soc (pu) - university of albertaapic/uploads/forum/poster7.pdf · the studied hybrid system is...
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H. M. Hassan, Student Member, IEEE, and Y. A. I. Mohamed, Senior Member, IEEE
[email protected], [email protected]
MPC constraints optimizer
Simulation Results
Conclusion This paper addresses the development of a comprehensive market-oriented EMS via MPC for a hybrid power system (WECS with BESS). The MPC aims at maximizing the daily profit by
dispatching the BESS. The real-time optimization process depends on receiving market prices and wind power predictions in each new sample. For expanding the BESS lifetime, constraints on the power rating, the DOD and the DNC are included. MPC constraints optimizer tightens the DNC and DOD constraints in order to achieve
the maximum profit with the minimal expended lifetime cost. Real wind power and market data of Alberta province shows that the proposed MPC constraints optimizer achieves the optimal profit with the minimal sacrifices in the BESS life. Comparison with other two MPC techniques shows that the proposed MPC has always managed to achieve the maximum net profit for the system owner
Abstract This work proposes a market-oriented energy management system
(EMS) for a hybrid power system composed of a wind energy conversion system and battery energy storage system (BESS).
The EMS is designed as a real-time model predictive control (MPC) system. The EMS dispatches the BESS to achieve the maximum net profit from the deregulated electricity market.
Further, the EMS aims at expanding the BESS lifetime by applying typical and practical constraints in the MPC problem on both the daily number of cycles (DNC) and depth of discharge (DOD).
MPC constraints optimizer is designed to tune the lifetime constraints optimally. It guarantees the optimal economic profit by finding the optimal DNC and DOD that achieve the maximum market revenue with the minimal life expended cost.
Furthermore, an optimal maintenance scheduler is designed to detect the most profitable maintenance periods.
The effectiveness of this work is all verified by comparison with conventional MPC used in previous works. Simulation is conducted using a real wind power and market data of Alberta province, Canada.
System Description
Problem Formulation
The objective of MPC controller is dispatching the BESS such that the net
profit is maximized by solving this optimization problem
𝜆
𝑀𝑎𝑥𝕦(𝑃𝑅𝑂)
𝑠. 𝑡.
𝑋 𝑘 + 1 = 𝐴𝑋 𝑘 + 𝐵𝑈 𝑘 + 𝐵𝑑𝑈𝑑(𝑘)
𝑌 𝑘 = 𝐶𝑋 𝑘 + 𝐷𝑑𝑈𝑑 𝑘 𝑋 0 = 𝑋0𝑃𝑏 𝑘 ≤ 𝑃𝑏 𝑘 ≤ 𝑃𝑏 𝑘
𝐷𝑂𝐷 𝑘 ≤ 𝐷𝑂𝐷 𝑘 ≤ 𝐷𝑂𝐷 𝑘
𝑁 ≤ 𝑁
∀ 𝑘𝜖[𝑇𝑜, 𝑇𝑜 + 𝑛𝑝𝑇𝑠]
The problem models the hybrid system by an accurate fourth order system .
MPC has battery DOD, power and number of cycles constraints The expended life cost of the BESS is nonlinear function in both DOD,
DNC The proposed constraint optimizer searches for the maximum net profit
for the system owner by detecting the most profitable DOD and DNC Receding horizon policy is used to deal with both market and wind
predictions’ errors.
the studied hybrid system is composed of a WECS and lead-acid BESS. However, the proposed control strategy is applicable with any other renewable source connected with other types of batteries.
Our work is validated by comparison between three different MPC approaches
used in energy management of hybrid systems:
A- MPC1: a MPC without DNC constraints and fixed SOC constraint
B- MPC2: a MPC with fixed single DNC, fixed SOC constraint
C- MPC3: the proposed MPC with constraints optimizer
0 10 200
10
20
30
40
Case-A
0 5 10 15 200
500
1000
Pool Price ($/MWh)
Case-B
0 5 10 15 200
200
400
600
Case-C
0 10 200
1000
2000
3000
4000
0 5 10 15 202000
3000
4000
5000
Wind power (KW)
0 5 10 15 201000
2000
3000
4000
0 10 20-4
-2
0
2
4x 10
-13
0 5 10 15 20
-1000
-500
0
500
1000
Battery power (KW)
0 5 10 15 20
-1000
-500
0
500
1000
0 10 200.3
0.3
0.3
0 5 10 15 200.2
0.4
0.6
0.8
1
SOC (PU)
0 5 10 15 200.2
0.4
0.6
0.8
1
MPC1
MPC2
MPC3
0.5
0.6
0.7
02
4
0
500
1000
1500
DNC
Case A
DOD
Net
Pro
fit
($)
0.50.6
0.7
01234
1.6
1.65
1.7
1.75
1.8
x 104
Case B
DNC
DOD
Net
pro
fit
($)
0.5
0.6
0.7
024
8000
8500
9000
9500Case C
DNC
DOD
Net
pro
fit
($)
MPC1,2,3MPC2,3
MPC1
MPC3
MPC1MPC2