A Committee of Machine Learning Techniques for Load Forecasting in a Smart Grid Environment

www.ijep.org International Journal of Energy and Power (IJEP) Volume 4, 2015 doi: 10.14355/ijep.2015.04.016

A Committee of Machine Learning Techniques for Load Forecasting in a Smart Grid Environment G. Sideratos*1,2, A. Ikonomopoulos1 and N. D. Hatziargyriou2 Institute of Nuclear & Radiological Sciences & Technology, Energy & Safety, National Center for Scientific Research “DEMOKRITOS”, 15310, Aghia Paraskevi, Attiki, Greece 1

National Technical University of Athens, 15773, Zografou, Attiki, Greece

2 *

Email: sideratos@ipta.demokritos.gr; joesider@power.ece.ntua.gr

Abstract Interoperability and adaptability form the core requirements of efficient smart grid operation. Meeting these requirements mandates load forecasting models employed in a smart grid environment to be self‐tuned and capable of capturing all customer idiosyncrasies. In that direction, the applicability of an expert committee as a load forecaster is examined under the prism of accurate short‐term prognosis in a smart grid implementation. The independent methods are intertwined in a load forecasting model structure that is a priori parameterized without the application of optimality criteria. The proposed model structure consists of four prediction units (PUs) and a combination unit (CU). The PUs provide independent forecasts processing diverse inputs through different predictors. An artificial intelligence implementation has been devised to combine these forecasts at the CU. The performance of the proposed expert committee when used for an actual load time series forecast obtained from a HV/MV substation located in a city neighborhood is illustrated. Four different machine learning architectures of the proposed load forecasting blueprint are devised and the results obtained are compared and discussed.

Keywords Load Forecasting; Smart Grid; Generalized Linear Models; Gaussian Processes; Multilayer Perceptrons; Random Forests

Introduction Integration of distributed generation, primarily from rooftop PV installations, along with the incorporation of new loads, like electric vehicles and heat pumps impose requirements for novel monitoring and control functionalities at the MV level of the distribution system [1‐3]. Among these functionalities, load forecasting at the distribution level becomes extremely important in operating the system in a secure way providing high quality of energy supply at the lowest cost [4]. At lower voltage levels, accurate load forecasting becomes more difficult because the smoothing effect of the diverse number of consumers is naturally reduced. Moreover, the actual consumption might be unknown or difficult to measure, in case consumers use in‐house distributed generation to satisfy their consumption under a net‐metering scheme.

In a smart grid environment, distribution networks become active and distribution system operators (DSOs) are responsible for the operation of their local networks. Load forecasting is a vital tool for them in order to operate the system efficiently and respond to the needs of the transmission system for congestion management, ancillary services provision, etc. Load forecasting is also crucial for retailers in order to schedule their participation in local markets, service providers and other stakeholders dealing with demand‐side management, storage scheduling and operation, etc. Finally, load predictions are expected to benefit the active consumer who wants to understand the electricity demand‐cost relationship and adopt electricity utilization patterns according to prices [5‐7].

The requirements for accurate forecasting at the distribution level posed by DSOs, the push for energy efficiency functionalities along with the growing customer awareness on energy consumption guide forecasters in trying to improve their models, as existing tools do not always perform at a satisfactory level [8]. At lower voltages, the load time series dynamics are influenced by the customer types and load forecasting models should be automatically adapted in a short time with low computational power and a limited amount of data [9‐10]. Researchers and

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practitioners in the field need to evaluate their computational schemes reconsidering the data selection and management process, the model development time and its ability to fit to every load profile. To that end, the methods utilized should be parameter‐free or employ fast estimation computing schemes.

During the last decade, various load forecasting approaches have been proposed. Among them, artificial neural networks and support vector machines when endowed with an optimization algorithm appear to be highly effective, characterized though by a large amount of data needed for model fitting [8]. This paper investigates the combinatorial application of four model architectures in a load forecasting tool. The proposed implementation employs four PUs accompanied by a CU. The set of PUs accommodates multiple predictors designed to capture the entire dynamics of possible load time series in order to perform accurately within a 24‐hour forecasting window. Moreover, four machine learning methodologies are comparatively assessed as PU predictors, namely: generalized linear models (GLMs), Gaussian processes (GPs), multilayer perceptrons (MLPs) and random forests (RFs). These methods are implemented with a default parameterization allowing the proposed forecasting tool to be able to be self‐constructed and self‐trained in a short time for a stand‐alone application. The forecasting structure is tested on an actual time series with specific characteristics.

