[IEEE 2014 Electric Power Quality and Supply Reliability Conference (PQ) - Rakvere, Estonia...

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Abstract—The paper presents approach for determination of

wind and solar generation probability to back-up power supply of customers in distribution network.The developed approach is based on the analysis and usage of statistics of real genera-tion from wind and solar resources connected to distribution network, as well as customers’ load diagrams. Proposed ap-proach was implemented in case studies that use information on real distribution network design principles, customers’ load diagrams and statistics of distributed energy resources genera-tion in Latvia. Case studies are based on previously performed calculations (case studies) presented by authors at PQ2012 conference in the paper “Assessment of Different Power Line Types’ Life-time Costs in Distribution Network From Reliabil-ity Point of View”. Results obtained from the calculations give well-grounded base for distribution network exploiting engi-neers as well as individual customers when trying to find network’s optimal reliability level by replacing overhead lines by other types of power lines (as one of network reliability centered maintenance and reconstruction activities of DSO) and by using small wind power generators and photovoltaics (as activity of customers).

Index Terms—cost benefit analysis, power distribution lines,

power system economics, power system reliability, photo-voltaics, wind energy integration.

I. INTRODUCTION In order to preserve the climate balance in the world,

carbon dioxide (CO2) emissions per capita should not exceed 3.8 tons per year. Carbon dioxide emissions in Latvia currently exceed this specified threshold, reaching 4.1 tons of CO2 per capita per year [1]. One of the ways to reduce CO2 emissions is increasing the use of energy resources that creates the least amount of CO2 emissions, such as wind or solar (PV). Total wind energy potential in Latvia is estimated up to 1.5 TWh (at present installed power capacity is 0.112 TWh) [2]–[4].

The role of power quality and supply reliability increase over the years as modern society becomes more dependent on demanding electrical appliances. Electric power quality and supply reliability are closely related to each other and sometimes have the same causes of appearance, e.g. trees can cause both voltage dips, temporary, as well as perma-nent faults for overhead lines [5].

The paper is supported by the European Social Fund project

"Assessment of wind energy potential in Latvia and environmental impact from wind energy installations", No. 2014/0010/1DP/1.1.1.2.0/13/APIA/ VIAA/033.

A. Lvovs, I. Priedite, and A. Snegirjovs are with Institute of Physical Energetics, Aizkraukles street 21, LV-1006, Riga, Latvia (e-mail: [email protected], [email protected], [email protected]).

Therefore, it is worthwhile to analyse the wind positive

effect of wind generators on network reliability and on reduction of customer costs of reliability.

II. APPROACH FOR DAILY WIND POWER GENERATION ESTIMATION

Availability of wind resources of Latvia shown in Fig. 1. It is obvious that the highest wind energy potential in Latvia is on the west coast. Irradiation level in of Latvia, that could be used for preliminary potential estimation of PV panel installation, shown in Fig. 2.

A case study was carried for Liepaja because it is located in the highest wind speed zone. Average hourly wind speed in Latvia at 4 meter height shown in Fig. 3. For calculations there has been used statistics of hourly wind speed for every hour from year 2011 and 2012 (2 × 8760 hours).

Fig. 1. Average yearly wind speed in Latvia 10 meter height [6].

Fig. 2. Global irradiation for optimally-inclined photovoltaic modules in Latvia [27].

Study on Reliability Improvement Possibilities Assuming Probabilistic Wind and Solar

Generation Aleksandrs Lvovs, Ilze Priedite, and Andrejs Snegirjovs

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Fig. 3. Average hourly wind speed in Latvia 4 meter height, 2011–2012 [7].

In order to estimate the wind speed at a certain height h, the relationship would be rearranged to:

00 h

hVVh

where Vh – wind speed at a certain height h; V0 – wind speed at a reference height h0; h0 – reference height;

= 0.16–0.24 empirically derived coefficient that varies dependent upon the stability of the atmosphere [8].

