1. Master Thesis
Presented by
Mohamed Allam
Presented to
ib Vogt
“Technical and Financial Optimization of
a utility scale battery storage system in
combination with a PV power plant in
Malawi”

2. Outline
• Abstract
• Introduction
• Case
• Model-Technical
• Model Financial
• Simulation
• Results
• Conclusion
• Recommendations
• References

3. Abstract
“Further penetration of solar PV as well as other types of renewables to the
grid is becoming a challenge as they are non-dispatchable and intermittent on
long, medium and short term.
A possible solution for such challenge is the reliance on electrochemical
batteries, giving PV and renewables more room for expansion in the energy
mix.
In this paper, a technical and financial optimization of a battery storage system
integrated with a 40 MVA PV plant in the country of Malawi is presented.”

4. Overview
Lithium Ion
Rated Capacity 11.5 MWh
Power 5 MW
LCOE 0.180 $/kWh

5. Introduction
Intermittency in renewables
(http://www.solarchoice.net.au/blog/wp-content/uploads/Average-NSW-household-in-winter-
electricity-consumption-vs-PV-generation.jpg)
(http://image.slidesharecdn.com/zlunch-robertharrington-090505174750-phpapp01/95/robert-harrington-solar-
generation-and-effects-on-electric-grid-4-728.jpg?cb=1241545703)

6. Introduction
Ramp rates and smoothing
• Affects grid stability
• Example: 10% per min (specified by operator)
(http://www.nrel.gov/docs/fy14osti/59003.pdf)

7. Introduction
Intermittency
Variability Index
(Stein et al,2012)

8. Introduction
Intermittency
Variability Index
(Stein et al,2012)

9. Introduction
Intermittency
Generation shifting
CHEAP
HYDRO
EXPENSIVE
DIESEL
(http://www.extremetech.com/wp-content/uploads/2015/05/Tesla-power-demand-illo.jpg)

10. Solution: Batteries

11. Introduction
Batteries
• Battery Capacity
• Usable/Design
• Rated
• Converter Power
• Depth of Discharge
• Cycles & lifetime
• State of Charge
Parameters
(http://image.slidesharecdn.com/9-system-sizing-150609152820-lva1-app6891/95/9-systemsizing-24-638.jpg?cb=1433863773)

12. Introduction
Batteries
Lead Acid Lithium Ion Sodium Sulfate
(http://tecsol.blogs.com/.a/6a00d8341bfe5d53ef01bb082b47e29
70d-450wi)
(http://www.betterworldsolutions.eu/wp-
content/uploads/2015/02/NaS-batteries.jpg)
(http://www.reuk.co.uk/OtherImages/lead-acid-battery.gif)
Technologies - Established

13. Introduction
Batteries
Flow Batteries Metal Air
(http://www.extremetech.com/wp-
content/uploads/2015/02/Imergy-vanadium-flow-
battery.jpg)
(http://www.ecotechninja.com/wp-
content/uploads/2009/01/zinc-air-battery.gif )
Technologies - New

14. Introduction
Batteries
(http://www.irena.org/documentdownloads/publications/irena_battery_storage_report_2015.pdf)
Increase in installed capacity Prices Projections
Comparison

15. Introduction
Batteries
(http://www.irena.org/documentdownloads/publications/irena_battery_storage_report_2015.pdf)
Increase in installed capacity Prices Projections
Comparison
Lithium Ion

16. Case: Malawi

17. Case: Malawi
Climate
(http://solargis.info/doc/_pics/freemaps/1000px/ghi/SolarGIS-Solar-
map-Malawi-en.png)
(http://www.myweather2.com/Holiday-
Destinations/Malawi/Lilongwe/climate-
profile.aspx)

18. Case: Malawi
Power Generation
• 95% Hydro
• 4 Hydro stations – 370MW
• Rest is Diesel Generation
• Deficit at least 14%
• Future plans for diesel:
• 10MW Central (Q1 16)
• 16MW Southern
• 6MW Northern
Nkula Hydro Station
(http://emagazine.european-times.com/articles/e-magazine-EPT-Malawi-web-resources/image/nkula2.jpg)

19. Case
Problem Definition
• Most of supply from Hydro
• Lake Malawi level dropping
• Demand growing
• Blackouts- peak
• Adding renewables may
cause instability

20. Objectives
• Stores energy for evening use
• Smoothens ramp rates
• Optimal in design and capacity
• Optimal in costs
• Offers savings on the Malawian side
Storage system that can

21. Model: How the
system works

22. Model Technical Perspective
Connection

23. Model Technical Perspective
Reserve
SOCset and SOCact
100%
17 hr

24. Model
• Smoothing
• Filling
• Shaving
• Correction of SOC
Functions

25. Model Technical Perspective
Block Diagram

26. Model Technical Perspective
Algorithm (Whole)

27. Model Technical Perspective
Output Outputpower(MW)

28. Model Financial Perspective
LCOS
• The levelized cost of storage over the life time of the plant
Total Cost/Total energy during lifetime
(Jülcha, et al., 2015)

