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Implementation of advanced functionalities for Distribution Management Systems: Load forecasting and modeling through Artificial Neural Networks ensembles
Electric Power Systems Research 167 (2019) 230–239
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Electric Power Systems Research journal homepage: www.elsevier.com/locate/epsr
Implementation of advanced functionalities for Distribution Management Systems: Load forecasting and modeling through Artificial Neural Networks ensembles
T
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M. Saviozzi , S. Massucco, F. Silvestro Department of Electrical, Electronics and Telecommunication Engineering and Naval Architecture, Università degli Studi di Genova, Via all’Opera Pia 11a, 16145 Genova, Italy
A R T I C LE I N FO
A B S T R A C T
Keywords: Distribution Management System Load forecasting Load modeling Artificial Neural Networks
Electric power systems are undergoing significant changes in all sectors at all voltage levels. The growing penetration of Renewable Energy Resources (RES), the liberalization of energy markets, the spread of active customers, the increasing diffusion of green energy policies to foster sustainable and low-emission policies, represent the main drivers in the evolution of the electric system. For these reasons, Distribution System Operators (DSO) are asked to adopt modern Distribution Management Systems (DMS) in order to manage RES uncertainties for an efficient, flexible and economic operation of distribution systems. In this context, the paper presents the design and the implementation in a real DMS of two advanced functionalities: load forecasting and load modeling. These two algorithms are based on an ensemble of Artificial Neural Networks (ANN). The good performances obtained on a real distribution network encourage the exploitation of the two proposed techniques to deal with demand uncertainties, in order to use efficiently the controllable resources and to face the stochastic behavior of RES.
1. Introduction
1.1. Load forecasting
Economic policies, technological improvements and social drivers have a crucial impact on electric power systems. The thrive of Renewable Energy Sources (RES) in a distributed configuration is radically changing systems operation. In the distribution system bi-directional power flows are becoming very common together with voltage violations, unexpected production and consumption peaks, while at transmission level these phenomena can cause stability/security issues and economic losses. For these reasons, Distribution System Operators (DSO) are asked to exploit advanced control systems [1] to implement specific strategies for the uncertainty management brought by RES [2]. These requests can be satisfied by modern Distribution Management Systems (DMS) [3,4]. Such systems are equipped with advanced functionalities that are able to manage controllable devices (distributed generators, storage systems, etc.), incentive the exploitation of RES and deal with active customers, providing economical benefits and a valuable support to DSO. Load forecasting and load modeling represent two important functionalities for an advanced DMS.
Load forecasting represents an important research field and a fundamental functionality for advanced DMS. In this last context, load forecasting is necessary to provide crucial inputs to other algorithms within DMS [5], such as state estimation, optimization procedures, voltage support, optimal reconfiguration, maintenance scheduling, etc. In general, a reliable load forecasting function is essential for the coordination of the uncertainty brought by renewable generation with actual demand, also considering the diffusion of active demand side management and load shaping strategies. The load forecasting methodologies with high accuracy can provide economic and environmental benefits, fostering the RES exploitation [6]. In addition, load forecasting is useful for grid security, providing crucial information in order to determine in advance imbalances and vulnerable scenarios. The load forecasting topic has been widely researched by the scientific community, which has proposed a variety of solutions in the last years. In [7,8] Wavelet Decomposition has been used for short-term load forecasting, while in [9] a probabilistic methodology based on quantile regression averaging has been exploited to predict load absorption. Authors in [10] propose a load forecasting model for
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Corresponding author. E-mail addresses: [email protected] (M. Saviozzi), [email protected] (S. Massucco), [email protected] (F. Silvestro).
https://doi.org/10.1016/j.epsr.2018.10.036 Received 5 February 2018; Received in revised form 9 September 2018; Accepted 30 October 2018 Available online 16 November 2018 0378-7796/ © 2018 Elsevier B.V. All rights reserved.
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The novelty of this paper is represented by the exploitation of the Basic Ensemble Method (BEM) coupled with a formal procedure for the selection of neurons number and ANN number involved in the ensemble. In addition, the BEM approach has been implemented within a real DMS and exploited for the load forecast/modeling of MV/LV substations. Thus, the proposed approach has been validated through online field tests. The rest of the paper is organized as follows: Section 2 describes a proposed and implemented DMS, Section 3 presents the test site that uses the DMS previously introduced; Section 4 is dedicated to the proposed load forecasting methodology. This section covers all the aspects of this advanced functionality from the description of the implemented strategy to the presentation of the real field results. Section 5 collects all the information about the proposed load modeling algorithm. Finally, Section 6 draws the conclusions of this work focusing also on possible future developments.
industrial application in Smart Grids based on Artificial Neural Networks (ANN), feature selection technique and enhanced differential evolution algorithm. In [11] and [12] single ANN approach has been implemented to predict electrical consumption of hospital facilities, while in [13] a short-term load forecasting procedure through boosted ANN is presented. Authors in [14] propose a hybrid method that combines support vector machine technique with an optimization search algorithm. Finally, in [15] an ensemble forecasting method is presented. 1.2. Load modeling The estimation process of a typical daily load curve (also called load profile, load shape or load pattern) of a generic customer (industrial, residential and commercial) or a distribution transformer/substation is called load modeling. For distribution system analysis, a very useful information is the modeling of the aggregated demand at feeder level. Load modeling of customers aggregation or Medium Voltage (MV)/Low Voltage (LV) substations can be very important for DSO, since it would be very expensive and not always feasible for them to monitor all their electrical networks [16–18], due to communication and infrastructure issues. Thus, load modeling is a crucial procedure in distribution systems because it is able to provide information about demand side behavior in order to:
2. Distribution Management System A Distribution Management System monitors and manages commercial and residential loads at distribution voltage level. A DMS is basically a distributed control system which is able to perform a huge amount of on/off-line analysis, concerning fault location and management, reconfiguration or restoration process, etc. [31]. The communication among the controllable devices (breakers, generators, etc.) is operated by a Supervisory Control And Data Acquisition (SCADA) system that is integrated with the DMS. SCADA is a central unit that acquires data from the field and sends commands to the controllable devices.
• study the possible impacts of load shedding or active demand response programs [19]; • analyze the technical and economic advantages that can be realized by the installation of new distributed energy resources [20]; • provide information for planning maintenance activities for DSO; • study possible implementations of special incentives/tariffs dedicated to each customer group [20]; correlate the variation of the power flow, node voltage profile or • •
2.1. Proposed architecture In this work the considered DMS (see Fig. 1) has been designed and implemented within SmartGen and Podcast projects [5]. A complete integration with a SCADA system allows to the abovementioned DMS to control a distribution grid at low and medium voltage level. In this case SCADA communicates with the field using standard protocols (IEC 61850, Modbus TCP/IP and Remote Terminal Unit (RTU)). The acquired data from the distribution network are exploited by advanced functionalities (included load forecasting and load modeling described in the following sections) that are implemented within the DMS. In addition, the DMS is equipped with two main database that collect respectively real time measures and historical data for experimental analysis.
network losses to the specific demand shapes of various consumers aggregations [20]; provide essential inputs for DMS advanced functionalities in order to face the uncertanties due to load demand [21,16,18].
The importance of load modeling in the research community is increasing in the last years and different interesting techniques have been proposed to estimate load profiles. In [22] a statistical method for modeling load uncertainty in distribution network is described, while in [17] a fuzzy logic technique is exploited to estimate load pattern for distribution transformers along MV feeders. In [23] Monte-Carlo method has been proposed to approximate residential load shapes, while with the same goal authors in [24] exploit Gaussian functions. In [25] and [19] bottom-up approaches are adopted respectively to train a probabilistic neural network and to find the best-fitting probability distribution for residential pattern. The works proposed in [20,26,27] model the domestic power consumption through Beta probability density function. The methodology presented in [28] unifies voltage dependent and constant real power model to estimate load patterns, while [29] describes a modified version of the K-means algorithm in power load curves profiling. In [30] typical load profiles for transformers at secondary substations have been approximated in order to assist planning engineers in network management. In [16] authors present an ANN approach to create pseudo-measurements in terms of typical load profiles, used in a DMS and tested on a generic distribution system. Finally, authors in [21] propose a load modeling procedure for MV/LV substation based on a single ANN. This work aims to describe the design and the implementation of two advanced functionalities: load forecasting and load modeling, within a real DMS. In particular, a day-ahead load forecasting procedure and a load modeling technique, based on an ensemble of Artificial Neural Networks, are presented.
