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Resources adequacy Analysis of renewable generation variability - - PowerPoint PPT Presentation

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Resources adequacy Analysis of renewable generation variability ING. MICHAELA LACHMANOV FACULTY OF ELECTRICAL ENGINEERING, CZECH TECHNICAL UNIVERSITY IN PRAGUE 15TH IAEE EUROPEAN CONFERENCE 2017 Outline Czech Republic does it

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Resources adequacy – Analysis of renewable generation variability

  • ING. MICHAELA LACHMANOVÁ

FACULTY OF ELECTRICAL ENGINEERING, CZECH TECHNICAL UNIVERSITY IN PRAGUE 15TH IAEE EUROPEAN CONFERENCE 2017

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Outline

  • Czech Republic – does it worth it?
  • Power sector and RES-E in the Czech Republic, solar boom
  • Resources adequacy – what is it? How can we measure it?
  • Methodology
  • Case study – 15 min RES generation in the Czech Republic
  • 15 min generation
  • 15 min gradients
  • Conclusion

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Czech Republic

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  • 10 mio inhabitants
  • GDP - 192.92 billion US dollars (2016)
  • 1st in beer consumption in the world (3rd Austria)
  • Pilsner Urquell, Budweisser
  • Sport nation (Jaromir Jagr, Petra Kvitova)
  • 1989 – Velvet revolution (centrally planned economy)
  • 2003 – member of EU
  • Traditionally focused on heavy industry (Škoda auto, Paramo,

Setuza)

  • Steel industry, petrochemical industry
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Czech power sector 1/2

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Czech power sector 2/2

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  • Nuclear and coal power
  • Heavy industry
  • Exporter
  • Overflows from Germany to Austria
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RES-E in Czech Republic

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  • RES significantly increased in the last five years
  • 2010, 2011 solar boom (around 500 EUR/MWH FIT)
  • The highest share of PVE sources with an installed capacity over 1 MW
  • The highest share of wind sources with an installed capacity over 2 MW
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Aspects of RES integration

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  • Sustainable and secure energy
  • Protection of the environment
  • The limited supplies of fossil fuels and the

negative impact of their use on the environment

  • Energy security - local independence on

energy sources

  • Social benefits of renewable energy.
  • High investment costs (recently lower)
  • Subsidies
  • High final electricity price
  • Merit order effect
  • Intermittency
  • Low energy area concentration
  • Public opinion
  • Traditional country

Renewables are going to be more widely implemented – we should recognize how and focus on technical point of view – this paper focuses on basic characteristic of RES

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Resources adequacy

Short term variability analysis leads to calculation of sources covering hourly gradients of RES production – hourly (15 min) changes in generation

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Generation adequacy

  • The ability of the generation in the power system to

match the load on the power system at all times.

  • More broadly in long-time period, it is also about

finding the optimal structure of sources for electricity generation taking into consideration all economic, environmental and technical aspects of all types of sources.

  • For RES integration is crucial to analyze renewable

production and determine short term characteristics to determine contribution of RES to generation adequacy

Capacity credit vs capacity factor

  • Capacity factor is the ratio of generator actual output
  • ver a period of time to its maximum generation

capacity over the same period.

  • Capacity credit or capacity value of a generator is the

amount of additional peak load that can be served by that generator.

  • Capacity factor and capacity credit are basic

characteristics of sources to determine the contribution of sources to generation adequacy.

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Methodology

  • Statistical analysis of renewable generation
  • 15 min data
  • Mean, median, standard deviation, 95% confidence interval
  • Capacity factor

𝑑 = 𝑞𝑢 𝑞𝑗𝑜𝑡𝑢

  • 15 min gradients
  • Mean, median, standard deviation, 95% confidence interval

𝑕𝑢 = 𝑞𝑢 − 𝑞𝑢−1

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Case study – PVE, wind generation 1/2

  • All wind generation in the Czech Republic, aggregated
  • All PVE generation in the Czech Republic, aggregated
  • 15 min data

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200 400 600 800 1000 1200 1400 00:00 01:15 02:30 03:45 05:00 06:15 07:30 08:45 10:00 11:15 12:30 13:45 15:00 16:15 17:30 18:45 20:00 21:15 22:30 23:45 Generation (MW)

