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Technical aspects of large-scale integration of renewables to the grid
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Prof. Janaka Ekanayake (BSc, PhD, FIEEE, FIET, FIESL, Ceng (UK and SL))
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Outline 2
• Consequence of operating the power system with high penetration of Renewables • Activities in different time scales • Global • Local
• Creating the landscape to accommodate more renewables • Concluding remarks 2
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Consequence of operating the power system with high penetration of Renewable
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Activities in different time scales GLOBAL Months → Weeks
Years
Power system experts
LOCAL Weeks → Days
hrs → seconds
Days → hrs
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Real time
Energy experts Economists Sociologist
Expansion planning Reliability evaluation Scenario analysis Production costing modelling
Demand prediction Maintenance planning Hydro coordination Demand / weather prediction
Fuel planning
Unit commitment
Economic dispatch Renewable predictions
Contingencies
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Years ahead 5
Electricity Demand (MW)
6000 5000
Coal Solar Wind (To Consumer) Wind (To Storage)
LNG/Oil Mini Hydro Battery Discharge
Major Hydro Biomass Pump Hydro Discharge
4000 3000 2000 1000
0015 0115 0215 0315 0415 0515 0615 0715 0815 0915 1015 1115 1215 1315 1415 1515 1615 1715 1815 1915 2015 2115 2215 2315
0 2500
Time
2000
80% renewables in 20XX 2030
1500 1000 500
0015 0130 0245 0400 0515 0630 0745 0900 1015 1130 1245 1400 1515 1630 1745 1900 2015 2130 2245 2400
0
Coal
Oil - CEB
Oil - IPP
Hydro
Wind
Technical Economical Social
Analysis
Today
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Months to weeks ahead Gross demand
Net demand
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JICA report, March 2018
• Dispatchable generation depends on the net demand • How accurately the solar or wind profile in 2030 could be predicted?
The Nationally Determined Contributions (NDCs) is to reduce the GHG emissions against 2010 values by 10% in transport sector by 2030.
CEB Generation Plan
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Weeks to days ahead 65% clean energy
5000
Electricity Demand (MW)
4500 4000
Coal
810
3500
LNG
900
3000
Biomass
225
2500
Hydro
1,040
2000
Wind
1,000
Solar
4,250
1500 1000
0
Battery Discharge
500
Pump Hydro Discharge
200
0015 0045 0115 0145 0215 0245 0315 0345 0415 0445 0515 0545 0615 0645 0715 0745 0815 0845 0915 0945 1015 1045 1115 1145 1215 1245 1315 1345 1415 1445 1515 1545 1615 1645 1715 1745 1815 1845 1915 1945 2015 2045 2115 2145 2215 2245 2315 2345
500
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Time
Coal Solar Battery Discharge
LNG Biomass Pump Hydro Discharge
Hydro Wind
From a study carried out by Mr. Chamitha Rathnayake <chamithar@gmail.com>
Operational issues • High rate of increase of LNG • Daily cycling of Coal
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Weeks to days ahead 14
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12 25MW+
8
50 kW-5 MW
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10-50 kW
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Jan May Sep Jan May Sep Jan May Sep Jan May Sep Jan May Sep Jan May Sep Jan May Sep Jan May Sep Jan May Sep Jan May Sep
2
2010
2011
2012
2013
2014
2015
2016
2017
2018
0
UK Solar Capacity (GW)
10
5-25 MW
Sunny summer day
2019
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Weeks to days ahead 9
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Weeks to days ahead 10
Solar variability Seasonal and daily variations Hourly variation due to cloud cover
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Weeks to days ahead Solar Predictions
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Nonlinear autoregressive exogenous model
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Weeks to days ahead 12
System demand may be smooth due to the stochastic cancellation of short-term variations 12
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Days to hours ahead 13
Reserve Requirement Estimation â&#x20AC;˘ Methodology 1. Generate Net-Load profile 2. Calculation of Up/Down variations at dispatch intervals 3. Get the distribution 4. Calculation of Reserve i. ii.