3. Test site For this work the considered test site is a portion of a real distribution system (Sanremo, Italy), managed by AMAIE S.p.A., which covers about half of the municipal area of Sanremo, including both urban and rural areas. The DMS presented in Section 2.1 has been installed in this electrical network. AMAIE S.p.A. operates an electrical distribution network at 15 kV Medium Voltage (MV) level, composed of ten feeders, which are connected to a unique primary substation High Voltage (HV)/MV and supply both urban and rural areas in the district of the city of Sanremo. On the MV side, each transformer is connected to a 154 kV busbar. The two bars are connected through a parallel switch, normally closed to allow the operation using one transformer at turn, and constitute a unique electrical node. From this node, ten MV feeders branch off to supply 186 public substations (MV/LV) and globally 17 MV users. A part of the cited ten feeders has been equipped with a measurement system at the secondary wiring of the local MV/LV transformer, as shown in Fig. 2, and remotely acquired by the SCADA system. The 231
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Fig. 1. Communication architecture.
where each MV feeder is highlighted with a different color. The feeders not equipped with any instrumentation have been included in the CIM model as an equivalent load, which is connected to the MV busbar in the primary substation, absorbing the active power measured by feeder protection, installed in the substation and acquired by the SCADA. The model, according to the CIM standard, is three-phase and includes the MV/LV transformers in each secondary substation, where the LV network is represented by an equivalent aggregated load. In the same way, potential LV capacitor banks, for reactive power compensation, have been modeled. The dataset exploited in this work is composed of all the data related to the load absorption of the monitored MV/LV substations, which are the objects of the forecasting and modeling procedures. Fig. 2. AMAIE test site, scheme of installation of the measurement devices in the secondary substation.
4. Load forecasting
measures are acquired with a granularity equal to one minute. The measurement system is composed of:
In this section a load forecasting procedure based on ensemble of neural networks is presented. In particular, Multi-Layer Perceptron (MLP) (see Fig. 4) has been chosen as the architecture of the networks within the Ensemble Averaging Method. MLP is a particular type of ANN composed of three different layers: input, hidden and output. This methodology is currently used in the presented DMS in order to predict the load absorption of the MV/LV substations described in Section 3. The time horizon of the load forecast has been set to 24 hours with a granularity of 15 min.
• three single-phase current transformers; • one multimeter “DIRIS A-10” produced by “Socomec”, which provides measures of current, voltage and instantaneous power; • modem router GPRS “InRouter” produced by “InHand Networks” as interface with the SCADA through Modbus RTU/Modbus TCP protocol.
Globally, 22 public substations (MV/LV) have been equipped with the monitoring system. These substations present RES (especially PV domestic plants) at LV side with unknown power injections. For all the mentioned substations the percentage of RES is not dominant with respect to the electrical load. The set of substations to monitor has been chosen in order to provide a significant scenario to the algorithms embedded in the DMS. According to the left plot of Fig. 3, the entire feeder n°8 (15 substations) and two of its possible re-closures, through the feeders n°4 and n°9 (three substations each), are monitored, with the addition of the large PV plant (470 kWp) connected along the feeder n°10. Due to communication issues it was not possible to install the measurement devices in all the substations along the feeders. The portion of the network depicted in Fig. 3 has been electrically modeled into the Common Information Model (CIM) database of the DMS. The global layout of the distribution system is provided in Fig. 3,
4.1. Ensemble Averaging Method Ensemble Averaging Method is usually implemented in order to achieve more accurate results than a single ANN. The main idea of this method is to train different networks and combining their outputs in order to have a better prediction of the load. Two fundamental factors can contribute to improve the prediction results:
• The combined effect of several networks compensates the different •
random initializations. Indeed, errors between different neural networks are usually not correlated; Each concurrent MLP employs a slightly different number of hidden units. There are several types of ensemble methods. In this work the Basic
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Fig. 3. AMAIE test site, HMI layout of the grid portion modeled in the proposed DMS.
Table 1 ANN inputs example. 15 min. intervals of the day
Day of week
24 h average load [kWh]
Day ahead load [kWh]
7 days ahead load [kWh]
Weekday Holiday
1 2 ⋮ 96
7 7 ⋮ 7
144.20 144.22 ⋮ 153.24
111.94 112.36 ⋮ 90.21
110.11 105.18 ⋮ 96.36
0 0 ⋮ 0
4.3. Key Performance Indicators Fig. 4. General multi-layers perceptron.
In this work two Key Performance Indicators (KPI) have been exploited in order to assess the forecast quality:
Ensemble Method (BEM) has been analyzed and implemented. The BEM output is defined as:
fBEM (t ) =
1 n
• Mean Absolute Percentage Error (MAPE), defined as:
n
∑ fi (t ); i=1
MAPE = (1)
where n is the total number of neural networks and fi(t), with i = 1, …, n, are the single ANN outputs defined as a function of time index t.
1 N
N
|yi − yˆi | ·100% yi
∑ i=1
(2)
where N is the cardinality of the test set, yi is the measured load of the ith element in the test set and yˆi is the ith predicted value. Notice that
|yi − yˆi | ·100% yi
represents the percentage error on the ith element.
• Daily Peak Mean Absolute Percentage Error (DPMAPE), defined as
4.2. ANN inputs
follows:
One of the most important parts for the implementation of an accurate forecast procedure with the exploitation of ANN is the choice of appropriate input variables. According to [11,12] the inputs for all the neural networks used in this work are the following:
• • • • • •
DPMAPE =
1 D
D
∑ i=1
|max(yd ) − max(yˆd )| ·100% max(yd )
(3)
where D is the days number considered in the test set, yd is the maximum measured load of the dth day of the test set, yˆd is the maximum forecast value on the same day. DPMAPE helps to analyze the forecasting behavior with respect to the load peaks. Notice that the maximum predicted value may be occurred in a different time of the day from the maximum measured value.
96 numbers between 1 and 96 defining the 15-minute intervals of the day; 96 values ranging from 1 = Sunday to 6 = Saturday representing the day of the week; 96 values defining the 24-hour-ahead average load; 96 values related to the day-ahead load; 96 values for the 7 days-ahead load; 96 Boolean values to distinguish between holiday and weekday (0 = holiday, 1 = weekday).
In addition to these indexes also the Lilliefors test has been exploited in this work. The Lilliefors test allows to establish if a vector comes from a distribution in the normal family [32]. In the final simulations this test has been used to verify if the error distribution can be approximated through a Normal distribution with null mean.
An example of inputs for a single ANN is shown in Table 1. Notice that the output of the proposed method is composed of 96 values representing the load for the next 24 h with 15 min granularity, which represents the standard monitoring interval adopted by the Italian DSO. The data related to the historical load are stored by the SCADA system in a dedicated database that is directly available by the algorithms, implemented within the DMS, for the training of the different networks and for the parameters selection (see Section 4.4).
4.4. Parameters selection After the inputs definition, another important task for the design of a robust forecasting procedure is the parameter selection of the multilayer perceptrons. The parameters for a generic MLP can be summarized in the following list: 233
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Table 2 ANN parameters selection.
Table 3 Best five neurons number for the considered MV/LV substations.
Hidden Layers number
1
Substation
Optimal
2nd
3rd
4th
5th
Transfer functions, input-hidden layer Transfer function, hidden-output layer Training algorithm
Hyperbolic Tangent Sigmoid Function Hyperbolic Tangent Sigmoid Function Resilient Backprogation
9 10 11_1 12 14 18 19 47_1 47_3 48 79 87_1 087_2 103 107 126 131 142 200 217 220 222
25 13 6 34 4 19 49 12 15 23 23 12 16 15 16 13 17 8 21 36 7 18
20 7 3 40 26 11 35 14 21 44 59 5 38 27 14 56 3 76 28 8 29 21
15 5 8 26 3 15 27 3 18 81 55 31 11 34 15 42 40 47 17 61 14 26
30 33 4 22 12 53 44 8 22 51 44 7 45 31 45 46 33 24 12 59 6 44
35 40 9 32 33 30 10 7 13 60 25 41 22 5 41 61 46 37 20 30 11 19
• number of hidden layers; • neurons number in the hidden layers; • transfer functions among each layer. These functions define the relation of the inputs and the outputs among each layer; training algorithm. In order to use an ANN for any possible appli• cation, the network has to be trained on a knowledge database. This set is called training set and consists of an input vector and a score vector. The training algorithm defines the training methodology.
In addition to these parameters related to a single MLP, for the BEM method is also crucial the choice of the ANN number (i.e. parameter n in (1)). For the selection of the number of hidden layers, the training algorithm and the transfer functions, the methodology proposed in [11], [12] has been exploited on a single MLP. Table 2 collects the final results for these parameters. This configuration has been exploited for the forecasting of all the MV/LV substations described in Section 3.
simulation resulted to be 15, with a MAPE of 4.37%. Top 5 neuron numbers for each substation are collected in Table 3. These results have been exploited in the selection of the best ANN number for the BEM.