Case study described further included 3 types wind generators with rated power 1 kW, 3 kW and 5 kW. More detailed technical specification of these wind turbines shown in Table I [9].

TABLE I. TECHNICAL SPECIFICATION OF WIND TURBINES

1 kW 3 kW 5 kW Tower height 8 m 9 m 12 m Cut-in wind speed 2.5 m/s 3 m/s 3 m/s Working wind speed 2.5–40 m/s 3.5–40 m/s 3.5–40 m/s

Power curves for these wind turbines shown in Fig. 4.

Fig. 4. Power curves for 3 types wind turbine with rated power 1 kW, 3 kW, 5 kW [9].

For the approach proposed in the paper information on at least hourly statistics of energy generation of PVs and wind turbines should be obtained. For the study performed and described in the paper there have been used information on hourly values of wind speed, as stated previously, as well as hourly information of PV production installed on the roof of Institute of physical energetics. In case if someone has no information on hourly production values, consultation with specialists that have PV and wind turbine installations is needed. Usually such specific companies have some infor-mation on production at their web page. Example of data of such company is illustrated at Fig. 5 [29], [30].

Fig. 5. Energy generation of 1 kW PV and 1 kW wind turbine in year 2013.

III. APPROACH FOR COST ESTIMATION OF UTILITY For comparison of economic performance of the net-

works, formed by four different types of power lines (overhead line (OHL), covered conductor overhead lines (COHL) or cable lines (CL)), from construction, mainte-nance and fault elimination costs point of view, detailed data about number of maintenance works, failure rates, costs of materials and work, etc. should be known. These data are used in the algorithm of methodology for costs estimation (see Fig. 6). Algorithm’s diagram and equations for calcula-tion of CL, OHL or COHL total costs described in details in [10].

No

Yes

Credit used?

Network’s technical information input

Calculation of power lines’ total length. Definition of different type power lines’ parameters for all sections

Cost information input Fault statistics input

Start of network’s cost evaluation calculations

Interest rate, inflation, credit

usage time

Use NPV method for construction cost calculations

NoYes Cables used?

Calculation of CL total costs (2)

Calculation of OHL or COHL total costs (2)

Interest rate, inflation, credit usage time are equal to „0”

Calculation of total annual costs

Results Fig. 6. Algorithm for cost estimation methodology [11].

In common situation expenses related with construction and operation of the network can be calculated using (2).

.1

( )n

i mi fe ii

C C C C

where C – total costs of network for some period of time,

monetary units/time period; Ci – total construction costs of lines with parameters i,

monetary units; Cmi – total preventive repair/maintenance costs of lines

with parameters i , monetary units;

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Cfe.i – total fault elimination costs for lines with parame-ters i, monetary units;

i – number, corresponding to power line with specific parameters;

n – total number of lines with different parameters used for the network.

Equation (2) gives total costs of network for some period of time, e.g. 1 year or life time of power lines, taking into account power lines’ life time and different costs for lines of the same type (OHL, COHL, CL) but different parameters, e.g. cross section [11].

IV. APPROACH FOR ESTIMATION OF CUSTOMER COSTS OF RELIABILITY

In the most cases analysis of customer costs of reliability (further – CCR) and energy not supplied (further – ENS) is performed using average loads. In such case energy consumption is replaced by average load of load point during some long period of time – season, like in [12], year, like in [13] or other time period, like in [14]. For calculation of ENS average load of year/season/other period is multi-plied with average power supply interruption time of customers.

According to results of surveys of customer about power supply interruption related costs, the costs are not constant and they change not only in terms of seasons of a year, but also in short time periods – month and time of a day [15]–[18].