3Ď&#x192; Method Confidence Interval Method
5. At two different time horizons i. ii.
4 hourly ahead Reserve Estimation 24 hourly (Day) ahead Reserve Estimation (Similar to annual reserve estimation)
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Days to hours ahead 14
Year
Mean Maximum Solar Installed Capacity Demand (MW)
(MW) Addition
Wind Installed Capacity (MW)
Total
Addition
Total
2018
2599
-
220
-
120
2019
2718
+80
300
+60
180
2020
2886
+150
450
+100
280
2021
3012
+150
600
0
280
2022
3147
+100
700
+100
380
2023
3287
+100
800
0
380
2024
3433
+100
900
+100
480
2025
3591
+100
1000
+250
730
Solar 4250 Wind 1000
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Days to hours ahead 15
Variation of Annual Reserve requirement
(Comparison between the use of Actual and predicted PV Generation) 400 300
Reserve (MW)
200 100 0 2018
2019
2020
2021
2022
2023
2024
2025
-100 -200 -300 -400
Year Pos. Reserve
Neg. Reserve
Pos. Reserve (P)
Neg. Reserve (P)
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Days to hours ahead 16
Comparison between 4hrs ahead and 24hrs ahead reserve requirment 400 300 200
0 -100 -200
00 - 04 04 - 08 08 - 12 12 - 16 16 - 20 20 -24 00 - 04 04 - 08 08 - 12 12 - 16 16 - 20 20 -24 00 - 04 04 - 08 08 - 12 12 - 16 16 - 20 20 -24 00 - 04 04 - 08 08 - 12 12 - 16 16 - 20 20 -24 00 - 04 04 - 08 08 - 12 12 - 16 16 - 20 20 -24 00 - 04 04 - 08 08 - 12 12 - 16 16 - 20 20 -24 00 - 04 04 - 08 08 - 12 12 - 16 16 - 20 20 -24 00 - 04 04 - 08 08 - 12 12 - 16 16 - 20 20 -24
Reserve (MW)
100
2018
2019
2020
2021
2022
2023
2024
2025
-300 -400 -500
Year and Time Period Pos. Reserve - 4h
Pos. Reserve - 24h
Neg. Reserve - 4h
Neg. Reserve - 24h
Pos. Reserve - 4h (P)
Pos. Reserve - 24h (P)
Neg. Reserve - 4h (P)
Neg. Reserve - 24h (P)
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Days to hours ahead Total operating cost
•
9,000,000,000
•
8,000,000,000 7,000,000,000
•
Cost (Rs./=)
6,000,000,000 Case 1
5,000,000,000
Case 2 4,000,000,000
Case 3
•
Case 4
3,000,000,000
Case 5
2,000,000,000
•
1,000,000,000 0 2018
2019
2020 Year
2021
2022
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Case 1 - The actual average load Case 2 - With 5% of load as reserve requirement. Case 3 - With reserve calculated using 3σ method considering NCRE and net load Case 4 – With reserve calculated using Exceedance level method considering NCRE and net load Case 5 – With reserve calculated using Exceedance level method considering NCRE and net load at 4hr ahead
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Hours to seconds ahead 18
Solar Predictions Frame (f)
Frame (f+1)
Frame (f+2)
Frame (f+3)
1 min ahead
Frame (f+4)
5 min ahead
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Hours to seconds ahead 19
An optimum control strategy that can control the smart transformer, smart inverter and demand side should be developed to maximize PV penetration while considering the network constrains based on the predicted state of the network, loads and PV influx.
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Seconds to hours Voltage rise
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Seconds to hours Statistically similar networks
Generate a network to be analysed
generator (SSNG)
Transfer data to OpenDSS
Randomly generate PV capacity, node and phase
Yes
A generalized method was developed to evaluate voltage rise and unbalances due to solar PV generation in LV distribution networks
Run 4 wire load flow in OpenDSS Record the results
Impact results (presentation format to be
No
Maximum line flows Maximum neutral current
(About 25)
decided)
Yes
Maximum voltage
Another network to Analyse?
