Berlin, 2014-11-10
Dr. Harald G Svendsen
SINTEF Energy Research
Trondheim, Norway
Technology for a better society
1

Presentation of a framework for modelling and analysis of large-scale grid integration of renewable energy in large and interconnected power systems
Approach based on reduced power system models including grid, market, storage representation
Implementation as open source Python package powerGAMA
Validation of the approach against present day data
Case study: 2030 scenario

Euro-Mediterranean cooperation on research and training in sun-based renewable energies
• solar PV
• solar CSP
• grid integration www.eurosunmed.eu

Renewable energy potential is large [1]
Targets are ambitious [2]
What happens when all this renewable energy is pushed into the electricity grid?
Grid bottlenecks?
Cost of generation?
Price variations?
Balancing?
How to study these questions?
[1] Müller et al, Renewable Energy: Markets and Prospects by Region , in IEA Information paper . November 2011.
[2] Griffiths, MENA Renewable Energy; Challenges of Meeting Future Regional Power
Targets . 2012, Masdar Institute.

Power market:
Step-wise (hour by hour) optimal power flow analysis
Minimise cost of generation
A single market where all power is traded for each time step
Grid :
Reduced, but detailed grid model
Linearised power flow equations
Active power only
Energy storage :
Utilisation strategy encoded via storage values
Keep it simple

Western Mediterranean grid model
Europe : Reduced model ( Hutcheon/Bialek ) http://dx.doi.org/10.1109/PTC.2013.6652178
Morocco : Reduced model derived from detailed model based on similarity of power flow criterion
Algeria, Tunisia : Simple representation
•
Line impedance and transmission capacity
•
Power demand distribution
•
Generator locations
•
Time series for demand variations and renewable energy

time-series
Generator costs (€/MWh) per type and country
Minimise total cost of generation per time step, subject to grid constraints
Energy storage systems: Cost = storage value
(depends on filling level and time)
Examples of storage value curves
( storage utilisation strategy )

Open source Python package
Light-weight tool for high level analysis of renewable energy integration in large interconnected power systems
Universal description of generators, no hard-coded generator types
Based on methods used previously in several projects to study wind integration in Northern Europe https://bitbucket.org/harald_g_svendsen/powergama
PowerGAMA is inspired by SINTEF's Matlab-based PSST (see
TradeWind D3.2 - Grid modelling and power system data )

Input data
Country
Power demand [GWh/y]
58690 38010 243900 447110 311260 25141.2 48580 12940
Generator Capacity [MW] Cost (€/MWh)
Coal
Gas
Oil
Nuclear
Hydro
Other
Solar
CSP
Solar PV
CH
100
3263
14900
300
DZ
15596
124
249
ES
11466
31555
5877 11898 11962
7567
14419
3560
762
FR
7177
5811 49037
63130
20652
3189
IT
9772
17095
4365
17928
MA
1745
3763
446
1737
PT
1855
3885
2765
4499
161
TN
2077
62
60
70
162
11
3
50
0.5
0.5
Wind 24 21674 8254 8552 4731 54 0.5
Variable wind and solar power, derived from Reanalysis weather data
Example: MA central region
Variable demand
Sun Mon Tue Wed Thu Fri Sat

Energy mix (annual sum)
Simulation gives good match with real energy mix
Reflects generation capacities per type and generator costs

Power flow FR-ES-MA-DZ
Over-estimated MA/DZ transmission (connection not much in use)
Energy exchange
In most cases, energy exchange matches well with reality
However, there are significant discrepancies
•
"Perfect market" assumption
•
Limits on power transfer due to stability criteria not included

Case description in line with renewable energy targets and realistic assumptions and projections
Updated demand
Updated generation capacities
Added HVDC connections (e.g. IT-TN, ES-DZ)
Power demand increase
Unchanged costs
Large-scale renewable energy (solar PV, CSP, wind)

Daily storage utilisation
CSP with thermal storage
Time (h)
Example :
Store energy for evening when prices are high basecost = 60 €/MWh
Storage curve = storage value
= relative value × basecost
Filling level(%)
Storage curve = storage utilisation strategy

Grid congestion in Morocco
Preliminary results
Something is not right
1 st week in 2030
New solar power plants
Increased demand
→ Transmission grid limits optimal power flow
→ Nodal price gradients
Low nodal price: Cheap power cannot be exported
Possible causes:
•
Model reduction is poor (line capacity & impedance)
•
Distribution of demand increase and/or generation changes is poor
•
Grid is inadequate to cope with 2030 situation

•
A suitable framework for analysis of large-scale renewable energy integration in interconnected power systems
•
Open source implementation: PowerGAMA
•
Western Mediterranean case study
–
Validated for present day situation
–
On-going work to investigate 2030 scenario – part of the EuroSunMed
(EU FP7) project
•
To do
–
Reduce uncertainty regarding 2030 grid (verify/improve)
–
Create baseline case + some variations for sensitivity analysis, e.g. towards changes in generation costs, storage strategies, total demand

Technology for a better society
16