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0
Dr Tobias Bischof-Niemz
Chief Engineer
Planning for future energy systems : A mini-IRP
for the CSIR campus in Pretoria
P...
1
Agenda
• Objectives of the study
• Scope and boundary conditions
• Methodology
• Input data and assumptions
• Results
• ...
2
Objectives of the study
Background
• Future energy systems will largely be based on Distributed
Energy Resources (DER) –...
3
Objectives of the study
The objective of the study was to determine least cost generation capacity mix for
CSIR’s Pretor...
4
Agenda
• Objectives of the study
• Scope and boundary conditions
• Methodology
• Input data and assumptions
• Results
• ...
5
Scope
1) The modelling timeframe is from 2016 – 2022
2) Includes existing power plants (single axis, dual axis and rooft...
6
Boundary conditions within which optimisation was based on
 The modelling considers the time of use tariff from Tshwane...
7
Agenda
• Objectives of the study
• Scope and boundary conditions
• Methodology
• Input data and assumptions
• Results
• ...
8
Study Methodology
Modelling
framework
(PLEXOS®)
LT1 techno-economic
least-cost optimisation in
long term horizon
MT/ST2 ...
9
Agenda
• Objectives of the study
• Scope and boundary conditions
• Methodology
• Input data and assumptions
• Results
• ...
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 ...
11
Plant Performance characteristics:
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 ...
13
Typical weekly generation profile of a single tracking system in Pretoria
campus
The average capacity factor
for single...
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,...
17
Agenda
• Objectives of the study
• Scope and boundary conditions
• Methodology
• Input data and assumptions
• Results
•...
18
Results presentation format
As-is capacity case assumes a case where the
CSIR is not doing any new installations of sup...
19
Agenda
• Objectives of the study
• Scope and boundary conditions
• Methodology
• Input data and assumptions
• Results
...
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...
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
...
24
Agenda
• Objectives of the study
• Scope and boundary conditions
• Methodology
• Input data and assumptions
• Results
...
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 20...
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
Instal...
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
2...
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...
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
1...
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...
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
...
32
Total discounted system cost over the study period
300
250
200
150
100
50
0
Total cost (R )
Optimized
BAU scenario
274
...
33
Agenda
• Objectives of the study
• Scope and boundary conditions
• Methodology
• Input data and assumptions
• Results
•...
34
Conclusions and way forward
 Leaving the campus energy consumption as it is today will be costly for the CSIR in the n...
51
Thank you
Re a leboga
Siyathokoza
Enkosi
Siyabonga
Re a leboha
Ro livhuha
Ha Khensa
Dankie
Note: “Thank you” in all off...
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NCPC IE Conference 2017 P Mukoma

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NCPC IE Conference 2017 P Mukoma

  1. 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. 2. 1 Agenda • Objectives of the study • Scope and boundary conditions • Methodology • Input data and assumptions • Results • Conclusion
  3. 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. 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. 5. 4 Agenda • Objectives of the study • Scope and boundary conditions • Methodology • Input data and assumptions • Results • Conclusion
  6. 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. 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. 8. 7 Agenda • Objectives of the study • Scope and boundary conditions • Methodology • Input data and assumptions • Results • Conclusion
  9. 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. 10. 9 Agenda • Objectives of the study • Scope and boundary conditions • Methodology • Input data and assumptions • Results • Conclusion
  11. 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. 12. 11 Plant Performance characteristics:
  13. 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. 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. 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. 16. 17 Agenda • Objectives of the study • Scope and boundary conditions • Methodology • Input data and assumptions • Results • Conclusion
  17. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 30. 33 Agenda • Objectives of the study • Scope and boundary conditions • Methodology • Input data and assumptions • Results • Conclusion
  31. 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. 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

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