In Section II, the time series used is described and its peculiarities are shown compared with a load time series corresponding to power system level. In Section III, the load forecasting model structure is introduced. The four machine learning methodologies (GLM, GP, MLP, RF) tested in the PUs of the forecasting tool are overviewed and the evaluation results during three weeks are compared and discussed in Section IV. The performance of the aggregate structure compared with the individual prediction methodologies is finally investigated.

Case Study The proposed model architecture is applied on an actual load time series recorded from May 1, 2013 until April 5, 2014 along with the corresponding daily maximum predicted temperature values. The load time series has been recorded with a 15‐minute step at a ΗV/ΜV substation feeding an urban neighborhood in a city. The load time series is endowed with special characteristics coming from the two university campuses located in this area. The time series idiosyncrasies have mandated a different modeling approach from those followed in traditional load forecasting applications [11]. The large number of students (a consumer group following non‐ordinary and highly stochastic electricity consumption patterns) living in the area, but who are not permanent residents, influences load consumption in several ways. It is customary for university students to leave the city during summer holidays (July and August) as well as during Christmas and Easter breaks. Contrary to the known Christmas and Easter break periods, it is unknown when the student summer holiday period starts and ends due to the variability of the university exam periods. Figure 1 presents the recorded load time series where it appears that students use air conditioners for heating during the winter period but rarely utilize them for cooling during summer.

Figure 1 depicts the different periodicities of the studied load pattern as compared with typical load time series at the transmission system level [11]. It is observed that there are no significant differences in the daily consumption patterns during spring, summer and fall seasons. Generally, there are large consumption fluctuations as the annual minimum load during spring represents only 12% of the maximum annual load. In winter months, high daily variability is observed with a daily peak roughly three times the corresponding low (Figure 2b). The maximum demand observed during winter is 65% higher than the maximum demand recorded during the summer period.

Taking also under consideration of the fluctuations of the daily load curves (Figures 2 (a) & (b)), the applied case study becomes a challenge for existing load forecasting models.

Figures 2 show the load demand during a week in (a) summer and (b) winter. In both cases, the daily load profiles appear to randomly vary throughout the week, while large random fluctuations emerge during different hours each day. The level of uncertainty in the recorded time series renders this case study challenging to predict and thus suitable for testing the proposed methodology. In load time series modeling, it is customary to consider Sundays as special days as the demand is lower than other days. However, in the application discussed, week days with the lowest energy consumption are Saturdays (shown in Figures 2), while Sundays appear to have a behavior similar to normal days. In fact, application of the Kruskal‐Wallis hypothesis test [12] shows that Mondays, Wednesdays and Thursdays exhibit similar behavior, while Tuesdays, Fridays, Saturdays and Sundays form

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separate populations. In this application, Saturdays are considered as special days as they possess similar characteristics as holidays.

The load timeseries 1.2

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FIG. 1. LOAD TIME SERIES FROM MAY 1, 2013 UNTIL APRIL 5, 2014.

A week during the winter period

A week during the summer period

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FIG. 2. A) LOAD DEMAND DURING A WEEK IN SUMMER, B) LOAD DEMAND DURING A WEEK IN WINTER.

The correlation between load demand and atmospheric temperature is well‐documented [13, 14]. This is also the case in this study where the load demand is strongly correlated with the high temperature values recorded during the summer period. However, the student summer holidays reduce the high temperature influence on load demand at the area of interest while increased temperature/load correlation appears for temperatures below 20o C. Figure 3 illustrates the maximum daily temperature values against the daily energy consumption showing clearly the strong effect of low temperature in consumption.

Since special days include national and religious holidays, it is customary to exhibit different load profiles. For example, the Christmas Day and New Year’s Eve present a different profile compared with the remaining days of the Christmas season that possess similar profiles. The load profile of the Holy Saturday is different from that of Easter Sunday. The load time series during the national holidays appear similar to each other, though different from other special days. The Kruskal‐Wallis test [12] provides grouping of the special days according to their load profile similarity which is expressed by a suitable index.

Special days load curves

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FIG. 3. DAILY CONSUMED ENERGY VERSUS DAILY FIG. 4. LOAD CURVES RECORDED DURING SPECIAL DAYS. MAXIMUM TEMPERATURE.