4.4.1. Neurons number selection For the implementation of the BEM within the described DMS a new method for the optimal selection of neurons number in the hidden layer has been introduced. The number of neurons has been chosen for each MV/LV monitored substations, thus one ensemble for each substation has been trained and used to predict its load absorption. This selection procedure consists of training and testing (on the same training and test set) single multi-layer perceptrons having different number of neurons in the hidden layer starting from 3 and increasing by 1 for each iteration. For this selection the training set is composed of 8-months of data from all the seasons, while the test set consists in a single month. The MLP inputs used in this phase have been the ones presented in Section 4.2, while the ANN configuration has been the one reported in Table 2. The number of neurons has been chosen considering the best MAPE value in the test phase. If the MAPE of a certain network with k neurons has been higher than 10% (i.e. the considered threshold), the neural networks with more neurons than k have not been considered in the selection procedure. An example of this selection procedure is reported in Fig. 5. In this case a net with 52 neurons number providing an unsatisfying 25% of MAPE and also a very high DPMAPE. This is an example of over-fitting. Notice that the best neurons number for this
4.4.2. ANN number for BEM In order to select the best ANN number for the ensemble averaging method for each MV/LV substation, 5 different perceptrons, with the top 5 neurons number collected in Table 3, and with all the parameters reported in Table 2 have been considered. These 5 different networks have been trained on 14 different random initialization of the weights related to the transfer functions, thus 70 nets in total. Training set has been composed of 8-months of data, while test set consists in a single month. The neural networks have been ordered with respect to the MAPE error on the common test set. Then, they are combined starting from the best by adding to the BEM mean the best remaining net. The best combination of the n first networks is chosen as the best ensemble. Fig. 6 represents the MAPE as a function of networks number involved in the ensemble for substation 9. The error function decreases and reaches its minimum for n = 3, then it starts to increase. Thus, in this case (substation 9), 3 is chosen as ANN number for the BEM. Table 4 contains the best ANN number for the ensemble method. The parameters selection, that also includes the ANN training, is completely automatic and embedded in the proposed DMS. It presents a computational duration that varies from 30 to 90 s for a single
Fig. 5. Neurons number selection for substation 103.
Fig. 6. ANN number selection for substation 9. 234
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single ANN as proposed in [11,12]. Single ANN has been trained and used with the same inputs and parameters exploited in the ensemble approach. The optimal number for the neurons in the hidden layer, collected in Table 3, have been used for the single ANN approach. The last column of Table 3 reports the results of Lilliefors test with a 10% significance level. As can be seen from Table 6 the results in terms of MAPE for the BEM are always under 5%. In addition, the proposed method is able to improve the results provided by the single ANN approach in all the considered substations. The error distribution can be approximated with a normal function in 20 cases out of 22 according to the Lilliefors test. Results testify also that the presented methodology is not affected by the distributed RES connected at LV level in a case wherein the power injections are not prevalent with respect to the electrical load (less than 25%). For other scenarios, which can include also reverse power flows, the exploitation of a dedicated RES forecasting procedure [5] (currently implemented in the proposed DMS), is more reliable [33–35]. This can be used with the proposed approach in order to estimate accurately the power absorption/injection (net load) of a MV/LV substation with a high RES penetration level. Table 7 collects the load forecasting results in terms of DPMAPE. Also for this KPI the values related to the BEM are lower in almost all the substations. This establishes that the ensemble approach is better and more reliable in the prediction of the load peaks compared to the single MLP approach. Fig. 7 depicts the load forecasting results for substation 87_1. In this figure the black curve represents the output of the BEM, while in grey is reported the actual substation load absorption. As can be seen from Fig. 7 the results of the proposed procedure are very satisfactory. Fig. 8 reports the error distribution and the normal QQ-plot of substation 10 for a month of the load forecasting proposed procedure. These results coupled with the Lilliefors test state that the error distribution can be approximated through a Gaussian distribution. In [36] an Auto Regressive Integrated Moving Average (ARIMA) model has been implemented to forecast the consumption of MV/LV substations with errors in terms of MAPE included from 4% to 8%. MV/ LV substations have been the objects of the load forecast also in [37], wherein a time series model has been exploited with MAPE around 15%, and in [38], where authors reach a MAPE close to 10% with an ANN based methodology. In [39] bus forecasting models based on clustering techniques and ANN present an average MAPE slightly over 5%. In [40] ANN hybrid approaches have been used to predict transmission/distribution bus loads with MAPE values from 3% to 7.5%. This literature comparison proves that the proposed BEM approach is a robust and an accurate procedure, representing a useful and reliable advanced functionality for a real DMS.
Table 4 ANN number selection for the BEM. Substation
Size ensemble
Substation
Size ensemble
9 10 11_1 12 14 18 19 47_1 47_3 48 79
3 2 8 4 2 3 5 3 7 3 5
87_1 87_2 103 107 126 131 142 200 217 220 222
4 7 9 3 3 2 5 3 4 5 3
Table 5 Computer specifications. Processor
RAM
Operating system
Intel Xeon CPU E3-1245 @ 3.40 GHz
16 GB
Windows 7 Professional
substation. The computer specifications are reported in Table 5, while the software used for the methodology implementation has been Matlab R2016a. The results in terms of computational speed support the applicability of the proposed procedure in a real distribution network. For this kind of algorithms also a cloud computing solution can be a valuable resource in order to speed up the training phase for large systems. 4.5. Real field results In this section the load forecasting results, obtained with the BEM, are presented. The results are related to the forecast of the 2016 load absorption of the MV/LV substations of network managed by AMAIE. All neural networks exploited in the DMS, described in Section 3, have been trained on the same dataset used for the optimal neurons number selection and for the choice of ANN number for the BEM. The ANN parameters used for the BEM are the ones presented in Section 4.2. Table 6 collects the results in terms of MAPE for the BEM forecasting procedure and compares them with the ones obtained exploiting a Table 6 MAPE on the same test sets for networks ensemble and single network. Substation
BEM MAPE
Single Net MAPE
Lilliefors Test
9 10 11_1 12 14 18 19 47_1 47_3 48 79 87_1 87_2 103 107 126 131 142 200 217 220 222
3.36% 4.60% 3.98% 4.86% 4.13% 3.46% 3.60% 3.33% 4.24% 4.83% 3.24% 2.92% 3.47% 4.36% 3.34% 4.84% 3.62% 4.54% 3.81% 3.64% 4.70% 3.65%
3.43% 4.81% 4.61% 7.01% 6.17% 5.38% 3.66% 4.46% 4.50% 6.06% 3.32% 3.02% 3.59% 4.37% 3.36% 6.00% 3.70% 4.65% 4.76% 3.73% 6.08% 3.69%
✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✗ ✓ ✓ ✓ ✓ ✓ ✓ ✗ ✓ ✓ ✓ ✓
4.6. ANN re-training The proposed DMS has been configured in order to monitor the Table 7 DPMAPE on the same test sets for networks ensemble and single network.
235
Sub.
BEM DP
Single Net DP
Sub.
BEM DP
Single Net DP
9 10 11_1 12 14 18 19 47_1 47_3 48 79
1.35% 3.70% 4.99% 5.56% 4.08% 2.33% 3.57% 2.72% 1.96% 3.14% 3.12%
1.70% 4.78% 7.08% 5.85% 4.13% 2.34% 3.66% 3.20% 2.19% 3.18% 3.15%
87_1 87_2 103 107 126 131 142 200 217 220 222
1.70% 2.72% 2.44% 2.60% 3.16% 2.29% 3.09% 3.01% 4.04% 6.14% 3.65%
1.85% 2.88% 2.83% 2.84% 4.62% 2.24% 3.33% 3.03% 4.33% 6.73% 3.52%
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The good performances reported in this work suggest that this methodology can be used to forecast other types of load at different levels on the grid. Finally, load forecasting techniques with this level of accuracy can significantly help to deal with uncertainties due to the demand, in order to exploit the controllable resources efficiently and to face the stochastic behavior of renewables. 5. Load modeling In this section a technique based on ANN, data collected from field measurements and information related to networks customers is described. In particular, the proposed methodology is very similar to the one described in Section 4 for load forecasting. This algorithm is able to produce MV/LV substations typical load profiles identifying a correlation among the aggregated patterns and the customer types. The proposed procedure adopts ANN coupled with Fourier Decomposition. It has been tested and validated with real field results to model the aggregated load patterns of the secondary MV/LV substations described in Section 3.
Fig. 7. Load forecasting with BEM for substation 87_1 (first week of May 2016).
performance of the ANN ensemble. A low level of accuracy in the load forecast of a specific substation can be related to:
• grid faults or malfunctions; • changes in the network topology and/or in the composition of the load/generation.
5.1. Dataset
If the MAPE value related to the load prediction of a certain substation is higher than a predefined threshold, the DMS is enabled to send a warning to the DSO that can decide if it is necessary to re-train the ANN ensemble or to not consider the unsatisfactory forecast (temporary issue).
