Competitive Demand Response Trading in Electricity

Competitive Demand Response Trading in Electricity Markets: Aggregator and End-User Perspective NUR MOHAMMAD BSc (EEE) MSc (EEE) Presentation for the final seminar of Doctor of Philosophy Supervised by Dr. Yateendra Mishra and Prof. Gerrard Ledwich School of Electrical Engineering and Computer Science Queensland University of Technology December 18, 2017

Presentation Outlines ➢ ➢ ➢ ➢ ➢

Background, Why Demand Response (DR)………………………………..….…Slide 3,4 Electricity Price on the Rise, How Fluctuating the Price is?................Slide 5,6 Policy and Tariff Reformation………………………………...…………...............Slide 7 Hypothesis, Research Questions, Literature Review, Objectives………Slide 9-11 The Proposed Market Model and Optimization……………………….….….Slide 13-15 -A Framework of DRX Integrated Market Clearing Model (MCM) -Bi-level Optimization, Solution, Complexity

➢ Model#1, Problem Formulation without Wind Energy Firm (WEF)...Slide 17-20 - Upper-Level (SCED Problem), Lower-Level (DRX Problem) - Flow Chart, GenCos Revenue and Aggregator’s Payoff

➢ Data, and Simulation Results#1………………………...................................Slide 22-31 - Network, its Data - Bidding Data of GenCo and Aggregators - Operation Cost, LMP, Gen Dispatch, - DR Supply, Payoff for Aggregators and Compensation for Users, Summary

➢ Model#2, Problem Formulation with a WEF………………….……….………Slide 33-34 - Modified Optimization Model - Wind-DR Pairing, Conditional Priority Scheme

➢ Data, and Simulation Results#2…………………………………………….………….Slide 36-41 - Network, its Data , Case Formation - Results, Discussion, and Summary

➢ Conclusions, Limitation, and Future Works…………..............................Slide 44-46 2

Background - Demand Response (DR)? • Demand Response (DR) • A reduction/shifting of electricity usage from usual consumption at a certain times to balance/reduce the gap between supply and demand of electricity. • DR sees to adjust load demand instead of adjusting generation supply. • Like pseudo generation from demand side. • User may reduce electricity consumption in response to (1) price change over time, (2), financial incentives as compensation. [Dept. of Energy, USA 2005]

3

Why Demand Response?

*Defer network infrastructure cost, *Power system modernization, *Make gird responsive

*Minimize network congestion and cost, *Reduces carbon footprint

*Faster ramping rate, *better for renewable growth

*Provide ancillary services • Voltage • Frequency

4

$/MWh

Electricity Price on The Rise

The data sourced from AEMO 5

How Fluctuating the Wholesale Price is? 655 555 455

NSW

355 255 155

RRP > $55/MWh

RRP > $55/MWh

655

560 460

455

VIC

355 255 155 55 2008 2009 2010 2011 2012 2013 2014 560

660 SA

360 260 160 60 2008 2009 2010 2011 2012 2013 2014

RRP > $60/MWh

RRP > $60/MWh

55 2008 2009 2010 2011 2012 2013 2014

555

460

QLD

360 260 160 60 2008 2009 2010 2011 2012 2013 2014 6

Policy and Tariff Reformation How DR Service Provider Get Rewarded? DR selling price should be at LMP • In 2010, FERC order 745 • Practices in PJM, • CAISO

Dynamic Price: RealTime (RT), ToU • ComEd’s (Commonwealth Edition) • 5% customers left the RT in 2014 • Due to RT hikes/spike

LMP: Locational Marginal Price FERC: Federal Electricity Regulatory Commission

Incentive: Direct Load Control (DLC) • Undesirable interruption • Users are less committed • Misreporting • Less visible from load control perspective

7


Background, Why Demand Response (DR)………………………………..….…Slide 3,4 Electricity Price on the Rise, How Fluctuating the Price is?................Slide 5,6 Policy and Tariff Reformation………………………………...…………...............Slide 7 Hypothesis, Research Questions, Literature Review, Objectives……Slide 9-11 The Proposed Market Model and Optimization……………………….……..Slide 13-15 -A Framework of DRX Integrated MCM -Bi-level Optimization, Solution, Complexity