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Forecasting Tool Architecture Α load forecasting tool is proposed that utilizes four independent prediction units, identified as PU I, II, III and IV and assembles their individual predictions within a CU to provide forecasts for a 24 hour forecasting horizon [11].

The independent PUs analyze the incoming data based on the hours of the day or the month of the year. They receive different input data and have specialized structures tuned for better performance either in short‐ or in long‐ term horizons. They have a complementary architecture that attempts to capture the entire time series dynamics through varying time windows. The structure of the proposed load forecasting model is illustrated in Figure 5 while details are discussed in the following sections.

Prediction Units I and III PUs I and III (Figure 6) consist of 24 predictors for normal days and 2 predictors for special days. For normal days each predictor is trained to predict the load demand for each separate hour of the day. For special days two predictors are applied: one at the high consumption periods of the day (from 09:00 to 15:45 and from 18:00 to 22:45) and one during the low consumption hours (from 16:00 to 17:45 and from 23:00 to 08:45). The dataset corresponding to special days is divided into two parts and the subsets are used to train the respective predictors.

PU I has been designed for short forecasting horizons while PU III is customized for horizons longer than six hour‐ ahead. PU I receives as input the most recent data and produces the load prediction for the next 15 minutes. More specifically, the PU I input vector contains the 15 min load values recorded during the last twelve hours and the 15 min load values that correspond to the 4 hours before the prediction hour of the last seven days. PU I is executed every 15 min utilizing the prediction made in the previous time step as the most recent load value. In other words, it runs 96 times to conclude 15 min predictions for a 24 hour‐ahead horizon.

FIG. 5. STRUCTURE OF THE PROPOSED LOAD FORECASTING MODEL.

FIG. 6. STRUCTURE OF PUs I AND III.

PU III receives as input a vector that contains the 15 min load values recorded during the 4 hours before the

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prediction hour of the last seven days. PU III is executed every 15 min and runs 96 times to conclude 15 min predictions for a 24 hour‐ahead horizon. While PU I employs also predictions made during the forecasting process, PU III employs only historical data. In addition, both PU input vectors contain calendar data, maximum forecasted temperature values and special indices identifying the prediction day as well as the last seven days.

Prediction Units II and IV PUs II and IV (Figure 7) also comprise 24 predictors used in regular days and 2 predictors used in special days. For the regular days, each two predictors correspond to a different month. In particular, the regular days are divided into two time periods: the low demand time period (from 23:00 to 08:45 and from 16:00 to 17:45) and the high demand time period (from 09:00 to 15:45 and from 18:00 to 22:45) periods. For every month the two predictors are trained with data from each time period. Regarding special days, there is a predictor trained with data recorded at the special days of the high temperature year period (i.e., from April to September) and a second built on special days during the low temperature year period (i.e., from October to March) using the corresponding data. The PU II input vector is similar to the one of PU I. It runs every 15 min using as input the forecasting tool prediction made in the previous time step. It differs from PU I in employing the month indicator as input variable instead of the hour indictor.

FIG. 7. STRUCTURE OF PUs II AND IV.

Similar to PU III, PU IV produces its predictions using always data from the load time series. PUs III and IV have similar input vectors and are built for long term forecasting. In all the above architectures, the features used for each PU predictor are selected through a stepwise regression analysis applied to the corresponding dataset [15].

Combination Unit The CU receives as input the four predictions made by the PUs for each 15 min forecasting step and provides the final load prediction of the proposed forecasting tool. It runs 96 times combining the PU predictions at each forecasting step. It consists of 24 MLPs where each MLP is activated four times for a specific hourly forecasting window. Namely, the CU uses at every four 15 min forecasting steps a different MLP. The applied MLPs have 24 linear hidden layer neurons in typical three layer architecture (i.e. an input, a hidden and an output layers) and are trained with the PU predictions obtained during the training period.

