Business Intelligence Solutions for Wind Power Plants Operation

Abstract

The European Union energy strategies imply significant changes in the power systems. These should contribute to sustainable development and protection of the environment by enabling the EU to achieve its targets set in the third package of legislative proposals for electricity and gas markets. Renewable energy sources (RES) in Romania have been encouraged since 2007 and this lead to a large volume of projects. It took several years to have the first MW installed in wind power plants (WPP). Nowadays, the installed wind power in Romania is about 2642 MW, most of them (about 85%) being concentrated in the south-eastern part of the country. Based on recorded data during four years in Romania, a couple of analyses have been performed. They contribute to a better integration of wind energy into power systems. This paper will describe how business intelligence solutions are applied on data regarding wind power plants operation and main conclusions that could be drawn.

Headnote

The European Union energy strategies imply significant changes in the power systems. These should contribute to sustainable development and protection of the environment by enabling the EU to achieve its targets set in the third package of legislative proposals for electricity and gas markets. Renewable energy sources (RES) in Romania have been encouraged since 2007 and this lead to a large volume of projects. It took several years to have the first MW installed in wind power plants (WPP). Nowadays, the installed wind power in Romania is about 2642 MW, most of them (about 85%) being concentrated in the south-eastern part of the country. Based on recorded data during four years in Romania, a couple of analyses have been performed. They contribute to a better integration of wind energy into power systems. This paper will describe how business intelligence solutions are applied on data regarding wind power plants operation and main conclusions that could be drawn.

Keywords: Business Intelligence, Wind Power Plants Operation, Data Integration, Aver- age/Maximum Output

1 Introduction

T he EU's energy and climate policy ob- jectives consist in completing the internal market in energy, guaranteeing security of supply, notably for gas and oil, reducing greenhouse gas emissions by 20%, increasing the share of renewable energy in the final en- ergy consumption to 20% and achieving a 20% increase in energy efficiency by 2020 [1].

Romania as one of the State Members has to fulfil its obligations related to EU's targets in terms of RES integration. The incentive sup- port scheme for RES has been enacted by Law no. 220/2008 for establishing the pro- moting scheme for energy produced out of RES, Law no. 139/2010 (modifying Law 220/2008) and a series of four governmental orders dated November 2011 [2]. As a result of the supporting scheme mainly based on green certificates, since 2007, a large number of projects have been noticed. Most of them are located in Dobrogea, Moldova and Banat areas as in Figure 1 [3].

This concentration of interest from the pri- vate investors coincides with the wind poten- tial map as in Figure 2 [4].

Starting from 2010, installed power increased from 13 MW to about 400 MW by the end of the year. In 2011, the installed power was almost double (700 MW) compared with the previous year. The maximum installed power was recorded in 2012 (800 MW), then in 2013 it decreased up to 500 MW and in 2014 as forecast it will decrease even more (270 MW). This evolution is well-related to the specific legislation that incentives RES de- velopment. These figures are given in Table 1. Due to the fact that the installing process is very dynamic, the total figures are approxi- mate.

Its trend is ascending, but in the last two years, it has been moderated by legislative means. For 2014 the increase for the first half of the year was double so that to get an approximate value for entire year.

2 Data Integration and Analysis Models

In the research project [5] we aim to develop a set of templates for data integration in a central database within the online platform, to define a set of performance indicators at macro level and to develop analytical and in- teractive reports for monitoring these indica- tors intended for supporting decisions. We'll propose simulation models of the operation of power plants at regional and national lev- el, models that are based on data mining techniques and developed with geospatial el- ements for tracking indicators through inter- active maps. A particularly important indica- tor on which to base an accurate forecast of the produced energy from renewable sources is the degree of simultaneity of operation of wind power plants located in different geo- graphical areas. Wind energy production is conditioned by several factors factors such as: slipstream effect, soil orography, power characteristics, losses up to the connection point of etc. These factors are identified and detailed in the fundamental works [6], [7]. The analytical component developed for na- tional authorities will contain a model for de- termining the degree of simultaneity which will allow a more accurate dimensioning of power reserves in the system. Thus, if some of the production companies in a given area will have an accurate prediction system, based on the degree of simultaneity of the model we will be able to determine and cor- rect the estimation of production for those power plants without efficient prediction sys- tems (for e.g. undispatchable units or units that have systems with big errors) as de- scribed in [8].

The proposed model will have advanced data analysis capabilities and it can be used to im- prove decision-making and ensure knowledge management. The component for national operators will allow the streamlining of the information flow, required statements and reports being obtained automatically via the online platform.