➢ Model#1, Problem Formulation without WEF……...............................Slide 17-20 - Upper-Level (SCED Problem), Lower-Level (DRX Problem) - Flow Chart, GenCos Revenue and Aggregator’s Payoff

➢ Data, and Simulation Results#1………………………..................................Slide 22-31 - Network, its Data - Bidding Data of GenCo and Aggregators - Operation Cost, LMP, Gen Dispatch, - DR Supply, Payoff for Aggregators and Compensation for Users, Summary

➢ Model#2, Problem Formulation with a WEF………………….……………..…Slide 33-34 - Modified Optimization Model - Wind-DR Pairing, Conditional Priority Scheme



Hypothesis, Research Questions Hypothesis • “A coordinated DR exchange (DRX) mechanism with a bid based compensation price setting from end-users can suppress LMP spikes and operating cost.”

Two research questions to be answered • (1), If a DRX mechanism is integrated then how we set it’s involvement margin (the amount of DR to be traded) from network perspective ? • (2), How can we use DR to smooth wind energy variability and what its impact? Provided that, a largescale wind energy firm (WEF) is in the system.

9

Pros Cons Obj.

Research Gap

Pros Cons Obj.

Literature Review Max Social Welfare

Cost Min, Revenue of LSE

Max SW

price setting, No TX

A single LSE, DR growth

Demand-side passive

DRX concept, UMCP

MCM, Coupon-based, LMP

Network Constraint Impact, LMP

To develop a DRX integrated Market Clearing Model (MCM) for operating cost and LMP reduction with a goal setting to compensate the end-users upon receiving the offer from independent DR aggregators

social-welfare

Profit of wind producer

Cost Min, SW Max

Scalability issue

practical Setting, MCM,LMP

silent about DR and WEF

Distribution side, MCM

Option DR, Risk, WEF, CVaR

SEUC, DRX, Price-based, LMP 10

Research Objectives 1) Providing a bid based DR compensation price and quantity settlement mechanism upon receiving the DR offer from the aggregators on behalf of the end-users. 2) Analyzing the operational and economic impacts (operating cost, emission, and LMP) of demand flexibility. 3) Providing a detailed DRX framework integrated it into a security constraint market clearing model (MCM). 4) Quantify how aggregator may get benefitted if the GenCos exercise its conflicting economic interest to uplift market price. 5) Analyzing how the DR paired with WEF smooth wind power variation and what are the cost benefit effects. 11


Background, Why Demand Response (DR)………………………………..….…Slide 3,4 Electricity Price on the Rise, How Fluctuating the Price is?................Slide 5,6 Policy and Tariff Reformation………………………………...…………...............Slide 7 Hypothesis, Research Questions, Literature Review, Objectives………Slide 9-11 The Proposed Market Model and Optimization………………………..…..Slide 13-15 -A Framework of DRX Integrated MCM -Bi-level Optimization, Solution, Complexity

➢ Model#1, Problem Formulation without WEF…….................................Slide 17-20 - Upper-Level (SCED Problem), Lower-Level (DRX Problem) - Flow Chart, GenCos Revenue and Aggregator’s Payoff

➢ Data, and Simulation Results#1………………………...................................Slide 22-31 - Network, its Data - Bidding Data of GenCo and Aggregators - Operation Cost, LMP, Gen Dispatch, - DR Supply, Payoff for Aggregators and Compensation for Users, Summary

➢ Model#2, Problem Formulation with a WEF………………….……..…………Slide 33-34 - Modified Optimization Model - Wind-DR Pairing, Conditional Priority Scheme



A Framework of DRX Integrated MCM SCED – Security Constraint Economic Dispatch DRX – Demand Response Xchange Upper-Level