Forecasting Methodologies Machine learning approaches, as used in MLPs, employ an algorithm to learn the relationship between predictors and their response assuming that the generating mechanism of the vector of predictors with their corresponding response is unknown. Thus, they search the space of possible functions to identify one that best approximates the unknown function. Contrary to looking for a single best hypothesis to explain the relationship, ensemble learning methods build a set of hypotheses (or “ensemble”), which are combined in some way, to provide a response to new predictors [16]. Boosting and bagging of classification trees have been widely applied and tested ensemble learning methods [17]. In random forests proposed by Breiman [18], each tree is constructed using a different bootstrap data sample and each node is split using the best among a subset of predictors randomly chosen at that node. The random forests algorithm for regression lists [17]:

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

From the original data draw NT bootstrap samples

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For every bootstrap sample grow a tree without pruning and at each node randomly choose a number of the predictors and select the best split among them

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Combine the predictions of the NT trees by taking their average

If more trees are added to a random forest, this will not overfit and its generalization error will achieve a limiting value [18]. As far as it concerns the RF computational time, this is cNT√MNlog(N) where c is a constant, NT is the number of trees, M is the number of variables and N is the number of instances [19].

An alternative to data modeling based on prior knowledge of the underlying functional form is to assume that data are drawn from a multivariate normal (Gaussian) distribution in a high‐dimensional space, where the individual data points are not assumed independent [20]. Utilizing the observed data points it is possible to look for the most probable values of other quantities of interest, or values at different points from those measured, employing a GP regression. A GP is defined as a collection of random variables, any finite number of which has a joint Gaussian distribution [21]. A finite‐dimensional Gaussian distribution is defined by its mean vector and its covariance matrix, whereas a Gaussian process is specified by its mean function and covariance function. For a real process f(x) the mean function m(x) and covariance function C(x, xʹ) are m(x) = E[f(x)] and C(x, xʹ) = E[(f(x) – m(x))(f(xʹ) – m(xʹ))] and the Gaussian process is f(x) ~ GP(m(x),C(x, xʹ)) [21].

The above methods are applied in the proposed model in order to investigate their prediction accuracy in a challenging case study without considering any special handling of their parameterization. As outlined in Section III, the proposed model requires training the 104 predictors utilized by the four PUs as well as the 24 MLPs of the CU. The computation time required for training is limited to several minutes [8] making the approach suitable for self‐adaptation. The GP and MLP model parameters are identified following a typical trial‐and‐error method to a randomly selected predictor and the effort outcome is subsequently assigned to all 104 predictors. On the contrary, the RF models are applied following Breiman’s recommendation, while the GLM models are used arbitrary in their simplest form.

The RF parameters that need to be defined are the number of trees forming the forest and the number of variables used to build a tree. Following some experimentation, the former parameter was set to 1000 while the latter – in accordance to Breiman’s proposal – [17] was equal to one third of the number of input variables and the randomForest package [17] was employed for this implementation. In case of the GP models, testing several functions in a single predictor, the most accurate results were received when a constant mean function equal to the response vector mean and linear covariance function were used.

Similar to GP, the MLP selected architecture was initially applied on a single predictor to estimate the optimal number of hidden layer neurons. Following successive experimentations with models containing 12, 18, 24, 28, 32 and 36 neurons, the best results were obtained from a network with 18 neurons at the hidden layer employing a hyperbolic tangent sigmoid as a transfer function. The simplest model developed was that of GLM employing a Gaussian distribution function and an ‘identity’ linkage function [15]. As expected, GLM was proved the fastest in training requiring less than one minute. Employment of RFs, GPs and MLPs in the four PUs increased the training time to 5.65, 18 and 21.42 min, respectively. The 24 MLPs forming the CU needed 30 minutes for training independently of the model version.

Case Study The four versions of the proposed forecasting tool were evaluated during three weeks that correspond to the last week of August, the last week of November and the last week of February; namely, the periods from 23/08/2013 to 31/08/2013, 22/11/2013 to 30/11/2013 and 22/02/2014 to 28/02/2014. The evaluation period was initially separated from the training period and was selected to include the main time series periodicities and idiosyncrasies. During this period the proposed forecasting tool was applied to update its hourly predictions in a 24 hour forecasting window with a 15 min time resolution. All forecasting tool versions are activated 96 times every hour providing 24 hour‐ahead predictions iteratively by feeding back the last prediction as the most recent load value input.