The prototype's interfaces will be developed so as to allow users single access to the sys- tem via mobile devices, and the use of the Cloud Computing platform will allow the connecting of servers, services and applica- tions necessary for the prototype, thus streamlining access to information to deci- sion makers and reducing infrastructure costs. The system will enable effective and real-time analysis of the operation of renew- able power plants. Also, using an integrated platform, through which there are monitored and analyzed in real time all the renewable power plants included in the system provides a competitive advantage when integrating with similar networks in the European Union. The first phase of the project involves identi- fying and analyzing the data sources, by de- signing the conceptual database diagrams and mappings between data. The conceptual data model will be designed. The system must implement the features of an integrated deci- sion support system, using multidimensional models through which we can implement technological and business workflows. We will define the Business Intelligence methods and technologies used for analysis and data presentation and we'll define the main com- ponents of the system based on the following levels: the data level, the model analysis lev- el and the presentation level. But first of all, an analyses of the WPP operation over time is needed, based on the data series recorded in the last 4 years.

3 Business intelligence analyses of WPP operation

Taking into account the large available rec- orded data set that describes the global opera- tion of WPP between 2010 and 2013 (over 200000 records), business intelligence solu- tions will be used. No business intelligence technique has been applied for wind power plants operation until now. Out of data set some interesting results are found such as hourly average WPP output grouped by stud- ied years, comparison among curves that de- scribe hourly average WPP output, relation between WPP output and installed power in WPP in terms of maximum and average val- ues and seasonal analyses on each studied year.

Figure 4 depicts average WPP output hour by hour in January. The first three studied years WPP output was almost flat, but in 2013 lower values were recorded around 5 and 12 o'clock and higher values were recorded at 21. Some similarities are identified between 2012 and 2013 curves up to 12 o'clock.

Table 2 shows average and maximum values for WPP output in January in 2011 and 2012. It shows the difference between two consecu- tive years. Average output in 2012 is double compared with 2011.

Figure 5 depicts average WPP output hour by hour in February. The middle two studied years (2011 and 2012) WPP output is quite similar.

Figure 6 depicts average WPP output hour by hour in March. The last two studied years (2012 and 2013) WPP output was similar be- tween 6 and 15 o'clock. The rest of time in- tervals trends are different. As for WPP out- put in 2011 is quite flat.

Table 3 shows average and maximum values recorded in March. The maximum value shows that for short time intervals WPP out- put was almost equal to installed power.

Figure 7 depicts average WPP output hour by hour in April. WPP output in 2011 and 2013 was slightly similar. WPP output in 2013 has many windings. WPP output in 2011 and 2012 are quite different, but with little wind- ings. As for WPP output in 2010 is quite flat up to June-July when significant power was installed.

Table 4 shows average and maximum values recorded in April.

These values ae significant because in 2013 the installed power increased over 2000 MW. Average values of WPP output is about 30% and maximum value is almost 88% of in- stalled power.

Figure 8 depicts average WPP output hour by hour in May. WPP output in 2013 is oppo- site with load curve and it does not help bal- ance of the power system. WPP output in 2011 and 2012 are similar, but with little windings.

Figure 9 depicts average WPP output hour by hour in June. All three important curves are similar and again they are opposite with load curve and do not help balance of the power system. This month the level of output is much smaller than in winter and spring time.

Table 5 shows average and maximum values recorded in June.

Average value of WPP output is about 13% and maximum value is almost 65% of in- stalled power.

Figure 10 depicts average WPP output hour by hour in July. All three important curves are similar and again they are opposite with load curve and do not help balance of the power system. This month the level of output is much smaller than in winter and spring time. WPP output in July is similar with WPP output in June.

Table 6 indicates average and maximum per- centage of installed power recorded in July 2013.

Figure 11 depicts average WPP output hour by hour in August.

WPP output in 2012 and 2013 are similar. WPP output in summer is similar.

Table 7 indicates average and maximum per- centage of installed power recorded in Au- gust 2011.

Figure 12 depicts average WPP output hour by hour in September. This month the level of WPP output is increasing

In 2011 and 2012 the curves are similar, but different from 2013 curve.

Figure 13 depicts average WPP output hour by hour in October. This month the level of WPP output is higher than WPP output of the previous month. In 2010 and 2011 the curves are similar and flat. In 2012 and 2013 the curves are very similar.

Table 8 indicates average and maximum per- centage of installed power recorded in Octo- ber 2010 and 2013.

Figure 14 depicts average WPP output hour by hour in November. This month the level of WPP output is the higher than WPP output of the previous months. In 2010 and 2011 the curves are similar and flat. In 2012 and 2013 the curves are very similar.

Table 9 indicates average and maximum per- centage of installed power recorded in Octo- ber 2010 and 2013.

Figure 15 depicts average WPP output hour by hour in December. This month the level of WPP output is much higher than WPP output of the previous months. In 2010 and 2011 the curves are similar and flat. In 2012 WPP out- put is almost flat, but in 2013 the curve has more windings.

Table 10 indicates average and maximum percentage of installed power recorded in December 2012 and 2013.

