short-term load forecasting was presented in [7] to capture load features via forecasting each frequency load component individually. To properly select the historical load fed into neural networks, similar day-based neural network was addressed in [8]. Methods reported about small areas load forecasting such as substation or a feeder load forecasting include single level forecasting models and hierarchical forecasting models. Single level forecasting model intended to forecast one substation/feeder load without considering aggregation or distribution level [9]. Hierarchical forecasting model can forecast the load at any defined hierarchy level [10], [11], [12]. Bus Load Distribution Factor (BLDF) was used in [10] to forecast the bus load from outputs of upper grid level (i.e., zonal load). The BLDF is the ratio of bus load to zonal load. With BLDF updated every forecasting horizon, real time bus load forecasting can be obtained from the proportion of zonal load forecasting results. However, since patterns of bus load could be quite different from that of zonal load, not all buses can be properly forecasted by distribution factors. Different load patterns of small regions within a large geographic area are addressed in [12]. Load diversity is quantified in [12] to represent the level that different regional load patterns affect overall system load. These previous research addressed load patterns analysis of small area and opened a new way for handling small area load forecasting. III.
SUBSTATION LOAD ANALYSIS AND SUBSTATION DIFFERENTIATION
It is time consuming and impractical to forecast each substation via individual model considering the large number of substations within one area. Our idea is to differentiate the substations into two types based on comparison of substation load pattern to that of zonal load and have particular methods to focus on each type. Therefore, load forecasting of all
substations would be simplified. Section III-A analyzes load features of substations compared with those of large system load. Section III-B describes a criterion to separate the substations into two types based on their load pattern similarity to zonal load. A.
Substation Load Features
The comparisons between a typical large system load and several substation loads are shown in Fig. 1. Clear load patterns can be expected from the total system load. However, different substations show different load features even though they share the similar weather information. This is because substation load could be dominated by a few major customers such as industrial companies or schools and varies due to factors hidden from the outside. Therefore, it is not proper to use the same method as what are used for large system load forecasting. In addition, considering the large number of substations (e.g., 200 substations in New Zealand), an efficient way to forecast load of all the substations is required.
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