Usually the reason of power supply interruption costs changes during time is supposed to be changes of load values. To check the assumption, comparison of ENS changes and CCR changes during a day (24 hour period) was made. Possible ENS values for each hour of a day were calculated taking into account average power supply interruption duration (that in Latvia in summer of year 2010 was ~8.5 hours, but during summer of year 2011 – ~7 hours [19]), information about typical load diagrams of the sectors’ customers [20] and statistical data about sectors’ energy consumption [21]. Comparison showed that not always CCR changes correspond to ENS (and therefore load) changes.

This was the reason for development of methodology that takes into account time-variable loads and customer costs.

Proposed approach for evaluation of CCR is based on evaluation of effect of interruptions occurring at distribution network level. This can be based by the fact that according to statistical data [22], power supply interruptions are caused mainly by outages in distribution network.

Flowchart given in Fig.7 illustrates structure of proposed approach.

Before calculation starts, information about network that includes failure rates of network elements, load diagrams, protection devices, etc. is specified. As soon as calculations can be carried out for different network configurations (states), e.g. different types of lines, number of reclosers, etc., information about networks` state number (indicated by n) is also given.

After network technical information for state n is known, calculations start. Taking into account that customer load diagrams, as well as failure rates of elements usually depend on time, different network operation modes re should be assumed. Regimes re represent such network operation

Network’s technical information input. Initial state (n=1)

Start

Calculation of load point indices re; Ure; rre

Load profile related information calculation

Calculation of CCRre

re=1

No

Yes

re=reend re=re+1

Calculation of CCR

No

Yes

n=nendn=n+1

End Fig. 7. Customer costs of reliability calculation flowchart.

modes like working days in winter or other season, holidays in winter and other seasons, etc.

When number of regime is known, calculation of load point indices starts. During this process, failure rate – re; unavailability – Ure and average power supply restoration time – rre values for each load point are calculated based on network configuration (state) n and regime re.

To calculate CCR for the regime re, ENS and interrupted load for each hour of a 24 hour (a day) period are calculated. CCR is calculated based on the information of each hour’s ENS and interrupted load. CCR is formed by costs caused by short (< 3 min.) and long (> 3 min.) power supply interrup-tions and can be calculated using (3):

ls CCRCCRCCRwhere

CCRs – customer costs of reliability caused by short (< 3 min.) power supply interruptions, monetary units.

CCRl – customer costs of reliability caused by long (> 3 min.) power supply interruptions, monetary units [10].

V. APPROACH FOR DETERMINATION OF WIND GENERATION PROBABILITY TO BACK-UP POWER SUPPLY

Small scale wind and PV generation is characterized with relatively high fluctuations of generation due to changes of wind speed and solar radiation in wide borders. Due to aforementioned fact, small scale wind and PV generation has highly probabilistic nature. Moreover, generation equip-ment of wind turbines has non-linear power generation characteristic. The fact determines the need to analyse fluctuations of power generation, but not just wind speed and its changes to evaluate possibility of reliability improve-ment by using small scale wind generators.

Diagram for determination of wind and PV generation probability to back-up power supply is given at Fig. 8. First of all specific location of wind generator and PV installation should be known to evaluate possible wind and solar potential at the place. Preliminary information on wind potential and solar radiation for region or country is usually

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illustrated by specialized maps of wind speed, like ones shown on Fig. 1 and Fig. 2. When installation place is known, information (statistics) on at least hourly wind speeds, should be gathered. Information on the change of solar radiation also would be valuable. The second step is generator and PV panel model (type) selection that would allow to evaluate possible generation output at specific location and with specific wind speeds and solar radiation (at the third step). In case if there is no information on the changes of solar radiation, for the PVs output evaluation at the 3rd step there could be used information on generation fluctuations of PVs of similar type installed in nearby region. At the third step distribution of small scale generator generation output is formed, as well as average generation and standard deviations are being calculated. At the fourth step energy consumption pattern of household or other consumption unit has to be made. This shall be done to evaluate average loads during different year seasons, as well as working and weekend days. Preliminary power supply reliability analysis has to be performed at the fifth step to identify number of short and long interruptions and their durations when wind generator and PV panel is not in-stalled. Number of long and short outages, as well as their durations in combination with results of energy consump-tion pattern analysis allows identifying energy amount not supplied during interruptions.

