Water and Climate Related Energies: Scale issues

Transcription

1 Water and Climate Related Energies: Scale issues M. Borga 1, François B 1,2., J.D. Creutin 1, K. Engeland 3, B. Hingray 1 1 University of Padova, TESAF, Padova, Italy 2 LTHE Université de Grenoble 1/CNRS, Grenoble, France 3 NVE, Oslo, Norway

2 BACKGROUND - 1 European Renewable Energy Directive (2009): 20% share of renewable energy in the EU overall energy mix by 2020 Focus on Climate Related Energies (CRE): Hydropower, Solar, Wind Weather and climate conditions influence both electricity production and demand Dubus, L., 2010: Practices, needs and impediments in the use of weather/climate information in the electricity sector. NATO Advanced Research Workshop on Weather/Climate Risk Management for the Energy Sector, Santa Maria di Leuca, Italy, Springer, Dordrecht, NL,

3 BACKGROUND - 2 Intermittency of wind and sun energy sources is recognized as a severe limitation of their penetration (Tanaka, 2011). Can be transformed in an excuse to not use it (Sovacool, 2009) Unless further investigation shows the tractability of these future systems, in particular thanks to improved consideration of how space-time aggregation scale smooth intermittency and variability of wind and sun energy sources Tanaka, N., 2011: Harnessing Variable Renewables: A Guide to the Balancing Challenge. International Energy Agency, Tech. Rep. Sovacool, B. K., 2009: The intermittency of wind, solar, and renewable electricity generators: Technical barrier or rhetorical excuse? Utilities Policy, 17, Renné, D. S., 2014: Emerging Meteorological Requirements to Support High Penetrations of Variable Renewable Energy Sources: Solar Energy. Weather Matters for Energy, Springer,

4 AIMS SPACE-TIME SCALE ISSUES - Hydrometeorological view of the energy systems; - Energy demand; - Temporal scales: integration of hydropower and PV in NE Italy - Conclusions 4

5 HYDROMETEOROLOGICAL VIEW ON ENERGY SYSTEMS - 1 5

6 HYDROMETEOROLOGICAL VIEW ON ENERGY SYSTEMS - 2 6

7 HYDROMETEOROLOGY AND ENERGY SYSTEMS - 3 Example for wind: 3 obvious peaks: one at around four days corresponding to the passage of weather systems; one at 12 hours reflecting a harmonic of the diurnal cycle; one around one minute corresponding to turbulence. - A power spectrum of wind speeds Van der Hoven, 1957: Power spectrum of horizontal wind speed in the frequency range from to 900 cycles per hour. Journal of Meteorology 1957, 14:

8 HYDROMETEOROLOGY AND ENERGY SYSTEMS - 3 Due to the finite size of weather systems, a way to help manage the integration of wind power into a network is to ensure that wind farms are geographically spread. This reduces the correlation in output between different wind farms and thus helps to smooth output. Correlations between pairs of wind farm sites in Texas compared with sites in Europe. Best fit exponential decay curves are shown for the two regions. 8

9 HYDROMETEOROLOGY AND ENERGY SYSTEMS - 4 (a) Average correlation length and (b) autocorrelation time inferred from ERA-40 data over a 44 year period. These plots show that the typical correlation length is between km and the autocorrelation time typically <2 days. Although interconnection could limit fluctuations in wind power across the continent, there are limitations and the effect of European-wide low wind speed events cannot be eliminated. 9

10 TEMPERATURE AND ENERGY DEMAND Energy demand is connected to temperature due to need for heating or cooling Also connected to wind and humidity 10

11 ENERGY DEMAND Base load Intermediate load Peak load High CRE system: reduced need for base load Increased need for intermediate and peak load (Haubt et al, 2014). Haupt, s.e., Mahoney, W.P., and Parks, K. (2014) Wind Power Forecasting. In: Troccoli, A., Dubus, L. and Haupt, S.E. (Eds) Weather matters for energy, Springer,