This section presents the essential inputs for the implemented load modeling procedure. The dataset is composed of historical load time series and data related to the network customers. 5.1.1. Historical data As previously described in Section 2.1, the SCADA system is able to manage a historical dataset acquired from the measurement system installed on the network managed by AMAIE. In order to design and validate the proposed load modeling procedure, data related to 18 MV/LV substations have been exploited. In particular, the available measurements of the active power, starting from 2013, have been used.
4.7. Final comments on load forecasting The proposed strategy for load forecasting has been validated on real data and with on-line tests. The comparison with the state of the art proves that the presented approach is robust, accurate and can be implemented within real DMS. Results have been very satisfactory with low MAPE and DPMAPE values. Also the statistical analysis of the forecast error proves that the uncertainties due the prediction procedures can be modeled as normal distribution. In addition, a detailed analysis and an innovative formal procedure for the selection of all the ANN parameters has been described and supported by simulation/field results. The computational speed and the simplicity of the BEM approach represent two fundamental advantages of the proposed approach.
5.1.2. Customer characterization The DSO (AMAIE) provided the commercial and technical information related to the customers connected to the above mentioned 18 MV/LV substations of the monitored feeder. For each of these substations the DSO is able to produce the following data:
• type of customers;
Fig. 8. Statistical analysis of the error for substation 10. 236
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Table 8 Contractual powers of customers. Substation
Contractual Power [kW]
9 10 11 14 18 19 47 48 79 87 103 126 131 142 159 200 217 222
Res.
Com.
P. L.
Total
800.0 441.0 840.0 238.5 1239.5 676.5 2282.0 801.5 1500.0 802.0 580.5 611.5 1011.5 598.5 647.0 1071.0 1153.5 594.5
57.5 56.3 97.5 170.5 178.5 92.3 608.5 293.5 509.3 1055.0 102.5 39.0 314.0 43.0 264.3 155.0 278.0 439.0
39.6 – – 7.5 20.0 – 99.8 – – 25.0 11.0 – – – 6.0 15.0 6.0 –
897.1 497.3 937.5 416.5 1438.0 768.8 2990.3 1095.0 2009.3 1882.0 694.0 650.5 1325.5 641.5 917.3 1241.0 1437.5 1033.5
Fig. 10. Load modeling with BEM for substation 222 (holiday, November 2016).
The data related to the contractual powers (non-monitored substations included) are all stored in a specific database managed by the SCADA system and always available for the DMS. This database is updated in case of changes in the grid configuration. 5.2. Typical power shapes
Table 9 Load modeling results with ensemble approach. Subst.
Weekday
217 (4; 5; 3) 222 (3; 3; 4)
Pre-holiday
Holiday
MAPE
MPE
MAPE
MPE
MAPE
MPE
6.20 % 7.09 %
14.19 % 16.67 %
7.00 % 5.08 %
13.75 % 15.06 %
6.17 % 5.05 %
13.46 % 18.74 %
The above-mentioned MV/LV substations are composed of different compositions of the three load types previously presented (see Table 8). Since these data represent all the available information, it is not possible to identify the share of the single customer type. This entails that the characterization of the typical load share of a generic MV/LV substation is a quite difficult problem, due to the absence of information related to a pure commercial, residential and public lighting load profile. For this reason, an ANN ensemble technique has been used for the estimation of the typical load shapes for a secondary substation, exploiting data related to the time of the day and contractual powers. For this goal, the typical shapes of the described monitored secondary substations have been evaluated calculating the mean daily active load profiles acquired by the SCADA with a time step equal to 15 min. In order to simplify the ANN training, 6th order Fourier Decomposition (FD) has been employed to the above-mentioned typical power shapes:
Table 10 Load modeling results with single ANN approach. Subst.
217 222
Weekday
Pre-holiday
Holiday
MAPE
MPE
MAPE
MPE
MAPE
MPE
7.52 % 7.17 %
30.89 % 18.97 %
8.39 % 5.71 %
35.19 % 16.77 %
10.18 % 5.32 %
42.13 % 20.68 %
6
y˜ (t ) = α 0 +
∑ αi cos(wi t ) + βj sin(wi t ),
t = 1, …, 96
i=1
(4)
where αi (i = 0, …, 6) and βi (i = 1, …, 6) are the Fourier coefficients, while t represents the time index on the 15 min intervals that compose a day. The 6th order FD has been chosen, since it has been the best in terms of approximation error. The application of FD is a fundamental step for the proposed modeling procedure in order to perform a reliable and robust training for the neural networks involved in the ensemble. The typical load shape for each substation has been estimated for three different day typologies: weekday, pre-holiday and holiday. This has been done to achieve the highest precision for the proposed load modeling functionality. Fig. 9. Load modeling with BEM for substation 222 (pre-holiday, November 2016).
5.3. BEM for load modeling In this work BEM method has been used also for the load modeling of the non-monitored MV/LV substations of AMAIE network. The ANN architecture exploited in the ensemble approach is MLP, i.e. the same implemented approach for the load forecasting. According to the dataset description (Section 5.1) the inputs for BEM in this application are:
• contractual power; • number of customers. These information are sufficient to aggregate the power absorption according to the type of customer in order to obtain the total contractual power of the same load type for each MV/LV substation. Table 8 reports the different contributions of contractual powers for the MV/LV monitored substations: Residential Customers (Res.), Commercial Customers (Com.), Public Lighting (P.I.) and Total (i.e. the sum of the three previous load types).
• 96 values defining the commercial Contractual Power; • 96 values representing the residential contractual power; • 96 values for the public lighting contractual power; • 96 values representing the total contractual power; 237
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• 96 values among 1 and 96 representing the 15-minutes intervals of
procedure is able to provide with very satisfactory accuracy the estimation of the typical active power profiles of real MV/LV substations with the goal to provide essential inputs for DMS advanced functionalities in order to face the uncertainties due to load demand. Results proved to be very interesting with fast computational speed and reasonable errors in terms of MAPE and MPE, as can be noticed from Table 9 and Figs. 9, 10. In addition, one of the strong points of this approach is represented by the fact that neural networks do not have to be retrained if there are changes in the load composition. The test showed that the ensemble approach can improve significantly the results obtained with a single ANN approach.
the day.
Notice that the inputs related to the contractual powers are constant since they do not change during the day. The output of the proposed load modeling algorithm based on BEM is composed of 96 values determining the typical load shape, presented in Section 5.2, of a generic secondary substation. One of the strong points of this approach is that the neural networks do not have to be retrained if there are changes in the load composition of the considered substation. In this case, it will be sufficient to update the inputs related to the loads configuration (see Section 5.1.2 and Table 8).
6. Conclusions In this work the design, the implementation and the validation of two advanced functionalities for a real DMS have been presented. In addition, a description of a proposed architecture for a DMS has been provided. The performance of the two algorithms on field tests has been very satisfactory, as proved also by a comparison in terms of accuracy with the literature. Future developments can investigate an evolution of Basic Ensemble Method exploited for both proposed procedures. Sophisticated ensemble approaches are able to weigh the neural networks involved, in order to use mainly the ones that can provide the highest accuracy. This has to be done without forgetting that one of the strong point of the BEM is its simplicity.
5.4. Parameters selection The parameters selection for the MLP exploited in the ensemble approach has been performed according to [21]. Except for neurons number in the hidden layer and the ANN number for the BEM. These two choices have been done according to the formal procedure presented in Sections 4.4.1 and 4.4.2. The final configuration for the neural networks in the ensemble is collected in Table 2. The computational time for the training and parameters selection is around 30 seconds (see Table 5 for the computer specifications). 5.5. Real field results
References
This section presents the results related to the BEM load modeling. In order to validate the proposed methodology the algorithm has been tested to estimate the typical power shape for each day type (holiday, pre-holiday and weekday) of two monitored substations (217, 222). For these tests, three neural networks, one for each type of day, have been trained with the typical active power profiles, evaluated as the mean of the load curves of November 2016, for all the substations except 217 and 222. In addition to MAPE, in this application, also the Maximum Percentage Error (MPE) has been exploited to evaluate the presented modeling algorithm. Table 9 reports the results obtained with the BEM in terms of MAPE and MPE for the load modeling of the typical profile of November 2016 for substations 217 and 222. The ANN number composing the ensembles are shown in parenthesis in the first column of Table 9, while Table 10 collects the results for the load modeling of substations 217 and 222 using a single ANN approach (described in [21]). The previous tables show that the ensemble approach is able to improve significantly the results of the single ANN approach. MAPE and MPE values are lower in the BEM approach in all cases. Figs. 9 and 10 illustrate the comparison between the characteristic active power profiles and the ANN output. As can be seen from these figures the shapes provided by the proposed neural networks are very close to the typical active power profiles, for this reason the results are very satisfying. As occurred for the load forecasting procedure, the BEM method is able to provide a great level of accuracy in a scenario that presents a RES penetration that is not dominant with respect to the electrical load. For high RES penetration cases (more than 25% of the power absorption), it is recommended to use a dedicated RES forecasting technique [5], and to subtract the results of this procedure to the load modeling output in order to have a reliable estimation of the typical power shape (net load).