On the basis of results from the third step, at the sixth step Z score values of wind generator and PV panel power generation distribution is being selected in such way that following expression is fulfilled:

.* .int.. iiavg AZA

where Ag.av. – average energy generated by wind generator

during 24 hour period, kWh; – standard deviation of energy generated by wind

generator or PV panel during 24 hour period, kWh; Zi – needed Z score value of wind generator or PV

panel power generation distribution (to cover not supplied energy of interruption i);

i – depending on i value, short or long interruption is taken into account;

Aint.i. – energy amount not supplied during interruption i, kWh.

Taking into account Z score value at the seventh step probability to cover not supplied energy during short and long interruptions is being determined. Probability to cover not supplied energy is defined as the area below wind generation distribution probability function to the right of Z score value (see Fig. 9 for idea illustration purposes).

For example, if according to (4) wind generator or PV panel is able to cover not supplied energy during interrup-tion with Z score equal to Z = 1.4 (sum of generator’s/PV panel’s average daily energy production + 1.4 standard deviation is equal to energy amount not supplied during interruption), than probability to cover not supplied energy will be equal to P = 0.08076. In case if wind generator or PV panel would produce more energy during a day, mean value would move more to the right, Z score value needed to satisfy expression (4) would decrease and move more to the left (due to increased mean value) and due to that probability to cover energy demand (P) would increase.

Wind speed and PV generation at specific location

Generator and PV panel model selection

Evaluate possible generation output – average generation and standart

deviations

Energy consumption pattern analysis

Calculate Z score values of generation distribution

Calculate probabilities to cover cosumption during supply interruption

Preliminary power supply reliability analysis

Final power supply reliability analysis

Results Fig. 8. Diagram for determination of wind and PV generation probability to back-up power supply.

Fig. 9. Illustration to the approach of determination the probability to cover not supplied energy during interruption according to Z score value

At the eighth step power supply reliability is recalculated taking into account probabilities to back up power supply – number of power supply interruptions is being reduced by multiplying them with following coefficient K:

1 w pvK P P

where Pw, Ppv – respectively probability of wind generation and

PV panel to back up power supply during specific interrup-tion.

Case study given further implemented all the approaches presented at the paper.

VI. CASE STUDY The aim of case study was to estimate reliability improve-

ment possibilities assuming probabilistic wind and PV panel generation and to evaluate economic feasibility of PV and small scale wind generation installation from reliability of supply point of view. In the case study there have been estimated changes of reliability level for customers due to wind generation installation, due to PV panel installation

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together with wind generation (using different combinations of installed PV and wind generator power), and due to some other reliability improvement activities related with usage of different types of power lines with and without renewables. In section “Results” reliability level is represented by the “Interruption costs” – the bigger are reliability costs, the worse is reliability level. Costs of wind generator and PV panel installation in “Results” are being represented by generator and PV costs. Costs of renewables consist of wind generator or PV panel costs + costs of batteries needed to store energy from the energy source. The number of batteries has been selected to be able to store such volume of energy that is equal to: average daily energy generation + 2 standart deviations of energy generation. This condition ensures that with 97.7% probability all generated energy during any day with any production volumes will be stored in batteries. Efficiency coefficient of the batteries has been estimated at the level of 85%.

Utility costs related to construction and operating costs of networks with different types of power lines and their combinations have been taken already calculated and taken into account as well (in section “Results” called “TUC”).

Sum of the two aforementioned costs give total costs of reliability that are used for finding optimal reliability level for network.

This case study is based on previous case study of authors that is described in details in [10]. For detailed information about initial data on the case study please refer to the aforementioned paper. For case study it was used medium voltage (MV) network scheme that takes into account typical structure of MV network used in Latvia [23].