12 THE INTERMITTENCY CHALLENGE - 1 Energy transport Geographical smoothing Energy diversity A energy portfolio Energy storage Hydro reservoirs Energy information Smart grids. Example of time series of normalized power output from a single WT, a group of wind power plants and all WTs in Germany ( ) (extracted from Holttinen et al., 2009 page 21). 12

13 THE INTERMITTENCY CHALLENGE - 2 Optimization of energy portfolio: Minimizing the portfolio variance provided the average output is above a certain limit (Traube et al, 2008) Var( p p ) n i n a a Cov( p 1 j 1 i j i j, p ) Minimize the storage energy capacity Heide et al (2011) Minimize the balancing energy Minimize the balancing power Heide,D., Greiner, M., von Bremen, L., Hoffmann, C. (2011) Reduced storage and balancing needs in a fully renewable European power system with excess wind and solar power generation, Renewable Energy 36, , doi: /j.renene Traube, J., L. Hansen, B. Palmintier, and J. Levine, 2008: Spatial and temporal interactions of solar and wind resources in the next generation utility. SOLAR 2008 Conference and Exhibition. San Diego, CA,

14 THE TEMPORAL SCALES EXAMPLE OF ENERGY INTEGRATION IN NE ITALY 14

15 Can we use solar and run-of-the river hydro power complementarity? Focus on small run-of-the-river power plant (P<10 MW) Wild energy source: no upstream dam controlling inflow into the plants Mainly located on small tributaries (mainly un-gauged catchments) Source: IPPBC 15

16 Climate change?? Period: Solar: Veneto plains (17 stations) average power generation. Hydro: 2 catchments - snowmelt dominated - rainfall dominated Energy demand: Italian model Climate change?? 16

17 The power generation scenario 17

18 Deviation between supply and demand: Examples: hourly deviation time series considering two different scenarios of CRE mix: 18

19 Deviation between supply and demand: Effect of CRE mix on deviation variability ( ) Color map: Standard deviation of the deviation time series between CRE power generation and energy demand black dot represent the optimal value More than 400 different scenarios of PV and RoR power generation combinations have been considered CRE mix Scenario 1 Hourly time scale CRE mix Scenario 2 19

20 Deviation between supply and demand: Effect of CRE mix on deviation variability ( ) Color map: Standard deviation of the deviation time series between CRE power generation and energy demand black dot represent the optimal value CRE mix Scenario 1 Hourly time scale Daily time scale CRE mix Scenario 2 20

21 Deviation between supply and demand: Effect of CRE mix on deviation variability ( ) Color map: Standard deviation of the deviation time series between CRE power generation and energy demand black dot represent the optimal value For a given CRE mix, deviation time series variability depends on time scale 21

22 Effect of CRE mix on system performance: Reliability, Resilience, Vulnerability (Hashimoto et al. 1982) Reliability (%) % % % % CRE mix reliability seems related to deviation variability Resilience and Vulnerability assessments are on going 22

23 Effect of CRE mix on storage requirement Deviation Cumulative deviation CRE mix Scenario 1 CRE mix Scenario 2 Philosophy: As soon as the energy generated can be stored, the cumulative deviation time series represent the storage fluctuations required to fulfill the energy load 23

24 Effect of CRE mix on storage requirement Deviation Cumulative deviation CRE mix Scenario 1 Storage capacity required CRE mix Scenario 2 Philosophy: As soon as the energy generated can be stored, the cumulative deviation time series represent the storage fluctuations required to fulfill the energy load Storage capacity required = difference between Max and Min values of cumulative curve 24

25 Effect of CRE mix on storage capacity required Color map: difference between Max and Min values of cumulative deviation time series black dot represent the optimal value storage capacity requirement depends on time scale and energy mix 25

26 SUMMARY Assessing co-variability between weather variables and with hydro systems is essential for designing energy portfolios. Most studies have implicit assumptions about co-variability, e.g. by using outputs form numerical models or observations. Research needs: Understand and model co-variability at relevant scales Understand and model co-variability across «new» variables and scales become important (e.g. streamflow in the Alps and in Scandinavia, wind in Germany and runoff in Norway)) Analyze complex systems and interactions 26