Energy Presentations

1. 0
Dr Tobias Bischof-Niemz
Chief Engineer
Planning for future energy systems : A mini-IRP
for the CSIR campus in Pretoria
Peter Mukoma
Principal Engineer, CSIR
Mamahloko Senatla (Msenatla@csir.co.za)
Henerica Tazvinga (HTazvinga@csir.co.za)
Enose Moholisa (Emoholisa@csir.co.za)
NCPC Industrial Efficiency Conference 2017
Cape Town, 15 September 2017

2. 1
Agenda
• Objectives of the study
• Scope and boundary conditions
• Methodology
• Input data and assumptions
• Results
• Conclusion

3. 2
Objectives of the study
Background
• Future energy systems will largely be based on Distributed
Energy Resources (DER) – a combination of Variable
Renewable Energy (VRE), storage and demand response
technologies
• Technology and systems innovations are required to design,
build and operate such energy systems in an optimal manner
• The business model of utilities will also be affected
Response: Energy-Autonomous Campus Programme
The CSIR has established a programme where it implements its
research findings as a test bed for future energy systems
• Demonstrate how to cost-efficiently design and operate an
energy system based on DER
• Integration of energy storage in form of batteries & hydrogen
• Energy savings and demand response measures
• Key outcomes: System design/operations,
technology demonstration, future utility
business model

4. 3
Objectives of the study
The objective of the study was to determine least cost generation capacity mix for
CSIR’s Pretoria campus between 2016 and 2022 by considering the following energy
usage scenarios:
1) Business as usual (BAU) scenario
 Increasing the electrical energy consumption by 0.7% per annum until 2022
2) Energy Efficiency (EE) scenario
 Decreasing the electrical energy consumption by 20% by the year 2022. 5%
decrease annually starting in 2018
1. The normal load increase is 0.7%, therefore the net decrease is 4.3%
1
30.4 GWhr
6 MW
3 MW
R31 m (24% Demand Charges)
2016 Energy usage
Maximum Demand
Base load
Total cost

5. 4
Agenda
• Objectives of the study
• Scope and boundary conditions
• Methodology
• Input data and assumptions
• Results
• Conclusion

6. 5
Scope
1) The modelling timeframe is from 2016 – 2022
2) Includes existing power plants (single axis, dual axis and rooftop in bldg. 17), approximately 1.011 MW
3) The supply technology options considered for optimization in this iteration of the model are:
 PV
 Wind
 Biogas and
 Tshwane supply
4) Tshwane’s Megaflex tariff as Variable operating and maintenance costs for
 Energy tariff increasing at 9.43% annually and peak demand charge increasing at 0.7% annually

7. 6
Boundary conditions within which optimisation was based on
 The modelling considers the time of use tariff from Tshwane as is in 2016 and the structure is assumed not
to change until 2022. The only change is the increasing cost of electricity and peak demand charges
(R/MWh and R/MW).
 This means that this modelling does not consider the SSEG tariff which will be implemented once NERSA
approves it.
 The optimization assumes balancing of demand for the CSIR campus, excess demand is allowed but by its
nature PLEXOS will minimize overcapacity, hence minimizing generation of excess energy
 The excess energy is not given any value, assumed to be wasted
 Tshwane is providing unlimited supply to the campus

8. 7
Agenda
• Objectives of the study
• Scope and boundary conditions
• Methodology
• Input data and assumptions
• Results
• Conclusion

9. 8
Study Methodology
Modelling
framework
(PLEXOS®)
LT1 techno-economic
least-cost optimisation in
long term horizon
MT/ST2 production cost
testing detailed
generation dispatch in
short and long term
horizon
Outputs
For Business-As-Usual scenario:
 Total generation costs
 Capacity expansion (MW)
 Energy share (MWh)
For EE scenario:
 Weekly typical generation profiles
Inputs
1) 2 Demand Forecasts
 BAU with 0.7% growth
 EE with 4.3% reduction
2) Existing and committed plants:
 Committed plants (2.011 MW)
 Plant performance
3) New Supply Options:
 Wind, PV rooftop, biogas, batteries
 Plant costs assumptions
 Plant performance
4) Tshwane Tariff:
Megaflex (Time of Use)
 Peak (P)
 Standard (S)
 Off_Peak (O)
1 LT = Long-term
2 MT/ST = Medium-term/Short-term
O
P
S
The peak demand charges are done after optimising system for energy.