Construction and maintenance costs were obtained from Latvian DSO “Sadales tikls” and private companies that construct and help to maintain network in operating state.

Information about time-variable customer costs of reliability was taken from [24]. In calculations cost variation depending on season and working/not working hours was taken into account. Also it was assumed that at each load point maximal active load is 100 kW. Each load point included load from different consumption sectors, e.g. Households, Commerce and services sector, etc. Load diagrams for the sectors were constructed using information about load diagrams of consumers from [20].

Customer costs of reliability were estimated including costs created by permanent faults (power supply interruption time > 3 min.) when there is need for repair works (~7h duration) or switchings for power supply restoration (~1h duration) and temporary faults (power supply interruption

3 min.). In initial state, studied network is fully formed by OHL

and customers do not have wind generation and PV panels. Case study included 3 variants: Network is fully formed by OHL (initial state) – Variant Nr. 1; One section of three sections and corresponding branches are replaced by underground cable lines (OHL+CL) – Variant Nr. 2; One section of three sections and corresponding branches are replaced by COHL (OHL+COHL) – Variant Nr. 3. In the case study each variant has also been supplemented

with calculations carried for the cases when wind generators with rated power 1 kW, 3 kW and 5 kW are being used by household customers. As additional variants to the wind

generation usage, variants with combination of PV panels (with installed power 1 kW, 3 kW and 5 kW) and wind generation have been evaluated. In total 39 combination of network development variants, wind generators and PVs have been studied.

In the case study wind generators and PV panels are being installed to household users only. For household users there was assumed max. allowed load 32 amps in 1 phase network and with cos = 0.93, that gives max power of about 6.8 kW. In calculations of energy not supplied there have been used load diagrams from Latvian DSO “Sadales tikls” [20].

Information on small scale wind generators has been taken from [9]. Information on PV output is based on real PV output installed in Institute of Physical Energetics in Laboratory of Energy Resources [28]. Information on batteries used to store energy from wind generator has been taken from [25]. Generator and batteries related costs in section “Results” called “gen.costs”.

As it was mentioned in introduction to the paper, usage of renewable energy sources helps to decrease emissions of greenhouse gases. Authors of the paper have studied literature on the renewable energy sources effect on the greenhouse gas emissions, e.g. [26] for wind power generation. It can be concluded is that almost only big penetration of renewables (e.g. wind farms polygon with installed power of several megawatts, that usually are connected to high voltage network) can affect reduction of greenhouse gases emissions in reasonable manner. As can be seen in [26], some positive net effect of wind generation in years 2010–2015 is only in case if total installed power is above 500 MW. Due to the fact that this paper focuses on small scale generators and PV panels with installed power up to 5 kW, positive net effect of wind generation and PVs on greenhouse gases emissions was not considered. At the same time authors of the paper believe that massive usage of renewables (not only wind and solar power, but also bio-mass, etc.) in both transmission and distribution networks can positively affect emissions.

When analysing positive effect of wind generators and PVs on network reliability and on reduction of customer costs of reliability, there also has been considered economic benefit that get household users by installing renewables – such users do not pay for electricity produced by wind generators and PV panels. In Latvian case additional econ-omy is ~0.15 EUR/kWh.

Further section of the paper provides results of calcula-tions according to proposed approached.

VII. RESULTS The results of probabilities of outage load coverage for

wind generators are shown in Table II. But the results of probabilities of outage load coverage for PV panels are shown in Table III.

As can be seen in Table II that higher probability of outage load coverage for wind generator is in winter time and smaller probability of outage load coverage in summer. It is also logical, that wind generators with higher installed power have higher probability of outage load coverage than smaller power generators – especially difference can be seen for the cases of middle and long interruptions.

