Presentation at the World of Energy Solutions 2015, Stuttgart.

1. World of Energy Solutions
14.10.2015
Zentrum für Sonnenenergie- und Wasserstoff-Forschung
Baden-Württemberg (ZSW)
Center for Solar and Hydrogen Research Baden-Württemberg
PV Storage Systems –
sizing and operation to serve owner and grid
Benjamin Matthiß

2. - 2 -
ZSW is a non-profit foundation with 220 employees in Stuttgart and Ulm
The focus is on • Photovoltaics – Thin-Film Technology
• Fuel Cells and Hydrogen Technology
• Electrochemical Storage
• Renewable Fuels and Reformers
• Systems Analysis and Consulting
We work on the whole value chain from materials research to product
development and production.
Zentrum für Sonnenenergie- und Wasserstoff-Forschung
Baden-Württemberg (ZSW)

3. controlled
appliances
heat pump
PV inverter
electrical storage
thermal storage
Energy Source:
Energy drawn from
sun und grid
Energy Sink:
heat,
Refrigeration, mobility,
communication, etc.
Grid Feed-in
EPV
EPV,SC
EPV
smart
controls
The goal: increased
self consumption
and autarky
Eel
Motivation I – My Home is my Power Station
To Grid
13ct/kWh
27 ct/kWh
From Grid
EPV,SC = PV self-consumption
Limit Grid Strain

4. Outline
• Motivation
• Feed-In Limitation for PV-Storage Systems
• Orientation
• Curtailment Losses
• Operation Strategies
• Charge Algorithms
• Comparison
• Summary
- 4 -

5. Feed-In Limitation – Curtailment
- 5 -
• Energy above the injection limit has to be curtailed:
• Energy is lost
Grid feed-in limit [W]
PV Generation [W]
Load [W]
Grid feed-in [W]
SOC Battery [Wh]
W/Wh
03:00 06:00 09:00 12:00 15:00 18:00 21:00
0
1000
2000
3000
4000
Time
Feed-In power limitation to avoid
• grid overloading / voltage limit violations.
• grid expansion
How much?

6. - 6 -
Feed in Limits and Curtailed Energy
Step 1:
• curtailment for a given feed-in limit as it depends on azimuth and tilt.
• Based on inverter output, no self-consumption

7. - 7 -
Step 2:
• PV and local loads
(no control)
Step 1:
• curtailment for a given feed-in limit as it depends on azimuth and tilt.
• Based on inverter output, no self-consumption
Feed in Limits and Curtailed Energy

8. - 8 -
Step 3:
• PV and local loads
• Battery Storage
Step 4:
• Charge Algorithms
• Demand Side Managemt.
Step 1:
• curtailment for a given feed-in limit as it depends on azimuth and tilt.
• Based on inverter output, no self-consumption
Feed in Limits and Curtailed Energy
Step 2:
• PV and local loads
(no control)

9. Outline
- 9 -
• Motivation
• Feed-In Limitation for PV-Storage Systems
• Orientation
• Curtailment Losses
• Operation Strategies
• Charge Algorithms
• Comparison
• Summary

10. Feed-in limit [W]
PV Generation [W]
Load [W]
Feed-in [W]
SOC Battery [Wh]
W/Wh
Time
03:00 06:00 09:00 12:00 15:00 18:00 21:00
0
1000
2000
3000
4000
W/Wh
Time
03:00 06:00 09:00 12:00 15:00 18:00 21:00
0
1000
2000
3000
4000
Time
W/Wh
03:00 06:00 09:00 12:00 15:00 18:00 21:00
0
1000
2000
3000
4000
Direct charging
Linear delayed charging
Peak shaving
+ High self-consumption
- Large curtailment
+ Little curtailment
- Little self-consumption
Battery charge algorithms
sample profiles
- 10 -
•Low to medium curtailment
•Medium to high self-
consumption

11. Model Predictive Control
operating principle
- 11 -
Control signalsMeasurements
Optimizer
Model
Prediction
Controller
PV–Storage–System
Control Unit
MPC
1. Prediction
• Local load
• Local generation
2. Optimization
• Calculate the optimized trajectory to achieve goals
• Objectives: - minimizing energy costs
- comply with grid limits
3. Controller
• Processes setpoints from MPC
• Calculate battery setpoints
According to: Dittmar, Pfeiffer; Modellbasierte prädiktive Regelung.
Energy tariff (24h ahead)

12. InfeedPower[W]
0 500 1000 1500 2000 2500 3000
0
500
1000
1500
2000
2500
3000
3500
4000
Time [h]
Infeed Limit: 25 % PVpeak
Feed-In Duration Curve
including household self consumption
results shown for PV = 4500Wp, battery = 4500Wh, limit 1125 W (25% of PV rating), yearly load 4500 kWh/a
- 12 -
No Battery25.2% 0%
Increase in
Self-Cons.
Curtailed
Energy