[SCED] supply schedule

Lower-Level

[DRX] DR schedule

13

Bi-Level Optimization SCED – Security Constraint Economic Dispatch DRX – Demand Response Xchange [SCED] Upper-Level Problem solved by EMO

[DRX] Lower-Level Problem Solved by DRXO

Min

Upper-Level Objective Function [Operation Cost] OPC

Subject to Upper-Level Constraints

  

Min

• Network security constraints   • Supply-demand constraint • GenCos supply limit constraints  • DR requirement in buses  

Lower-Level Objective Function [DR Transaction Cost] DRTC

Subject to

Lower-Level Constraints

• Regulatory constraint   • DR limit per user type   • Aggregator’s payoff limits   14

Conversion of Bi-Level Optimization MPEC – Mathematical Program with Equilibrium Constraints [Hu & Ralph 2007] KKT – Karush-Kuhn-Tucker

A single-level MPEC

Min Upper-Level Objective Function Subject to Upper-Level Constraints KKT Optimality Conditions associated with Lower-Level Problem(s)

KKT Optimality Conditions: 1). Primal and dual feasibility constraints, 2). Equalities obtained from differentiating the Lagrangian w.r.t. variables, 3). Complementary conditions [Dreves et al. 2011; Karush & W 2008] 15


Background, Why Demand Response (DR)………………………………..….…Slide 3,4 Electricity Price on the Rise, How Fluctuating the Price is?................Slide 5,6 Policy and Tariff Reformation………………………………...…………...............Slide 7 Hypothesis, Research Questions, Literature Review, Objectives………Slide 9-11 The Proposed Market Model and Optimization……………………….……..Slide 13-15 -A Framework of DRX Integrated MCM -Bi-level Optimization, Solution, Complexity

➢ Model#1, Problem Formulation without WEF……...........................Slide 17-20 - Upper-Level (SCED Problem), Lower-Level (DRX Problem) - Flow Chart, GenCos Revenue and Aggregator’s Payoff


➢ Model#2, Problem Formulation with a WEF………………….………………..Slide 33-34 - Wind-DR Pairing, Conditional Priority Scheme - Modified Optimization Model



Upper-Level Problem (SCED Model) i: index for bus n: index for supply k: index for time

GenCos cost

 cn ( Pgnk ) Pgnk 

Min

nN g

Minimizing cost Demand-supply balance constraint Transmission line Generation limit Ramp rate limits

DR constraints



 

d λ  mk d mk

mN r

total DR amount

Subject to :

 Pg   (1   ) d B     B      F (i, j )  N , k nk

n

ik

iN b

b

ik

min ij

 Fij  F

F

DR Transaction Cost [DRTC]

jk

b

ik



b

ij

max ij

jk





F k

Pg nmin  Pg nk  Pg nmax  {S,g,kmin , S,g,kmax } Rndn  Pg nk  Pg nk 1  Rnup , k

imin    imax ,i 1  0 0  d ik ,  w  R 0  d mk  arg Min DRTC

λki

The LMP at Bus i demand at Bus i

dual variable for line congestion charge dual variable for gen. capacity limits

 17

Lower-Level Problem (DRX Model) j: index for buyer m: index for seller k: index for time



Min DRTC

s

DR offer price DR to be transected

k k ( d ) d m ,u mj mj

m|( m , j )N a

Subject to : A d  Ard k a mj

A d

k a mj

m|( m , j )N a

buyer design matrix k jm

seller design matrix compensation price DR tuning parameter

 χ d ik ,  

d jm

rmjmin  d mjk  rmjmax , directed graph L:= (N, A),

| d mjk  d mjk -1 |  d

.

pick-up/drop-off rate

0  d mk  d mmax

DR capacity limit 1 1  0 Aa   0 0  0

T

0 0 0 0 1 0  , Ar 1 0 0 1  0 1

T

  1 0  0  1     1 0   , A  A a A r  0  1     1 0    0  1 18

The Flow Chart

DR Transaction Cost

•

Upper-Level Constraints [SCED]

•

argmin: Lower-Level Problem [DRX]