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Contents lists available at ScienceDirect
Electric Power Systems Research journal homepage: www.elsevier.com/locate/epsr
Implementation of advanced functionalities for Distribution Management Systems: Load forecasting and modeling through Artificial Neural Networks ensembles
T
⁎
M. Saviozzi , S. Massucco, F. Silvestro Department of Electrical, Electronics and Telecommunication Engineering and Naval Architecture, Università degli Studi di Genova, Via all’Opera Pia 11a, 16145 Genova, Italy
A R T I C LE I N FO
A B S T R A C T
Keywords: Distribution Management System Load forecasting Load modeling Artificial Neural Networks
Electric power systems are undergoing significant changes in all sectors at all voltage levels. The growing penetration of Renewable Energy Resources (RES), the liberalization of energy markets, the spread of active customers, the increasing diffusion of green energy policies to foster sustainable and low-emission policies, represent the main drivers in the evolution of the electric system. For these reasons, Distribution System Operators (DSO) are asked to adopt modern Distribution Management Systems (DMS) in order to manage RES uncertainties for an efficient, flexible and economic operation of distribution systems. In this context, the paper presents the design and the implementation in a real DMS of two advanced functionalities: load forecasting and load modeling. These two algorithms are based on an ensemble of Artificial Neural Networks (ANN). The good performances obtained on a real distribution network encourage the exploitation of the two proposed techniques to deal with demand uncertainties, in order to use efficiently the controllable resources and to face the stochastic behavior of RES.
1. Introduction
1.1. Load forecasting
Economic policies, technological improvements and social drivers have a crucial impact on electric power systems. The thrive of Renewable Energy Sources (RES) in a distributed configuration is radically changing systems operation. In the distribution system bi-directional power flows are becoming very common together with voltage violations, unexpected production and consumption peaks, while at transmission level these phenomena can cause stability/security issues and economic losses. For these reasons, Distribution System Operators (DSO) are asked to exploit advanced control systems [1] to implement specific strategies for the uncertainty management brought by RES [2]. These requests can be satisfied by modern Distribution Management Systems (DMS) [3,4]. Such systems are equipped with advanced functionalities that are able to manage controllable devices (distributed generators, storage systems, etc.), incentive the exploitation of RES and deal with active customers, providing economical benefits and a valuable support to DSO. Load forecasting and load modeling represent two important functionalities for an advanced DMS.
Load forecasting represents an important research field and a fundamental functionality for advanced DMS. In this last context, load forecasting is necessary to provide crucial inputs to other algorithms within DMS [5], such as state estimation, optimization procedures, voltage support, optimal reconfiguration, maintenance scheduling, etc. In general, a reliable load forecasting function is essential for the coordination of the uncertainty brought by renewable generation with actual demand, also considering the diffusion of active demand side management and load shaping strategies. The load forecasting methodologies with high accuracy can provide economic and environmental benefits, fostering the RES exploitation [6]. In addition, load forecasting is useful for grid security, providing crucial information in order to determine in advance imbalances and vulnerable scenarios. The load forecasting topic has been widely researched by the scientific community, which has proposed a variety of solutions in the last years. In [7,8] Wavelet Decomposition has been used for short-term load forecasting, while in [9] a probabilistic methodology based on quantile regression averaging has been exploited to predict load absorption. Authors in [10] propose a load forecasting model for
⁎
Corresponding author. E-mail addresses: [email protected] (M. Saviozzi), [email protected] (S. Massucco), [email protected] (F. Silvestro).
https://doi.org/10.1016/j.epsr.2018.10.036 Received 5 February 2018; Received in revised form 9 September 2018; Accepted 30 October 2018 Available online 16 November 2018 0378-7796/ © 2018 Elsevier B.V. All rights reserved.
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The novelty of this paper is represented by the exploitation of the Basic Ensemble Method (BEM) coupled with a formal procedure for the selection of neurons number and ANN number involved in the ensemble. In addition, the BEM approach has been implemented within a real DMS and exploited for the load forecast/modeling of MV/LV substations. Thus, the proposed approach has been validated through online field tests. The rest of the paper is organized as follows: Section 2 describes a proposed and implemented DMS, Section 3 presents the test site that uses the DMS previously introduced; Section 4 is dedicated to the proposed load forecasting methodology. This section covers all the aspects of this advanced functionality from the description of the implemented strategy to the presentation of the real field results. Section 5 collects all the information about the proposed load modeling algorithm. Finally, Section 6 draws the conclusions of this work focusing also on possible future developments.
industrial application in Smart Grids based on Artificial Neural Networks (ANN), feature selection technique and enhanced differential evolution algorithm. In [11] and [12] single ANN approach has been implemented to predict electrical consumption of hospital facilities, while in [13] a short-term load forecasting procedure through boosted ANN is presented. Authors in [14] propose a hybrid method that combines support vector machine technique with an optimization search algorithm. Finally, in [15] an ensemble forecasting method is presented. 1.2. Load modeling The estimation process of a typical daily load curve (also called load profile, load shape or load pattern) of a generic customer (industrial, residential and commercial) or a distribution transformer/substation is called load modeling. For distribution system analysis, a very useful information is the modeling of the aggregated demand at feeder level. Load modeling of customers aggregation or Medium Voltage (MV)/Low Voltage (LV) substations can be very important for DSO, since it would be very expensive and not always feasible for them to monitor all their electrical networks [16–18], due to communication and infrastructure issues. Thus, load modeling is a crucial procedure in distribution systems because it is able to provide information about demand side behavior in order to:
2. Distribution Management System A Distribution Management System monitors and manages commercial and residential loads at distribution voltage level. A DMS is basically a distributed control system which is able to perform a huge amount of on/off-line analysis, concerning fault location and management, reconfiguration or restoration process, etc. [31]. The communication among the controllable devices (breakers, generators, etc.) is operated by a Supervisory Control And Data Acquisition (SCADA) system that is integrated with the DMS. SCADA is a central unit that acquires data from the field and sends commands to the controllable devices.
• study the possible impacts of load shedding or active demand response programs [19]; • analyze the technical and economic advantages that can be realized by the installation of new distributed energy resources [20]; • provide information for planning maintenance activities for DSO; • study possible implementations of special incentives/tariffs dedicated to each customer group [20]; correlate the variation of the power flow, node voltage profile or • •
2.1. Proposed architecture In this work the considered DMS (see Fig. 1) has been designed and implemented within SmartGen and Podcast projects [5]. A complete integration with a SCADA system allows to the abovementioned DMS to control a distribution grid at low and medium voltage level. In this case SCADA communicates with the field using standard protocols (IEC 61850, Modbus TCP/IP and Remote Terminal Unit (RTU)). The acquired data from the distribution network are exploited by advanced functionalities (included load forecasting and load modeling described in the following sections) that are implemented within the DMS. In addition, the DMS is equipped with two main database that collect respectively real time measures and historical data for experimental analysis.
network losses to the specific demand shapes of various consumers aggregations [20]; provide essential inputs for DMS advanced functionalities in order to face the uncertanties due to load demand [21,16,18].
The importance of load modeling in the research community is increasing in the last years and different interesting techniques have been proposed to estimate load profiles. In [22] a statistical method for modeling load uncertainty in distribution network is described, while in [17] a fuzzy logic technique is exploited to estimate load pattern for distribution transformers along MV feeders. In [23] Monte-Carlo method has been proposed to approximate residential load shapes, while with the same goal authors in [24] exploit Gaussian functions. In [25] and [19] bottom-up approaches are adopted respectively to train a probabilistic neural network and to find the best-fitting probability distribution for residential pattern. The works proposed in [20,26,27] model the domestic power consumption through Beta probability density function. The methodology presented in [28] unifies voltage dependent and constant real power model to estimate load patterns, while [29] describes a modified version of the K-means algorithm in power load curves profiling. In [30] typical load profiles for transformers at secondary substations have been approximated in order to assist planning engineers in network management. In [16] authors present an ANN approach to create pseudo-measurements in terms of typical load profiles, used in a DMS and tested on a generic distribution system. Finally, authors in [21] propose a load modeling procedure for MV/LV substation based on a single ANN. This work aims to describe the design and the implementation of two advanced functionalities: load forecasting and load modeling, within a real DMS. In particular, a day-ahead load forecasting procedure and a load modeling technique, based on an ensemble of Artificial Neural Networks, are presented.
