Optimization of Wind and
Storage Dispatch for
Enhanced Market
Opportunities
Kwami Senam Sedzro
Ph.D. Student, Electrical Engineering
Lehigh University
Larry Snyder
Associate Professor, Industrial & Systems Engineering
Lehigh University
Optimization of Wind and Storage Dispatch for Enhanced Market Opportunities
Agenda
•
•
•
•
•
•
Problem Statement
System Layout
Control Strategy
Results
Future Work
Conclusion
Optimization of Wind and Storage Dispatch for Enhanced Market Opportunities
Problem Statement
Wind Intermittency => Grid reliability issues (ISO) + Limited Market opportunities (WF)
Wind farms can benefit from this variability of price
Optimization of Wind and Storage Dispatch for Enhanced Market Opportunities
System layout
Battery storage review*
Energy
Market
•
•
•
•
Pb-Ac
Li-Ion
NaS
Zebra (Sodium
Nickel Chloride)
• Flow Battery
• Ni-Cd
*http://www.sandia.gov/ess/publications/EPRI-DOE%20ESHB%20Wind%20Supplement.pdf
Optimization of Wind and Storage Dispatch for Enhanced Market Opportunities
Control Strategy: Optimization/DP
Objective : Maximize the Wind Farm’s REVENUE
Optimization of Wind and Storage Dispatch for Enhanced Market Opportunities
Control Strategy: Optimization/DP
• Inventory Model
Objective function (recursion)
𝜽𝒕 𝒙 = 𝒎𝒂𝒙𝝁𝒕 𝒑 𝒕 ∗ 𝒘𝒕 − 𝝁𝒕 ∗ 𝒘𝒕 − 𝝁𝒕 > 𝟎 − 𝒄 𝒕 ∗ 𝒘𝒕 − 𝝁𝒕 ∗ −𝒘𝒕 + 𝝁𝒕 > 𝟎 + 𝜽𝒕+𝟏 𝜼 ∗ (𝒙 + 𝝁𝒕 )
𝒘𝒕
Constraints
𝒘𝒕 − 𝝁𝒕
−𝒎𝒊𝒏 𝒙, 𝑹𝒐 ≤ 𝝁𝒕 ≤ 𝒎𝒊𝒏 𝑺 − 𝒙 , 𝑹𝒊
𝟎 ≤ 𝒙 ≤ 𝟎. 𝟖𝑺
𝑹𝒊 : Storage system charge rate
𝑹𝒐 : Storage system discharge rate
𝑺 : Storage system capacity
𝒙 : useful charge level
𝝁𝒕
Optimization of Wind and Storage Dispatch for Enhanced Market Opportunities
Control Strategy: Optimization/DP
Bidding
• Inventory
Model
policy x=0
x=1
x=2
t=1
t=2
t=3
t=4
t=5
t=6
t=7
t=8
t=9
t=10
t=11
t=12
t=13
t=14
t=15
t=16
t=17
t=18
t=19
t=20
t=21
t=22
t=23
t=24
29
25
32
32
20
11
10
9
-2
-3
-1
4
-2
-2
-1
1
0
8
12
20
21
23
26
30
30
26
33
33
20
11
10
10
-2
-3
-1
5
-2
-2
3
1
0
9
13
21
22
24
27
31
x=3
31
27
34
34
21
12
11
11
-1
-3
1
6
4
-1
4
2
1
10
14
22
23
25
28
32
x=4
32
28
35
35
22
13
12
12
-2
-3
2
7
5
5
5
3
2
11
15
23
24
26
29
33
x=5
33
29
36
36
23
14
13
13
5
-3
3
8
6
6
6
4
3
12
16
24
25
27
30
34
x=6
33
29
36
37
24
15
14
14
6
-3
4
8
6
6
7
5
4
12
16
24
26
27
31
34
x=7
33
29
36
37
25
16
15
14
3
-3
5
8
-2
6
7
1
5
12
16
24
27
27
32
34
x=8
33
29
36
37
26
17
16
14
4
-2
6
8
6
6
7
2
6
12
16
24
28
27
33
34
x=9
33
29
36
37
27
18
17
13
5
-1
7
8
6
6
7
3
7
12
16
24
29
27
34
34
x=10
33
29
36
37
28
19
18
14
6
5
7
8
6
6
7
4
8
12
16
24
29
27
34
34
33
29
36
37
24
15
18
14
6
2
7
8
6
6
6
5
5
12
16
24
29
27
34
34
Optimization of Wind and Storage Dispatch for Enhanced Market Opportunities
Results
Presented by Kwami Senam Sedzro
Master’s Eng. Electrical Engineering
Master’s Research Engineering Sciences/EE
Master’s Eng. ESE Student / Fulbright Scholar
For 1 day:
Optimization of Wind and Storage Dispatch for Enhanced Market Opportunities
Results: DAM/RTM
5
4
Optimum DAM bidding vs Current RTM bidding Zone A, 2011. ESS 100MWh
x 10
Revenue (DAM)