Lower-Level Problem: Min Objective DRTC Subject to •

✓ DR sale prices ✓ DR quantities share ✓ User’s compensation

Transacted DR

✓ LMPs ✓ Generation dispatch ✓ Gen’s Profit

Upper-Level Problem: Min Objective OPC Subject to

Upper-Level Constraint

KKT set for Aggregator j1 KKT set for Aggregator j2 • • • •

KKT set for Aggregator jNa Stationary conditions Complimentary slackness Primal feasibility Dual feasibility 19

Payoff of GenCos and Aggregator • GenCos Payoff

LMP at Bus where generation located generation amount settled

n 

* λ Pg  nk nk 

kT t



* * c ( Pg ) Pg  n nk nk

kT t

revenue



generation cost

generation cost

• Aggregator’s Payoff LMP at Bus i where the DR capable load sum over DR periods sum of all users

RA    λik d kjm*  n kT t

 revenue

 

kT t iN u

λ djm d kjm* compensation

 compensation cost 20


Background, Why Demand Response (DR)………………………………..…..…Slide 3,4 Electricity Price on the Rise, How Fluctuating the Price is?................Slide 5,6 Policy and Tariff Reformation………………………………...…………................Slide 7 Hypothesis, Research Questions, Literature Review, Objectives…….…Slide 9-11 The Proposed Market Model and Optimization……………………….……...Slide 13-15 -A Framework of DRX Integrated MCM -Bi-level Optimization, Solution, Complexity


➢ Data, and Simulation Results#1………………………................................Slide 22-31 - Network, its Data - Bidding Data of GenCo and Aggregators - Operation Cost, LMP, Gen Dispatch, - DR Supply, Payoff for Aggregators and Compensation for Users, Summary

➢ Model#2, Problem Formulation with a WEF………………….………………….Slide 33-34 - Wind-DR Pairing, Conditional Priority Scheme - Modified Optimization Model


➢ Conclusions, Limitation, and Future Works…………...............................Slide 43-46 21

Network Data Used

G1 (Coal), Bus#1 G2 (Diesel), Bus#1 G3 (Coal), Bus#3 G4 (Gas), Bus#4 G5 (Coal), Bus#5

Gen Limits Carbon Emissions [Pnmin Pnmax ] Rate (p.u.) (kgCO2/kWh) [0.25, 1.10] [0.25, 1.00] [1.50, 5.20] [0.50, 3.00] [2.25, 6.00]

0.940 0.778 0.940 0.581 0.940

Figure: PJM 5-Bus System. [Li & Rui 2007]

demand prevails

Gen (Fuel type)

supply prevails

Table: Generation Capacity limits, Emission rate, Lines and Load Data [Walawalkar et al. 2008]

Transmission Lines and Load Data Load Reacta Capacity From To Limit Demand, nce Bus i Bus j min max (xij) [Fij Fij ] Di, (p.u.) 1 2 2.81 [-8.75, 8.75] Bus#2=3.00 1.08 [-8.75, 8.75] Bus#3=3.00 2 3 2.97 [-8.75, 8.75] Bus#4=3.00 4 3 2.97 [-2.40, 2.40] 5 4 0.64 [-8.75, 8.75] 5 1 3.04 [-8.75, 8.75] 1 4 22

Supply and Demand-Side Bidding Data Table A: Cost coefficient, bidding quantities and prices for GenCos Gen [1st

Block] [an, bn] ($/MWh), ($/MWh, $/MWh2) (p.u) G1 [0.115, 11.50] 11.61, 0.5 G2 [0.115, 11.50] 11.61, 0.5 G3 [0.355, 12.50] 12.85, 1.0 G4 [0.425, 18.50] 18.92, 0.6 G5 [0.265, 10.50] 10.76, 1.2

Generation supply offer price Block] [3st Block] [4th Block] ($/MWh), ($/MWh), ($/MWh), (p.u) (p.u) (p.u) 17.36, 1.1 23.11 28.86 17.36, 1.0 23.11 28.86 30.60, 2.0 48.35, 3.0 66.10, 4.0 40.17, 1.2 61.42, 1.8 82.67, 2.4 24.01, 2.4 37.26,3.6 50.51, 4.8