10. 9
Agenda
• Objectives of the study
• Scope and boundary conditions
• Methodology
• Input data and assumptions
• Results
• Conclusion

11. 10
Demand Forecasts: modelled demand for both BAU and EE Scenarios
0
5
10
15
20
25
30
35
40
2006 2008 2010 2012 2014 2016 2018 2020 2022
24,6
31,7
30,4
Energy (GWh)
Energy Efficiency (EE) HistoricalBusiness as usual (BAU)
For BAU Scenario, energy
consumptions increases by
0.7% annually starting in 2017
For EE Scenario, energy
consumptions increases by
0.7% and also decreases by
5% starting in 2018. resulting
in overall annual decrease of
4.3%.
The difference between BAU
scenario and EE scenario is 7
GWh

12. 11
Plant Performance characteristics:

13. 12
Wind resource performance: Wind profile shows a very low load factor
of (16%)
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170
0,4
0,5
0,6
0,7
0,8
0,9
0,0
1,0
1,1
0,2
0,1
0,3
Normalisedwindprofile(1MW)
Average weekly generationWeekly generation
Unlike what the aggregation study from CSIR showed, where on average
South Africa has wind load factor around 38%. The campus is one of the low
wind sites with only 16% load factor. This means for every 1 MW of wind
installed, only 0.16 MW is useful to generate power.

14. 13
Typical weekly generation profile of a single tracking system in Pretoria
campus
The average capacity factor
for single tracking system is
21%.
The average capacity factor
for dual axis tracking
system 31%.
The average capacity factor
for rooftop PV is 17%.

15. 15
Time of use tariff in a typical week in winter and summer of 2016 and 2022
0 2 4 6 8 10 12 14 16 18 20 22 24
0,5
1,5
2,0
2,5
0,0
1,0
3,0
0,63
Hours
1,02
0,44
Tariff(R/kWh)
0 2 4 6 8 10 12 14 16 18 20 22 24
3,0
2,5
2,0
1,5
1,0
0,5
0,0 Hours
0,95
0,52
2,78
Tariff(R/kWh)
SundaySaturdayWeekday
Summer 2016
Winter 2016
0 2 4 6 8 10 12 14 16 18 20 22 24
2
3
0
5
4
1
Hours
0,76
1,08
1,75
Tariff(R/kWh)
0 2 4 6 8 10 12 14 16 18 20 22 24
5
3
4
1
2
0
1,63
4,77
0,89
Hours
Tariff(R/kWh)
Weekday Saturday Sunday
Summer 2022
Winter 2022
9.43%

16. 17
Agenda
• Objectives of the study
• Scope and boundary conditions
• Methodology
• Input data and assumptions
• Results
• Conclusion

17. 18
Results presentation format
As-is capacity case assumes a case where the
CSIR is not doing any new installations of supply
options. In this case, the analysis is interested in
the annual cost and the net present value of the
total system from 2016 to 2022.
Capacity case 1:
 As-is
Demand scenarios
 BAU
 EE
Capacity case 2:
 Optimized
Demand scenarios
 BAU
 EE
The Optimized case assumes that there are
some new investments allowed in the supply
side and then the analysis is interested in the
capacity to be installed (MW), the total annual
energy cost and the net present value of the
total system from 2016 to 2022
250
203
558
558
250
558
2016
1.011
2015
203
1.000
2.011
2017/8
Single axis tracker
Dual axis tracker
Rooftop PV
Rooftop 2
+
2017/8
CommittedandBuilt
2,011
Any new
least cost
build
options

18. 19
Agenda
• Objectives of the study
• Scope and boundary conditions
• Methodology
• Input data and assumptions
• Results
 As – is capacity case
 Optimized capacity case
• Conclusion

19. 20
As-is under BAU demand scenario
20
60
50
40
30
0
10
2022
57
47
3
7
2021
51
41
3
7
2020
46
36
3
7
2019
41
31
3
6
2018
37
27
3
6
2017
34
25
2
7
2016
31
23
Total cost (R million)
1
7
TshwanePVPeak demand cost
Tshwane energy tariff increases from R23 million
to R47 million by 2022 if CSIR decides to install
only 2.011 MW of PV (as the system is)
The peak demand charge costs, decreases by R1
million in 2018, when the 1 MW rooftop PV is
introduced into the system and starts to increase
by R1 million in 2020, due to increase peak
demand charges
The cost of generating electricity using PV
increases from R1 million in 2016 to R3 million in
2022
The total system cost (bill) increases from R31
million in 2016 to R57 million in 2022.

