Flexible Ramp Product Provision from Grid-Connected Energy Storage Systems

Sumanth Yamujala1, Anjali Jain1, Partha Das3, Rohit Bhakar1, Jyotirmay Mathur1, Priyanka Kushwaha2

1 Centre for Energy and Environment, 2 Department of Electrical Engineering

Malaviya National Institute of Technology Jaipur, India 3 Center for Study of Science, Technology and Policy, Noida, Uttar Pradesh, India

Email: rbhakar.ee@mnit.ac.in, sumanthyamujala@gmail.com

Submission ID #84

Background

Power System Flexibility

Contribution

Scheduling Problem

Data

Case Studies

Conclusions

Key References

2

Presentation Outline

Global power generation - A 62% increase between 2017 and 2040 [1]

Coal fired & Natural gas plants - key role in supplying the demand (~60%)

Dwindling fossil fuels, environmental concerns and policy implications Clean and sustainable sources of energy

Many nations are setting ambitious targets to increase the share of RES in the generation mix.

Intermittency of RE Challenges in power system operations

Background

Traditional System - Variability of load and inevitable contingencies

RE integrated systems - Variability of both demand and generation along with inevitable contingencies

Steeper net load ramps

Undesirable outcomes The system must rely on regulation services, market efficiency (penalties), leaning towards interconnections/curtailments

Necessitates flexibility requirements

Background (Cont…)

Controllable units with insufficient ramping capability

Flexibility - Generation, Transmission, Distribution, Market Operations and long term planning

Ability of system to vary energy production in a certain ramp rate to cope up with variations and uncertainties in net-load at minimum cost [1]

PS Flexibility

For Flexibility Enhancement

Physical Measures

Institutional Measures

Improved Grid Infrastructure Fast Start Resources Energy Storage

Improved Operations Market Structures Demand Response

PS Flexibility (Cont…)

Improved Operations

Demand Response

Improved Grid

Infrastructure Fast Start Resources

Energy Storage

Investment Cost

• Improved Forecasting

• Time resolution of scheduling

• BESS • PHES • CAES

• Gas Power plants

• Peaking units

• Distribution and Transmission corridors

• Retrofitting

• Time shifting

• Curtailable loads

Improved operations helps in effective NL handling

India

DA:15 min- 5 min

CAISO

DA: 1hr-15 mins

PS Flexibility (Cont…)

0

1000

2000

3000

4000

5000

6000

7000

t1 t3 t5 t7 t9 t11 t13 t15 t17 t19 t21 t23

Net

load

(M

W)

Time blocks (1 hour)

0

1000

2000

3000

4000

5000

6000

t1 t4 t7 t10

t13

t16

t19

t22

t25

t28

t31

t34

t37

t40

t43

t46

t49

t52

t55

t58

t61

t64

t67

t70

t73

t76

t79

t82

t85

t88

t91

t94

Net

-Loa

d

(

MW

)

Time blocks (15 minutes)

Fig.2: Hourly Net-load

Fig.3: Intra- hour Net-load

Market operators like CAISO, MISO etc., are implementing market-based FRP to address the RR in RT dispatch

Unlike AS, FRP is the capacity deployed to meet RR of consecutive real-time dispatch intervals [3]

FRP - FRU/FRD

Energy Storage - quick start and ramping ability, ESS have become an increased interest [9]

RR- Ramp Rate, FRP- Flexible Ramp Products

PS Flexibility (Cont…)

Ability of PHES in FRP provision and its impact on system operations

Mature technology

High power density and energy density

96% of total installed storage capacity

A detailed modelling of PHES is incorporated in a 15-minute temporal DA SCUC and 5-minute RT re-dispatch

Effectiveness of the proposed model is analysed on IEEE RTS 24 bus test system.

Performance parameters like load and RE curtailment, operating cost and cycling of units are studied.

Contribution

Objective Function:

Subject to- operational and network constraints of

Generator minimum and maximum generation

Generator up/down ramp

Generator minimum Up/Down Time

Transmission line limit

Scheduling Problem

D D D RT RToper ener res frpMin C C C C Lc Rc= + + + + …(1)

PHES Modelling

Scheduling Problem (cont...)