3. Test site For this work the considered test site is a portion of a real distribution system (Sanremo, Italy), managed by AMAIE S.p.A., which covers about half of the municipal area of Sanremo, including both urban and rural areas. The DMS presented in Section 2.1 has been installed in this electrical network. AMAIE S.p.A. operates an electrical distribution network at 15 kV Medium Voltage (MV) level, composed of ten feeders, which are connected to a unique primary substation High Voltage (HV)/MV and supply both urban and rural areas in the district of the city of Sanremo. On the MV side, each transformer is connected to a 154 kV busbar. The two bars are connected through a parallel switch, normally closed to allow the operation using one transformer at turn, and constitute a unique electrical node. From this node, ten MV feeders branch off to supply 186 public substations (MV/LV) and globally 17 MV users. A part of the cited ten feeders has been equipped with a measurement system at the secondary wiring of the local MV/LV transformer, as shown in Fig. 2, and remotely acquired by the SCADA system. The 231
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Fig. 1. Communication architecture.
where each MV feeder is highlighted with a different color. The feeders not equipped with any instrumentation have been included in the CIM model as an equivalent load, which is connected to the MV busbar in the primary substation, absorbing the active power measured by feeder protection, installed in the substation and acquired by the SCADA. The model, according to the CIM standard, is three-phase and includes the MV/LV transformers in each secondary substation, where the LV network is represented by an equivalent aggregated load. In the same way, potential LV capacitor banks, for reactive power compensation, have been modeled. The dataset exploited in this work is composed of all the data related to the load absorption of the monitored MV/LV substations, which are the objects of the forecasting and modeling procedures. Fig. 2. AMAIE test site, scheme of installation of the measurement devices in the secondary substation.
4. Load forecasting
measures are acquired with a granularity equal to one minute. The measurement system is composed of:
In this section a load forecasting procedure based on ensemble of neural networks is presented. In particular, Multi-Layer Perceptron (MLP) (see Fig. 4) has been chosen as the architecture of the networks within the Ensemble Averaging Method. MLP is a particular type of ANN composed of three different layers: input, hidden and output. This methodology is currently used in the presented DMS in order to predict the load absorption of the MV/LV substations described in Section 3. The time horizon of the load forecast has been set to 24 hours with a granularity of 15 min.
• three single-phase current transformers; • one multimeter “DIRIS A-10” produced by “Socomec”, which provides measures of current, voltage and instantaneous power; • modem router GPRS “InRouter” produced by “InHand Networks” as interface with the SCADA through Modbus RTU/Modbus TCP protocol.
Globally, 22 public substations (MV/LV) have been equipped with the monitoring system. These substations present RES (especially PV domestic plants) at LV side with unknown power injections. For all the mentioned substations the percentage of RES is not dominant with respect to the electrical load. The set of substations to monitor has been chosen in order to provide a significant scenario to the algorithms embedded in the DMS. According to the left plot of Fig. 3, the entire feeder n°8 (15 substations) and two of its possible re-closures, through the feeders n°4 and n°9 (three substations each), are monitored, with the addition of the large PV plant (470 kWp) connected along the feeder n°10. Due to communication issues it was not possible to install the measurement devices in all the substations along the feeders. The portion of the network depicted in Fig. 3 has been electrically modeled into the Common Information Model (CIM) database of the DMS. The global layout of the distribution system is provided in Fig. 3,
4.1. Ensemble Averaging Method Ensemble Averaging Method is usually implemented in order to achieve more accurate results than a single ANN. The main idea of this method is to train different networks and combining their outputs in order to have a better prediction of the load. Two fundamental factors can contribute to improve the prediction results:
• The combined effect of several networks compensates the different •
random initializations. Indeed, errors between different neural networks are usually not correlated; Each concurrent MLP employs a slightly different number of hidden units. There are several types of ensemble methods. In this work the Basic
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Fig. 3. AMAIE test site, HMI layout of the grid portion modeled in the proposed DMS.
Table 1 ANN inputs example. 15 min. intervals of the day
Day of week
24 h average load [kWh]
Day ahead load [kWh]
7 days ahead load [kWh]
Weekday Holiday
1 2 ⋮ 96
7 7 ⋮ 7
144.20 144.22 ⋮ 153.24
111.94 112.36 ⋮ 90.21
110.11 105.18 ⋮ 96.36
0 0 ⋮ 0
4.3. Key Performance Indicators Fig. 4. General multi-layers perceptron.
In this work two Key Performance Indicators (KPI) have been exploited in order to assess the forecast quality:
Ensemble Method (BEM) has been analyzed and implemented. The BEM output is defined as:
fBEM (t ) =
1 n
• Mean Absolute Percentage Error (MAPE), defined as:
n
∑ fi (t ); i=1
MAPE = (1)
where n is the total number of neural networks and fi(t), with i = 1, …, n, are the single ANN outputs defined as a function of time index t.
1 N
N
|yi − yˆi | ·100% yi
∑ i=1
(2)
where N is the cardinality of the test set, yi is the measured load of the ith element in the test set and yˆi is the ith predicted value. Notice that
|yi − yˆi | ·100% yi
represents the percentage error on the ith element.
• Daily Peak Mean Absolute Percentage Error (DPMAPE), defined as
4.2. ANN inputs
follows:
One of the most important parts for the implementation of an accurate forecast procedure with the exploitation of ANN is the choice of appropriate input variables. According to [11,12] the inputs for all the neural networks used in this work are the following:
• • • • • •
DPMAPE =
1 D
D
∑ i=1
|max(yd ) − max(yˆd )| ·100% max(yd )
(3)
where D is the days number considered in the test set, yd is the maximum measured load of the dth day of the test set, yˆd is the maximum forecast value on the same day. DPMAPE helps to analyze the forecasting behavior with respect to the load peaks. Notice that the maximum predicted value may be occurred in a different time of the day from the maximum measured value.
96 numbers between 1 and 96 defining the 15-minute intervals of the day; 96 values ranging from 1 = Sunday to 6 = Saturday representing the day of the week; 96 values defining the 24-hour-ahead average load; 96 values related to the day-ahead load; 96 values for the 7 days-ahead load; 96 Boolean values to distinguish between holiday and weekday (0 = holiday, 1 = weekday).
In addition to these indexes also the Lilliefors test has been exploited in this work. The Lilliefors test allows to establish if a vector comes from a distribution in the normal family [32]. In the final simulations this test has been used to verify if the error distribution can be approximated through a Normal distribution with null mean.
An example of inputs for a single ANN is shown in Table 1. Notice that the output of the proposed method is composed of 96 values representing the load for the next 24 h with 15 min granularity, which represents the standard monitoring interval adopted by the Italian DSO. The data related to the historical load are stored by the SCADA system in a dedicated database that is directly available by the algorithms, implemented within the DMS, for the training of the different networks and for the parameters selection (see Section 4.4).
4.4. Parameters selection After the inputs definition, another important task for the design of a robust forecasting procedure is the parameter selection of the multilayer perceptrons. The parameters for a generic MLP can be summarized in the following list: 233
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Table 2 ANN parameters selection.
Table 3 Best five neurons number for the considered MV/LV substations.
Hidden Layers number
1
Substation
Optimal
2nd
3rd
4th
5th
Transfer functions, input-hidden layer Transfer function, hidden-output layer Training algorithm
Hyperbolic Tangent Sigmoid Function Hyperbolic Tangent Sigmoid Function Resilient Backprogation
9 10 11_1 12 14 18 19 47_1 47_3 48 79 87_1 087_2 103 107 126 131 142 200 217 220 222
25 13 6 34 4 19 49 12 15 23 23 12 16 15 16 13 17 8 21 36 7 18
20 7 3 40 26 11 35 14 21 44 59 5 38 27 14 56 3 76 28 8 29 21
15 5 8 26 3 15 27 3 18 81 55 31 11 34 15 42 40 47 17 61 14 26
30 33 4 22 12 53 44 8 22 51 44 7 45 31 45 46 33 24 12 59 6 44
35 40 9 32 33 30 10 7 13 60 25 41 22 5 41 61 46 37 20 30 11 19
• number of hidden layers; • neurons number in the hidden layers; • transfer functions among each layer. These functions define the relation of the inputs and the outputs among each layer; training algorithm. In order to use an ANN for any possible appli• cation, the network has to be trained on a knowledge database. This set is called training set and consists of an input vector and a score vector. The training algorithm defines the training methodology.
In addition to these parameters related to a single MLP, for the BEM method is also crucial the choice of the ANN number (i.e. parameter n in (1)). For the selection of the number of hidden layers, the training algorithm and the transfer functions, the methodology proposed in [11], [12] has been exploited on a single MLP. Table 2 collects the final results for these parameters. This configuration has been exploited for the forecasting of all the MV/LV substations described in Section 3.
simulation resulted to be 15, with a MAPE of 4.37%. Top 5 neuron numbers for each substation are collected in Table 3. These results have been exploited in the selection of the best ANN number for the BEM.