29. Model Financial Perspective
LCOE for system
• To calculate how much storage adds to the system energy price
Weighted average

30. Model Financial Perspective
Diesel costs
(https://knoema.com/atlas/Malawi/Pump-price-for-diesel-fuel-USdollar-per-liter)
Diesel price currently stands at $1.14/liter

31. Model Financial Perspective
Diesel costs
Diesel Energy costs = $0.36/kWh

32. Simulation
• Control parameters:
• Eb (Variable)
• Pconv (Variable)
• MARR (constant)
• Outcomes:
• Compliance/min weekly
• LCOS
• LCOE
• Discharge time/min monthly percentage
Parameters

33. Simulation
Method
• Iteration and trying out all combinations of Eb and Pconv for each day
• Compiling optimum outcomes for all days of the month, then taking a
monthly average
• Taking the average of the months for a yearly average

34. Simulation
Method

35. Simulation
Method Eb/Pconv(MWh/MW)

36. Simulation
Method
0.00
2.00
4.00
6.00
8.00
10.00
12.00
1 2 3 4 5 6 7 8 9 10 11 12
Eb/Pconv(MWh/MW)
Months
OptEb
OptPconv
Linear (OptEb)
Linear (OptPconv)

37. Simulation
• Lithium Ion (NCA)
• DoD of 80%
• 4,000 cycles
• Fluctuations Reserve of 50% (Fixed for each run)
• Loss of 20% by EOL
• MARR is 1%
Since VI is proportional to MARR for same system size, for variability of 15
and MARR 10% the system size would stay the same for a VI of 1.5 and
MARR is 1%
Assumptions

38. Results

39. Results

40. Results
0.00
2.00
4.00
6.00
8.00
10.00
12.00
1 2 3 4 5 6 7 8 9 10 11 12
OptEb
OptPconv
Linear (OptEb)
Linear (OptPconv)
Optimum

41. Results
5000.00
6000.00
7000.00
8000.00
9000.00
1 2 3 4 5 6 7 8 9 10 11 12
Energy
Energy
0.900
0.950
1.000
1.050
1.100
1.150
1.200
1.250
1.300
1.350
1.400
1 2 3 4 5 6 7 8 9 10 11 12
VI
VI
Optimum

42. Results
Optimum

43. Results
0%
1%
1%
2%
2%
3%
3%
4%
1 2 3 4 5 6 7 8 9 10 11 12
Percentage
Months
Min Delievery Percentage per month
System Outcomes

44. Results
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Compliance
Weeks
Compliance
System Outcomes

45. Results
Conservative Optimistic
Storage (€/kWh) 400 300 200
+33% 0 -33%
Converter (€/kW) 800 600 400
+33% 0 -33%
CAPEX sensitivity
Scenarios

46. Results
CAPEX sensitivity
Conservative Normal Optimistic
LCOS 0.558 0.460 0.373
+21% 0 -19%
LCOE 0.183 0.180 0.177
+1.7% 0 -1.7%
On Optimum

47. Results
CAPEX sensitivity
New Optimums
Conservative Normal Optimistic
Eb 8.64 9.25 9.33
-6.6% 0 +0.9%
Pconv 4.93 4.96 4.93
-0.6% 0 -0.6%

48. Wrap up
Eb 9.25MWh
Rated Cap 11.5MWh
Pconv 5MW
Min Comp 71%
Average 91%
LCOE 0.18$/kWh
min % del 2.10%@ 4MW
av % del 3.20%@ 4MW
Vs Diesel @ $0.36/kWh

49. Wrap up
Sources of error
• Data inaccurate (1 min-15 min)
• Variability Index
• Too many variables involved
• Optimization method inaccurate
(Stein et al,2012)

50. Conclusion and Recommendations
For the application
• The use of a 11.5 MWh system and a 5 MW would be suitable for the
scenario
• So far, according to the data set, the ramp rates do not pose a danger to
the stability of the grid, however, more accurate data can give a different
indication
• According to LCOE calculation, implementing the project would be
profitable

51. Conclusion and Recommendations
For future development of method
• The battery model can be further improved in terms of the algorithm and further
optimized to achieve better results and smoother output.
• Calculate VI more accurately, which can be achieved through higher resolution data
and clear sky data that is more accurate
• The method of simulation could be improved to run faster and obtain more accurate
results.
• Calculation of IRR based on evening power
• Parameters not taken into consideration such as temperature’s and SOC effect on
efficiency can be considered in the future.

52. References
• Jülcha, V. et al., 2015. A holistic comparative analysis of different storage systems using
levelized cost of storage and life cycle indicators. Energy Procedia, pp. 18-28.
• Stein, J. S., Reno, M. J. & Hansen, C. W., 2012. THE VARIABILITY INDEX: A NEW AND
NOVEL METRIC FOR QUANTIFYING IRRADIANCE AND PV OUTPUT VARIABILITY, s.l.: s.n.

53. Thank you
Questions??