[2nd

Table B: The DR bidding prices. Aggregator

Capacity

A1 A2 A3

dmax 1.90 1.70 1.70

PJM [5th

Block] ($/MWh), ($/MWh) (p.u) 34.61 14.00 34.61 15.00 83.85, 5.0 30.00 103.92, 3.0 40.00 63.76, 6.0 10.00

2 s   (  d DRTC function m ,u m m mj   m (1  Γ u ) d mj )

DR cost coefficients αm 0485 0.092 0.068

βm 12.15 10.35 11.05

The DR offer price segments for the user group at ωm=1 U1 U2 U3 16.97 23.06 27.42 21.71 24.72 26.43 19.20 24.65 24.66

The bidding parameter, ωm in DR price is varied from 1:5.50 with different increment to rescaled price coefficient, αm and βm at different DR levels. 23

Operating Cost with and without DRX Table: At different level of DR, the operation cost with and without DR, emission. DR Amount

System demand 100 Base

p.u.

% DR

Operating Cost

Relative Cost Reduction

DRX Transaction Cost

Emissions

p.u.

%

k$

%

k$

[tonCO2]

0

202.26

0

747.49

0

0

19077

4.02

198.18

1.95

724.16

3.12

7.75

18806

12.21

194.08

5.92

686.31

8.18

29.34

18167

17.03

189.25

8.26

672.61

10.01

48.76

17764

21.86

184.42

10.60

679.22

9.13

74.32

17335

26.69

179.59

12.94

687.10

8.07

107.28

16881

31.52

174.76

15.28

734.44

1.74

178.48

16427

36.35

169.94

17.72

802.55

-7.36

266.92

15974

39.96

166.32

19.37

909.64

-21.69

382.34

15634

43.57

162.71

21.21

1040.62

39.21

516.08

15294

24

Operation Cost Trajectory and LMPs Figure: The operation cost trajectory with and without DR transaction.

Table: The average LMP for different incremental DR. Average LMP ($/MWh)

% DR Bus#1 Bus#2 Bus#3 Bus#4 Bus#5 0

50.51 51.82 55.25 56.49 16.28

1.95 47.92 49.14 52.3 53.46 16.28 5.92

46.3 47.45 50.46 51.55 16.28

8.26 45.76 46.89 49.85 50.92 16.28 10.6 44.14 45.21

48

49.01 16.28

12.94 44.14 45.21

48

49.01 16.28

15.28 44.14 45.21

48

49.01 16.28

17.72 44.14 45.21

48

49.01 16.28

19.37 44.14 45.21

48

49.01 16.28

21.21 44.14 45.21

48

49.01 16.28

25

Hourly LMPs over a Day Figure A: The hourly LMPs at Bus#3.

Figure B: The hourly LMPs at Bus#4.

26

Generation Dispatch for Three DR Levels Figure A: Generation dispatch mix [no elasticity to the demand]

Figure B: Generation dispatch mix [with 1.95% DR participation].

Figure C: Generation dispatch mix [with 5.92% DR participation].

27

Aggregators’ Payoff Share

41

Figure: Payoff trajectory of the aggregators across the incremental DR levels. J1 J2 J3 Poly. (J1) Poly. (J2) Poly. (J3)

Aggregator's Payoff (k$)

31 21 11

1 1.95 -9

5.92

8.26

10.6

12.94 15.28 17.72 19.37 21.21

% DR of demand w/o DR

-19

Table: The payoff receive by each aggregator. % DR 1.95 5.92 8.26 10.60 12.94 15.28

J1 15.15 23.05 26.09 22.93 26.37 2.92

-29

+𝑅𝐴

−𝑅𝐴

28

Payoff (k$) J2 13.75 19.35 17.33 15.89 15.98 -1.65

J3 14.41 20.59 19.27 16.19 16.09 -1.47

DR Offer Price and Compensation DR offer price ($/MWh)

35

Figure A: The DR supply offer price which increases if the DR demand rises. Figure B: The DR provided by each user types at different incremental DR levels.