4.4.1. Neurons number selection For the implementation of the BEM within the described DMS a new method for the optimal selection of neurons number in the hidden layer has been introduced. The number of neurons has been chosen for each MV/LV monitored substations, thus one ensemble for each substation has been trained and used to predict its load absorption. This selection procedure consists of training and testing (on the same training and test set) single multi-layer perceptrons having different number of neurons in the hidden layer starting from 3 and increasing by 1 for each iteration. For this selection the training set is composed of 8-months of data from all the seasons, while the test set consists in a single month. The MLP inputs used in this phase have been the ones presented in Section 4.2, while the ANN configuration has been the one reported in Table 2. The number of neurons has been chosen considering the best MAPE value in the test phase. If the MAPE of a certain network with k neurons has been higher than 10% (i.e. the considered threshold), the neural networks with more neurons than k have not been considered in the selection procedure. An example of this selection procedure is reported in Fig. 5. In this case a net with 52 neurons number providing an unsatisfying 25% of MAPE and also a very high DPMAPE. This is an example of over-fitting. Notice that the best neurons number for this
4.4.2. ANN number for BEM In order to select the best ANN number for the ensemble averaging method for each MV/LV substation, 5 different perceptrons, with the top 5 neurons number collected in Table 3, and with all the parameters reported in Table 2 have been considered. These 5 different networks have been trained on 14 different random initialization of the weights related to the transfer functions, thus 70 nets in total. Training set has been composed of 8-months of data, while test set consists in a single month. The neural networks have been ordered with respect to the MAPE error on the common test set. Then, they are combined starting from the best by adding to the BEM mean the best remaining net. The best combination of the n first networks is chosen as the best ensemble. Fig. 6 represents the MAPE as a function of networks number involved in the ensemble for substation 9. The error function decreases and reaches its minimum for n = 3, then it starts to increase. Thus, in this case (substation 9), 3 is chosen as ANN number for the BEM. Table 4 contains the best ANN number for the ensemble method. The parameters selection, that also includes the ANN training, is completely automatic and embedded in the proposed DMS. It presents a computational duration that varies from 30 to 90 s for a single
Fig. 5. Neurons number selection for substation 103.
Fig. 6. ANN number selection for substation 9. 234
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single ANN as proposed in [11,12]. Single ANN has been trained and used with the same inputs and parameters exploited in the ensemble approach. The optimal number for the neurons in the hidden layer, collected in Table 3, have been used for the single ANN approach. The last column of Table 3 reports the results of Lilliefors test with a 10% significance level. As can be seen from Table 6 the results in terms of MAPE for the BEM are always under 5%. In addition, the proposed method is able to improve the results provided by the single ANN approach in all the considered substations. The error distribution can be approximated with a normal function in 20 cases out of 22 according to the Lilliefors test. Results testify also that the presented methodology is not affected by the distributed RES connected at LV level in a case wherein the power injections are not prevalent with respect to the electrical load (less than 25%). For other scenarios, which can include also reverse power flows, the exploitation of a dedicated RES forecasting procedure [5] (currently implemented in the proposed DMS), is more reliable [33–35]. This can be used with the proposed approach in order to estimate accurately the power absorption/injection (net load) of a MV/LV substation with a high RES penetration level. Table 7 collects the load forecasting results in terms of DPMAPE. Also for this KPI the values related to the BEM are lower in almost all the substations. This establishes that the ensemble approach is better and more reliable in the prediction of the load peaks compared to the single MLP approach. Fig. 7 depicts the load forecasting results for substation 87_1. In this figure the black curve represents the output of the BEM, while in grey is reported the actual substation load absorption. As can be seen from Fig. 7 the results of the proposed procedure are very satisfactory. Fig. 8 reports the error distribution and the normal QQ-plot of substation 10 for a month of the load forecasting proposed procedure. These results coupled with the Lilliefors test state that the error distribution can be approximated through a Gaussian distribution. In [36] an Auto Regressive Integrated Moving Average (ARIMA) model has been implemented to forecast the consumption of MV/LV substations with errors in terms of MAPE included from 4% to 8%. MV/ LV substations have been the objects of the load forecast also in [37], wherein a time series model has been exploited with MAPE around 15%, and in [38], where authors reach a MAPE close to 10% with an ANN based methodology. In [39] bus forecasting models based on clustering techniques and ANN present an average MAPE slightly over 5%. In [40] ANN hybrid approaches have been used to predict transmission/distribution bus loads with MAPE values from 3% to 7.5%. This literature comparison proves that the proposed BEM approach is a robust and an accurate procedure, representing a useful and reliable advanced functionality for a real DMS.
Table 4 ANN number selection for the BEM. Substation
Size ensemble
Substation
Size ensemble
9 10 11_1 12 14 18 19 47_1 47_3 48 79
3 2 8 4 2 3 5 3 7 3 5
87_1 87_2 103 107 126 131 142 200 217 220 222
4 7 9 3 3 2 5 3 4 5 3
Table 5 Computer specifications. Processor
RAM
Operating system
Intel Xeon CPU E3-1245 @ 3.40 GHz
16 GB
Windows 7 Professional
substation. The computer specifications are reported in Table 5, while the software used for the methodology implementation has been Matlab R2016a. The results in terms of computational speed support the applicability of the proposed procedure in a real distribution network. For this kind of algorithms also a cloud computing solution can be a valuable resource in order to speed up the training phase for large systems. 4.5. Real field results In this section the load forecasting results, obtained with the BEM, are presented. The results are related to the forecast of the 2016 load absorption of the MV/LV substations of network managed by AMAIE. All neural networks exploited in the DMS, described in Section 3, have been trained on the same dataset used for the optimal neurons number selection and for the choice of ANN number for the BEM. The ANN parameters used for the BEM are the ones presented in Section 4.2. Table 6 collects the results in terms of MAPE for the BEM forecasting procedure and compares them with the ones obtained exploiting a Table 6 MAPE on the same test sets for networks ensemble and single network. Substation
BEM MAPE
Single Net MAPE
Lilliefors Test
9 10 11_1 12 14 18 19 47_1 47_3 48 79 87_1 87_2 103 107 126 131 142 200 217 220 222
3.36% 4.60% 3.98% 4.86% 4.13% 3.46% 3.60% 3.33% 4.24% 4.83% 3.24% 2.92% 3.47% 4.36% 3.34% 4.84% 3.62% 4.54% 3.81% 3.64% 4.70% 3.65%
3.43% 4.81% 4.61% 7.01% 6.17% 5.38% 3.66% 4.46% 4.50% 6.06% 3.32% 3.02% 3.59% 4.37% 3.36% 6.00% 3.70% 4.65% 4.76% 3.73% 6.08% 3.69%
✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✗ ✓ ✓ ✓ ✓ ✓ ✓ ✗ ✓ ✓ ✓ ✓
4.6. ANN re-training The proposed DMS has been configured in order to monitor the Table 7 DPMAPE on the same test sets for networks ensemble and single network.
235
Sub.
BEM DP
Single Net DP
Sub.
BEM DP
Single Net DP
9 10 11_1 12 14 18 19 47_1 47_3 48 79
1.35% 3.70% 4.99% 5.56% 4.08% 2.33% 3.57% 2.72% 1.96% 3.14% 3.12%
1.70% 4.78% 7.08% 5.85% 4.13% 2.34% 3.66% 3.20% 2.19% 3.18% 3.15%
87_1 87_2 103 107 126 131 142 200 217 220 222
1.70% 2.72% 2.44% 2.60% 3.16% 2.29% 3.09% 3.01% 4.04% 6.14% 3.65%
1.85% 2.88% 2.83% 2.84% 4.62% 2.24% 3.33% 3.03% 4.33% 6.73% 3.52%
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The good performances reported in this work suggest that this methodology can be used to forecast other types of load at different levels on the grid. Finally, load forecasting techniques with this level of accuracy can significantly help to deal with uncertainties due to the demand, in order to exploit the controllable resources efficiently and to face the stochastic behavior of renewables. 5. Load modeling In this section a technique based on ANN, data collected from field measurements and information related to networks customers is described. In particular, the proposed methodology is very similar to the one described in Section 4 for load forecasting. This algorithm is able to produce MV/LV substations typical load profiles identifying a correlation among the aggregated patterns and the customer types. The proposed procedure adopts ANN coupled with Fourier Decomposition. It has been tested and validated with real field results to model the aggregated load patterns of the secondary MV/LV substations described in Section 3.
Fig. 7. Load forecasting with BEM for substation 87_1 (first week of May 2016).
performance of the ANN ensemble. A low level of accuracy in the load forecast of a specific substation can be related to:
• grid faults or malfunctions; • changes in the network topology and/or in the composition of the load/generation.
5.1. Dataset
If the MAPE value related to the load prediction of a certain substation is higher than a predefined threshold, the DMS is enabled to send a warning to the DSO that can decide if it is necessary to re-train the ANN ensemble or to not consider the unsatisfactory forecast (temporary issue).
This section presents the essential inputs for the implemented load modeling procedure. The dataset is composed of historical load time series and data related to the network customers. 5.1.1. Historical data As previously described in Section 2.1, the SCADA system is able to manage a historical dataset acquired from the measurement system installed on the network managed by AMAIE. In order to design and validate the proposed load modeling procedure, data related to 18 MV/LV substations have been exploited. In particular, the available measurements of the active power, starting from 2013, have been used.