1.7.2016, wind and solar production, Czech Republic

20 40 60 80 100 120 140 160 00:00 01:15 02:30 03:45 05:00 06:15 07:30 08:45 10:00 11:15 12:30 13:45 15:00 16:15 17:30 18:45 20:00 21:15 22:30 Generation (MW)

1.1.2016, wind and solar production, Czech Republic

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PVE, wind generation 2/2 – capacity factor

0,00% 20,00% 40,00% 60,00% 80,00% 100,00% 1000 2000 3000 4000 5000 6000 7000 8000 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 FREQUENCY PRODUCTION (MW)

Wind generation, Czech Republic (2016)

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0,00% 20,00% 40,00% 60,00% 80,00% 100,00% 5000 10000 15000 20000 106 212 318 424 530 636 742 848 954 1166 1060 1272 1378 1484 1590 1696 FREQUENCY PRODUCTION (MW)

PVE generation (Czech Republic (2016)

Wind generation PVE generation Minimum 0,000 0,000 Maximum 0,839 0,829 Mean 0,163 0,76 Standard deviation 0,171 0,187 95% confidence interval top 0,000 0,000 95% confidence interval bottom 0,474 0,425

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15 min gradients 1/3

  • 0,02
  • 0,015
  • 0,01
  • 0,005

0,005 0,01 0,015 0,02 0,025 01.01.2016 00:15 01.01.2016 01:15 01.01.2016 02:15 01.01.2016 03:15 01.01.2016 04:15 01.01.2016 05:15 01.01.2016 06:15 01.01.2016 07:15 01.01.2016 08:15 01.01.2016 09:15 01.01.2016 10:15 01.01.2016 11:15 01.01.2016 12:15 01.01.2016 13:15 01.01.2016 14:15 01.01.2016 15:15 01.01.2016 16:15 01.01.2016 17:15 01.01.2016 18:15 01.01.2016 19:15 01.01.2016 20:15 01.01.2016 21:15 01.01.2016 22:15 01.01.2016 23:15

15 min gradient

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Wind (%) PVE (%) 9/6/2017

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  • 0,05
  • 0,04
  • 0,03
  • 0,02
  • 0,01

0,01 0,02 0,03 0,04 0,05 0,06 01.07.2016 00:00 01.07.2016 01:00 01.07.2016 02:00 01.07.2016 03:00 01.07.2016 04:00 01.07.2016 05:00 01.07.2016 06:00 01.07.2016 07:00 01.07.2016 08:00 01.07.2016 09:00 01.07.2016 10:00 01.07.2016 11:00 01.07.2016 12:00 01.07.2016 13:00 01.07.2016 14:00 01.07.2016 15:00 01.07.2016 16:00 01.07.2016 17:00 01.07.2016 18:00 01.07.2016 19:00 01.07.2016 20:00 01.07.2016 21:00 01.07.2016 22:00 01.07.2016 23:00

15 min gradient

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Wind (%) PVE (%)

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15 min gradients 2/3

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0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00% 80,00% 90,00% 100,00% 5000 10000 15000 20000 25000 FREQUENCY

Wind gradient histogram

0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00% 80,00% 90,00% 100,00% 5000 10000 15000 20000 25000

  • 0,15
  • 0,136
  • 0,122
  • 0,108
  • 0,094
  • 0,08
  • 0,066
  • 0,052
  • 0,038
  • 0,024
  • 0,01

0,004 0,018 0,032 0,046 0,06 0,074 0,088 Další FREQUENCY

PVE gradient histogram

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15 min gradients 3/3

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Wind generation PVE generation

Minimum

  • 0,148
  • 0,130

Maximum 0,140 0,087 Mean 0,000 0,000 Standard deviation 0,015 0,015 95% confidence interval top

  • 0,025
  • 0,025

95% confidence interval bottom 0,025 0,025

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Conclusion

  • Maximum generation 0,83 wind, 0,829 PVE
  • Capacity factor is very low – 7,6% PVE, 16% wind
  • Hourly (15 min) gradients
  • Maximum values - Germany (-80%;88%) vs Czech Republic (-14%;8%)
  • Hourly variability is high
  • Hourly gradient is not as high – 3%
  • Following research – dimensioning of backup sources, demand response…

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Thank you for your attention!

  • Ing. Michaela Lachmanová

Department of Economy, Management, Humanities Faculty of Electrical Engineering Czech Technical University in Prague Michaela.lachmanova@fel.cvut.cz

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