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TABLE II. PROBABILITIES OF OUTAGE LOAD COVERAGE BY WIND GENERATOR

Type of interruption

Winter Summer Working

days Weekends/

holidays Working

days Weekends/

holidays 1 kW wind generator

Short 0.59095 0.59095 0.52392 0.51994 Middle 0.01743 0.01255 almost zero probability Long 1 kW generator can not cover interrupted energy

3 kW wind generator Short 0.60257 0.60257 0.54776 0.5438 Middle 0.26435 0.24825 0.02018 0.01539 Long almost zero probability

5 kW wind generator Short 0.63307 0.63307 0.57926 0.57926 Middle 0.44038 0.44038 0.2177 0.20327 Long 0.00071 0.00038 almost zero probability

TABLE III. PROBABILITIES OF OUTAGE LOAD COVERAGE BY PV PANELS

Type of interruption

Winter Summer Working

days Weekends/

holidays Working

days Weekends/

holidays PV panels with total installed power of 1 kW

Short 0.71904 0.71904 0.97615 0.97615 Middle 0.02275 0.01659 0.52392 0.48405 Long 1 kW PV panel can not cover interrupted energy

PV panels with total installed power of 3 kW Short 0.72907 0.72907 0.97725 0.97725 Middle 0.40129 0.38591 0.91309 0.90824 Long almost zero probability 0.00539 0.00264

PV panels with total installed power of 5 kW Short 0.73237 0.73237 0.97778 0.97778 Middle 0.53983 0.5279 0.94738 0.9452 Long 0.00114 0.0006 0.2327 0.19215

Results presented in Table III show that situation of

outage load coverage by PVs is different from the situation with wind generators. Higher probability of outage load coverage for PV panels is in summer time and smaller probability of outage load coverage in winter. Such situation is logical, because of bigger number of sunny days during summer in Latvia. As can be seen from results, PV panels in overall show much better outage load coverage probabilities in comparison with results of wind generators.

Figs. 10–13 show annual costs (in EUR) for three network reliability improvement variants depending on wind genera-tor capacity and PV panel’s capacity.

It is obvious that the lowest annual costs are when one section of three sections and corresponding branches are replaced by underground cable lines (OHL+CL) and without wind generator and PV panel, i.e., variant Nr. 2 with option “without” at all Figs. 10–13. Similar annual costs are when one section of three sections and corresponding branches are replaced by COHL (OHL+COHL) and without use of wind generator and PV panel, i.e., variant Nr. 3 with option “without” at all Figs. 10–13. When comparing annual costs for all three variants, network fully formed by OHL (initial state), i.e., variant Nr. 1, is the least beneficial.

Results of performed calculations show that usage of distributed generation like PVs and wind generators is not the best option from technical-economical point of view when analyzing possibility to backup power supply. But it should be mentioned, that usage of analysed types of energy sources showed that they also are not the worst option for

reliability level improvement – the most economically effec-tive ways to improve reliability of power supply, after aforementioned variant Nr. 2 and Nr. 3, are variants with usage of combination of cable lines and PVs and wind generator with different power. The most economical options after variant Nr. 2 and Nr. 3 with option “without” are:

a) variant Nr. 2 with option “1 kW” at Fig. 11 (usage of OHL+CL and 1 kW wind gen. and 1 kW PV panels);

b) variant Nr. 2 with option “1 kW” at Fig. 13 (usage of OHL+CL and 1 kW wind gen. and 5 kW PV panels);

c) variant Nr. 2 with option “1 kW” at Fig. 12 (usage of OHL+CL and 1 kW wind gen. and 3 kW PV panels).

Previously explained results show that usage of solar energy is more beneficial in Latvia in comparison with small-scale wind generation, even taking into account the fact that in calculations there were used data of PV genera-tion form Riga (with relatively smaller solar irradiation than in the southern coast of Latvia) and with data of wind speeds from city Liepaja, where wind speeds are one of the highest in Latvia.

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Fig. 11. Annual costs (in EUR) for three variants depending on wind generator capacity and with 1 kW PV panel.

