Technical aspects of large-scale integration of renewables to the grid

29/01/2020

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

8

12 25MW+

8

50 kW-5 MW

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10-50 kW

4

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

8

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Weeks to days ahead 9

9

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

1

2

3

9

10

11

12

13

19

20

21

22

23

29

30

31

32

33

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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