Maximum values recorded in December re- veals that even if they are taken from two consecutive years, differences can be signifi- cant. Figure 16 depicts average WPP output hour by hour each month in 2013. At the be- ginning of the year about 2000 MW have been installed. By the end of the year about 2500 MW have been installed. In this figure WPP monthly output is compared. In winter time the level of WPP output is much higher (more than double) than WPP output in summer time. The lowest level is about 240 MW and the highest level is about 790 MW.

Figure 17 depicts average WPP output hour by hour each month in 2012. At the begin- ning of the year about 1100 MW have been installed. By the end of the year about 1900 MW have been installed. In this figure WPP monthly output is compared. In winter time the level of WPP output is much higher than WPP output in summer time. The lowest lev- el is about 120 MW recorded in June and the highest level is about 600 MW recorded in December. As for the rest of the months, the curves are quite close and compact.

Figure 18 shows the same average WPP out- put hour by hour each month in 2012 at smaller scale.

Figure 19 depicts average WPP output hour by hour each month in 2011. At the begin- ning of the year about 400 MW have been in- stalled. By the end of the year about 1100 MW have been installed. In this figure WPP monthly output is compared.

In winter time the level of WPP output is higher than WPP output in summer time. The lowest level is about 30 MW recorded in July and the highest level is about 220 MW rec- orded in December [9].

4 Conclusions

Starting from 2010, installed power increased from 13 MW to about 400 MW by the end of the year. In 2011, the installed power was almost double (700 MW) compared with the previous year. The maximum installed power was recorded in 2012 (800 MW), then in 2013 it decreased up to 500 MW and in 2014 as forecast it will decrease even more (270 MW). This evolution is well-related to the specific legislation that incentives RES de- velopment.

Taking into account the large available rec- orded data set that describes the global opera- tion of WPP between 2010 and 2013 (over 200000 records), business intelligence solu- tions developed by Oracle will be used. No business intelligence technique has been ap- plied for wind power plants operation until now. Out of data set some interesting results are found such as hourly average WPP output grouped by studied years, comparison among curves that describe hourly average WPP output, relation between WPP output and in- stalled power in WPP in terms of maximum and average values and seasonal analyses on each studied year.

The main conclusions regarding WPP opera- tion in Romania are:

- wind blows more at night that is not help- ful for system operation;

- summer months were less windy;

- if we compare WPP output in the same month in two consecutive years, the dif- ference could be significant;

- average WPP output could be considered no more than 30% and maximum WPP output could be considered around 80%.

Although in this paper several analysis re- garding WPP operation are presented for dif- ferent consecutive years, it is obvious that more data is required in order to obtain better correlations and more significant conclusion that could be useful in power systems opera- tion.

Acknowledgement

This paper presents some results of the re- search project: A. Bara (coord) - Sistem intel- igent pentru predictia, analiza si monitor- izarea indicatorilor de performanta a pro- ceselor tehnologice si de afaceri în domeniul energiilor regenerabile (SIPAMER), research project, PNII - Parteneriate în domeniile pri- oritare, PCCA 2013, code 0996, 2014

References

References

[1] Parlamenul European, "Directiva 72/2009/CE a Parlamentului European si a Consiliului privind normele comune pentru piata interna a energiei electrice si de abrogare a Directivei 54/2003/CE", 2009

[2] S. Oprea, D. Petrescu, D. Bolborici, O. Stanescu, "Aspects related to te wind power plants operation in Romania", CIE 2012 Oradea

[3] S. Oprea, "Aspecte privind accesul deschis la retelele electrice - Integrarea surselor regenerabile de energie", PhD thesis, Bucuresti, 2009

[4] ANM, Administratia Nationala de Mete- orologie - online reports available at http://www.meteoromania.ro/anm/?page_ id=138

[5] A. Bara (coord), "Sistem inteligent pentru predictia, analiza si monitorizarea in- dicatorilor de performanta a proceselor tehnologice si de afaceri în domeniul en- ergiilor regenerabile", (SIPAMER), re- search project, PNII - Parteneriate în domeniile prioritare, PCCA 2013, code 0996, 2014

[6] L. Landberg, G. Giebel, H.A. Nielsen, T.S. Nielsen, H. Madsen, "Short-term prediction - An overview", Wind Energy 6(3), 2003, pp.273-280;

[7] T. Ackerman, "Wind Power", John Wiley & Sons, 2005, 742 pp;

[8] A. Bara, A. Velicanu, I. Lungu, I. Botha, "Natural Factors that Can Affect Wind Parks and Possible Implementation Solu- tions in a Geographic Information Sys- tem", Proc. of the International Confer- ence on Development, Energy, Environ- ment, Economics, 2010, pp.50-54, ISBN 978-960-474-253-0

[9] Transelectrica SA, online reports availa- ble:http://www.transelectrica.ro/4Operare SEN/functionare.php