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Environmental impacts of hydro-peaking

Tor Haakon Bakken1, Roser Casas-Mulet2 & Michael Puffer2

1 SINTEF Energy Research & CEDREN

2 NTNU & CEDREN

Structure of my talk

CEDREN and research on renewable energy

Overview of EnviPEAK – environmental impacts of hydro-

peaking

Results from studies on physical processes

Results from some of the fish studies

Supplementary results from Ulrich Pulg

Mitigating

measures

3

Centre for environmental design of renewable energy – CEDREN

Climate agreement in Parlament (2008)

Energy efficiency

Renewable energy

CO2-neutral heating

Energy systems

Institutional framework analysis

CO2-capture and storage

Transportation

8 research

centres for renewable

energy established

New climate

agreement in

Parlament (2012)

PROJECTS

HydroPEAK

GOVREP

OptiPol

BirdWind

EnviPEAK

EnviDORR

Social

Economy Environm

SUSGRID

+ PILOTS

EcoManage

Example hydro-peaking regimes

Period 1

Period 2

Flow measurements 20 km

downstream outlet

Flow at outlet of HP-plant

Discharge station:

Constant production

in the week-days, no

production in the

week-ends

Discharge station:

Several start/stop

episodes every day.

No release/production

in the week-ends.

Example hydro-peaking regimes

Definition and drivers

Unclear definition, but characteristics are:

• More rapid start/stop than natural hydrological processes

• More frequent changes than naturally

• An element of periodicity

• Max value (much) lower than e.g. annual flood

Drivers are:

• Sale of power when high price

• Balancing the grid

• Development of non-regulated power production

Photo UNI Research

Coupling physical and biological studies

Photo: SINTEF

Coupling physical and biological studies

Photo: SINTEF

Coupling physical and biological studies

Photo: Arne Jensen, NINA

How much water is needed?

… and what about the dynamics …

Prior research about hydro-peaking

The impacts are largest in rivers, smaller in reservoirs, lakes &

fjords

The larger and more frequent variations the larger impacts

The impacts are determined by time and location specific

characteristics (type of HP-plant, type of ecosystem, time of the

day/year, etc.)

Impacts in rivers

Stranding as a problem

Rapid changes (decreases) in water level

might cause stranding

Reduced changes in water level drops

reduce the risk of stranding

Highest risk of stranding in cold water

(winter), at day-time and in river sections

with coarse substrate

Fish can survive stranding

Harby et. al, 2004 and other publications

Thumb of rule:

Water level drops slower than 13 cm/hour

Nid

elv

a, T

ron

dh

eim

Ca

tch

men

t sc

ale

Hydropower operation simulation

nMAG Operational

strategies

Mes

o-s

cale

Stranding areas calculation HEC-

RAS Physical habitat analysis

Dynamic mesohabitat

3D hydraulic model STAR CCM

Mic

ro-

sca

le

GW-SW interactions Physical and Biological

processes Salmon survival

Approach

Total catchment area: 395 km2

Average annual runoff: 381 Mm3/year Installed capacity: 61 MW Average annual production: 278 GWh

3 Regulated reservoirs 3 Power Plants 3 Interbasin transfers

Holtsjøen

Samsjøen

Sama

Sokna

Håen

Håen

0.3

0.4

0.5

0.6

0.7

0.8

0.9

11.

1

0 15 30 45 0 15 30 45 0 15 30 45 0 15 30 45 0 15 30 45 0 15 30 45 0 15 30 45 0 15 30 45 0 15 30 45 0 15 30 45 0 15 30 45 0 15 30 45 0 15 30 45 0 15 30 45 0 15 30 45 0 15 30 45 0 15 30 45 0 15 30 45 0 15 30 45 0 15 30 45 0 15 30 45 0 15 30 45 0 15 30 45 0 15 30 45

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

16.06.2010

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

16.06.2010

Wat

er le

vel(

m)

Increasing flow

Dewatering curve

Peak flow

Regular hydro-peaking operations

Study site: Lundesokna river system

DATA TYPE

EQUIPMENT OUTPUT

Ge

om

etr

y

Differential GPS x, y, z

points Laser scan

Data collection

+ Camera mounted to helicopter

DATA TYPE

EQUIPMENT OUTPUT

Hyd

rau

lic Pressure

transducers Discharge

Water levels Velocity profiles ADCP

0

5

10

15

20

25

30.2 30.4 30.6 30.8 31.0 31.2 31.4

Dis

ch

arg

e (

m3/s

)

Water level elevation (m)