The forecasting tool performance in its four versions is graphically depicted by the mean absolute percentage error

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(MAPE) and mean absolute error (MAE) during the evaluation period. As shown at Figures 8, 9 and 10, the RF architectures outperform the other methods during all three evaluation periods with the highest accuracy obtained during the August period (i.e., the period during which students are on holidays). The proposed PU architectures employing RF, GP and GLM predictors exhibit performance consistency during all three evaluation periods. On the other hand, the forecasting tool with the MLPs performs quite well during the summer and winter periods, but it presents the worst estimations during the fall period. This is attributed to the predefined number of neurons utilized in their hidden layer. The GLM and GP predictors’ performances is similar during the three testing periods and competitive to the RF performance during the winter period.

Performance during the summer testing period (23/08/2013-31/08/2013) 8 7.5

% Mean Absolute Percentage Error

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FIG. 8. MEAN ABSOLUTE PERCENTAGE ERROR OF THE PROPOSED FORECASTING TOOL IMPLEMENTATIONS IN MLP, GP, RF AND GLM MODEL ARCHITECTURES DURING THE PERIOD FROM AUGUST 23 TO AUGUST 31, 2013.

Performance during the fall testing period (22/11/2013-30/11/2013) 12

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FIG. 9. MEAN ABSOLUTE PERCENTAGE ERROR OF THE PROPOSED FORECASTING TOOL IMPLEMENTATION IN MLP, GP, RF AND GLM MODEL ARCHITECTURES DURING THE PERIOD FROM NOVEMBER 22, 2013 TO NOVEMBER 30, 2013.

Performance during the winter testing period (22/02/2014-28/02/2014) 11

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FIG. 10. MEAN ABSOLUTE PERCENTAGE ERROR OF THE PROPOSED FORECASTING TOOL IMPLEMENTATIONS IN MLP, GP, RF AND GLM MODEL ARCHITECTURES DURING THE PERIOD FROM FEBRUARY 22, 2014 TO FEBRUARY 28, 2014.

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MAE of the proposed model during the entire evaluation period 0.045 0.04

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FIG. 11. MEAN ABSOLUTE ERROR OF THE PROPOSED FORECASTING TOOL IMPLEMENTATIONS IN MLP, GP, RF AND GLM MODEL ARCHITECTURES DURING THE ENTIRE EVALUATION PERIOD.

The MAEs of the four forecasting tool version estimations during the entire evaluation period are illustrated in Fig. 11. The RF model architecture outperforms the other implementations but at the first 15 minute time step where the GLM model is more accurate than the rest. As the prediction horizon elongates, the GP, GLM and MLP model performances converge. The overall forecasting tool performance – in all architectures – may be considered satisfactory and applicable in a smart grid environment since the average load prediction error does not exceed 0.045pu [8]. The following table shows the mutual information of all model architecture predictions with the corresponding observations.

TABLE I THE MUTUAL INFORMATION BETWEEN OBSERVATIONS AND PREDICTIONS OF THE PROPOSED FORECASTING TOOL IMPLEMENTATIONS IN MLP, GP, RF AND GLM MODEL ARCHITECTURES DURING THE ENTIRE EVALUATION PERIOD

Method

Mutual information

MLP

1.5211

GP

1.5965

RF

1.6461

GLM

1.5711

An extra step is taken to exhibit the forecasting tool accuracy – especially the RF method implementation – by drawing (Figures 12 a‐c) the 3 hour‐ahead predictions made during two consecutive days at each evaluation period (summer, fall and winter). Figures 12 a‐c show the sharp time series variability during the high load demand hours where the largest prediction errors are estimated. The more accurate performance of the RF model architectures, as compared with the remaining methodologies, is clear in the majority of the observations. However, there are cases where a different method outperforms the rest giving rise to the hypothesis that better results may be achieved if a committee is formed by four sub‐committees that implement a different machine learning architecture and have their predictions combined in an aggregate CU. Three hour-ahead load predictions on August 27-28, 2013

Three hour-ahead load predictions on November 27-28, 2013

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Three hour-ahead predictions on February 27-28, 2014 0.9 MLP GP RF GLM Observation

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(c) FIG. 12. THREE HOUR‐AHEAD PREDICTIONS OF THE PROPOSED FORECASTING TOOL IN TWO DAYS DURING A) SUMMER, B) FALL AND C) WINTER.

Figures 12 a‐c show the plasticity of the load forecasting model in capturing the large fluctuations observed in the load profile during the different seasons. The predictions made by all forecasting tool versions appear quite similar during the hours of low consumption (from 23:00 to 11:00). However, during high load hours (from 12:00 to 22:00), when the time series exhibit high variability, all predictions appear to deviate from the measured values.