20

DR (p.u.)

8

5.92 12.94 19.37

24.72

23.06

U2

26.43

U3 24.65 24.66

21.71 19.2

16.97

15 10 5 0 A1

A2

A3

Table: DR compensation benefit share among the user groups

% DR of demand w/o DR 1.95 10.6 17.72

27.42

25

12 10

U1

30

8.26 15.28 21.21

A1 U2

U1

A2 U2

U3

U3

1.95 2.31

0

0

2.88

0

5.92 9.06

0

0

10.76

8.26 17.05

0

0

16.82

10.6 30.14 4.28

0

12.94 36.84 14.75 15.28 52.66 32.97

DR

U1

U1

A3 U2

U3

0

2.553

0

0

0

0

9.519

0

0

0

0

14.88

0

0

25.37 1.23

0

23.22 1.22

0

0

30.82 6.59

0

30.73 6.57

0

0

42.37 17.22

0

42.25 17.17

0

6

4 2 0 U1

U2

U3

U1

U2

U3

U1

U2

A1 A2 A3 DR provided by end-user group under each Aggregatror j

U3

29

Impact on Aggregator’s Payoff Table: Comparison of aggregator’s payoff due degree of strategy adopted by GenCos. All GenCos Competitive Price Rigging ($/MWh) Aggregator’s Payoff (k$) without DR Aggregator’s Payoff (k$) with 5.92% DR Aggregated Payoff (k$) Relative payoff variation (k$) and %

0

The G3 Strategic (Scenario#1) $83.80 /$66.10

The G4 Strategic (Scenario#2) $103.92 /$82.67

The G3, G4 Both Strategic (Scenario#3) $83.80 $103.92

0

0

0

{23.05, 19.35, {23.42,19.70, {21.69, 18.06, {24.72, 20.94, 20.59} 20.95, 19.31} 22.18} 62.29

64.07

63.72

67.84

(64.07 - 62.59) (63.72 - 62.29) (67.84 - 62.29) = 1.78 (2.85%) = 1.13 (1.82%) = 5.55 (8.90%)

NB: Strategic Bidding Hours: 7 pm, 8 pm, and 9 pm 30

Summary of Model#1 ✓ In Model#1, a DR integrated MCP settled in two levels is modelled. ✓ DRXO determines optimal DR amount traded and the DR compensation price in lower-level. ✓ The EMO considers the lower-level transacted DR and its cost into MCM at upper-level to find generation dispatch and the LMPs. ✓ Simulation results compare the operating cost, LMPs, aggregator’s payoff, the of DR should be transacted, it's cost. ✓ DR compensation benefit consist of the DR compensation price with the allocated DR is regarded as compensation benefit for the users ✓ Beyond a critical DR level, the DR compensation price become higher and outweigh the DR benefit both the upper and lower level

31





➢ Model#2, Problem Formulation with a WEF………………….……………….Slide 33-34 - Wind-DR Pairing, Conditional Priority Scheme - Modified Optimization Model



SCED Problem Considering Wind WEF’s offer price

Min

 c (P

nN g

n

g nk ) Pg nk 

 c (P

nN w

n

wnk ) Pwnk 

Subject to :



nN g

Pg nk 

P

nN w

wnk



 (1  

iN b

w

λ

mN r



d

)d ik  Bb  ik   jk  

λki

wind power variable WIDR parameter

Bb  ik   jk   Fij (i, j )  N b , k Fijmin  Fij  Fijmax

d mk mk

kF

Pg nmin  Pg nk  Pg nmax  {S,g,kmin , S,g,kmax }

wind power availability limits

0  Pwnk  P

max wnk

Rndn  Pg nk  Pg nk 1  Rnup , k Rndn  Pwnk  Pwnk 1  Rnup , k

imin    imax ,i 1  0 0  Dik ,  w  R,, 0  d mk  arg Min DRTC

 33

Dynamic Wind-DR Pairing

Conditional Priority Scheme

34






➢ Data, and Simulation Results#2…………………………………………………….Slide 36-41 - Network, its Data , Case Formation - Results, Discussion, and Summary