20. 21
As-is under EE demand growth scenario
25
20
15
10
5
0
30
45
40
35
2022
43
35
3
5
2021
41
32
3
5
2020
39
30
3
5
2019
35
26
3
6
2017
34
25
2
7
36
31
23
1
7
28
3
6
Total cost (R million)
20182016
TshwanePeak demand cost PV
The peak demand charge costs, decreases by R1
million in 2018, when the 1 MW rooftop PV is
introduced into the system and starts to increase
by R1 million in 2020, due to increase peak
demand charges

21. 24
Agenda
• Objectives of the study
• Scope and boundary conditions
• Methodology
• Input data and assumptions
• Results
 As – is capacity case
 Optimized capacity case
• Conclusion

22. 25
BAU demand scenario new least cost capacity
2 2 2 2
2
4 4 4
2
1
1
0
1
2
3
4
5
6
7
20172016 2022
6
2021
6
2020
6
2018 2019
4
Installedcapacity(MW)
New PV
Commited/Approved

23. 26
EE demand scenario new least cost capacity
2 2 2 2
2
3 3 3
2
1
1
4,0
5,5
5,0
4,5
1,5
3,5
3,0
2,5
2,0
1,0
0,5
0,0
Installedcapacity(MW)
20172016 2019
4
5
2020
5 5
20222018 2021
Commited/Approved
New PV

24. 27
Optimised BAU demand scenario
40
35
30
25
50
20
45
15
10
5
0
3
2017
34
25
2
7
2016
31
23
1
7
Total cost (R million)
6
2018
5
37
27
2022
50
36
8
6
2021
45
31
8
5
2020
40
27
8
5
2019
38
27
6
PV TshwanePeak demand cost
Under the optimized capacity case,
the total system cost increases from
R31 million in 2016 to R50 million in
2022
PV generation cost increases from R1
million in 2016 to R8 million in 2022.
This is due to increased capacity
equalling 6 MW by 2022.

25. 28
Optimized EE demand scenario
25
5
30
0
35
10
20
50
15
45
40
2022
6
10
2 4
8
Total cost (R million)
2021
4
7
27
48
45
25
7
4
2020
41
23
7
4
2019
39
24
6
5
2018
37
26
3
6
2017
34
25
2
7
2016
31
23
1
7
EE cost
PV
Tshwane
Peak demand cost
Under the optimized capacity case,
the total system cost increases from
R31 million in 2016 to R48 million in
2022
In comparison to the BAU demand
scenario, the EE optimized case is
cheaper than the BAU optimized by
R2 million (This R50 – R48)

26. 29
0
1
2
3
4
5
6
7
1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
86
91
96
101
106
111
116
121
126
131
136
141
146
151
156
161
166
Demand(MW)
2016 PV Supply
Tshwane
Demand
Monday Tuesda Wednesday Thursday Friday
Saturday
Sunday
Typical generation in a week in 2016 with only 558 kWp of dual axis
Hours

27. 30
0
1
2
3
4
5
6
7
1
5
9
13
17
21
25
29
33
37
41
45
49
53
57
61
65
69
73
77
81
85
89
93
97
101
105
109
113
117
121
125
129
133
137
141
145
149
153
157
161
165
Demand(MW)
Hours
2018 Excess
PV Supply
Tshwane
Demand
SundaySaturdayFridayThursdayWednesdayTuesdayMonday
Typical generation in a week in 2018 after installing 2.011 MW of PV

28. 31
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
86
91
96
101
106
111
116
121
126
131
136
141
146
151
156
161
166
Demand(MW)
Hours
2022 Excess
PV Supply
Tshwane
Demand
Monday Thursday SaturdayFriday SundayTuesday Wednesday
Even during times of excess generation, energy is not wasted everyday

29. 32
Total discounted system cost over the study period
300
250
200
150
100
50
0
Total cost (R )
Optimized
BAU scenario
274
196
37
41
As-is BAU
scenario
295
230
19
46
As-is EE
scenario
258
198
19
41
242
172
33
Optimized
EE scenario
37
Pead demand cost
PV
Tshwane
If the CSIR decides not to investment in power
plants any further and does not invest in EE
measures, the organization will pay a total
amount of R295 million between 2016 and
2022. If it decides not to invest, but improve
the energy system by implement EE measures,
CSIR will pay R258 million (excluding DSM
investment costs).
Therefore its economic to install an additional 3
MW and 4 MW in both the cases where
demand is decreasing and increasing.
By investing in PV, the CSIR will respectively
save between R16 and R21 million for the cases
where efficiency measures are implemented
and when efficiency measures are not
implemented

30. 33
Agenda
• Objectives of the study
• Scope and boundary conditions
• Methodology
• Input data and assumptions
• Results
• Conclusion

31. 34
Conclusions and way forward
 Leaving the campus energy consumption as it is today will be costly for the CSIR in the next
5 years
 The least cost additional new generation capacity to install in the campus is 4 MW for
Business as usual scenario
 The least cost additional new generation capacity to install in the campus is 3 MW for
Energy Efficiency scenario
 The Optimised EE scenario is the most economic option saving R53m against BAU
 Biogas and wind are not economic during the modelled timeframe
 Energy audits to continue to identify and implement Energy Efficiency and Demand
Response measures

32. 51
Thank you
Re a leboga
Siyathokoza
Enkosi
Siyabonga
Re a leboha
Ro livhuha
Ha Khensa
Dankie
Note: “Thank you” in all official languages of the Republic of South Africa