3.5
3
Revenue ($)
2.5
NoStockR evenue (RTM)
Annual DAM revenue: $18,396,000
Annual RTM revenue: $16,295,000
Delta: $2,100,700
2
1.5
1
0.5
0
-0.5
50
100
150
200
Day
250
300
350
Optimization of Wind and Storage Dispatch for Enhanced Market Opportunities
Results: DART/RTM
5
4
Strategic bidding DAM/RTM on a daily basis, ZoneA,2011, ESS 100MWh
x 10
Strategic Bidding (DA/RT)
RTM Revenue
3.5
3
Revenue ($)
2.5
Annual strategic revenue : $19,575,000
Annual RTM revenue: $16,295,000
Delta = $3,279,600
2
1.5
1
0.5
0
-0.5
50
100
150
200
Day
250
300
350
Optimization of Wind and Storage Dispatch for Enhanced Market Opportunities
Results : Revenue per Storage capacity – 10, 20 & 30 MWh
5
4
5
Zone A, 2011, ESS 10MWh
x 10
4
RTM Revenue
DAM Revenue
2.5
2.5
Revenue ($)
3
2
1.5
2
1.5
1
1
0.5
0.5
0
0
-0.5
0
50
100
150
200
Day
250
300
5
4
350
-0.5
400
0
3
100
150
RTM Revenue
DAM Revenue
RTM Revenue: $16,295,000
DAM Revenue: $16,730,000
Profit: $434,480
3.5
50
Zone A, 2011. ESS 30 MWh - Revenues
x 10
2.5
2
1.5
1
0.5
0
-0.5
0
50
100
150
200
Day
RTM Revenue
DAM Revenue
RTM Revenue: $16,295,000
DAM Revenue: $16,640,000
Profit = $345,000
3.5
3
Revenue ($)
Revenue ($)
3.5
ZoneA, 2011. ESS 20MWh - Revenues
x 10
250
300
350
400
200
Day
250
300
350
400
Optimization of Wind and Storage Dispatch for Enhanced Market Opportunities
Results : Revenue per Storage capacity
4
RT/DA Strategic Profit
DAM Profit
3
Profit ($)
v s ESS Capacity . DAM and RT/DA Strategic Bidding
2
1
0
0
20
40
60
Storage Capacity
80
100
Zone A, 2011. Profitability vs ESS Size. DAM and RT/DA vs Strategic Bidding
Ratio Strategic RT-DA / DAM Profit
6
x 10Zone A, 2011. Prof itability
8
6
4
2
0
0
20
40
60
Storage Capacity
80
• As storage capacity increases, the DAM strategic bidding profit
tends to catch up with the mixt (RT-DA) bidding
100
Optimization of Wind and Storage Dispatch for Enhanced Market Opportunities
Future Work
Integrate in the analysis:
• Stochasticity of Wind power generation
o account for intertemporal probability distribution
o compare online decision vs ex-ante strategy
• Lifecycle cost evaluation of energy storage
• Grid stability constraints
Optimization of Wind and Storage Dispatch for Enhanced Market Opportunities
Conclusion
This project resulted in
• the development of algorithms that maximize wind farms’ revenue
• by optimally dispatching wind and energy storage systems on daily basis,
• based on energy market prices and wind power forecasts
• The revenue as a function of the storage capacity is presented.
• Wind power producers will be able to have a significant share even in the
day-ahead market
• With more data (at least 10 years), we can build a RTMP/DAMP model in
order to predict when to bid into the DAM or into the RTM
• The extra $ gained in the DAM and the grid reliability made possible by this
project are reasons for its implementation
Optimization of Wind and Storage Dispatch for Enhanced Market Opportunities
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