4.7. Final comments on load forecasting The proposed strategy for load forecasting has been validated on real data and with on-line tests. The comparison with the state of the art proves that the presented approach is robust, accurate and can be implemented within real DMS. Results have been very satisfactory with low MAPE and DPMAPE values. Also the statistical analysis of the forecast error proves that the uncertainties due the prediction procedures can be modeled as normal distribution. In addition, a detailed analysis and an innovative formal procedure for the selection of all the ANN parameters has been described and supported by simulation/field results. The computational speed and the simplicity of the BEM approach represent two fundamental advantages of the proposed approach.
5.1.2. Customer characterization The DSO (AMAIE) provided the commercial and technical information related to the customers connected to the above mentioned 18 MV/LV substations of the monitored feeder. For each of these substations the DSO is able to produce the following data:
• type of customers;
Fig. 8. Statistical analysis of the error for substation 10. 236
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Table 8 Contractual powers of customers. Substation
Contractual Power [kW]
9 10 11 14 18 19 47 48 79 87 103 126 131 142 159 200 217 222
Res.
Com.
P. L.
Total
800.0 441.0 840.0 238.5 1239.5 676.5 2282.0 801.5 1500.0 802.0 580.5 611.5 1011.5 598.5 647.0 1071.0 1153.5 594.5
57.5 56.3 97.5 170.5 178.5 92.3 608.5 293.5 509.3 1055.0 102.5 39.0 314.0 43.0 264.3 155.0 278.0 439.0
39.6 – – 7.5 20.0 – 99.8 – – 25.0 11.0 – – – 6.0 15.0 6.0 –
897.1 497.3 937.5 416.5 1438.0 768.8 2990.3 1095.0 2009.3 1882.0 694.0 650.5 1325.5 641.5 917.3 1241.0 1437.5 1033.5
Fig. 10. Load modeling with BEM for substation 222 (holiday, November 2016).
The data related to the contractual powers (non-monitored substations included) are all stored in a specific database managed by the SCADA system and always available for the DMS. This database is updated in case of changes in the grid configuration. 5.2. Typical power shapes
Table 9 Load modeling results with ensemble approach. Subst.
Weekday
217 (4; 5; 3) 222 (3; 3; 4)
Pre-holiday
Holiday
MAPE
MPE
MAPE
MPE
MAPE
MPE
6.20 % 7.09 %
14.19 % 16.67 %
7.00 % 5.08 %
13.75 % 15.06 %
6.17 % 5.05 %
13.46 % 18.74 %
The above-mentioned MV/LV substations are composed of different compositions of the three load types previously presented (see Table 8). Since these data represent all the available information, it is not possible to identify the share of the single customer type. This entails that the characterization of the typical load share of a generic MV/LV substation is a quite difficult problem, due to the absence of information related to a pure commercial, residential and public lighting load profile. For this reason, an ANN ensemble technique has been used for the estimation of the typical load shapes for a secondary substation, exploiting data related to the time of the day and contractual powers. For this goal, the typical shapes of the described monitored secondary substations have been evaluated calculating the mean daily active load profiles acquired by the SCADA with a time step equal to 15 min. In order to simplify the ANN training, 6th order Fourier Decomposition (FD) has been employed to the above-mentioned typical power shapes:
Table 10 Load modeling results with single ANN approach. Subst.
217 222
Weekday
Pre-holiday
Holiday
MAPE
MPE
MAPE
MPE
MAPE
MPE
7.52 % 7.17 %
30.89 % 18.97 %
8.39 % 5.71 %
35.19 % 16.77 %
10.18 % 5.32 %
42.13 % 20.68 %
6
y˜ (t ) = α 0 +
∑ αi cos(wi t ) + βj sin(wi t ),
t = 1, …, 96
i=1
(4)
where αi (i = 0, …, 6) and βi (i = 1, …, 6) are the Fourier coefficients, while t represents the time index on the 15 min intervals that compose a day. The 6th order FD has been chosen, since it has been the best in terms of approximation error. The application of FD is a fundamental step for the proposed modeling procedure in order to perform a reliable and robust training for the neural networks involved in the ensemble. The typical load shape for each substation has been estimated for three different day typologies: weekday, pre-holiday and holiday. This has been done to achieve the highest precision for the proposed load modeling functionality. Fig. 9. Load modeling with BEM for substation 222 (pre-holiday, November 2016).
5.3. BEM for load modeling In this work BEM method has been used also for the load modeling of the non-monitored MV/LV substations of AMAIE network. The ANN architecture exploited in the ensemble approach is MLP, i.e. the same implemented approach for the load forecasting. According to the dataset description (Section 5.1) the inputs for BEM in this application are:
• contractual power; • number of customers. These information are sufficient to aggregate the power absorption according to the type of customer in order to obtain the total contractual power of the same load type for each MV/LV substation. Table 8 reports the different contributions of contractual powers for the MV/LV monitored substations: Residential Customers (Res.), Commercial Customers (Com.), Public Lighting (P.I.) and Total (i.e. the sum of the three previous load types).
• 96 values defining the commercial Contractual Power; • 96 values representing the residential contractual power; • 96 values for the public lighting contractual power; • 96 values representing the total contractual power; 237
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• 96 values among 1 and 96 representing the 15-minutes intervals of
procedure is able to provide with very satisfactory accuracy the estimation of the typical active power profiles of real MV/LV substations with the goal to provide essential inputs for DMS advanced functionalities in order to face the uncertainties due to load demand. Results proved to be very interesting with fast computational speed and reasonable errors in terms of MAPE and MPE, as can be noticed from Table 9 and Figs. 9, 10. In addition, one of the strong points of this approach is represented by the fact that neural networks do not have to be retrained if there are changes in the load composition. The test showed that the ensemble approach can improve significantly the results obtained with a single ANN approach.
the day.
Notice that the inputs related to the contractual powers are constant since they do not change during the day. The output of the proposed load modeling algorithm based on BEM is composed of 96 values determining the typical load shape, presented in Section 5.2, of a generic secondary substation. One of the strong points of this approach is that the neural networks do not have to be retrained if there are changes in the load composition of the considered substation. In this case, it will be sufficient to update the inputs related to the loads configuration (see Section 5.1.2 and Table 8).
6. Conclusions In this work the design, the implementation and the validation of two advanced functionalities for a real DMS have been presented. In addition, a description of a proposed architecture for a DMS has been provided. The performance of the two algorithms on field tests has been very satisfactory, as proved also by a comparison in terms of accuracy with the literature. Future developments can investigate an evolution of Basic Ensemble Method exploited for both proposed procedures. Sophisticated ensemble approaches are able to weigh the neural networks involved, in order to use mainly the ones that can provide the highest accuracy. This has to be done without forgetting that one of the strong point of the BEM is its simplicity.
5.4. Parameters selection The parameters selection for the MLP exploited in the ensemble approach has been performed according to [21]. Except for neurons number in the hidden layer and the ANN number for the BEM. These two choices have been done according to the formal procedure presented in Sections 4.4.1 and 4.4.2. The final configuration for the neural networks in the ensemble is collected in Table 2. The computational time for the training and parameters selection is around 30 seconds (see Table 5 for the computer specifications). 5.5. Real field results
References
This section presents the results related to the BEM load modeling. In order to validate the proposed methodology the algorithm has been tested to estimate the typical power shape for each day type (holiday, pre-holiday and weekday) of two monitored substations (217, 222). For these tests, three neural networks, one for each type of day, have been trained with the typical active power profiles, evaluated as the mean of the load curves of November 2016, for all the substations except 217 and 222. In addition to MAPE, in this application, also the Maximum Percentage Error (MPE) has been exploited to evaluate the presented modeling algorithm. Table 9 reports the results obtained with the BEM in terms of MAPE and MPE for the load modeling of the typical profile of November 2016 for substations 217 and 222. The ANN number composing the ensembles are shown in parenthesis in the first column of Table 9, while Table 10 collects the results for the load modeling of substations 217 and 222 using a single ANN approach (described in [21]). The previous tables show that the ensemble approach is able to improve significantly the results of the single ANN approach. MAPE and MPE values are lower in the BEM approach in all cases. Figs. 9 and 10 illustrate the comparison between the characteristic active power profiles and the ANN output. As can be seen from these figures the shapes provided by the proposed neural networks are very close to the typical active power profiles, for this reason the results are very satisfying. As occurred for the load forecasting procedure, the BEM method is able to provide a great level of accuracy in a scenario that presents a RES penetration that is not dominant with respect to the electrical load. For high RES penetration cases (more than 25% of the power absorption), it is recommended to use a dedicated RES forecasting technique [5], and to subtract the results of this procedure to the load modeling output in order to have a reliable estimation of the typical power shape (net load).
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