1. Oemof user meeting
09-10/05/2017 Berlin RLI

2. Agenda How I have used oemof until now:
Multi-nodes Energy system model for Italy (electricity sector)
What I‘m working on at the moment:
Coupling of a Multi-objective evolutionary algorithm to oemof

3. The idea
Multi-nodes Energy system model for Italy
(MW)

4. Italian energy system model: installed power 2014

5. Italian energy system model
Model based on real data (source: TERNA, Italian TSO)
At the moment the developed model is only on the electricity sector (in the future the heat and transport sectors will be integrated)
First results
This is an example of an evaluation done on the Italian energy system divided in the 6 main zones of the electricity market. This scenario considers an increase of the installed power of wind and PV equal to the 75% of their potential (120 GW for PV, 49 GW for wind power).
This evaluation highlights few problems of the Italian energy system:
The highest potential of variable renewable energy is in the south of Italy while the majority of the installed power of electric storage (pumped hydro) is placed in the north.
The current trasmission constraints of the electric grid are not sufficient to absorb the overgeneration from RES without congenstion problems and curtailment.

6. Agenda How I have used oemof until now:
Multi-nodes Energy system model for Italy (electricity sector)
What I‘m working on at the moment:
Coupling of a Multi-objective evolutionary algorithm to oemof

7. Multi objective evolutionary algorithm coupled to oemof
Generate initial population
Evaluate each individual
(on annual costs and CO2 emissions)
Rank each individual: Fitness function
Stopping
criteria met? No
Yes
STOP
Generate a new population
Through operators:
Parent selection
Crossover
Mutation
Input variables:
Capacities of RES
Capacity of electric storage
Trasmission constraints
Oemof model
Used as simulation dispatch model (minimizing CO2 emissions)
Output:
Distribution data: PV
Wind …
Total annual costs
CO2 emissions or %RES
Oemof
Multi
Objective
Evolutionary
Algorithm
(MOEA)

8. Optimization variables
Current values [GW]
Potential
[GW]
8.1
0.1
0.6
55.5
1000 [GWh]
Capacities of VRES
Capacity of electric storage
Trasmission constraints
Current values [GW]
Potential
[GW]
2.2
0.1
0.7
12.4
1000 [GWh]
Current values [GW]
Potential
[GW]
2.6
1.5
8.6
26.1
1000 [GWh]
Current values [GW]
Potential
[GW]
0.7
1.0
5.6
3.3
1000 [GWh]
Current values [GW]
Potential
[GW]
3.5
4.1
23.5
13.9
1000 [GWh]
Current values [GW]
Potential
[GW]
1.3
1.7
9.8
10.1
1000 [GWh]

9. Thanks for your attention
Contact us:
Tel