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VIII. CONCLUSIONS Wind power integration and economics is important topic

for stakeholders, end users and regulatory institutions. New approaches for estimation customer costs of reliability and utility are proposed and illustrated in the paper.

In the case study there have been estimated changes of reliability level for customers due to wind generation and PV installation, and due to some other reliability improve-ment activities related with usage of different types of power lines. Changes of reliability level affect customer costs of reliability.

Results of performed study show that in Latvia small scale wind generation is less competitive in comparison with small-scale PV, because PVs show bigger energy generation in average and more stable generation (standard deviations of generation distributions have smaller value). The situation can be even better for PVs, if there would be available and used information on PVs generation installed in regions of Latvia with higher solar energy potential.

As stated previously, output of renewables and their technical efficiency highly depends on their installation place even in such relatively compact country like Latvia. But the main and valuable conclusion that can be made on the basis of the results of the study presented in the paper is that already today renewable energy sources’ technologies can if not compete, than at least supplement traditional network reliability improvement activities in technically and economically effective way – as according to the results the 2nd, 3rd and 4th place (of 39 combination of network develop-ment variants) go to the variants with usage of both types of renewables.

However, it should be noted that these results reflect situation of Latvia town and cannot be generalized because each country and even part of country can have different wind and solar irradiation potential.

REFERENCES [1] Regional energy agency of Zemgale: CO2 emissions and Latvija [in

Latvian] http://www.zrea.lv/lv/energoefektivitate/menesa_aktualitate/co2_emisijas_un_latvija/

[2] Ministry of Economics of the Republic of Latvia: Wind energy [in Latvian] – http://wiki.em.gov.lv/wiki/V%C4%93ja_ener%C4%A3ija

[3] Central Statistical Bureau of Latvia: Energy balance of Latvia in 2012 [in Latvian] http://www.csb.gov.lv/sites/default/files/nr_35_latvijas_energobilance_2012_13_00_lv.pdf

[4]

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[5] R. E. Brown, “Electric power distribution reliability”. New York: Marcel Dekker Inc., 2002. pp. 40–114.

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BIOGRAPHIES

Aleksandrs Lvovs was born in 1985 in Riga, Latvia. He received the B.Sc. and Mg.Sc.ing. and Dr.Sc.Ing. degrees from the Riga Technical University (RTU) in 2007, 2009 and 2013 respectively. His major field of study is power energetics.

His employment experience includes Ministry of Economics of the Republic of Latvia – Division of energy markets, as well as Projection division and Operational planning devision (current position) of Latvian TSO – JSC ”Augstsprieguma tikls”. Currently he also holds

position of researcher in Institute of Physical Energetics. His special field of interests includes methods for reliability evaluation of electrical networks and systems and methods for development of power systems planning.

He received „Werner von Siemens Excellence Award” in 2009 for his master’s work. In 2007 he received paper of distinction form JSC ”Latvenergo”, LEEA and LIF for victory at the contest of scientifical works in field of power and electrical engineering.

Ilze Priedite was born in 1986. She received the B.Sc., Mg.Sc.ing. and Dr.Sc.Ing. degrees from the Riga Technical University (RTU) in 2007, 2009 and 2014 respectively. Her major field of study is power energetics.

Her employment experience includes Institute of Physical Energetics- Laboratory of Electric Power System Simulation. Her special field of interests includes methods for touch voltage calculation, methods for selection of neutral grounding mood, methods for reliability evalua-tion of medium voltage electrical networks and

systems and methods for development of medium voltage power systems planning.

Andrejs Snegirjovs was born in 1987. He received the B.Sc., Mg.Sc.ing. degrees from the Riga Technical University (RTU) in 2011, 2012 respec-tively. His major field of study is heat, gas and water technology.

His employment experience includes Institute of Physical Energetics. His special field of interests includes renewable energy sources, solar energy use technology, optimization of the energy and fuel balance, using the mathematical models, combined heats supply systems, sustainable development of the energy system.

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