Maximum unbalance factor
Generate scatter and bar plots
Another PV penetration
No
level to analyse? (about 100)
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Seconds to hours Voltage rise
Voltage unbalances 9
1.16
8 Unbalance factor (%)
Voltage (pu)
1.14 1.12 1.1 1.08 1.06 1.04
7 6 5 4 3 2 1
1.02 0
10000
20000 30000 Momentum (kWm)
40000
0 0
0.2
0.4 0.6 Mean absolute deviation
0.8
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Seconds to hours South Australia Blackout - 28 Sep 2016
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Seconds to hours London Blackout - 9 August 2019
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24
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Seconds to hours
25
Seconds to hours H Hydros Combined
constant 3
Operating capacity mix 40%
40%
40%
40%
40%
40% 20% 0 5.2
30% 20% 10% 4.4
20% 20% 20% 3.6
10% 20% 30% 2.8
0 20% 40% 2
Minimum frequency (Hz)
46.5
46.35
46.17
45.95
45.64
ROCOF (Hz/s)
0.6
0.664
0.732
0.804
1.33
Cycle Coal Renewables Equivalent H
8 4 0
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Creating landscape to accommodate more renewables
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Energy storage 28
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Energy storage 29
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Energy storage 129MWh battery energy storage system deployed by Tesla and developer Neoen in South Australia
South Australiaâ&#x20AC;&#x2122;s Planning Council gave approval in June to a 500MW (AC) solar farm project with a 250MW / 1,000MWh of battery energy storage.
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California mandates utilities to procure 1325 MW of storage by 2024
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Visibility all the way to consumers
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HVDC connection to India 32
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DNO to DSO transition • TSO uses FLEXIBILITY services to run the system more efficiently through controlling power and energy flows across network infrastructure
• With DGs and EVs, DNO needs to manage its own networks and the need to obtain FLEXIBILITY services 33
Flexibility services for DSO • Automated Load Transfer (ALT) • TSO needs to be balancing energy volumes • DSO needs to be balancing power capacity in discrete network zones • One section of a network is at capacity, another may have spare. Therefore automated load transfer schemes allow a DSO to move power around to solve constraints.
• Dynamic Asset Rating (DAR) • Windy and cool conditions an overhead line can have its rating increased
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Flexibility services for DSO â&#x20AC;˘ Voltage reduction (VR) â&#x20AC;˘ manipulating the voltage at which electricity is delivered to customers it has been shown that demand can be increased or decreased
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Frequency following smart distribution transformer
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Flexibility services for DSO • Power Electronic Equipment • SVC and STATCOM have the ability to be dynamically controlled and rapidly adjust system voltage through the injection or absorption of reactive power. • SST, UPFC can be used as sources of flexibility, delivering either real or reactive power.
• ANM – Active Network Management • full dynamic control of the network, generation and demand
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DSO let market
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Future Power System Architecture
35 new technical/commercial functions â&#x20AC;˘ Many are 'whole-system' in nature Achieving Black Start Demand management Smart energy systems
Smart EV charging Community Energy Network management
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Clustering approach 40
Community cluster
(Household or industry Prosumers, consumers, renewable energy sources, network infrastructure owned by communities
Hybrid cluster ( Microgrids)
Virtual cluster Future power system could be a collection of clusters connected through SOPs
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Community cluster 41 Wind turbine 600 kW
33/0.69 kV
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2
3
9
10
11
12
13
19
20
21
22
23
29
30
31
32
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39
40
33/0.4 kV
Grid Substation
Residential network Community based network
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Concluding remarks • Renewable future will not only depend on the capital and operating costs but by many other factors that was discussed in different time scales • Is there a maximum value of renewables that can be absorbed to the power grid? • Should we connect to the grid? Cost • If there is sufficient Energy Storage to overcome prediction errors and power dips due to cloud cover, can’t we consider renewables as a dispatchable source? Visibility • Can’t we use smart grid initiatives such as HVDC, Demand Side Management, Active Voltage Control and system approaches such as MicroGrid and VPP to create landscape for more renewable absorption? Cost 42
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Q&A?
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Thank you!!!
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