Network and Data Used Table: Generation, Emission, Lines, and Load Data Gen (Fuel type)

Capacity [Pnmin Pnmax ] (p.u.) G1 (Coal) [3.00 16.50] G2 (Gas) [1.50 9.50] WEF (Wind) [0.00 4.75]

Carbon Emissions Rate (kgCO2/kWh)

Figure: Sample 4-bus System

0.940 0.581 0.009

Transmission Lines and Load Data Capacity Load and its From To Reactance [Fijmin uncertainty (p.u.) Bus i Bus j (xij) max Fij ] 1 4 0.146 [-2.25 3.50] Bus#2=2.00 1 2 0.120 [-3.50 5.75] Bus#3=3.00 Base, 0.120 [-3.50 5.75] 2 3 N (1.75,0.12) Shoulder, 0.100 [-4.00 6.50] 4 3 N (3.25, 0.25)

1

3

0.126

[-3.70 5.85]

Peak, N (6.25,0.65) 36

Modified IEEE 24-Bus system [C. Grigg and P. Wong, 1999]

System consists of 24 Buses, 38 lines, 17 loads and 11 Gen units.

Gen • The WEF at Bus#9;

Load • at Bus#1, #3, #6, #7, #13, #15 and #18 are assumed to be elastic and have DR capability

Line • interconnectors between Bus#4 to Bus#5 and Bus#5 to Bus#7

37

Case Formation • The WEF is modelled as Weibull distribution with scale (c=6.34) and shape (s=5.68) parameters. [Donk et al. 2005] • The cut-in (vco), rated (vr) and cut-out (vco) speed are assumed to be 2.25 m/s, 6.75 m/s and 18.15 m/s respectively.

Case#1

Case#3

Case#3

(Low-Wind, High-DR)

(Med-Wind, Med-DR)

(High-Wind, Low-DR)

v < vL,

vL < v vH,

vL =2vci

vM = vr

vH =1/2vco 38

Results: Portfolio of WEF and DR Table A: Profit of WEF and DR in 4-Bus System. Cases

WEF Portfolio

DR Portfolio DR [%]

Profit [$K]

Energy [%]

Profit [$M]

Emissions [tonCO2]

Case#1

14.17

35.89

4.150

3.459

28555

Case#2

10.87

26.82

30.11

27.48

22762

Case#3

8.94

16.38

48.80

43.20

17763

Table B: Profit of WEF and DR in 24-Bus System. Cases

WEF Portfolio

DR Portfolio DR [%]

Profit, [$M]

Energy [%]

Profit [$M]

Emissions [M tonCO2]

Case#1

13.98

0.739

9.850

66.73

0.2889

Case#2 Case#3

11.35 7.77

0.680 0.569

11.77 13.54

92.43 100.84

0.2994 0.3011 39

Results: User’s Price Rigging Strategy Table A: Aggregator’s Payoff for Different Cases. Cases

Case#1

Case#2

Case#3

Payoff [$]

9875.72

6644.29

5506.66

DR [U1,U2, U3]

6.5, 5.2, 2.3

6.2, 3.3, 1.0

5.8, 2.6, 0.7

Table B: User’s Compensation Benefit due to different type value [Case#1 ] Groups

Benefit [$]

Benefit [$]

Benefit [$]

ˆ  0%  u

ˆ  10%  1

ˆ  10%  2

ˆ  10%  3

U1 U2

6677.64

6202.92

5945.64

7026.12

4899.93

8525.82

U3

2763.76

2763.76

4896.31

4126.47

Benefit [$]

7760.21

9257.96

40

Summary of Model#2 ✓ The Model#2 presents how to smooth various wind power levels using the DR.

✓ A conditional priority scheme is used to improve shortfall and to minimize operation cost. ✓ The least cost operation is achieved either by increasing DR when the wind is low or decreasing the DR during high winds.