, ,( ).turb UR turbph t g ph tP gH Qη ρ=

, ,( ). /pump pumpLRmph t ph tP gH Qρ η=

, , 1 ,,( )pumpUR UR turbph t ph t ph tph tL L Q Q t−= + −

, , 1 , ,( )pumpLR LR turbph t ph t ph t ph tL L Q Q t−= + −

_, ,,

g stor stor turbph t ph tph tP RU P+ ≤

_,, ,

m stor pumpstorph tph t ph tP RD P− ≤

…(3)

…(2)

…(4)

…(5)

…(6)

…(7)

max, ,, pumpturb

ph t phph tQ Q Q≤

_ min _ max,, ,

LR LRLRph tph t ph tL L L≤ ≤

_ min _ max,, ,

UR URURph tph t ph tL L L≤ ≤ …(10)

…(9)

…(8)

RT ramp requirement:

Scheduling Problem (cont…)

_1max(0, )RT up RT RT

m m mNLR NL NL −= −

_1max(0, )RT dn RT RT

m m mNLR NL NL−= −

_ _ _, ,RT gen RT stor RT up

mi m i mi ph

RU RU NLR+ =∑ ∑

_ _ _, ,RT gen RT stor RT dn

mi m i mi ph

RD RD NLR+ =∑ ∑

…(11)

…(12)

…(13)

…(14)

The proposed model is implemented on IEEE RTS 24 bus test system with a peak load of 2650 MW

Solar and wind generation - buses 3,5,20 and 23

Solar radiation and wind speed - CAISO for July 2018 [12]

Load Profile – normalized CAISO hourly load profile wrt peak demand of test case

Costs:

Opportunity cost for the reserve : $3.34/MW

FRP procurement: 20% higher than the average market clearing price of energy.

Case Study

Data (Cont…)

0100200300400500600700800

0

500

1000

1500

2000

2500

3000t1 t4 t7 t10

t13

t16

t19

t22

t25

t28

t31

t34

t37

t40

t43

t46

t49

t52

t55

t58

t61

t64

t67

t70

t73

t76

t79

t82

t85

t88

t91

t94

Sola

r an

d W

ind

Gen

erat

ion

(MW

)

Tota

l Loa

d (M

W)

Time (15 minutes block) Load Solar Wind

Figure 2. Day-ahead load and renewable profile at 30% RE integration

Case-1: FRP Provision from conventional units Case-2: FRP Provision from conventional units and PHES

FRP in DA is estimated from the standard deviation of historical data

Result Analysis

0

100

200

300

400

500

600

700

800

0

500

1000

1500

2000

2500

3000

t1 t5 t9 t13

t17

t21

t25

t29

t33

t37

t41

t45

t49

t53

t57

t61

t65

t69

t73

t77

t81

t85

t89

t93

RE

_ge

n/R

am_

req

(MW

)

Tota

l Loa

d/C

onv_

gen

(MW

)

Time (15 minutes block)

Conv_gen Total Load Ramp_req RE_gen

Figure 3. Day-ahead scheduling at 30% RE integration

Case-1:

Commitment status and scheduling of committed units is considered

Changes in net-load is supplied by up and down FRPs

Ramping incapability between dispatch intervals resulted in load curtailment with a max. value of 285 MW at block m190

Result Analysis (Cont…)

0

50

100

150

200

250

300

0

500

1000

1500

2000

2500

3000

m1

m8

m15

m22

m29

m36

m43

m50

m57

m64

m71

m78

m85

m92

m99

m10

6m

113

m12

0m

127

m13

4m

141

m14

8m

155

m16

2m

169

m17

6m

183

m19

0m

197

m20

4m

211

m21

8m

225

m23

2m

239

m24

6m

253

m26

0m

267

m27

4m

281

m28

8

FRU

/FR

D/L

oad_

curt

(MW

)

Con

v_di

spat

ch/N

et_

load

(MW

)

Time (5 minutes block)

P_Dispatch Net_load FRU FRD Load_Curt

Figure 4. Real-Time Power balance at 30% RE integration

PHES participation in energy, reserve and FRP markets resulted in low operating costs

Result Analysis (Cont…) Case- II

0

50

100

150

200

250

0

500

1000

1500

2000

2500

3000

t1 t4 t7 t10

t13

t16

t19

t22

t25

t28

t31

t34

t37

t40

t43

t46

t49

t52

t55

t58

t61

t64

t67

t70

t73

t76

t79

t82

t85

t88

t91

t94

PHE

S_ge

n/PH

ES_

mot

/Ram

p_re

q (M

W)

Con

v_ge

n/N

et_

load

(MW

)

Time (15 minutes block)

Conv_gen Net_load PHES_gen PHES_mot Ramp_req

Figure 5. Day-ahead scheduling of conventional units and PHES at 30% RE integration

Maximum Value of load curtailment in the case has decreased to 136 MW

Deviation of PHES pumping in real time is observed compared to its day-ahead schedule

Result Analysis (Cont…)