Furthermore, during August 27 and 28, 2013 (Figure 12a), when the maximum daily temperature reached 36oC and 38oC, respectively, the random time series fluctuations can be better captured by the RF predictors. The worst estimations during these days are computed during daylight hours between 09:00 and 14:00. During November 27‐ 28, 2013 (Figure 12b) when the daily load curves present similarities and are characterized by the same maximum daily temperature, the RF models architecture failed to predict accurately the load demand only in the interval between 16:00 and 20:00 where the MLP model offered the most accurate forecasts. The maximum daily temperatures on February 27 and 28, 2014 (Figure 12c) were 17oC and 13oC, respectively and the most accurate predictors were made by the GP models architecture while the RF and MLP predictors’ performances are comparable.

In order to capture the best performance of each method in each time period, an additional CU is applied for providing 24‐hour predictions. It has the same structure with the proposed model CU and is trained with the predictions of the four methods implemented in the proposed forecasting tool. Figure 13 shows the MAPE of the proposed model implementations and the results of their combination. It is shown that the combination of the predictions of the four methods outperforms any individual method; however the improvement achieved does not exceed 2.5%.

MAPE during the entire evaluation period

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FIG. 13. MEAN ABSOLUTE PERCENTAGE ERROR OF THE PROPOSED MODEL IMPLEMENTATIONS IN MLP, GP, RF AND GLM ARCHITECTURES AND OF THEIR COMBINATION DURING THE ENTIRE EVALUATION PERIOD.

Conclusions This paper presents a load forecasting tool in order to provide improved hourly, 24‐hour ahead predictions. To optimize its performance, it compares the performance of four machine learning implementations, namely generalized linear models, Gaussian processes, multi‐layer perceptrons and random forests in four modeling

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architectures that form a committee of independent forecasters tuned at different prediction horizons. The prediction committee is fused in a combination unit that produces predictions in a 24‐hour forecasting window with a 15 min time resolution. Particular emphasis has been paid to the time required for building the committee of prediction units in the proposed forecasting tool in view of the large amount of metering points. A load time series obtained from an HV/MV substation has been used as a test‐bed of all four forecasting tool versions. The applied time series presents severe variability and peculiarities that differentiate it from typical distribution system load time series. The results obtained from model evaluation during three distinct periods proved the committee of random forest experts as the most accurate, while all committees showed satisfactory performance for this application. The combination of the predictions obtained by the four implementations via a CU showed a small improvement compared with the best (RF) expert results.

ACKNOWLEDGEMENT

G. Sideratos and A. Ikonomopoulos acknowledge funding from the Greek General Secretariat for Research and Technology and the European Regional Development Fund under the Action “Development Grants For Research Institutions–KRIPIS” of OPCE II. REFERENCES

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George Sideratos was born in Chios, Greece in 1976. He received the Diploma in Electrical and Computer Engineering from the National Technical University of Athens (NTUA), in 2002 and Ph.D. degree in Electrical Engineering from NTUA, in 2010. He is currently a senior researcher in the Power System laboratory of NTUA. His research interests include wind power and load forecasting and artificial intelligence techniques.

Andreas Ikonomopoulos received the Ph.D. degree from the University of Tennessee, Knoxville, in 1992. He serves as a Senior Researcher with the National Center for Scientific Research “DEMOKRITOS,” Aghia Paraskevi, Attiki, Greece. His research interests are focused on the application of novel instrumentation and control technologies in industrial installations in order to enhance the man–machine interface and operator decision support systems, leading to augmented system safety and performance.

Nikos D. Hatziargyriou was born in Athens, Greece, in 1954. He received the Dipl.‐Electr. Mech. Eng. degree from the National Technical University of Athens in 1976 and the M.Sc. and Ph.D. degrees in electrical engineering from the University of Manchester Institute of Science and Technology (UMIST), Manchester, U.K., in 1979 and 1982, respectively. He is professor at the Power Division of the Electrical and Computer Engineering Department of NTUA. His research interests include dispersed and renewable generation, artificial intelligence techniques in power systems, and dynamic security assessment of power systems. He is Fellow Member of IEEE, immediate past Chair of the Power System Dynamic Performance Committee, member of CIGRE, Convener of SCC6, member of the BoD of EURELECTRIC and member of the EU Advisory Council of the Technology Platform on SmartGrids.

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