✓ Simulation results shows the profit of WEF and the aggregators. The DR, emission, and user’s compensation are compared. ✓ The effect of user’s reported type value on compensation benefit is investigated. ✓ Due to strategy-proof, no end-users can achieve a higher benefit by reporting a type value different from its true type value.

✓ Thereby, the end-users get a fair allocation of DR and the compensation benefits. 41







➢ Conclusions, Limitation, and Future Works…………............................Slide 43-46 42

Conclusions ✓ We studied how to trade DR among multiples aggregators and endusers in DRX market environment.

✓ A DRX integrated MCM without and with WEF using a bi-level optimization method is proposed. ✓ The transaction cost, compensation price, and the amount of DR allocated between the end-users and the aggregator are obtained.

✓ Plausible bidding behavior of the GenCos and its effect on MCM, and DRX market have been investigated. ✓ DR share causes the reductions in purchases of electricity from the expensive generators.

✓ When the GenCos bid strategically, aggregator payoff could be higher than the competitive bidding. ✓ The DR would be profitable as long as its transaction is cost-effective and economic. 43

Conclusions ✓ The proposed WIDR allow WEF to better compete with other generation company. ✓ WEF and DR pairing pushes operating cost and LMP lower. ✓ Due to strategy-proof, the user may not get more compensation benefit by misreporting of the type. ✓ Due to receive compensation (payback), the users reduce the energy consumption cost. ✓ All benefit can be comprehended, if the operating cost reduction at upper-level does not outweigh the compensation given to the endusers at lower-level. ***Besides the presentation contents, we have modelled additional two market mechanism. ✓ (1) Retailer’s Risk-Aware Trading Framework with the DR aggregators. ✓ (2) A DR integrated bi-level transactive market clearing model (TMC). 44

Publications • Conference Papers (1). N. Mohammad and Y. Mishra, "Competition driven bi-level supply offer strategies in dayahead electricity market," 2016 Australasian Universities Power Engineering Conf. (AUPEC), Brisbane, Australia, 2016, pp. 1-6. (2). N. Mohammad and Y. Mishra, " Transactive Market Clearing Model with Coordinated Integration of Large Scale Solar PV Firm and Demand Response Capable Loads," 2017 Australasian Universities Power Engineering Conf. (AUPEC), Victoria, Australia, 2017, pp. 1-6. • Book Chapter N. Mohammad, Y. Mishra, “Demand-side Management and Demand Response for Smart Grid.” In: Kabalci Yasin, Kabalci Ersan, editors. Handbook of Smart Grid Communication Systems. 1st ed. New York: Springer; will be appear in March 2018. • Draft Articles to be Submitted (1) N. Mohammad, Y. Mishra, “A Critical Review on Market Based Demand-Side Management – PJM and NEM Case Studies.” (2) N. Mohammad, Y. Mishra, “Incremental DR, GenCos Strategic Bidding and its Impact.” (3) N. Mohammad, Y. Mishra, “Wind-Induced Demand Response Pairing to Minimize Operation Cost in Day-Ahead Electricity Markets.” (4) N. Mohammad, Y. Mishra, “Retailer’s Risk-Aware Trading Framework with DR Aggregators in Short-Term Electricity Markets.” 45

Limitation and Future Works • This work focused on DR trading in transmission and wholesale level. • Collecting the DR from large-customers is a vast area of research and could be investigated in future works. • Especially industrial customers require excessive energy consumption with normal loads of hundreds of MWs. • Compare to the residential users, industrial DR would be complex due to interdependent industrial tasks being difficult to isolate. • (1) Considering task/process scheduling constraints, DR can be challenging and need to be studied. • (2) Further, a transactive control for industrial HVAC cold-storage may be modelled to participate in electricity markets. • A small changes in temperature setting have insignificant effect in plants while it may save huge energy and make a lot of money. • Adding additional constraints, our proposed model can be modified to optimize those two models. 46

THANKS FOR ATTENTION