13. 0 500 1000 1500 2000 2500 3000
0
500
1000
1500
2000
2500
3000
3500
4000
Feed-In Duration Curve
including household self consumption
results shown for PV = 4500Wp, battery = 4500Wh, limit 1125 W (25% of PV rating), yearly load 4500 kWh/a
- 13 -
Direct Charging
25.2%
16.5%
0%
29%
Algorithm: direct (earliest) charging of the Battery
No Battery
Infeed Limit: 25 % PVpeak
InfeedPower[W]
Time [h]
Increase in
Self-Cons.
Curtailed
Energy

14. 0 500 1000 1500 2000 2500 3000
0
500
1000
1500
2000
2500
3000
3500
4000
Feed-In Duration Curve
including household self consumption
results shown for PV = 4500Wp, battery = 4500Wh, limit 1125 W (25% of PV rating), yearly load 4500 kWh/a
- 14 -
Peak Shaving
25.2%
16.5%
0%
29%
17.8%
Algorithm: Peak Shaving
6.2%
No Battery
Direct Charging
Infeed Limit: 25 % PVpeak
InfeedPower[W]
Time [h]
Increase in
Self-Cons.
Curtailed
Energy

15. 0 500 1000 1500 2000 2500 3000
0
500
1000
1500
2000
2500
3000
3500
4000
Feed-In Duration Curve
including household self consumption
results shown for PV = 4500Wp, battery = 4500Wh, limit 1125 W (25% of PV rating), yearly load 4500 kWh/a
- 15 -
Peak Shaving
25.2%
16.5%
6.2%
0%
29%
17.8%
8.4% 23.6% Delayed Charging
Algorithm: linear delayed Charging
No Battery
Direct Charging
Infeed Limit: 25 % PVpeak
InfeedPower[W]
Time [h]
Increase in
Self-Cons.
Curtailed
Energy

16. 0 500 1000 1500 2000 2500 3000
0
500
1000
1500
2000
2500
3000
3500
4000
Feed-In Duration Curve
including household self consumption
results shown for PV = 4500Wp, battery = 4500Wh, limit 1125 W (25% of PV rating), yearly load 4500 kWh/a
- 16 -
Model Predictive Control8% 27,6%
Peak Shaving
25.2%
16.5%
6.2%
0%
29%
17.8%
8.4% 23.6%
Algorithm: model predictive charging of the Battery
No Battery
Direct Charging
Delayed Charging
Infeed Limit: 25 % PVpeak
InfeedPower[W]
Time [h]
Increase in
Self-Cons.
Curtailed
Energy

17. results shown for PV = 4500Wp , yearly load 4500 kWh/a
- 17 -
Injection Power Plots
no battery
battery 4.5 kWh, MPC
limit of injection = 50% of PV-kWp
battery 4.5 kWh, earliest charging battery 4.5 kWh, MPC
limit of injection = 25% of PV-kWp
Day of the yearTime of day
Power(kW)
Day of the yearTime of day
Power(kW)
Day of the yearTime of day
Power(kW)
Day of the yearTime of day
Power(kW)

18. Battery Size Vs Self-Consumption and Curtailment
- 18 -
PVpeak = 4500W, Grid Infeed Limit 1125W (25% PVpeak),
Heating Supply with Heatpump
Curtailment[%] Battery Size [kWh]
4 6 8 10 12 14
0
5
10
15
20
Direct Charging Peak Shaving Delayed Charging
Model Predictive Control for Batterie Charging
Curtailment
Self-Consumption[%]
Battery Size [kWh]
4 6 8 10 12 14
40
50
60
70
80
90
Self-Consumption

19. Summary
- 19 -
Curtailment losses of distributed PV systems are reduced
significantly
• With deviance from optimal orientation (at cost of self consumption
and yearly yield)
• With the use of battery storage
time date
Intelligent control schemes can increase the use of
renewables with low grid impact and energy
curtailment:
• Curtailment reduced from ~16% to ~8% at a cost
of ~1% self-consumption

20. - 20 -- 20 -
Thank you!
// Energy with a future
// Zentrum für Sonnenergie- und Wasserstoff-
Forschung Baden-Württemberg (ZSW)
Contact:
benjamin.matthiss@zsw-bw.de,
Tel. +49 (0) 711 7870 272
www.zsw-bw.de
Stuttgart:
Photovoltaics Division (with
Solab), Energy Policy and
Energy Carriers, Central
Division Finance, Human
Resources and Legal
Solar Test Fields:
Widderstall and Girona (ES)
Ulm:
Electrochemical Energy Technologies Division with eLaB