Data collection

Macro-scale

Model tools: The nMAG program: • Simulates hydropower operations for whole systems with several power

plants, reservoirs and transfers

Meso-scale 1D: Dynamic mesohabitat prediction

Aim: To develop a tool to predict mesohabitat changes linked to flow variations using a 1D hydraulic model

Tools: • Hec-RAS / ArcGIS • Norwegian Mesohabitat Classification

method (Borsany, 2005) • Field assessment • Comparison

Meso-scale 1D: Dynamic mesohabitat prediction

Field Assessment:

Meso-scale 1D: Dynamic mesohabitat prediction

Comparison:

0 10 20 30 40 50 6030

31

32

33

34

35

36

37

38

L_SF_Subcritical_15.3Q Plan: L_SF_Subcritical_15.31Q 16.08.2010

Station (m)

Ele

vation

(m

)Legend

WS PF 1

Ground

Bank Sta

OWS PF 1

.07

Surface pattern

Surface gradient

Surface velocity

Water depth

Froude number

Water level / distance

Average velocity

Depth

Field assessment Hec-RAS simulation

Meso-scale II: Stranding areas calculation

Aim: To find the 'optimum' geometrical representation in a 1D to quantify stranding areas

Tools: • Hec-RAS • High quality field data: geometry and hydraulic

data

Approach: - Basic : One transect at each extremity 100-120 m - Add 1 : 1 transect in between 50-60 m - Add 3 : 3 transects in between 25-30 m - Add 7 : 7 transects in between 12-15 m - Add 15 : 15 transects in between 6-7 m - Add 31 : 31 transects in between 3-4 m

Meso-scale II: Stranding area calculation

Results: More transects

Meso-scale 3D: Steady simulation

Film Simulation

Meso-scale 3D: Unsteady simulation

Micro-scale: GW-SW interactions

Aim: To assess the surface vs subsurface water dominance in the hyporheic zone during both dewatering and watering events stranding and egg survival

Experimental Setting:

Micro-scale: GW-SW interactions

Results:

• Hydraulic processes differ between flow decrease and increase: • Water level increases in hyporheic zone than it decreases • The results vary very much within short distances (very heterogenic), due to diff. in

conductivities • The water temperature determined by surface water/ groundwater ratio

Integrating energy production and impacts on the ecosystem

Energy production simulations

Habitat quality

Hyd

rolo

gy

Hydraulics

Integrating energy production and impacts on the ecosystem

Energy production simulations

Habitat quality

Hyd

rolo

gy

Hydraulics

Stranding experiment in Ims, Norway

Preliminary results from winter experiment

Unpublished results: Puffer, Berg and more

Hydropeaking experiment in Paltamo, Finland

Preliminary results from winter experiment

Unpublished results:

Puffer, Berg, Vehanen and more

The effects

on fat are a

little larger in

the Summer

experiments.

Hydropeaking experiment in Paltamo

Preliminary results from winter experiment

Unpublished results:

Puffer, Berg, Vehanen and more

The effects on

body mass are

significant!

Experimental setup:

• Fish density 1 fish/m2 vs. 3 fish/m2

• Intercohort competition large fish present vs. absent

• Time of day daytime vs. nighttime

• Season of the year summer, autumn, winter & spring

Habitat preferences of juvenile Atlantic salmon

Winter

No density effect !

Unpublished results:

Puffer, Berg, Vehanen and more

Conclusion habitat preferences

Highest probability of finding small fish in shallow habitats:

Spring

Autumn night time

Hydro-peaking events during these times most problematic Unpublished results: Puffer, Berg, Vehanen and more

39

Measures to reduce conflicts

Administrative measures Selection of plants dedicated to hydro-peaking

Compensation

Habitat restoration

”Master plan” for hydro-peaking

Operational measures Measures at each individual plant

Start/stop speed at plant

Timing and frequency

Base-flow

Physical/biological measures instream Dampening reservoir

Habitat restoration, leading of water, gravel

Stocking

Etc.

Go to www.cedren.no –

All publications

Summing up

The results need to be verified, handled

statistically and 'peer-reviewed'

• Physical studies:

• Methods to increase efficiency of data collection developed

• Methods to increase precision and volume of data developed

• Models and model integration further developed

• Several tools "ready to use

• Fish: less physiological stress than expected?

• Fish and spawning – wait for Ulrich Pulg, UNI

42 42

Information about CEDREN

www.cedren.no (official web-site)

tor.haakon.bakken@sintef.no (project leader EnviPEAK)

atle.harby@sintef.no (Director of CEDREN)

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