0

10

20

30

40

50

60

020406080

100120140160

m1

m9

m17

m25

m33

m41

m49

m57

m65

m73

m81

m89

m97

m10

5m

113

m12

1m

129

m13

7m

145

m15

3m

161

m16

9m

177

m18

5m

193

m20

1m

209

m21

7m

225

m23

3m

241

m24

9m

257

m26

5m

273

m28

1 PHE

S_FR

U/P

HE

S_FR

D (M

W)

Load

_cu

rt/C

onv_

FRU

/Con

v_FR

D

(MW

)

Time (5 Minutes block) Load_curt Conv_FRU Conv_FRD PHES_FRU PHES_FRD

Figure 7. FRP provision from conventional units and PHES in real-time at 30% RE integration

Result Analysis (Cont…)

Parameter 30% RE Integration

40% RE Integration

Case-I Operating Cost

($) 4596032 4165769

Load Curtailment

(MWh) 515 358

FRU deployed (MWh) 716.25 740

FRD deployed (MWh) 10 22.5

Case-II Operating Cost

($) 4170586 3904470

Load Curtailment

(MWh) 307 250

FRU deployed (MWh) 799.16 861

FRD deployed (MWh) 18 13

Table 1: Comparison of cases at different RE Integration

Variability and uncertainty associated with RES necessitates flexible resources in the system.

Improved operations+ Energy Storage

Flexibility product from the coordinated operation of conventional units and PHES is proposed along with improved operations

Integration of fast start units like PHES resulted in reduction in cycling of conventional units.

Use of peaking units decreased with FRP from PHES

Load curtailment and operating costs decrement with PHES participation at different RE penetrations is observed.

Conclusion

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IEEE Transactions on Sustainable Energy, vol. 6, no. 3, pp. 800-809, July 2015. 3. B. Zhang and M. Kezunovic, "Impact on Power System Flexibility by Electric Vehicle Participation

in Ramp Market," in IEEE Transactions on Smart Grid, vol. 7, no. 3, pp. 1285-1294, May 2016. 4. Kyle Westendorf, " Flexible Ramping Product UncertaintyCalculation and Implementation Issues,"

CAISO, Apr. 18, 2018. Accessed on: Jun. 30, 2019. [Online]. Available: Available: https://www.caiso.com/Documents/FlexibleRampingProductUncertaintyCalculationImplementationIssues.pdf

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8. J. Devlin, K. Li, P. Higgins and A. Foley, "System flexibility provision using short term grid scale storage," in IET Generation, Transmission & Distribution, vol. 10, no. 3, pp. 697-703, 2 18 2016.

9. M. B. Anwar, H. W. Qazi, D. J. Burke and M. J. O’Malley, "Harnessing the Flexibility of Demand-Side Resources," in IEEE Transactions on Smart Grid, vol. 10, no. 4, pp. 4151-4163, July 2019.

10. P. Kushwaha, V. Prakash, R. Bhakar, U. R. Yaragatti, A. Jain and Y. Sumanth, "Assessment of Energy Storage Potential for Primary Frequency Response Adequacy in Future Grids," 2018 8th IEEE India International Conference on Power Electronics (IICPE), JAIPUR, India, 2018, pp. 1-6.

11. A. Jain, S. Yamujala, P. Das, R. Bhakar, J. Mathur and P. Kushwaha, "Stacked Benefits of Energy Storage in Microgrid Scheduling," 2018 8th IEEE India International Conference on Power Electronics (IICPE), JAIPUR, India, 2018, pp. 1-5.

12. Y. Sumanth, A. Jain, P. Das, R. Bhakar, J. Mathur and P. Kushwaha, "Operational Strategy of Energy Storage to Address Day-Ahead Scheduling Errors in High RE Scenario," 2018 8th IEEE India International Conference on Power Electronics (IICPE), JAIPUR, India, 2018, pp. 1-6.

13. “Hourly and 15 minute wind and solar data sets”. Accessed on: Apr. 15, 2019. [Online]. Available: http://www.soda-pro.com/web-services

14. Technical Report, “Pinnapuram Integrated Renewable Energy With Storage Project (IRESP)”, Greenko Energies Pvt. Ltd., Accessed on: Jul. 05, 2019. [Online]. Available: http://environmentclearance.nic.in/writereaddata/Online/TOR/16_Apr_2018_13182853391O82IHRPinnapuramPFRFinalToR.pdf

15. Juan M. Morales, Antonio J. Conejo, Henrik Madsen, Pierre Pinson and Marco Zugno, “Integrating Renewables in Electricity Markets”, 2014.

References (Cont…)

Thank You !!

sumanthyamujala@gmail.com